International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Presentation for UP Health Informatics HI201 under Dr. Iris Tan and Dr. Mike Muin. The topic for discussion Interoperability & Standards, a healthcare scenario was given regarding two disparate information systems, one found in a clinic, another with a hospital information system. #MSHI #HI201
Multi-agent systems applied in Health CareAntonio Moreno
This document discusses the application of multi-agent systems in healthcare. It provides an overview of some projects developed by ITAKA, including a web-based platform for home care services and a system for managing clinical guidelines. It also outlines some research challenges in using agents for healthcare, such as standardization, security, and integration with existing systems. Overall, the document argues that agents are well-suited for coordinating distributed healthcare tasks and knowledge, but challenges remain in adoption due to technical and organizational issues.
Poster presented at the 2014 European Conference on Artificial Intelligence - Unsupervised semantic clustering of Twitter hashtags - automatic topic detection in Twitter
This document summarizes a systematic mapping study on microservice analysis and architecture evolution. It identifies five common analytical approaches used in literature (static analysis, dynamic analysis, machine learning, architecture reconstruction, and model-based analysis) to address seven categories of problems (monitoring, testing, migration, degradation, security, fault detection, and root cause analysis). It also discusses how microservice analysis relates to and differs from other software analysis areas, and identifies potential future research directions in this area.
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...Editor IJAIEM
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared
with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client
software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we
propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with
traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to
comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of
interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the
forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve
progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion
algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the
synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is
illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for
big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use
them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble
Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of
classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers.
Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
In this paper, the authors describe an approach for sharing sensitive medical data with the consent of the
data owner. The framework builds on the advantages of the Semantic Web technologies and makes it
secure and robust for sharing sensitive information in a controlled environment. The framework uses a
combination of Role-Based and Rule-Based Access Policies to provide security to a medical data
repository as per the FAIR guidelines. A lightweight ontologywas developed, to collect consent from the
users indicating which part of their data they want to share with another user having a particular role.
Here, the authors have considered the scenario of sharing the medical data by the owner of data, say the
patient, with relevant persons such as physicians, researchers, pharmacist, etc. To prove this concept, the
authors developed a prototype and validated using the Sesame OpenRDF Workbench with 202,908 triples
and a consent graph stating consents per patient.
DETERMINING BUSINESS INTELLIGENCE USAGE SUCCESSijcsit
Business intelligence systems are highly complex systems that senior executives use to process vast
amounts of information when making decisions. Business intelligence systems are rarely used to their full
potential due to a poor understanding of the factors that contribute to system success. Organizations using
business intelligence systems frequently find that it is not easy to evaluate the effectiveness of these
systems, and researchers have noted that there is limited scholarly and practical understanding of how
quality factors affect information use within these systems. This quantitative post positivist research used
the information system (IS) success model to analyze how information quality and system quality influence
information use in business intelligence systems. This study was also designed to investigate the
moderating effects of maturity constructs (i.e., data sources and analytical capabilities) on the
relationships between quality factors and information use.
Towards to an agent oriented modeling and evaluating approach for vehicular s...Zac Darcy
Agent technology is a software paradigm that permits to implement large and complex distributed
applications. In order to assist the development of multi-agent systems, agent-oriented methodologies
(AOM) have been created in the last years to support modeling more and more complex applications in
many different domains. By defining in a non-ambiguous way concepts used in a specific domain, Meta
modeling may represent a step towards such interoperability. In the Transport domain, this paper propose
an agent-oriented meta-model that provides rigorous concepts for conducting transportation system
problem modeling. The aim is to allow analysts to produce a transportation system model that precisely
captures the knowledge of an organization so that an agent-oriented requirements specification of the
system-to-be and its operational corporate environment can be derived from it. To this end, we extend and
adapt an existing meta-model, Extended Gaia, to build a meta-model and an adequate model for
transportation problems. Our new agent-oriented meta-model aims to allow the analyst to model and
specify any transportation system as a multi-agent system. Based on the proposed meta-model, we proposes
an approach for modeling and evaluating the Transportation System based on Stochastic Activity Network
(SAN) components. The proposed process is based on seven steps from “Recognition” phase to
“Quantitative Analysis” phase. These analyzes are based on the Dependability models which are built
using the formalism Stochastic Activity Network. A real case study of Urban Public Transportation System
has been conducted to show the benefits of the approach.
Presentation for UP Health Informatics HI201 under Dr. Iris Tan and Dr. Mike Muin. The topic for discussion Interoperability & Standards, a healthcare scenario was given regarding two disparate information systems, one found in a clinic, another with a hospital information system. #MSHI #HI201
Multi-agent systems applied in Health CareAntonio Moreno
This document discusses the application of multi-agent systems in healthcare. It provides an overview of some projects developed by ITAKA, including a web-based platform for home care services and a system for managing clinical guidelines. It also outlines some research challenges in using agents for healthcare, such as standardization, security, and integration with existing systems. Overall, the document argues that agents are well-suited for coordinating distributed healthcare tasks and knowledge, but challenges remain in adoption due to technical and organizational issues.
Poster presented at the 2014 European Conference on Artificial Intelligence - Unsupervised semantic clustering of Twitter hashtags - automatic topic detection in Twitter
This document summarizes a systematic mapping study on microservice analysis and architecture evolution. It identifies five common analytical approaches used in literature (static analysis, dynamic analysis, machine learning, architecture reconstruction, and model-based analysis) to address seven categories of problems (monitoring, testing, migration, degradation, security, fault detection, and root cause analysis). It also discusses how microservice analysis relates to and differs from other software analysis areas, and identifies potential future research directions in this area.
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...Editor IJAIEM
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared
with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client
software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we
propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with
traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to
comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of
interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the
forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve
progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion
algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the
synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is
illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for
big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use
them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble
Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of
classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers.
Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
In this paper, the authors describe an approach for sharing sensitive medical data with the consent of the
data owner. The framework builds on the advantages of the Semantic Web technologies and makes it
secure and robust for sharing sensitive information in a controlled environment. The framework uses a
combination of Role-Based and Rule-Based Access Policies to provide security to a medical data
repository as per the FAIR guidelines. A lightweight ontologywas developed, to collect consent from the
users indicating which part of their data they want to share with another user having a particular role.
Here, the authors have considered the scenario of sharing the medical data by the owner of data, say the
patient, with relevant persons such as physicians, researchers, pharmacist, etc. To prove this concept, the
authors developed a prototype and validated using the Sesame OpenRDF Workbench with 202,908 triples
and a consent graph stating consents per patient.
DETERMINING BUSINESS INTELLIGENCE USAGE SUCCESSijcsit
Business intelligence systems are highly complex systems that senior executives use to process vast
amounts of information when making decisions. Business intelligence systems are rarely used to their full
potential due to a poor understanding of the factors that contribute to system success. Organizations using
business intelligence systems frequently find that it is not easy to evaluate the effectiveness of these
systems, and researchers have noted that there is limited scholarly and practical understanding of how
quality factors affect information use within these systems. This quantitative post positivist research used
the information system (IS) success model to analyze how information quality and system quality influence
information use in business intelligence systems. This study was also designed to investigate the
moderating effects of maturity constructs (i.e., data sources and analytical capabilities) on the
relationships between quality factors and information use.
Towards to an agent oriented modeling and evaluating approach for vehicular s...Zac Darcy
Agent technology is a software paradigm that permits to implement large and complex distributed
applications. In order to assist the development of multi-agent systems, agent-oriented methodologies
(AOM) have been created in the last years to support modeling more and more complex applications in
many different domains. By defining in a non-ambiguous way concepts used in a specific domain, Meta
modeling may represent a step towards such interoperability. In the Transport domain, this paper propose
an agent-oriented meta-model that provides rigorous concepts for conducting transportation system
problem modeling. The aim is to allow analysts to produce a transportation system model that precisely
captures the knowledge of an organization so that an agent-oriented requirements specification of the
system-to-be and its operational corporate environment can be derived from it. To this end, we extend and
adapt an existing meta-model, Extended Gaia, to build a meta-model and an adequate model for
transportation problems. Our new agent-oriented meta-model aims to allow the analyst to model and
specify any transportation system as a multi-agent system. Based on the proposed meta-model, we proposes
an approach for modeling and evaluating the Transportation System based on Stochastic Activity Network
(SAN) components. The proposed process is based on seven steps from “Recognition” phase to
“Quantitative Analysis” phase. These analyzes are based on the Dependability models which are built
using the formalism Stochastic Activity Network. A real case study of Urban Public Transportation System
has been conducted to show the benefits of the approach.
DOMAIN ONTOLOGY DEVELOPMENT FOR COMMUNICABLE DISEASEScscpconf
Web has become the very first resource to search for any kind of information. With the emergence of semantic web, our search queries have started generating more informed results.Ontologies are at the core of any semantic web application. They help in rapid development of
distributed systems by providing information on the fly. This key feature of distribution and
sharing of information has made ontologies as a new knowledge representation mechanism. A
mechanism which is strongly backed by a sound inference system. In this paper, we shall discuss the development, verification and validation of an ontology in a health domain.
Domain ontology development for communicable diseasescsandit
This document discusses the development of a domain ontology for communicable diseases. The researchers developed an ontology with concepts like diseases, symptoms, and causes arranged in a taxonomy. They created over 600 concepts with properties and relations. The ontology development process included specification, conceptualization, creation of instances, and evaluation using a description logic reasoner to verify the concepts and relations were correctly represented. The ontology will be expanded to include more diseases and connections to related web content to provide information retrieval.
Interdisciplinary Science, Engineering, and Management,
Systems theory,
Systems thinking,
System development life cycles,
Synergy,
Project management,
Engineering Domains (Industry 4.0) , and
Communities (INCOSE and IEEE SMC Society).
The document discusses data and information fusion (DIF) techniques. It introduces several DIF models including the Intelligence Cycle, Boyd Control Loop, Waterfall Model, Dasarathy's Model, and the Joint Directors of Laboratories (JDL) Data Fusion Model. The JDL model is described as having five levels (Level 0 to Level 4) aimed at facilitating coordination between data fusion communities. Applications of DIF techniques discussed include strategic defense, computer security, crisis management, and biomedical fields.
Complex systems,
Software systems,
Database systems,
Operating systems,
Bioinformatics systems,
Social Systems,
Service Oriented Systems,
Cloud Systems,
Ubiquitous systems,
Distributed Version Control Systems (GitHub), and
Software Container Systems (DockerHub and Google App Engine).
Multi-objective NSGA-II based community detection using dynamical evolution s...IJECEIAES
Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.
Exploration exploitation trade off in mobile context-aware recommender systemsBouneffouf Djallel
Most existing approaches in Context-Aware Recommender Systems (CRS) focus on recommending relevant items to users taking into account contextual information, such as time, loca-tion, or social aspects. However, none of them have considered the problem of user’s content dynamicity. This problem has been studied in the reinforcement learning community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles the user’s content dynamicity by modeling the CRS as a contextual bandit algorithm. It is based on dynamic explora-tion/exploitation and it includes a metric to decide which user’s situation is the most relevant to exploration or exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demon-strate that our algorithm outperforms surveyed algorithms.
Hybrid-e-greedy for mobile context-aware recommender systemBouneffouf Djallel
This document proposes a hybrid-ε-greedy algorithm for mobile context-aware recommender systems that combines bandit algorithms and case-based reasoning. It summarizes related works that aim to follow the evolution of user interests or manage the user's context, but not both. The proposed approach uses case-based reasoning to consider the user's context in the bandit algorithm's exploration-exploitation strategy. It also uses content-based filtering with the ε-greedy algorithm.
A contextual bandit algorithm for mobile context-aware recommender systemBouneffouf Djallel
Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user’s content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.
The document discusses the challenges of healthcare informatics and proposes multilevel modeling (MLM) and open source software as solutions. It argues that MLM separates reference and knowledge models, allowing knowledge to adapt over time without replacing the whole system. Open specifications and tools support sharing knowledge artifacts and ensuring interoperability. MLM and open source align with principles of beneficence, non-maleficence, and efficiency in healthcare informatics. The approach has potential to address issues like information chaos, system failures, and costs that traditional approaches have faced.
This document proposes an accuracy-constrained privacy-preserving access control framework for relational data. It aims to satisfy both privacy requirements like k-anonymity through anonymization techniques, as well as accuracy constraints for authorized access by roles. The framework formulates this as a k-anonymous Partitioning with Imprecision Bounds (k-PIB) problem. Heuristics are proposed to approximate solutions for the k-PIB problem by using top-down multi-dimensional partitioning that satisfies imprecision bounds for roles' access permissions, while achieving lower total imprecision than existing methods. Empirical evaluation shows the approach satisfies imprecision bounds for more permissions.
This document discusses and compares several agent-assisted methodologies for developing multi-agent systems:
- It reviews Gaia, HLIM, PASSI, and Tropos methodologies, outlining their key models and phases. Gaia focuses on analysis and design, HLIM models internal and external agent behavior, and PASSI and Tropos incorporate UML modeling.
- It then proposes a new MAB methodology intended to address shortcomings of existing approaches. MAB includes requirements, analysis, design, and implementation phases and models such as use case maps and agent roles.
- Finally, it concludes that agent technologies represent a promising approach for developing complex software systems, but that matching methodologies to problem domains and developing princip
Ontological and clustering approach for content based recommendation systemsvikramadityajakkula
This document proposes a novel content-based recommendation system that uses ontological graphs and dynamic weighted ranking. It builds an adaptive ranking mechanism based on user selections and preferences to improve recommendation accuracy over time. The system segments data into ontological groups and identifies relationships between entities. It then calculates similarity between entities using feature vectors and ranks entities based on weights assigned to their connections in the ontological graph. These weights are updated dynamically based on user feedback to personalize recommendations for each user. The paper describes testing this approach in a recipe recommendation tool called RecipeMiner, which produced coherent recommendations that adapted to user preferences.
An Enhancement Role and Attribute Based Access Control Mechanism in Big Data IJECEIAES
To be able to leverage big data to achieve enhanced strategic insight and make informed decision, an efficient access control mechanism is needed for ensuring end to end security of such information asset. Attribute Based Access Control (ABAC), Role Based Access Control (RBAC) and Event Based Access Control (EBAC) are widely used access control mechanisms. The ABAC system is much more complex in terms of policy reviews, hence analyzing the policy and reviewing or changing user permission are quite complex task. RBAC system is labor intensive and time consuming to build a model instance and it lacks flexibility to efficiently adapt to changing user’s, objects and security policies. EBAC model considered only the events to allocate access controls. Yet these mechanisms have limitations and offer feature complimentary to each other. So in this paper, Event-Role-Attribute based fine grained Access Control mechanism is proposed, it provide a flexible boundary which effectively adapt to changing user’s, objects and security policies based on the event. The flexible boundary is achieved by using temporal and environment state of an event. It improves the big data security and overcomes the disadvantages of the ABAC and RBAC mechanisms. The experiments are conducted to prove the effectiveness of the proposed Event-Role-Attribute based Access Control mechanism over ABAC and RBAC in terms of computational overhead.
Social Media Datasets for Analysis and Modeling Drug Usageijtsrd
This paper based on the research carried out in the area of data mining depends for managing bulk amount of data with mining in social media on using composite applications for performing more sophisticated analysis. Enhancement of social media may address this need. The objective of this paper is to introduce such type of tool which used in social network to characterised Medicine Usage. This paper outlined a structured approach to analyse social media in order to capture emerging trends in medicine abuse by applying powerful methods like Machine Learning. This paper describes how to fetch important data for analysis from social network. Then big data techniques to extract useful content for analysis are discussed. Sindhu S. B | Dr. B. N Veerappa "Social Media Datasets for Analysis and Modeling Drug Usage" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25246.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/25246/social-media-datasets-for-analysis-and-modeling-drug-usage/sindhu-s-b
A SURVEY OF LINK MINING AND ANOMALIES DETECTIONIJDKP
This document discusses link mining and its application in detecting anomalies. It begins by defining link mining as focusing on discovering explicit links between objects, as opposed to data mining which aims to find patterns within datasets. The document then surveys different types of anomalies that can be detected through link mining, including contextual, point, collective, online, and distributed anomalies. It also discusses challenges in link mining like logical vs statistical dependencies and the skewed class distribution problem in link prediction. Applications of link mining mentioned include social networks, epidemiology, and bibliographic analysis. Overall, the document provides an overview of the emerging field of link mining and its relevance for detecting unusual or anomalous links within linked datasets.
This document summarizes a PhD defense presentation on an observer/controller and ontology/rule-based architecture for context-aware pervasive computing systems. The presentation introduces ubiquitous/pervasive computing and discusses context-aware computing approaches. It proposes a new generic context model called Ubiq-OntoRule-CM that uses an ontology for context modeling and rules for context reasoning and service selection. A new generic architecture is also proposed that supports context detection, rapid adaptation, and context modeling/reasoning techniques. The architecture applies an observer/controller concept from organic computing to supervise adaptive systems.
Collaborative filtering analyzes data from multiple sources to develop profiles of people with similar tastes. It finds similarities between users based on their preferences and provides recommendations based on the preferences of similar users. Collaborative filtering requires a large amount of stored user data to make reliable recommendations, and the more users in the population, the more useful the recommendations will be. However, with small datasets it can produce false connections or poor predictions.
Learning and inference engine applied to ubiquitous recommender systemBouneffouf Djallel
The document discusses a learning and inference engine applied to a ubiquitous recommender system. It aims to help users access information by guiding them based on their context and situation. The system should be able to recommend information to help users achieve their goals. Major challenges include avoiding expert intervention, starting with no prior knowledge, quick learning, and adapting to changing user interests. The document presents a scenario where the recommender system infers relevant information to recommend to new employees without expert input, based on analyzing the actions of other employees in their teams. It aims to start with a predefined set of actions from social groups and progressively adapt recommendations to individual users.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
La interna del Frente para la Victoria en Pilar sigue sin definirse, aunque Zúccaro afirmó haber ganado por 152 votos según sus datos. Molina había declarado su victoria el domingo pero luego se llamó a silencio. El recuento final se definirá ante la justicia electoral. Nicolás Ducoté del PRO fue el candidato más votado en las PASO, aunque el Frente para la Victoria obtuvo más votos en total.
Este documento apresenta vários números e medidas aparentemente sem contexto ou significado claro. As informações fornecidas são muito limitadas para resumir o conteúdo ou propósito do documento de forma significativa.
DOMAIN ONTOLOGY DEVELOPMENT FOR COMMUNICABLE DISEASEScscpconf
Web has become the very first resource to search for any kind of information. With the emergence of semantic web, our search queries have started generating more informed results.Ontologies are at the core of any semantic web application. They help in rapid development of
distributed systems by providing information on the fly. This key feature of distribution and
sharing of information has made ontologies as a new knowledge representation mechanism. A
mechanism which is strongly backed by a sound inference system. In this paper, we shall discuss the development, verification and validation of an ontology in a health domain.
Domain ontology development for communicable diseasescsandit
This document discusses the development of a domain ontology for communicable diseases. The researchers developed an ontology with concepts like diseases, symptoms, and causes arranged in a taxonomy. They created over 600 concepts with properties and relations. The ontology development process included specification, conceptualization, creation of instances, and evaluation using a description logic reasoner to verify the concepts and relations were correctly represented. The ontology will be expanded to include more diseases and connections to related web content to provide information retrieval.
Interdisciplinary Science, Engineering, and Management,
Systems theory,
Systems thinking,
System development life cycles,
Synergy,
Project management,
Engineering Domains (Industry 4.0) , and
Communities (INCOSE and IEEE SMC Society).
The document discusses data and information fusion (DIF) techniques. It introduces several DIF models including the Intelligence Cycle, Boyd Control Loop, Waterfall Model, Dasarathy's Model, and the Joint Directors of Laboratories (JDL) Data Fusion Model. The JDL model is described as having five levels (Level 0 to Level 4) aimed at facilitating coordination between data fusion communities. Applications of DIF techniques discussed include strategic defense, computer security, crisis management, and biomedical fields.
Complex systems,
Software systems,
Database systems,
Operating systems,
Bioinformatics systems,
Social Systems,
Service Oriented Systems,
Cloud Systems,
Ubiquitous systems,
Distributed Version Control Systems (GitHub), and
Software Container Systems (DockerHub and Google App Engine).
Multi-objective NSGA-II based community detection using dynamical evolution s...IJECEIAES
Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.
Exploration exploitation trade off in mobile context-aware recommender systemsBouneffouf Djallel
Most existing approaches in Context-Aware Recommender Systems (CRS) focus on recommending relevant items to users taking into account contextual information, such as time, loca-tion, or social aspects. However, none of them have considered the problem of user’s content dynamicity. This problem has been studied in the reinforcement learning community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles the user’s content dynamicity by modeling the CRS as a contextual bandit algorithm. It is based on dynamic explora-tion/exploitation and it includes a metric to decide which user’s situation is the most relevant to exploration or exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demon-strate that our algorithm outperforms surveyed algorithms.
Hybrid-e-greedy for mobile context-aware recommender systemBouneffouf Djallel
This document proposes a hybrid-ε-greedy algorithm for mobile context-aware recommender systems that combines bandit algorithms and case-based reasoning. It summarizes related works that aim to follow the evolution of user interests or manage the user's context, but not both. The proposed approach uses case-based reasoning to consider the user's context in the bandit algorithm's exploration-exploitation strategy. It also uses content-based filtering with the ε-greedy algorithm.
A contextual bandit algorithm for mobile context-aware recommender systemBouneffouf Djallel
Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user’s content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.
The document discusses the challenges of healthcare informatics and proposes multilevel modeling (MLM) and open source software as solutions. It argues that MLM separates reference and knowledge models, allowing knowledge to adapt over time without replacing the whole system. Open specifications and tools support sharing knowledge artifacts and ensuring interoperability. MLM and open source align with principles of beneficence, non-maleficence, and efficiency in healthcare informatics. The approach has potential to address issues like information chaos, system failures, and costs that traditional approaches have faced.
This document proposes an accuracy-constrained privacy-preserving access control framework for relational data. It aims to satisfy both privacy requirements like k-anonymity through anonymization techniques, as well as accuracy constraints for authorized access by roles. The framework formulates this as a k-anonymous Partitioning with Imprecision Bounds (k-PIB) problem. Heuristics are proposed to approximate solutions for the k-PIB problem by using top-down multi-dimensional partitioning that satisfies imprecision bounds for roles' access permissions, while achieving lower total imprecision than existing methods. Empirical evaluation shows the approach satisfies imprecision bounds for more permissions.
This document discusses and compares several agent-assisted methodologies for developing multi-agent systems:
- It reviews Gaia, HLIM, PASSI, and Tropos methodologies, outlining their key models and phases. Gaia focuses on analysis and design, HLIM models internal and external agent behavior, and PASSI and Tropos incorporate UML modeling.
- It then proposes a new MAB methodology intended to address shortcomings of existing approaches. MAB includes requirements, analysis, design, and implementation phases and models such as use case maps and agent roles.
- Finally, it concludes that agent technologies represent a promising approach for developing complex software systems, but that matching methodologies to problem domains and developing princip
Ontological and clustering approach for content based recommendation systemsvikramadityajakkula
This document proposes a novel content-based recommendation system that uses ontological graphs and dynamic weighted ranking. It builds an adaptive ranking mechanism based on user selections and preferences to improve recommendation accuracy over time. The system segments data into ontological groups and identifies relationships between entities. It then calculates similarity between entities using feature vectors and ranks entities based on weights assigned to their connections in the ontological graph. These weights are updated dynamically based on user feedback to personalize recommendations for each user. The paper describes testing this approach in a recipe recommendation tool called RecipeMiner, which produced coherent recommendations that adapted to user preferences.
An Enhancement Role and Attribute Based Access Control Mechanism in Big Data IJECEIAES
To be able to leverage big data to achieve enhanced strategic insight and make informed decision, an efficient access control mechanism is needed for ensuring end to end security of such information asset. Attribute Based Access Control (ABAC), Role Based Access Control (RBAC) and Event Based Access Control (EBAC) are widely used access control mechanisms. The ABAC system is much more complex in terms of policy reviews, hence analyzing the policy and reviewing or changing user permission are quite complex task. RBAC system is labor intensive and time consuming to build a model instance and it lacks flexibility to efficiently adapt to changing user’s, objects and security policies. EBAC model considered only the events to allocate access controls. Yet these mechanisms have limitations and offer feature complimentary to each other. So in this paper, Event-Role-Attribute based fine grained Access Control mechanism is proposed, it provide a flexible boundary which effectively adapt to changing user’s, objects and security policies based on the event. The flexible boundary is achieved by using temporal and environment state of an event. It improves the big data security and overcomes the disadvantages of the ABAC and RBAC mechanisms. The experiments are conducted to prove the effectiveness of the proposed Event-Role-Attribute based Access Control mechanism over ABAC and RBAC in terms of computational overhead.
Social Media Datasets for Analysis and Modeling Drug Usageijtsrd
This paper based on the research carried out in the area of data mining depends for managing bulk amount of data with mining in social media on using composite applications for performing more sophisticated analysis. Enhancement of social media may address this need. The objective of this paper is to introduce such type of tool which used in social network to characterised Medicine Usage. This paper outlined a structured approach to analyse social media in order to capture emerging trends in medicine abuse by applying powerful methods like Machine Learning. This paper describes how to fetch important data for analysis from social network. Then big data techniques to extract useful content for analysis are discussed. Sindhu S. B | Dr. B. N Veerappa "Social Media Datasets for Analysis and Modeling Drug Usage" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25246.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/25246/social-media-datasets-for-analysis-and-modeling-drug-usage/sindhu-s-b
A SURVEY OF LINK MINING AND ANOMALIES DETECTIONIJDKP
This document discusses link mining and its application in detecting anomalies. It begins by defining link mining as focusing on discovering explicit links between objects, as opposed to data mining which aims to find patterns within datasets. The document then surveys different types of anomalies that can be detected through link mining, including contextual, point, collective, online, and distributed anomalies. It also discusses challenges in link mining like logical vs statistical dependencies and the skewed class distribution problem in link prediction. Applications of link mining mentioned include social networks, epidemiology, and bibliographic analysis. Overall, the document provides an overview of the emerging field of link mining and its relevance for detecting unusual or anomalous links within linked datasets.
This document summarizes a PhD defense presentation on an observer/controller and ontology/rule-based architecture for context-aware pervasive computing systems. The presentation introduces ubiquitous/pervasive computing and discusses context-aware computing approaches. It proposes a new generic context model called Ubiq-OntoRule-CM that uses an ontology for context modeling and rules for context reasoning and service selection. A new generic architecture is also proposed that supports context detection, rapid adaptation, and context modeling/reasoning techniques. The architecture applies an observer/controller concept from organic computing to supervise adaptive systems.
Collaborative filtering analyzes data from multiple sources to develop profiles of people with similar tastes. It finds similarities between users based on their preferences and provides recommendations based on the preferences of similar users. Collaborative filtering requires a large amount of stored user data to make reliable recommendations, and the more users in the population, the more useful the recommendations will be. However, with small datasets it can produce false connections or poor predictions.
Learning and inference engine applied to ubiquitous recommender systemBouneffouf Djallel
The document discusses a learning and inference engine applied to a ubiquitous recommender system. It aims to help users access information by guiding them based on their context and situation. The system should be able to recommend information to help users achieve their goals. Major challenges include avoiding expert intervention, starting with no prior knowledge, quick learning, and adapting to changing user interests. The document presents a scenario where the recommender system infers relevant information to recommend to new employees without expert input, based on analyzing the actions of other employees in their teams. It aims to start with a predefined set of actions from social groups and progressively adapt recommendations to individual users.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
La interna del Frente para la Victoria en Pilar sigue sin definirse, aunque Zúccaro afirmó haber ganado por 152 votos según sus datos. Molina había declarado su victoria el domingo pero luego se llamó a silencio. El recuento final se definirá ante la justicia electoral. Nicolás Ducoté del PRO fue el candidato más votado en las PASO, aunque el Frente para la Victoria obtuvo más votos en total.
Este documento apresenta vários números e medidas aparentemente sem contexto ou significado claro. As informações fornecidas são muito limitadas para resumir o conteúdo ou propósito do documento de forma significativa.
Este documento promove um produto milagroso que promete transformar homens feios, gordos e sem habilidades sociais em galãs irresistíveis para mulheres, garantindo que elas irão se apaixonar. O produto é apresentado como uma cura para a baixa autoestima e timidez masculinas.
7 produtos para aproveitar o máximo do hdmiCirilo Cabos
Este documento apresenta sete produtos relacionados à tecnologia HDMI, incluindo cabos, adaptadores e conversores que permitem conectar dispositivos de forma flexível preservando a qualidade de imagem e som. Os itens descritos facilitam a transmissão de sinal entre diferentes portas e aparelhos de forma prática e eficiente.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
Useful and Effectiveness of Multi Agent Systemijtsrd
A multi agent system MAS or self cooperating system is a computerized system organized of multiple interacting intelligent agents. The problems that are difficult to solve for an individual agent or a monolithic system can be solved by multi agent system easily. MAS is a loosely coupled of software agents' network that interact to solve problems that are beyond the individual capacities or knowledge of each software agent. Distributed systems with a group of intelligent agents that communicate with other agents to achieve goals are directed by their masters. MAS group aims to develop new theory and computational models of higher order social cognition between people and computer systems by producing their abilities to reason about one another automatically. More specifically, multi agent control systems are fundamental parts of a wide range of safety critical engineering systems, and are commonly found in aerospace, traffic control, chemical process, power generation and distribution, flexible manufacturing, chemical processes, power generation and distribution, flexible manufacturing, robotic system design and self assembly structures. Moe Myint Myint ""Useful and Effectiveness of Multi-Agent System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23036.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23036/useful-and-effectiveness-of-multi-agent-system/moe-myint-myint
This document presents a multi-agent system approach for clinical diagnosis. It proposes an algorithm for an intelligent medical diagnosis system called Clinical Diagnosis System (CDS) that uses multiple intelligent agents. The CDS architecture includes various agents like the User Agent, Master Agent, Specialist Doctor Agents, and Report Agent. The algorithm describes the step-by-step process of how a problem or symptom is input, analyzed, assigned to a specialist agent, diagnosed and reported back to the user. The proposed CDS system aims to provide an improved and user-friendly clinical diagnosis approach.
A Literature Survey on Recommendation Systems for Scientific Articles.pdfAmber Ford
This document summarizes a literature survey on recommendation systems for scientific articles. It begins by outlining problems faced by researchers, including information overload from searching large amounts of non-structured data. It then reviews different types of recommender systems, including content-based, collaborative, knowledge-based, semantic-based, and hybrid approaches. The objective of the survey is to develop a framework for a semantic-based recommender system that integrates ontologies to help researchers more efficiently find relevant scientific articles.
RUNNING HEADER: Analytics Ecosystem 1
Analytics Ecosystem 4
Analytics Ecosystem
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastasia Rashtchian’s B288 Business Analytics Course.
This paper looks at the nine clusters of the ecosystem. Clustering refers to a system of grouping functions that are similar so as to set them out from others. It begins by highlighting them before proceeding to defining them. It then identifies clusters that represent technology developers and technology users. Peer reviewed materials are used in this endeavor.
They include executive sponsor cluster which contains information that concerns administrators for directing the system. Another one is end-user tools and dashboards cluster that is made of functions that facilitate ability of persons to ultimately engage the system. Data owners cluster is made up of programs that are related to persons who have data in the system. Business users’ cluster is made up of functions that are related to clients of the system. Business applications and systems cluster is made up programs related to features of a given system. Developers cluster is made of programs that are related to the development of programs in the system. Analyst cluster is made up of materials that are related to analysis of data in the system. SME cluster that is made up switches that run SME applications in the system. Lastly, operational data stores that are made up of programs that are concerned with storage of data in a system (Pitelis, 2012).
While developers cluster is made up of technology developers in the system, business users’ cluster is made up of technology users in the system. In conclusion, clustering serves to bring roles together as well as separating roles that are not related in a system (Cameron, Gelbach & Miller, 2012).
They can be represented as follows:-
References
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2012). Robust inference with multiway clustering. Journal of Business & Economic Statistics.
Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-creation, and appropriability: a conceptual framework. Industrial and Corporate Change, dts008.
Infrastructure
Executive Sponsor Cluster
End-user tools and dashboards cluster
operational data stores
Data Owners Cluster
Business users' cluster
Business systems and applications cluster
Developers Cluster
Analysts Cluster
SME cluster
4
Running head: Sentiment analysis
Sentiment Analysis
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastashia Rashtcian’s B288 Business Analytics course.
Sentiment analysis has played a significant role in the concurrent marketing field, specifically in product marketing. According to Somasundaran, Swapna, (2010), the process’ operational module is structured on a data mining sequence, whereby the end users of given particulars the feedback pertaining a used.
Proactive Intelligent Home System Using Contextual Information and Neural Net...IJERA Editor
Nowadays, cities around the world intend to use information technology to improve the lives of their citizens.
Future smart cities will incorporate digital data and technology to interact differently with their human
inhabitants.
Among the key component of a smart city, we find the smart home component. It is an autonomic environment
that can provide various smart services by considering the user’s context information. Several methods are used
in context-aware system to provide such services. In this paper, we propose an approach to offer the most
relevant services to the user according to any significant change of his context environment. The proposed
approach is based on the use of context history information together with user profiling and machine learning
techniques. Experimentations show that the proposed solution can efficiently provide the most useful services to
the user in an intelligent home environment.
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Improving the quality of information in strategic scanning system network app...ijaia
Integrating Business Intelligence (BI) processes in an information system requires a form of strategic
scanning system for which the information is the main source of efficiency and decision support. A process
of strategic scanning system network is primarily a cooperative approach to sharing knowledge that actors
are "producers" of information. The dynamics of the actor’s interactions allow gradual building of shared
knowledge. This paper proposes Multi Agent System (MAS) architecture which facilitates the integration of
a process of strategic scanning system network in the information system, to emerge relevant information
from simple information while ensuring the quality and safety information. In particular, this approach is
geared towards supporting system properties specially focused on cooperative multi-agent system. It gives
finally an overview of implementation of a prototype of the proposed solution limited for the moment to the
integration of processes most used in the majority of information systems.
A Survey On Ontology Agent Based Distributed Data MiningEditor IJMTER
With the increased complexity in number of applications and due to large volume
of availability of data from heterogeneous sources, there is a need for the development of
suitable ontology, which can handle the large data set and present the mined outcomes for
evaluation intelligently. In the era of intensive data driven applications distributed data mining can
meet the challenges with the support of agents. This paper discusses the underlying principles for
effectiveness of modern agent-based systems for distributed data mining
Identifying Structures in Social Conversations in NSCLC Patients through the ...IJERA Editor
The exploration of social conversations for addressing patient’s needs is an important analytical task in which
many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains
the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in
order to generate insight from social media data, which can be considered as one of the most challenging source
of information available today due to its sheer volume and noise. This study is based on the work by Scott
Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a
topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The
mechanism for automatically identifying and generating a taxonomy from social conversations is developed and
pressured tested using public data from media sites focused on the needs of cancer patients and their families.
Moreover, a novel method for generating the category’s label and the determination of an optimal number of
categories is presented which extends Scott and Jeffrey’s research in a meaningful way. We assume the reader is
familiar with taxonomies, what they are and how they are used.
Identifying Structures in Social Conversations in NSCLC Patients through the ...IJERA Editor
The exploration of social conversations for addressing patient’s needs is an important analytical task in which
many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains
the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in
order to generate insight from social media data, which can be considered as one of the most challenging source
of information available today due to its sheer volume and noise. This study is based on the work by Scott
Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a
topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The
mechanism for automatically identifying and generating a taxonomy from social conversations is developed and
pressured tested using public data from media sites focused on the needs of cancer patients and their families.
Moreover, a novel method for generating the category’s label and the determination of an optimal number of
categories is presented which extends Scott and Jeffrey’s research in a meaningful way. We assume the reader is
familiar with taxonomies, what they are and how they are used.
UML MODELING AND SYSTEM ARCHITECTURE FOR AGENT BASED INFORMATION RETRIEVALijcsit
In this current technological era, there is an enormous increase in the information available on web and
also in the online databases. This information abundance increases the complexity of finding relevant
information. To solve such challenges, there is a need for improved and intelligent systems for efficient
search and retrieval. Intelligent Agents can be used for better search and information retrieval in a
document collection. The information required by a user is scattered in a large number of databases. In this
paper, the object oriented modeling for agent based information retrieval system is presented. The paper
also discusses the framework of agent architecture for obtaining the best combination terms that serve as
an input query to the information retrieval system. The communication and cooperation among the agents
are also explained. Each agent has a task to perform in information retrieval.
FirstReview these assigned readings; they will serve as your .docxclydes2
First:
Review these assigned readings; they will serve as your scientific sources of accurate information:
http://www.closerlookatstemcells.org/Top_10_Stem_Cell_Treatment_Facts.html
http://www.closerlookatstemcells.org/How_Science_Becomes_Medicine.html
http://www.newvision.co.ug/news/649266-fighting-ageing-using-stem-cell-therapy.html
http://www.nature.com/news/stem-cells-in-texas-cowboy-culture-1.12404
http://www.cbc.ca/radio/whitecoat/blog/stem-cell-hype-and-risk-1.3654515
http://stm.sciencemag.org/content/7/278/278ps4.full
Next:
Use a standard Google search for this phrase: “stem cell therapy.” Do not go to Google Scholar. Select one of the websites, blogs, or other locations that offer stem cell therapies.
Save the link for your selected site.
Read the materials provided on your selected site and find out who the authors and sponsors of the site are by going to their “home” or “about us” pages.
Finally, submit your responses to the following in an essay of 500-750 words (2-3 pages of text—use a separate page for a title and for your references):
You are going to prepare a critique of the site you located and compare it to the scientific information available on this therapy.
Give the full title of the website, web blog, or other site that you selected, along with the link.
Describe the therapy that is being offered and what conditions it is designed to treat.
Who are the authors and sponsors of the site you selected?
Compare the claims about the therapy offered to what is said in the assigned readings about this type of therapy. You may have to use our library, as well, to determine what scientists and researchers have to say about the use of stem cells to treat this condition.
Would you say that the therapy you found is a well-established, proven technique for humans, or more of an experimental, unproven approach?
What about the type of language discussed in the Goldman article? Is the therapy you found using sensationalist claims and terminology that are not supported by the scientific research?
Would you recommend that a patient with this condition go ahead and participate in this treatment? Why or why not?
Literature review on how Information Technology has impacted governing bodies’ ability to align public policy with stakeholder needs
Nowadays, the governing bodies both in public and private sectors are dealing with complex systems on a day to day operations. These systems are made up of different components which present varying interactions and interrelationships with and/or among each other; therefore, making their management to be difficult or challenging. Indeed, Ruiz, Zabaleta & Elorza (2016), highlighted that public policymakers have to deal with complex systems which involve heterogeneous agents that act in non-linear behaviors making their management difficult. Neziraj & Shaqiri (2018) also stated that the policymakers are faced with problems which are complex and non-uniform due to a lot of uncertainties and risk situ.
Behavioural Modelling Outcomes prediction using Casual FactorsIJMER
This document describes a new approach to machine science that uses crowdsourcing to generate predictive models of human behavioral outcomes. Users collectively formulate predictive survey questions and provide responses, which are then used to build predictive models without requiring domain expertise. Two experiments implementing this approach successfully generated models to predict monthly household electricity usage and user body mass index based on the crowdsourced survey data. The approach harnesses the "wisdom of crowds" to both discover predictive factors and collect data, representing an innovative way to generate predictive models without expert involvement at each step of the process.
An improvised model for identifying influential nodes in multi parameter soci...csandit
Influence Maximization is one of the major tasks in the field of viral marketing and community
detection. Based on the observation that social networks in general are multi-parameter graphs
and viral marketing or Influence Maximization is based on few parameters, we propose to
convert the general social networks into “interest graphs”. We have proposed an improvised
model for identifying influential nodes in multi-parameter social networks using these “interest
graphs”. The experiments conducted on these interest graphs have shown better results than the
method proposed in [8].
Detection of Fake News Using Machine LearningIRJET Journal
This document summarizes a literature review on detecting fake news using machine learning. It discusses how machine learning classifiers can be trained to automatically identify fake news. Specifically, it addresses three research questions: 1) Why machine learning is needed to detect fake news, 2) Which machine learning classifiers can be used, and 3) How the classifiers are trained. It finds that common classifiers like support vector machines, naive Bayes, logistic regression, random forests, and recurrent neural networks have been effectively used to detect fake news by analyzing content. Accuracy of the classifiers depends on how well they are trained on labeled datasets.
1) Values in Computational Models RevaluedComputational mode.docxmonicafrancis71118
1) Values in Computational Models Revalued
Computational models are mathematical representations that are designed to study the behaviour of complex systems. Systems under study are usually nonlinear and complex to the extent that conventional analytics cannot be used. Scholars have tried to establish the role played by trust and values in the use of such models in the analysis of public administration.
Public decision-making is itself a complex endeavour that involves the input of multiple stakeholders. Usually, there are a lot of conflicting interests that influence the final outcome of such decision-making processes (Klabunde & Willekens, 2016). In a computational model, a number of factors equally influence the outcome of the process. One of them is the number of actors involved –the presence of more actors normally implies increased mistrust. Another factor is the amount of trust that already exists among the decision makers. In cases where the group is homogenous, there is likely to be more trust and thus, less concern about the number of actors involved.
Given the importance of these two factors, the designer of any such model bears the largest burden in assuring the value of the model. He or she can choose to implement agency by humans or by technology depending on the number of actors and trust among them. Also, model designer determines the margins of error from each scenario while modelling (Gershman, Markman & Otto, 2014). Since in conventional decision-making processes different actors have different roles, the model designer may decide to accord different levels of authority to different actors. Nevertheless, they must ensure that such a decision does not affect the trust of the system. Overall, what values are sought from a computational model in a public decision-making context?
References
Gershman, S. J., Markman, A. B., & Otto, A. R. (2014). Retrospective revaluation in sequential decision making: A tale of two systems.
Journal of Experimental Psychology: General
,
143
(1), 182-194.
Klabunde, A., & Willekens, F. (2016). Decision-making in agent-based models of migration: state of the art and challenges.
European Journal of Population
,
32
(1), 73-97.
2) Active and Passive Crowdsourcing in Government
The authors of the article “Active and Passive Crowdsourcing in Government” discuss the application of the idea of crowdsourcing by public agencies. It leverages Web-based platforms to gather information from a large number of individuals for solving intricate problems (Loukis and Charalabidis 284). The scholars revealed that the concept of crowdsourcing was first adopted by organizations in the private sector, especially creative and design firms. Later on, state agencies began to determine how to leverage crowdsourcing to obtain “collective wisdom” from citizens aimed at informing the formulation and implementation of public policies.
Active and passive approaches to crowdsourcing are similar as they are both.
This document proposes a framework called the Context-Influence Framework for managing contexts in interactive system development. The framework defines contexts as collections of influences that affect system behaviors and users. Contexts are analyzed at the level of "Use Subsystems" which are specific combinations of users, tasks, and interfaces. Contexts can be divided into domains to capture information from different perspectives. Contexts are also organized hierarchically to allow representation at different levels of detail. The framework is intended to help communication between teams and allow contexts to be added without increasing complexity.
A Novel Technique For Creative Problem-Solving By Using Q-Learning And Associ...Andrew Parish
This document presents a novel technique for creative problem solving using Q-learning and association algorithms. It introduces an intelligent agent called the Intelligent Agent (IA) that is equipped with learning capabilities and can generate ideas using three principles of human association: similarity, contiguity, and contrast. The Collective Ideation Decision System (CIDS) is proposed as an architecture and environment that allows multiple IAs to learn, share knowledge, and generate ideas through their inference mechanisms based on these association principles. The goal is to develop an automated decision agent and e-brainstorming system that can improve upon human cognitive limitations by removing constraints on working memory and attention through the use of intelligent agents.
Here are the key points about using content-based filtering techniques:
- Content-based filtering relies on analyzing the content or description of items to recommend items similar to what the user has liked in the past. It looks for patterns and regularities in item attributes/descriptions to distinguish highly rated items.
- The item content/descriptions are analyzed automatically by extracting information from sources like web pages, or entered manually from product databases.
- It focuses on objective attributes about items that can be extracted algorithmically, like text analysis of documents.
- However, personal preferences and what makes an item appealing are often subjective qualities not easily extracted algorithmically, like writing style or taste.
- So while content-based filtering can
Health Informatics: An International Journal (HIIJ)
ISSN : 2319 - 2046 (Online); 2319 - 3190 (Print)
https://airccse.org/journal/hiij/index.html
Volume 10, Number 4
Towards a Standard of Modelling Annotations in the E-Health Domain
For more info
https://airccse.org/journal/hiij/Current2021.html
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
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Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
JavaLand 2024: Application Development Green Masterplan
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RESEARCH ARTICLE
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OPEN ACCESS
Medical Application Using Multi Agent System - A Literature
Survey
Sougata Chakraborty*, Shibakali Gupta**
*(Department of Computer Science, UIT, The University of Burdwan, Burdwan, West Bengal, India)
** (Department of Computer Science, UIT, The University of Burdwan, Burdwan, West Bengal, India)
ABSTRACT
In this paper we have projected on the involvement of multi-agent system in medical or health care domain. The
objective of this study is to provide future researchers more resourceful and focused review of various research
papers in this domain. Multi-agent system is most suitable for healthcare paradigm, as the properties of agent
based systems deals with heterogeneous multiple agents. Data distribution and data management in a dynamic
and distributed environment with multi-user cooperation, made multi-agent system more significant in this field.
The disposition of this paper is classified on the basis of theoretical and application approach. We have tried to
cover few relevant papers published on last decade. The main aim of this literature survey is to provide a
complete road map on multi agent system based research on medical health care platform.
Keywords - agent, health care, medical, multi-agent system, multi user collaboration
I. INTRODUCTION
Agent based system which is a part of
Artificial Intelligence, has become a growing area to
manipulate and execute simple or complex problems.
An agent is a computer program designed to work on
behalf of its user or owner. An agent must have the
following attributes: a) Autonomous, b) Pro-activity,
c) Reactivity, d) Communication, e) Co-operation, f)
Negotiation, g) Learning, etc. There are many
features of agents, which helps to solve the particular
task. An autonomous agent has the ability to adapt
and learn from its environment. It can also react
whenever its environment changes and also can take
decision to fulfill its goals. The combination of
multiple agents is known as multi-agent system
(MAS). The key idea behind MAS is to form the
communication and co-operation between agents, so
as they can work together to solve a desired task. The
major applications of MAS are in telecommunication,
internet, robotic applications and medical domain
applications, etc.
The aim of this survey is to set a road map for the
future researchers to find the key idea behind the use
of multi-agent system in medical domain.
The organization of this paper is as follows. In
section 2 we discussed about the background of agent
systems and multi agent system. Section 3 we
discussed about the involvement of MAS in medical
domain. Section 4 describes literature survey of
relevant papers. Finally the paper is concludes in
section 5.
II.
BACKGROUND
The progress of research on artificial
intelligence during the mid-1960s and early 1970s led
to the evolution of agents in the 1970s. In 1966,
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Professor Joseph Weizenbaum of MIT wrote the first
software agent known as ELIZA [3], which was a
computer therapist program, programmed to suggest
a conversation and answer many question [4]. In the
mid to late 1980s, research began in earnest on multi
agent system; researchers understand the concept of
organizations and communications between agents.
In the 80's and during the 90's huge changes occur in
Artificial Intelligence as multi-agent system have
replaced single agent as the computing prototype. In
1990s ABM (Agent based Modeling) was expanded
within the social sciences. In 1990 large-scale ABM
Sugarscape, developed by Joshua M. Epstein and
Robert Axtell, which used to model the role of social
and cultural development [5]. In 1990s developments
there included another famous development, the
Carley ABM, developed by Carnegie Mellon
University's Kathleen, to explore the co-evolution of
social networks and culture [6]. In 2000 Samuelson
done a survey over the evolution of agent based
systems [7], and In 2006 Samuelson and Macal, they
researched on the recent improvements in multi-agent
systems [8] [9]. More recently, Ron Sun developed
methods for basing agent-based simulation on models
of human cognition, known as cognitive social
simulation [10].
III.
MULTI AGENT SYSTEM IN HEALTH
CARE DOMAIN
In recent years agent based systems has
become growing approach to solve the limitations in
the medical health care domain. Medicine domain is
a huge environment distinguished by its common and
distributed decisional characteristics and its
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management
of
care,
which
requires
a
communication and a complex management between
the various medical departments, doctors and
patients. The introduction of multi-agent systems into
the medical domains make it easy for the
management to take its decisions and the actions, and
ensures the communication and coordination by
minimizing the errors of analysis and treatment, and
by improve time required to look for the medical
resources, and other medical departments.
IV.
LITERATURE SURVEY
In this section we will go through several
relevant papers to describe significant applications in
the health care domain that have been developed
using the multi-agent system. The key idea about
each relevant paper is discussed below:
Multi agent Systems, by Katia P. Sycara
(1998): [11]
In this article the author presents some of the
significant concepts in multi-agent system and the
research work related to them. Here the author argued
that multi-agent system can be defined as a loosely
coupled network of problem solvers that interact to
solve problems that are beyond the individual
capabilities or knowledge of each problem solver
[12]. Here the author tries to show the efficient
overall problem-solving logic of multi-agent systems
architecture. Rationality was a global property of the
multi-agent systems which was measured by the
efficiency and consistency of a global solution. But a
multi-agent system lacks in global perception, global
control or global data. The author in his own work
RETSINA, a multi agent infrastructure, shown that
each RETSINA agent was a BDI (belief desire
intention) type agent [13] [14] that integrates
p1anning, scheduling, execution, information
gathering, and coordination with other agents [15]
and [16]. Here the author proposed the idea of
creating software that operates in distributed and
open environments, such as the internet.
A Road map of Agent Research and
Development, by Nicholas R. Jennings, Katia
P. Sycara and Michael Wooldridge (1998):
[17]
In This paper they present a survey of study and
improvements in the field of autonomous agents and
multi-agent systems. The main aim was to find the
idea and applications, and to find the interconnection
between them. Agents are being used in an
increasingly wide variety of applications ranging
from Comparatively small systems such as
personalized email filters to large, complex, mission
Critical systems such as air-traffic control At first
sight, It may appear that such extreme1y different
types of system can have little in common And yet
that was not the case in the key abstraction used that
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of an agent. It was the naturalness and ease with
which such a variety of applications can be
characterized in terms of agents that lead researchers
and developers to be so excited about the potential of
the approach. Indeed, several researchers found that
some feature of agents are being not fitted for the
solution, and that needs to be stopped, otherwise
agent based system will endure a same criticism to
that experienced by the AI community in the 1980s
[18].
Implementation Scheme for Online Medical
Diagnosis System Using Multi Agent System
with JADE, by Mr. Shibakali Gupta,
Arindam Sarkar, Indrani Pramanik and
Bauani Mukherjee (2012): [19]
This paper proposed the concept of online medical
service system for internet users. Here they proposed
the idea of OnlineDocs.com, where the user can get
access to the details of the closest and best health
care system like hospital, medical clinic etc. the multi
agent based system may works well but, the
challenges concerned mainly in five main areas:
(1) Intercommunication between services.
(2) Security of data.
(3) Transparency of environments of different
agents.
(4) Interconnection between users and agents.
(5) Synchronization between distributed services.
The standards for agent interoperation was set by,
The Foundation for Intelligent Physical Agents
(FIPA). Agent Cities was a project, whose target was
to construct a publicly available network of FIPA
based agent architecture. The final aim of the project
was to build a system which could be able to run in
all environments, like Agent Cities network. The
Message Transport System also called Agent
Communication Channel (ACC) [19] was the
software module controls the communication within
the same platform and the remote platform. This
architecture was build using JADE [22] and
whenever a JADE platform was launched, ACC
module started intercommunication by creating the
Agent Management System (AMS) and Directory
Facilitator (DF).
Multi-Agents System for Medical Diagnosis,
by Okba Kazar, Zaidi Sahnoun and Louis
Frecon (2013): [23]
Here the author discussed about the introduction of
multi-agent system into the medical fields, which
helps the management to take the decisions and
actions, and also ensures the communication and
coordination by reducing the errors of diagnosis and
treatment, and by improving time required to search
for the medical resources, and other medical
departments. The multi-agent system approach can be
used in medical domain mainly for the
communication between human and other types of
services. Another aspect of using multi-agent system
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in medical domain was to build the phenomena
simulation of interactions of genes and proteins,
which make it possible to reproduce the way in which
the patients demand with the treatments.
The appreciation of agent move towards the
awareness of a medical application was founded on
the supply of the data and the background drive. The
medical diagnosis was the different compiling data of
different regions; these are mainly divided into two
parts: a) symptoms (subjective) and b) The signs
(objective). The multi-agent system is mainly
developed to serve for the medical field to help the
doctors in their diagnosis process. The architecture of
the multi-agent system using blackboard control is
composed of three elements, these are: a) Agents, b)
the blackboard [25], c) the mechanism of control.
The objective of this paper was to model a multiagent system of support to the medical diagnoses.
Here they used the approach multi-agent system
which composed of three agents, where each agent
with a different subject. These agents communicate
using the blackboard and which helps to give a final
diagnosis. They have noted during recognition of
their system that the multi-agent system was
currently in growing phase, which helps to
communicate between autonomous agents. The
possible prospects for their works can be an
adjustment so that the system was more flexible for
the addition or the removal of an agent.
An Efficient Multi-agent System for E-health
Functionalities, by Mohammad Kalmarzi
Moghaddam,
Mohammad
Shojafar,
Mohammad Reza Nami and Hassan Rashidi
(2013): [26]
Here the author’s concentrated on the concept of
agent oriented software engineering. The main goal
of this agent oriented software engineering was to
build process, tools and services for training and
maintenance of the agent-based software. To
construct and execute multi-agent environments, here
they use Jade Technology. Jade was a distributed
middleware which was a flexible structure that can
easily expand. Jade framework implement agent and
a set of graphical tools so as to make agent oriented
system design very simple and easy. As jade was
completely java based so it can be added to a Library
in java language. Following are the several Jade
characteristics focused hare:
Multi-part application;
Interoperability;
Having open source;
Diversity;
Easy to use mobile software.
Features of E-health like interaction and
communication have amplified the necessity of agent
abased system. The properties of multi-agent system,
which included here:
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Necessity of some domain to multi-agent
systems;
To increase the speed with parallelism;
Reliability;
Extensibility,
Programmer easier.
In recent times, E-health technology [27] developed a
way for tele-medical care [28] which was supported
by modern digital mass communication media.
Finally this paper tried to study the agent-oriented
software engineering and E-Health which provides
agent-based system for the hospital by using the
agent properties and architecture. Though more
improvement is needed in agent technology which
causes additional services improvement and to
overcome E-health communication challenges.
Multi Agent System based Efficient
Healthcare Service, by Byung-Mo Han,
Seung-Jae Song, Kyu Min Lee, Kyung-Soo
Jang, Dong-Ryeol Shin (2006): [29]
This paper mainly deals with the idea of multi-agent
system as a health care system, which composed of
medical sensor modules with combinations of
wireless telecommunication technology. The
projected system comes with various services which
include telemedicine in mobile, monitoring patient,
disaster management and communication between
patients and medical staff. The projected system also
provides a computing platform between the BAN
(Body Area network) [30] and the supportive system
in the hospitals, and communicates with agent
network of BAN’s PDAs via peers. The
communication and message transfer in a hospital
was done by the help multi-agent system
environment. The JADE (Java Agent Development
Framework), a FIPA - certified development
framework which was used to develop the multi
agent system JADE mainly follows these properties:
Interoperability
Uniformity and portability
Easy to use
Pay-as-you-go philosophy
Here they also used LEAP (Lightweight Extensible
Agent Platform), the most known agent platform for
small devices. LEAP becomes an autonomous
development branch of JADE under the LGPL
license. JADE continues its development towards its
monitoring facilities, visualization, ontology’s and
policies. However, LEAP focus on its lightweight
and extensible aspects.
The final objective of this system was to monitor and
verify the patient's health by checking their medical
report and to provide proficient medical service with
the help of medical staff’s assistance. JADE can be
used to develop multi-agent system for healthcare
system and they used the agent platform LEAP for
PDA. The future research aspect involves the
construction of the mediator between BAN and
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hospital domain to implement agents, for more
complex computing on PDA.
The CMDS Medical Diagnosis System, by
Barna Iantovics (2008): [35]
In this paper they proposed the idea of a medical
diagnosis multi agent system called CMDS (Contract
Net Based Medical Diagnosis System), which was a
hybrid system with human and artificial agent
members. The proposed system solves the medical
diagnosis problem by the adaptation [36] of the
cooperative problem solving method, the contract net
protocol [37, 38, 39, 40]. The solution of medical
diagnosis problem based on the identified illness
(illnesses) and the associated treatment (treatments)
which must be applied to cure the illness (illnesses).
Different difficulties in the medical diagnoses
elaborations are analyzed in [41, 42, 43].
In this paper, a system called Feline [44] which
composed of five autonomous agents (expert systems
with some proprieties of the agents) capable with
medical knowledge was proposed. This paper also
proposed a general methodology based on Computer
Algebra for creating medical expert systems [45].
This paper also proposed a Web-centric extension to
a previously developed expert system specialized in
the glaucoma diagnosis. For the implementation of
the medical expert systems the CoCoA language is
proposed [46]. Here they used Cooperative Medical
Diagnosis Elaboration algorithm to show the problem
solving abilities of medical agents. Advantages of
using CMDS System were that the CMDS medical
diagnosis multi agent system can solve medical
diagnosis problems using the artificial agents and
physicians abilities and capacities. The medical
agents (human being and artificial) also can learn
autonomously from each other during the problems
solution replaying processes. The key idea was to use
multi-agent oriented model to solve several health
care related problems by merging and expanding
different problem solving technologies.
A multi agent intelligent environment for
medical knowledge, by Rosa m. vicari, cecilia
D. Flores, Andre M. Silverstre, Louise J.
Seixas, Marcelo Ladeira and Helder Coelho.
(2003): [47]
This paper proposes a multi-agent oriented learning
environment aimed at learning using a positive
approach to perform diagnostic reasoning and
modeling of a domain using AMPLIA [47], which
was mainly focused on medical domain. The
AMPLIA compares its domain with the one that
learner has built; if they are not matched then the
environment starts a process in order to encourage
the learner to review its model. Here they choose
Bayesian network approach to deal with uncertain
knowledge. Uncertainty was represented by
probability and probabilistic reasoning. The
AMPLIA composed of three agents: leaner agents,
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domain agent and mediator agents. The AMPLIA
consist of two phases; that was:
(1) Phase1: AMPLIA Model construction- under
which qualitative and quantitative review of the
construction is done.
(2) Phase2: Diagnostic hypothesis evaluation, which
consists of the evidence input and their
propagation into learner’s Bayesian network.
And it was also based on qualitative and
quantitative Diagnostic hypothesis evaluation.
This paper further discussed about investigating the
use of Bayesian network in agent’s belief modeling
and mental states to guide the negotiation process
using three cognitive agents(leaner agents, domain
agent and mediator agents), two databases(expert
knowledge built-in model database and pedagogical
strategy database) and the interface module in the
AMPLIA architecture.
Data warehousing through MAS in the
medical arena, by Antonio abelha, Jose
machado, Victor Alves and Jose Neves (2004):
[48]
In this paper they proposed an idea of AIDA [49],
Agency for Integration, Diffusion and Archive of
Medical Information is an agency that offers
intelligent electronic workers, commonly known as
pro-active agents. This pro-active agent interacts with
the various systems by transferring medical
information, and thus categorize and saving the
information to respond information requirements
with the necessary resources to their acceptable and
in time execution. Medical Information Systems
(MIS) are seen as a way of optimizing the use of the
accessible health care communications without
resorting to new and costly structure. AIDA offers
web based service tools to interact with human
agents. Based on these conditions, a Healthcare
Information System (HIS) was subjected to in terms
of a) Administrative Information System (AIS), b)
Medical Support Information System (MSIS), c)
Nursing Support Information System (NIS), d)
Electronic Medical Record Information System
(EMR) etc. Multi-agent system used as a problemsolving via theorem proving i.e., agent based
computing has been hailed as a significant
breakthrough in prob1em solving, as a new
revolution in software development and analysis. The
data warehousing [50] [51] for healthcare units for
maintaining the data, the documents of everyday
operations is done using RDBMS. Data warehousing
was there to help manage the system and must also
deal with changing worlds or environments. It mainly
applied on Radiological information system,
Electronic medical board etc.
Multi-Agent Systems for Active, Dynamic
Activity Areas, by Andreas Schweiger and
Helmut Krcmar (2004): [52]
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This paper mainly concerned in the area of health
care by care for patients, the reduction of costs and
the improved decision making process in the areas of
diagnosis and therapy. The multi-agent system was
the technology suited for providing solutions to the
mentioned problems. In this paper current workings
of the German National Science Foundation project
ASAinLog are presented. Properties of the health
care domain are: Cooperation, Communication and
Coordination. The present results of the project
ASAinLog shows that agent technology was suitable
for the software based realization of active, dynamic
activity areas and contributes to the solution of
problems of information logistics in the health care
domain. Central elements of the proposed solution
are active medical documents. Agents perform tasks
of interpretation and concatenation of data, arranging
access control, controlling complex medical
processes, monitoring appointments, associating
similar disease patterns, and recommending potential
therapies. Agent connectors’ support complex,
flexible and domain specific relationships for
communication, cooperation, and coordination.
Software agents act on behalf of their real world
representatives.
A Hybrid Technology for a Multi agent
Consultation System in Obesity Domain, by
Rohana Mahmud, Hairul Aysa Abdul Halim
Sithiq, and Haryuna Mohd Taharim(2009):
[54]
Here the author presented hybrid architecture of a
multi-agent consultation system for obesity oriented
health problems. This hybrid architecture composed
of an expert system (ES) and an intelligent agent
(IA). The author proposed that they will continue to
produce and collect biomedical knowledge
effectively and efficiently using multi-agent system
in the medical domain. The main idea behind this
research was to study the capability of hybrid multi
agent with expert system which capable of
diagnosing symptoms and provide consultation
related to obesity problems [55]. Obesity was treated
as a chronic non-communicable disease, like
diabetes, cardiovascular diseases and cancer. But
these are often curable locally if appropriate
diagnosis and treatment plans are provided
adequately. In the multi-agent system, the service of
hybrid technologies 1) pull technology and 2) push
technology allows an agent to help efficiently in
solving problems and share professional knowledge.
The system needs to be correct at any point in
analyzing the health and indication of a patient as it
deals with patient’s life. The proposed multi-agent
system was composed with the following
components which consist of collection of agents,
namely
(1) Diagnosis agent: This was responsible for
providing expertise knowledge.
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(2) Treatment agent: this agent consulted the
diagnosis results and suggests an appropriate
treatment plan.
(3) Collaboration agent: it helps to build the
communication between the above two agents.
The communication was done using two
methods which are: a) Image-text mapping and
b) Lexical relation ontology.
(4) User interface agent: it will integrate all the
knowledge bases of the expert systems in the
diagnosis and treatment agents.
The system was available to support users who have
obesity problems. This architecture directs the people
being aware of the necessity of fitness and good
health for long healthy life. However, in future
additional work has to be done before applying the
commercial multi agent consultation system for
obtaining the knowledge [54].
Naive Bayesian Learning based Multi agent
Architecture for Telemedicine, by Ei Ei Chaw
(2013): [56]
In this paper they discussed about Naive Bayesian
machine learning (NBMS) based multi-agent system
architecture for telemedicine and simulates a model
system for cancer classification. An agent for
different medical problem solving consists of
different properties:
a) Increased autonomy in operation,
b) Capability of communication,
c) Autonomous learning capability and
d) Capability to interact with the environment.
These properties permit the agents to synchronize
with other agents and human during different
problem solving. Multiple agents coordinate with
each other, balance each other's capabilities, and
distribute each other's knowledge to provide good
quality services [61]. Telemedicine [57] [58] was the
practice of medical health care where the interaction
between medical personnel and patients was done via
audio-video communication system without the
common physical physician-patient interaction.
Industry Canada uses telemedicine to provide
healthcare services in remote areas [59]. In this
architecture three types of agents are used these are:
a) Initial agent: It was an agent which interacts with
the user of the system, and it accepts the problem
and sends the problem to organizer agent for
further processing.
b) Coordinator agent: It was like a broker agent or
middle agent, which sends the problem to
desired assistant agent based on its own
knowledge. It applies Naïve Bayesian learning
mechanism for building the agent's knowledge.
c) Assistant agent: These agents actually process
the problem. They have own knowledge to
process the certain problem. They use Naïve
Bayesian classification and are trained with
training datasets.
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Here they developed the application using the
METATEM [60], which was a language based upon
the direct execution of temporal formulae. This
language was based upon executable logic; the
language can be used as part of the design and
prototyping of hasty systems. Also, as it uses
temporal logic, the language provides a high-level
programming detail in which the dynamic Attributes
of individual parts can be concisely represented. In
this system, each agent has their own different
knowledge. It was built on match maker based
coordination mechanism Assistant Agents do not
need to keep the information of other same level
agents. The agents initially know nothing about their
environment and learn to coordinate gradually. The
chain rule based architecture [56] was built on ring
based architecture. Each agent has their own different
knowledge. Assistant Agents need to keep the
information of other same level agents. Middle agent
or coordinator agent keeps the information about the
problem and agents' capabilities in the system.
Coordinator Agent sends the problem to one of
assistant agents (AG) [56]. Comparison between the
proposed systems with Chain Rule based system
architecture has been performed. The comparison
was made on the number of messages, complexity,
extendibility, efficiency and processing time.
According to these the proposed system architecture
was more suitable. But CHA based system was better
than the proposed system when the service Provider
agent was found directly. However, directly found
system was not always occurring. Besides the
processing time for CHA system was unstable. Thus
the proposed system was better than the CHA.
Multi Agent System Based Clinical Diagnosis
System. An Algorithmic Approach, by
Shibakali Gupta, Sripati Mukhopadhyay
(2012): [65]
In this paper they have proposed an operational
algorithm to describe the operations of a hybrid multi
agent system based intelligent medical diagnosis
system called Clinical Diagnosis System (CDS) [66].
User friendly interface like medical diagnosis system
required to be providing high performance, reliability
and functionality. The Clinical Diagnosis System
consists of several types of agents:
1) User Agent (UA): it was responsible for taking
the user or patients inputs.
2) Master Agent (MA): it converts the raw input
data collected by UA to knowledge. The
knowledge is stored in the Global Knowledge
base (GKB).
3) Specialist doctor agent (SDA): it was selected by
MA to assign a particular task for solution.SDA
was associated with a individual local knowledge
bases (LKB) to reach its desired solution.
After having the solution, SDA will give it to the
Master Agent (MA) and that solution will be stored
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in the solution knowledge base (SKB).The proposed
agent oriented clinical diagnosis system (CDS) Can
takes care of all possible stage of patients which
includes patients initial check up, treatment, and
report for the patients. Whereas the CDS algorithm
was capable to handle the system efficiently. In
future the proposed system can be developed and
applied to fulfill the demand of the medical scenario
in India.
A Multi-Agent Learning Paradigm for
Medical Data Mining Diagnostic Workbench,
by Sam Chao and Fai Wong (2009): [67]
This paper proposed the model of practical data
mining diagnostic which intends to support real
medical diagnosis by two emerging technologies data mining [68] and multi-agent system [69][70]. To
fulfill this effort they implemented an intelligent and
cooperative diagnostic structure with multi agent
approach called i+DiaMAS [71]. i+DiaMAS provides
an integrated architecture that includes a variety of
preprocessing agents as well as learning agents
through interactive interface agent [67]. The data
mining systems are only capable of dealing with
homogeneous [72] and centralized data. So here they
proposed the concept of addition of intelligent agents
into a data mining system can perfectly balance the
most of existing data mining methods. In this
architecture the key idea is to distribute a complex
problem among several agents that have the ability to
execute in parallel and communicate by sending
messages. Overall there are five types of agents
which deals with various data mining task:
1) Coordinator Agent (AgCor): it forms the core of
i+DiaMAS by coordinating with several agents’
behavior. Its breaks a task into sub-tasks and
then merge the results from several agents to
form the overall response to a task.
2) Data Preprocessing Agent (AgPre): the primary
key factor of a successful data mining tool was
using the right data [73]. This agent filters the
useful data from the raw data by data
preprocessing technology.
3) Data Mining Agent (AgDM): it contains a set of
learning agents that each of which implements a
methodology to diagnose a specific disease.
4) Optimizer Agent (AgOpt): this agent optimizes
the learned outcomes from AgDM. The main
activity of AgOpt contains:
a) Pruning: deals with overfitting [74] problem.
b) Integration: integration task was necessary
to assemble the results from different
learning modules.
c) Knowledge presentation: both inputs and
outputs of a data mining system must be
simple
enough to be understood.
Visualization and visual data mining play an
important role in biomedical data mining
[75].
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5) Supporting Agent (AgSup): it interacts between
users and system, and handles various
manipulations regarding data files, such as file
operations and format transformations, etc.
Additionally, their agent-based learning approach
was experimented in terms of classification accuracy
over sixteen bench-mark datasets drawn from UCI
repository [76] against two learning algorithms: C4.5
and ITI (Incremental Tree Induction) [77]. Here in
data mining agent, they included the i+ learning
module based on i+ learning (intelligent and
incremental learning) algorithms, which makes the
decision trees a more powerful, flexible, accurate,
and widely accepted model. i+DiaMAS aim to solve
the growing learning problem that in medical
domain, through the adoption of multi-agent strategy
and data mining methods.
Cooperative medical Diagnoses Elaboration
by Physicians and Artificial Agents, by Barna
Laszlo Iantovics (2007): [78]
Here the author discussed about Cooperative medical
diagnosis systems which used to solve complex
medical analysis problems. In this paper, a medical
multi-agent system called MAMSM (Medical
Assistant Multi agent System) that facilitates the
doctor and medical personnel to do their job more
professionally and rapidly. The teamwork of the
human and artificial belief judgment advances in the
diagnostics improvement. The proposed multi-agent
system was a composite system which was composed
of relatively simple agents. The simple agents can
resolve several medical problems which may be
unnoticed by the doctor or medical personnel. A
medical diagnosis problem represents the description
of a patient's illness. The solution of the problem
represents the identified illness and the proposed
diagnostic to cure the illness. To build these kinds of
agent systems some methodologies were proposed,
like: HIM [79], GAIA [80] and PASSI [81]. Here
they analyze different aspects of the multi agent
systems
specialized
in
medical
diagnosis.
Understanding such systems needs a high-level visual
view of how the systems work to achieve some
application related purpose. Here the method of
visualizing the behavior of a medical multi-agent
system based Use Case Maps was projected. Medical
expert systems represent comparatively typical
applications in the medical diagnosis. Examples of
well known medical expert systems are MYCIN [82],
GIDEON [83], CARDIAG2 [84], PUFF [85] and
CASNET [86]. In this paper, a cooperative MAMAS
specialized in medical diagnosis called CMDS
(Contract Net Based Medical Diagnosis System) is
proposed, which was a complex system composed
from medical expert system agents that cooperatively
solve medical diagnosis problems. The Advantages
of these software mobile agents are that they are
capable of change their location in the network where
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they operate. The main disadvantages of the mobile
agents are related with their limited Communication
capability, protection capability, Intelligence and
capability to use knowledge bases in the problems
solving. These disadvantages limit the use of the
mobile agents for medical problems solving in
insecure networks.
A Multi-Agent System Architecture for
Monitoring Medical Protocols, by T. Alsinet,
R. Bejar, C. Fernandez and F. Manya (2000):
[87]
Here they describe the construction of a multi-agent
system that used to support and regulate the
implementation of health care or medical practice in
hospital environments. A medical protocol performs
a series event in front of medical diagnosis systems.
The work reported here was a part of the SMASH
project. SMASH deals with the construction of multiagent systems to deal with complex problems of
distributed nature in hospital environments. The
purpose of this paper was to identify a system for the
support and management of the execution of medical
protocols in hospital environments. They have
implemented a user-friendly environment called
JAFDIS to graphically represent negotiation
processes in multi-agent system based on the concept
of dialogical Institutions [88]. Here they discussed
about the multi-agent system architecture, which
consists of:
Dynamic location: The system must be available
from any point of the Internet (or of the hospital
intranet) and allow agents to interact
independently of their physical location (the
network address).
Information
confidentiality:
Agent
communications, can involve private information
about patients so all message must be sent
encrypted.
Information integrity: The agent system must
identify the third party manipulation in order to
maintain the information integrity.
Agent’s authentication: To maintain the message
integrity all messages will be digitally signed.
Medical protocols monitoring: The system must
supervise all SDA interaction in order to register
invalid actions and to support SDA decisions.
Medical protocols delivery: The system must
deliver MP patterns with a suitable format.
They also discussed about 3 types of agents:
1) Certification agent (CA): Agents need their
certificates for authenticate messages.
2) Supervisor agents (SA): These agents keep eye
on the SDA’S interactions and their validity.
3) Medical protocols server agent (MPSA): It
distributes the available MPs to SDA’s and also
distributes MP specification to SDA’s.
Here they also discussed about the communication of
the different agents via some communication
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interfaces; here they use different network
communication protocol stated below:
Communications are symmetrically encrypted by
using the triple DES algorithm to provide
confidentiality. The symmetric encryption key
was negotiated in every session.
The negotiation of the symmetric key was
performed by using the public key HSA
algorithm.
This
algorithm
allows
an
authenticated communication between the
involved agents. Therefore every agent has a
public and a private key.
The public and private keys are generated from
the login and password of the agent.
Two agents transfer one to each other their
public key through their certificates.
A message authentication code (MAC) was used
to compute a digest of the plain message. This
digest is encrypted wit h the private key of the
sender agent.
In future they plan to use this prototype in real
hospital environment and also plan to build an agent
that will automatically discover the medical history
and progress of the patients.
Medical Image Segmentation using a MultiAgent System Approach, by Mahsa Chitsaz
and Woo Chaw Seng (2013): [89]
Here the author proposed an idea, where they design
a framework to extract concurrently several objects
of interest from Computed Tomography (CT) images
by using some priori-knowledge. Their method uses
the properties of agent in a multi-agent environment.
Firstly, most image segmentation solutions are
problem-based
Secondly;
medical
image
segmentation methods have limitations because
medical images have very similar gray level and
texture among the concerned objects. The following
properties are represented for a hardware or softwarebased computer system agent:
1. Autonomy.
2. Social Ability
3. Reactivity.
4. Pro-activeness.
5. Robustness.
The idea of Liu and Tang based on multi-agent
system to segment a MRI image of brain. The brain
has the four basic elements like as: a) outline, b)
branching region, c) enclosing region and d) tumor
region. For detecting each four regions, they assign
some threshold range. The agent behavior is one of
these four types: a) breeding, b) Pixel labeling, c)
Diffusion, and d) Decay.
The main improvement of this projected method was
that it almost automatic; it works without user
interaction in segmenting the image. The most
significant benefit of this method was segmenting
image into more than two regions in a parallel way.
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Towards Maturity in Multi-agent Based
Remote Patient Monitoring, by Fiaz Majeed,
Aslam Muhammad, Rizwan Saleem and
Martinez-Enriquez A. M. (2011): [90]
Here the author proposed the idea of implementing
multi-agent system with RPM (remote patient
monitoring).The main purpose of this paper was to
find weaknesses in present system and propose an
idea to improve the system’s functionality. Recently
multi-agent systems have been switched from PC's to
mobile technology. So patient at home or road can be
treated by the medical staff using mobile technology,
the doctors may be alerted about patient condition
simultaneously. The GPRS based communication
activity is wastage of resource and cost as there is
rare communication
held between patient and
medical staff in multi-agent system. So Short
Messaging Service (SMS) was a possible solution
which is proposed in this RPM architecture. Features
for comparison with existing systems architectures:
Mobility: The system should keep running in any
location.
Security: The whole system must be centrally
controlled.
Recovery: It should have ability to recover from
failure.
Efficient communication: the information
exchange between sender and receiver instantly.
Updated technologies: It should have the
capability to adapt with newer technologies.
Centralized DB: It maintain a centralized DBMS
which secures integrity of data.
Co-operation: It should maintain the architecture
in such a way that agents can easily co-operate.
The main advantages was that it resolved significant
problems in the existing multi-agent based RPM
infrastructure such as alternative solution for
continuous GPRS connection was provided, a way to
increase battery life was proposed and data
management in the network is resolved in our
framework.
Finally, prototype system demonstrates its practical
viability. The system work efficiently and securely in
the MAS environment.
Modeling a massive multi agents system for
the knowledge of practices in African
traditional medicine, by Ghislain A.
Atemezing, Laure P. Fotso, and Alain Cardon
(2004): [91]
African traditional medicine which was strictly
bound to the culture of the people was a complex
system of treatment in which there was interference
of several actors from several domains. It treats
disease as a social illness which should be removed
from the root. This traditional knowledge was not
frequently prepared because it generally passed
verbally. The study of ontology shows that several
concepts consisted of several categories called
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semantic traits [91]. The approach which they
proposed the adaptive qualities of the multi-agent
system. An adaptive system having ability to develop
the sense of the production and the manipulation of
knowledge. The adaptive systems with thoughtful
action type and its resolution fall into the Scope of a
massive multi-agent system (MMAS) [91]. Here the
adaptive agents systems applied various types of
agents these are:
Aspectual Agents: These agents are defined from
the ontology related to physical aspects observed
in the traditional medicine.
Pregnancy agent: These agents can communicate
among them, and can regroup, and also
communicate with aspectual agents to guide and
modify them to form major behavioral groups.
Here they identify four types of pregnancy agents
related to traditional medicine. These are 1) Nuisance
Agent (NA), 2) Divination Agent (DA), 3) Protection
Agent (PA), 4) Intimidation Agent (IA), 5)
Mystification Agent (MA).
Organization agents: Its action guide the group
of aspectual agents to accept a particular
structure appreciated globally, taking into
account the environment of the system.
Specific Agents: It also divided into : 1) Interface Agents: Used for the filtering of data
coming from the user.
2) Information Agents: Used for the collection of
information coming from an existing data base.
3) Analysis Agents: Used as a pattern matching
of the information coming from the sensors of
the system.
4) Geographic Agents: Used for geo positioning
for the communication with all the agents
involved in this model.
The future aspect of this project was how to produce
the artificial environment, as the language in
traditional medicine is the one used to exchange
knowledge.
Use of Ontology-based Multi-Agent Systems
in the Biomedical domain, by Maja Hadzic
and Elizabeth Chang (2005): [92]
Here they proposed the design of ontology, called
Generic Human Disease Ontology (GHDO). They
also design multi-agent system architecture from
several different information resources. This multiagent System uses the common GHDO ontology for
query processing, data recovery and information
incorporation. The ontology works on different levels
of information retrieval such as:
1) It is used to locate and recover requested
information.
2) It is used for enabling agents to communicate
with each other during the process of information
recovery.
3) It is used to examine and manipulate the
recovered information.
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4) It is used to represent the recovered information
to the user in a significant way.
Here they describe four different types of agents and
four different phases in the process of problem
solving within the multi-agent system. Here they
discussed about agent cities, AADCare, BioAgent,
Holonic Medical Diagnostic System (HMDS).
Agent Cities- It was a multi-agent system (MAS)
composed of agents that provide medical
services.
AADCare- It was agent architecture [93]
comprises several levels of information, a
memory, a Communications manager and a
human computer interface.
BioAgent- It was a mobile agent system suitable
to support bio scientist during the process of
genome analysis and explanation.
Holonic Medical Diagnostic System (HMDS) - It
was a medical diagnostic system. This system
combines the Advantages of the holonic
paradigm, multi-agent system technology and
swarm intelligence in order to realize Internetbased diagnostic system for diseases.
Agent Cities and AADCare are designed as Multi
agent architecture, where Bio-Agent and HMDS are
systems used as mobile agents. The advantages of
this architecture, firstly it allows users on a
worldwide basis to intelligently access new scientific
information much more rapidly. Secondly, it
improves research efficiency and effectiveness.
Thirdly, it improves interoperation which make
possible to divide a big job among different research
teams.
Although they have implemented this system but still
lacks in security concerns, testing and validation, and
development of user view interfaces, which needs to
be developed.
Intelligent Scheduling in Health Care
Domain, by Srividya Bhat , Nandini S. Sidnal
, Ravi S. Malashetty and Sunilkumar. S.
Manvi (2011): [94]
Here they presented this work to construct an agent
based information services for mobile users. An
agent was distinguished by its property of
Situatedness, autonomy and Flexibility. Multi-Agent
systems (MAS) were suitable for many medical
domains, as it was an upcoming technology that
promises to make much easier to design and execute.
Here multi-agent system uses java platform to
achieve better quality of service. This system
schedule the meeting between the patients and the
relevant doctors meeting in a professional way for
schedule and emergency services. System with above
featured properties offers the possibility of lower the
cost and Improve the performance of operations by:
Automating routine tasks.
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Enabling users to complete tasks that would
otherwise be very difficult, time consuming,
costly or just impossible.
Adapting to unexpected events or changes in the
environment automatically.
Multi-agent system can generate a complex system
by a collection of agents, which can have the
following ability:
Social ability: agents have to communicate with
each other and their users.
Reactivity: agents have to perceive their
environment.
Pro-activity: agents not only react on signals, but
also take autonomous actions, to achieve a
desired goal.
Agents can be classified by their capabilities and
method of implementation as given bellow:
1. Collaborative agents: these are autonomous and
they interact with each other.
2. Interface agents: it can ask for guidance from
other agents and user can also instruct interface
agents.
3. Mobile agents: these are agents on the move.
4. Reactive agents: They response to an impulse
which can be triggered events and are
appropriate for handling sensor data.
5. Hybrid agents: it was the combinations of the
above agents.
In future the latest version of JADE, JADE-LEAP
v2.4 can be used to achieve better results and
consistency than the existing architecture.
Communicating Agents in Open multi agent
systems, by Terry R. Payne, Massimo
Paolucci, Rahul Singh, and Katia Sycara
(2002): [95]
In this paper the author concentrated on the idea of
how agents communicate with each other. In multiagent system, agents often use the services of other
agents to perform a desired task. To perform this,
agents first have to locate the specific service
provider agent and then communicate with it. To
interact with a service provider, an agent requires: 1)
the service provider agent's interface; 2) the ontology
that defines concepts used by the provider agent; and
3) the agent communication language (ACL) [95].
Currently multi-agent system encodes the interface
description and the ontology of a service provider,
but
assumes
a
common
ACL
between
communicating agents. Many multi-agent systems
achieve this through the use of a Middle Agent, such
as the OAA Facilitator [96], the RETSINA
Matchmaker [97] and the Info sleuth Broker [98].
Agents can be classified as service providers or
service requesters, or both, depending on their role
within the multi-agent system, or the context in
which they are used. Agent Communication
Languages (ACLs) such as KQML [99], FIPA [100],
and emerging web service communication languages
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such as SOAP [101] propose the adoption of a
common ACL for all agents. Some class of Middle
Agents acting as Brokers or Facilitators [96,102]
which maintains knowledge of both the capabilities
and preferences of different agents and coordinate the
communication between them. Another sub-class of
Middle Agents was generally known as
Matchmakers, Yellow Pages or Directory Agent
systems [103, 104, 99, 105]. The matching process is
done depending upon the capability description
language (CDL) used. Infrastructures such as JINI
[106] and UDDI [107] provide class-based or
business- based lookup mechanisms to identify
registered, distributed services.
Finally formal semantics and ontology specification
languages such as the DARPA Agent Markup
Language (DAML) [108] are emerging, which
support semantic interoperation between agents.
Concepts defined in DAML can be readily combined
with message templates, thus increasing the
flexibility of template based messages, and hence
accessibility and interoperability of services within
open environments.
Decision-making with Trust and Control in
multi agent systems, by Chris Burnett,
Timothy J. Norman and Katia Sycara (2010):
[112]
Here author argued that in these environments, it was
difficult for agents to form a stable trust relationship
which was essential for secure communications.
Trust was considered as one of the major properties
of open multi-agent system (MAS), where agents
may be self interested, diverse and deceptive
[110][111]. Here they find that agents also use
controls to ease the risk in initial interactions. Here
they consider three kinds of controls which can be
used in the cases where trust is not sufficient. These
three kinds of controls are:
1. Explicit incentives: The trustor creates a contract
specifying the compensation (in terms of utility)
that the trustee will receive, dependant on the
outcome [109].
2. Monitoring: The trustor expends additional effort
in order to observe the behavioural choices of the
trustee [109].
3. Reputational Incentives: The trustor calculates
the reputational gain (or damage) that a trustee
will experience as a result of good (or bad)
feedback being communicated to the society
[109].
Here they have proposed a probabilistic trust
evaluation model; where in case of risk a trustworthy
partner was selected based upon decision-theoretic
approach to the risk situation approaches in order to
Support decision-making in multi-agent systems.
In future, an approach which aims to overcome the
problem of building trust in dynamic environments
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[112] [113] still need a base of communications to
form generalization.
Autonomic Management for Multi-agent
Systems, by Nadir K .Salih, Tianyi Zang, G.K
.Viju and Abdelmotalib A. Mohamed (2011):
[114]
This paper proposed multi-agent system with
autonomic characteristic that completely hides its
complexity from users/services. To provide web
based service to the users here they used Java
Application Development Framework [115] as
platform of this environment. The combination of
multi agent support system it also provides the
following properties like adaptability, intelligence,
collaboration, goal oriented interactions, flexibility,
mobility and persistence in software systems [114]. A
new platform called Agent Oriented Programming
(AOP) is implemented here. Being autonomous it can
autonomously carry out complex, and often longterm, tasks [115]. A planner section is used to create
multiple step scripts or sequences of actions
necessary to achieve the high-level goals [116]. The
agent design is done using FraMaS advanced
behavior wrappers [117], where Agents was mobile.
The limitations of multi-agent System were:
1. Individual agent has incomplete information or
capabilities for solving the problem.
2. There is no system global control.
3. Data are decentralized, and
4. Computation is asynchronous [118].
So here they included the concept of Bean Generator
which is a JADE supported tool that supports
message content ontology’s for creating agents.
There are several languages like be XML, RDF,
DAML-OIL, XMI, SQL or UML which was used to
signify these ontologies [119]. They have used the
jademx JADE add-on, which offers two major
abilities: 1) to interface JADE agents with Java JMX
(Java Management Extensions) and 2) to unit test
JADE agents using JUnit. The construction of a
jademx agent can be done programmatically or the
XML [115]. They also used OMACS which was a
meta model for agent systems. It enables the multiagent system to autonomously reconfigure at runtime,
which deals with unpredictable situations in a
dynamic environment [120]. They also introduced
MAGE, a powerful development environment for
autonomous computing, which supports agent-based
requirement analysis, design, development and
deployment [121]. Here they mentioned JADE was
implemented to provide programmers with the ready
to-use and easy-to-customize facilities. They have
suggested an agent-Web service that has the
properties of both the agent technology and Web
services technology and viewed as an independent
multi-agent based support system.
Towards a canonical framework for designing
agents to support healthcare organizations, by
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John Fox, David Glasspool, Sanjay Modgil,
Pancho Tolchinksy, Liz Black and members
of the ASPIC project (2006): [122]
Here the author showed that although multi-agent
system is giving promising outcome, but still the
design and execution of the agents and their
communications varied from one application to
another, which has harmful hypothetical and
technical consequences. The ASPIC project was
subjected to realize the environment of agent systems
and survey a standard agent architecture that was
hypothetically organized and reused among several
applications. The ASPIC project also supports the
conclusion and management components of domino
model, which includes added abilities to explore new
ideas and learning for the improvement of medical
diagnosis [122]. The aim of ASPIC project was to
develop a software platform in which application
developers can generate agents that utilize some
properties that are essential for agents to work
autonomously in health care organizations which
includes the objective management, trouble
management and development. The agent
architecture that is being developed within the ASPIC
project shows that the COGENT simulation package
is being used to prototype the ASPIC agent, and
initial versions of the CARREL and CREDO
applications.
In future they expect the generalization of this agent
model in terms of canonical signatures for its core
computational functions which are universal to many
agent architecture and technologies, as well as
providing a theoretical aspect for designing and
understand complex multi-agent system.
Use and Modeling of Multi-agent Systems in
Medicine, by Maja Hadzic, Darshan S. Dillon
and Tharam S. Dillon (2009): [124]
In this paper they represent an ontology-based multiagent system (MAS) specially planned to retrieve
information from human diseases. The human disease
ontology is classified into four ways: a) disease
types, b) Symptoms, c) Causes and d) treatments.
Here the MAS consist of four different types of
agent:
1. Interface agent: It constructs Specific Human
Disease Ontology (SHDO) templates from
GHDO, which can be sent to the master and
smart agent.
2. Manger agent: It allocates tasks to the different
Information Agents.
3. Information agent: This agent retrieves the
requested information from huge biomedical
databases. And
4. Smart agent: It accumulates and study the
information received from Information Agents
and assembles the information based on the
structure specified by the SHDO template.
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Here they proposed an idea of Generic Human
Disease Ontology (GHDO)-based multi-agent
system, where the agents are sociable means they can
be able to communicate with each other in order to
cooperate and negotiate with respect to the
information, knowledge and services. GHDO is used
to develop the Specific Human Disease Ontology
template (SHDO template). They used UML 2.1 to
represent the social and goal oriented behavior of
agent. They used a Composite Structure Diagram to
represent the goal driven nature of an agent. The
UML 2.1 Sequence Diagram is used to collect and
represent the sociable behavior of agents. The
different roles of an agent have been represented by
the different ports which matched with the ports of
the UML 2.1 Composite Structure Diagram.
An Ontology-Based Intelligent Agent System
for Semantic Search in Medicine, by Jung-Jin
Yang (2003): [125]
Here the author illustrates an ontology based
information retrieval agent system in medicine
through Bio-related literature database specially
MEDLINE. The task of interface agent system was to
help patients by examine both existing medical
ontology’s and its own agent ontology to generate
queries in order to get useful and suitable
information. Here agent ontology was in the form of
the Semantic Web languages. The Semantic Web
community deals with these concerns by defining
standard mark-up languages like RDF, RDF Schema,
DAML+OIL, and OWL [126]. This paper describes
the design and execution of a system called OnSSA
(Ontology-based Semantic Search Agent) which uses
the Semantic Web languages for the systematic
recovery of literature in medicine. Here the system
supports DAG (direct acyclic graph) based query
models to match ranked output recovery with a query
by the user. They also analyze the system with a
simple query and a query with clinical query option
in PubMed, a biomedical search engine and the
Ontology part was implemented with DAML+OIL
language and OilEd editor was used partly. In
addition to the Semantic Web languages, the rest of
work is done using Java. They examine both free-text
search and MeSH terms for subject heading search
separately with a simple query [127]. This task was
closely related with another ontology based
information retrieval system called MELISA
(Medical Literature Search Agent) [128]. The
advantage of this system was that, it advances the
quality of information recovery and reduces the
user’s load in information search. Further they will
attempt to help user by incorporate patient’s profiles
to provide related information through user’s
personal interests and genetic inheritances.
An Access Control Scheme for Multi-agent
Systems over Multi-Domain Environments,
by C. Martinez-Garcia, G. Navarro-Arribas,
www.ijera.com
www.ijera.com
J. Borrell, and A.Martin-Campillo (2009):
[129]
In this paper they discussed about MedIGS (Medical
Information Gathering System) [130], which was a
multi-agent middleware for the medical data
allocation between hospitals which enables the use of
applications in multi-domain environment. Here a
distributed access control for MedIGS was presented
which proposes a solution with a Minimum impact in
the local access control systems of the hospitals
based on attribute conversion. MedIGS application
had to develop a generic model based on Role-based
Access Control (RBAC) [131]. The aim of MedIGS
was to provide support for Virtual Electronic Patient
Records (VEPR), which is the collection of all the
medical documents referring to a given patient
among several hospitals. Here they use MAS which
is a collection of several types of agents:
a) Collector agents,
b) Document Broker Agent,
c) Remote Broker Agent, etc.
The main idea was to create an attribute mapping
between the different domains, through which agents
can be interoperable; means an agent working in a
foreign domain can access to the local access control
policies defined in the local domain. The MedIGS’
access control system has two main parts:
1. First is the hospital’s access control system,
which is supposed to have at least a Policy
Enforcement Point (PEP), a Policy Decision
Point (PDP) and a set of access control policies
(ACP).
2. Second was the MedIGS extension, which
consists of an Attribute Management Service
(AMS), the attribute conversion policies (Cp)
and a local repository of certificates (Crep)
[132].
Moreover, it has been described an access control
system for the MedIGS scenario, and its integration
analysis, which can be easily modified to any multiagent system in a multi-domain environment.
A Multi-Agent Prototype System for Medical
Diagnosis, by Qiao Yang and John S. Shieh
(2008): [133]
This paper proposed a model of multi-agent diagnosis
helping system (MADHS), which consists of four
different kinds of agents: Coordinator, Examiners,
Specialists and Joint Decision Maker. This Proposed
model is similar to the e-medicine system design
presented by Tian and Tianfield [134]. The model
and reasoning mechanisms were implemented using
Java, Java Agent Development Framework (JADE),
Java Expert System Shell (JESS) and NRC Fuzzy J
Toolkit. Fuzziness and uncertainty have been
integrated into the decision trees to form the
reasoning mechanism of agents. JADE supports two
Message Transport Protocols (MTPs): IIOP and
HTTP. The inter-platform communication mainly
539 | P a g e
13. Sougata Chakraborty et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.528-546
depends on the default settings of JADE i.e. HTTP.
JADE has provided several useful tools to develop
the multi-agent platform, including Directory
Facilitator (DF), Agent Management System (AMS)
and Remote Monitoring Agent (RMA) to create
distributed Containers and Agents [135].
In Traditional Chinese Medicine (TCM) [136],
concepts, terms, rules are often fuzzy; and the facts
and conclusions are often uncertain. In order to
encode the fuzzy sets, fuzzy rules and to perform
fuzzy and uncertain inference, they proposed a fuzzy
knowledge-based system MADHS which consists of
knowledge representation method, Fuzzy Decision
Tree. Here the Fuzzy Decision Tree combined with
certainty factor calculation was proposed to deal with
fuzziness and uncertainty and to deduce the diagnosis
results. A prototype system of MADHS has been
implemented according to the presented novel model.
An organisation-based Multi Agent System
for medical emergency assistance, by Roberto
Centeno, Moser Fagundes, Holger Billhardt,
Sascha Ossowski, Juan Manuel Corchado,
Vicente Julian and Alberto Fernandes. (2009):
[137]
In this paper they represented an application for a
real-time Health scenario, the mobile medical
emergency management in an urban area. The
representation was implemented using the THOMAS
approach to open multi-agent system (MAS) based
on an executive metaphor. The THOMAS approach
platform uses the idea of organization as a key
abstraction to build multi-agent system in Real time
environment. This approach it provides a way to
build Service Oriented Multi-Agent Systems
(SOMAS). It defines an abstract architecture, as well
as a software platform, where agents interact
according to norms and rules put forward by the
organizational structure. Their system was based on
real-world data provided by the Medical Emergency
Service of the Region of Madrid (SUMMA112)
[137]. This service handles the medical emergencies,
including the transportation of patients to hospitals. A
typical medical emergency assistance starts when a
patient calls SUMMA112, Asking for assistance. The
basic roles within medical emergency assistance
processes are:
a) Patient: agents playing role patient represent
potential users of medical emergency assistance.
b) Hospital: this role represents a hospital within
the organization.
c) Ambulance: agents joining the organization with
the role ambulance.
d) Emergency Coordinator: this agent was the
main piece in the mHealth organization.
Now the web services used to implement the
resources are the following:
www.ijera.com
www.ijera.com
a)
Emergency Centre Finder: this service finds the
responsible emergency co-ordinator for a given
location.
b) Medical Record Store: it allows agents to store
and retrieve patients' medical history information
through the user name and password.
c) Ambulance Finder: This service was able to find
an ambulance for a particular patient, taking into
account information such as his position, his
symptoms, etc.
d) Hospital Finder: The service was able to find a
hospital taking into account the patient’s
position, his symptoms.
The main components of THOMAS approach are the
following:
a) Service Facilitator (SF): this component provides
simple and complex services to the active agents
and organizations.[137]
b) Organization Manager Service (OMS): it was
mainly responsible of the management of the
organizations and their entities.
c) Platform Kernel (PK): it mainly allows agents to
communicate with each other.
They developed their application as an application of
the THOMAS abstract architecture, which openly
supports MAS. Moreover, the THOMAS platform
facilitates the addition of non active entities as web
services, following a SOMAS approach. In order to
show the system was dynamic, they have
implemented a case study based on real data provided
by the Madrid Medical Emergency Service
SUMMA112.
In future they planned to work more on the dynamic
nature of the architecture which decrease the
response time and cost. And they also planned to
increase the functionality of the application.
Virtual Medical Board a Distributed Bayesian
Agent Based Approach, by Animesh Dutta,
Sudipta Acharya, Aneesh Krishna and
Swapan Bhattacharya (2012): [139]
Here they proposed the idea of medical board in rural
areas to provide a sophisticated medical facility. To
form this kind of virtual medical board the based
Multi-agent based distributed decision support
system. Multi-agent system (MAS) based decision
support system was a system where number of
software agents takes a decision of a given problem
collectively. An agent was a computer system or
software that can act autonomously in any
environment to achieve a purpose MAS. Also two or
more agents can communicate or work together to
perform a set of tasks or to satisfy a set of goals. Here
number of software agents act as group of expert
physicians forming medical board. In their system
they characterize the knowledge base (KB) of expert
agent in the form of Bayesian network. A Bayesian
network or Bayes network or belief network was a
probabilistic graphical model as medical diagnosis
540 | P a g e
14. Sougata Chakraborty et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.528-546
was a probabilistic method so in our system we use
Bayesian network to represent the knowledge base of
each expert agent by specifying probabilistic
relationships between diseases and symptoms [139].
Ontology [139] was useful for the suitable
representation of domain knowledge .The domain
specific Ontology contains some rules and
methodologies which describe how to take a final
decision by determine different problem and
argument during group discussion of set a of decision
maker. They also proposed an optimization function,
minimization of which raises the final decision
utility.
An Agent-Based Knowledge Discovery from
Databases applied in Health Care Domain, by
Souad Benomrane, Mounir Ben Ayed and
Adel M.Alimi (2013): [140]
The idea of Knowledge Discovery from Databases
(KDD) was a complex, iterative and interactive
process to discover knowledge from database. For its
implementation, several modules should be
developed like:
a) Module for data storage,
b) Module for processing data,
c) Data mining module,
d) Evaluation module,
e) Knowledge management module.
The objective of this paper was to recommend an
idea that includes every module to an agent. These
agents help the patient to make the most suitable
decision by communicate with each other. In this way
the process of KDD can be compare to multi-agent
system. To prove their approach, they have applied a
process of KDD for the fight against nosocomial
infections within an intensive care unit (ICU) of a
hospital. Technically, they have developed a software
tool for decision making support in Java/XML
through the agent platform Madkit [140]. The various
stages of the KDD process were:
1. Problem formulation: identifying the objectives,
defining the objectives and checking the needs.
2. Data retrieval: identifying the information and
the sources, checking their quality and
accessibility.
3. Data selection: choosing the data related to the
requested analysis.
4. Data cleansing: detecting and correcting the
inaccuracies and/or errors in the data.
5. Data transformation: formatting the data in
preparation for the mining operation.
6. Data mining: applying one or more techniques
such as neuronal networks, Bayesian networks or
decision trees to extract interesting patterns.
7. Result evaluation: estimating the quality of the
patterns extracted, interpreting the patterns’
meaning. (8) Knowledge integration (putting the
pattern or its results into the company’s
information system).
www.ijera.com
www.ijera.com
Here they represent the functional description of the
system by an identification of the five dimensions
proposed by A.E.I.O.U. approach. The five
dimensions of multi-agent systems: 1) Agents, 2)
Interactions, 3) Environment, 4) Organization, 5)
User.
Here they discussed about 5 types of agents:
1) Data preparation agent (dpa): It combines three
modules these are data selection, data cleansing
and data transformation.
2) Data mining agent (da): These agents are applied
in order to extract data patterns.
3) Evaluator agent (ea): These agents are applied in
order to specify those patterns which represent
knowledge based on some defined measures.
4) Coordinator Agent (ca): It was a broker that
handles communication among the several agents
using the suitable protocol.
5) Graphical Interface Agent (GIA): It allows the
patients to analysis the new knowledge discovered
by the agents and assign agents to discover new
goals.
There are two main advantages of this paper, Firstly,
a multi agent based KDD process with a rule agent
for mining in medical databases and secondly, the
application of the whole proposed model is based on
medical databases. In future they proposed to develop
an agent for every service in the hospital and select
the best communication protocol and algorithm to
resolve the local problems.
V.
CONCLUSION
In this survey paper we have shown how multi-agent
system (MAS) has been incorporated in medical
domain to solve various medical diagnosis and
related problems. Still there are some advantages and
disadvantages of using MAS in medical domain. This
survey paper aimed at providing the basic
information and medical domain related work using
multi-agent system. Here we have provided the
review of different types of approach of multi-agent
system in medical domain like application oriented
paper survey, concept oriented paper survey etc. The
future researchers in this field might get help from
our survey paper as different varieties of papers are
incorporated in this study. The agent based systems
also have few disadvantages like, there does not exist
any universally accepted health care ontology for
generating and analysis of medical or health care
information. So it would be hard to communicate
between several systems developed in different areas.
As sensitive medical information is transmitted
between several agents so proper confidentiality
measurement is to be done to secure the medical or
health care information. Multi-agent system still
lacks in implementing intelligent and autonomous
agents for mobile devices, such as mobile phones and
PDA’s, which minimizes the wide use of agent based
541 | P a g e
15. Sougata Chakraborty et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.528-546
systems to access information from anywhere,
anytime. Despite these problems still Multi-agent
system is an emerging field in medical or health care
domain.
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