The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications
January 2021: Top Ten Cited Article in Computer Science, Engineering IJCSEA Journal
International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
New Research Articles 2019 July Issue International Journal of Artificial Int...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas
International Journal of Wireless & Mobile Networks (IJWMN)ijwmn
This document compares emerging wireless technologies for the Internet of Things (IoT), including ZigBee, 6LoWPAN, Bluetooth Low Energy, LoRa, and various versions of Wi-Fi such as 802.11ah. It evaluates the capabilities of each technology in terms of data range and rate, network size, frequency channels, bandwidth, and power consumption. The document concludes that a multifaceted approach is needed to enable interoperable and secure communication in IoT applications using different wireless technologies and standards.
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
Feasibility of Artificial Neural Network in Civil Engineeringijtsrd
An Artificial neural network ANN is an information processing hypothesis that is stimulated by the way natural nervous system, such as brain, process information. The using of artificial neural network in civil engineering is getting more and more credit all over the world in last decades. This soft computing method has been shown to be very effective in the analysis and solution of civil engineering problems. It is defined as a body which works out the more and more complex problem through sequential algorithms. It is designed on the basis of artificial intelligence which is proficient of storing more and more information's. In this work, we have investigated the various architectures of ANN and their learning process. The artificial neural network based method was widely applied to the civil engineering because of the strong non linear relationship between known and un known of the problems. They come with good modelling in areas where conventional approaches finite elements, finite differences etc. require large computing resources or time to solve problems. These includes to study the behaviour of building materials, structural identification and control problems, in geo technical engineering like earthquake induced liquefaction potential, in heat transfer problems in civil engineering to improve air quality, in transportation engineering like identification of traffic problems to improve its flexibility , in construction technology and management to estimate the cost of buildings and in building services issues like analyzing the water distribution network etc. Researches reveals that the method is realistic and it will be fascinated for more civil engineering applications. Vikash Singh | Samreen Bano | Anand Kumar Yadav | Dr. Sabih Ahmad ""Feasibility of Artificial Neural Network in Civil Engineering"" 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/ijtsrd22985.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/22985/feasibility-of-artificial-neural-network-in-civil-engineering/vikash-singh
New research articles 2019 may issue international journal of computer networ...IJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
This document lists publications related to SDN/NFV, cloud computing & data center networks, network virtualization, and optical networks by Hongfang Yu and collaborators. It includes 29 publications related to SDN/NFV, 16 related to cloud computing & data center networks, 23 related to network virtualization, and 15 related to optical networks. The publications span conference proceedings, journal articles, and book chapters covering topics such as flow table aggregation, service function chaining, virtual network mapping, coflow scheduling, data center topology design, network function consolidation, reliable virtual infrastructure design, and impairment-aware routing.
Trends in Advanced Computing in 2020 - Advanced Computing: An International J...acijjournal
Advanced Computing: An International Journal (ACIJ) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
January 2021: Top Ten Cited Article in Computer Science, Engineering IJCSEA Journal
International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
New Research Articles 2019 July Issue International Journal of Artificial Int...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas
International Journal of Wireless & Mobile Networks (IJWMN)ijwmn
This document compares emerging wireless technologies for the Internet of Things (IoT), including ZigBee, 6LoWPAN, Bluetooth Low Energy, LoRa, and various versions of Wi-Fi such as 802.11ah. It evaluates the capabilities of each technology in terms of data range and rate, network size, frequency channels, bandwidth, and power consumption. The document concludes that a multifaceted approach is needed to enable interoperable and secure communication in IoT applications using different wireless technologies and standards.
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
Feasibility of Artificial Neural Network in Civil Engineeringijtsrd
An Artificial neural network ANN is an information processing hypothesis that is stimulated by the way natural nervous system, such as brain, process information. The using of artificial neural network in civil engineering is getting more and more credit all over the world in last decades. This soft computing method has been shown to be very effective in the analysis and solution of civil engineering problems. It is defined as a body which works out the more and more complex problem through sequential algorithms. It is designed on the basis of artificial intelligence which is proficient of storing more and more information's. In this work, we have investigated the various architectures of ANN and their learning process. The artificial neural network based method was widely applied to the civil engineering because of the strong non linear relationship between known and un known of the problems. They come with good modelling in areas where conventional approaches finite elements, finite differences etc. require large computing resources or time to solve problems. These includes to study the behaviour of building materials, structural identification and control problems, in geo technical engineering like earthquake induced liquefaction potential, in heat transfer problems in civil engineering to improve air quality, in transportation engineering like identification of traffic problems to improve its flexibility , in construction technology and management to estimate the cost of buildings and in building services issues like analyzing the water distribution network etc. Researches reveals that the method is realistic and it will be fascinated for more civil engineering applications. Vikash Singh | Samreen Bano | Anand Kumar Yadav | Dr. Sabih Ahmad ""Feasibility of Artificial Neural Network in Civil Engineering"" 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/ijtsrd22985.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/22985/feasibility-of-artificial-neural-network-in-civil-engineering/vikash-singh
New research articles 2019 may issue international journal of computer networ...IJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
This document lists publications related to SDN/NFV, cloud computing & data center networks, network virtualization, and optical networks by Hongfang Yu and collaborators. It includes 29 publications related to SDN/NFV, 16 related to cloud computing & data center networks, 23 related to network virtualization, and 15 related to optical networks. The publications span conference proceedings, journal articles, and book chapters covering topics such as flow table aggregation, service function chaining, virtual network mapping, coflow scheduling, data center topology design, network function consolidation, reliable virtual infrastructure design, and impairment-aware routing.
Trends in Advanced Computing in 2020 - Advanced Computing: An International J...acijjournal
Advanced Computing: An International Journal (ACIJ) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
International Journal on Web Service Computing (IJWSC)ijwscjournal
Web Service Computing is a recent evolution in Distributed Computing series and it is an emerging and fast growing paradigm in the present scenario. Web Service Computing is a diversified discipline suite that related to the technologies of Business Process Integration and Management, Grid / Utility / Cloud Computing paradigms, autonomic computing, as well as the business and scientific applications. It applies the theories of Science and Technology for bridging the gap between Business Services and IT Services. Service oriented computing addresses how to enable the technology to help people to perform business processes more efficiently and effectively, ultimately resulting in creating WIN-WIN strategy between the business organizations and end users. The greatest significance of the web services is their interoperability, which allows businesses to dynamically publish, discover, and aggregate a range of Web services through the Internet to more easily create innovative products, business processes and value chains both from organization and end user points of views. Due to these, this cross discipline attracts the variety of researchers from various disciplines to conduct the versatile research and experiments in this area.
This document summarizes a book and 28 journal articles and conference papers authored or co-authored by C.G. Dethe. It includes the following key information:
- A book on performance evaluation of intelligent WLAN systems in multipath fading environments.
- 28 journal articles published in various international journals between 2009-2012 related to topics like UWB antenna design, resource allocation in MC-CDMA, medical image analysis, and video processing.
- 22 papers presented at international conferences between 2003-2013 on similar topics as the journal articles.
- 7 papers presented at national conferences in India between 2009-2014 related to wireless sensor networks, network traffic analysis, and intelligent transportation systems.
ADAPTIVE MODELING OF URBAN DYNAMICS DURING EPHEMERAL EVENT VIA MOBILE PHONE T...ieijjournal
The communication devices have produced digital traces for their users either voluntarily or not. This type
of collective data can give powerful indications that are affecting the urban systems design and
development. In this study mobile phone data during Armada event is investigated. Analyzing mobile
phone traces gives conceptual views about individuals densities and their mobility patterns in the urban
city. The geo-visualization and statistical techniques have been used for understanding human mobility
collectively and individually. The undertaken substantial parameters are inter-event times, travel distances
(displacements) and radius of gyration. They have been analyzed and simulated using computing platform
by integrating various applications for huge database management, visualization, analysis, and
simulation. Accordingly, the general population pattern law has been extracted. The study contribution
outcomes have revealed both the individuals densities in static perspective and individuals mobility in
dynamic perspective with multi levels of abstraction (macroscopic, mesoscopic, microscopic).
In this deck from the HPC User Forum, Rick Stevens from Argonne presents: AI for Science.
"Artificial Intelligence (AI) is making strides in transforming how we live. From the tech industry embracing AI as the most important technology for the 21st century to governments around the world growing efforts in AI, initiatives are rapidly emerging in the space. In sync with these emerging initiatives including U.S. Department of Energy efforts, Argonne has launched an “AI for Science” initiative aimed at accelerating the development and adoption of AI approaches in scientific and engineering domains with the goal to accelerate research and development breakthroughs in energy, basic science, medicine, and national security, especially where we have significant volumes of data and relatively less developed theory. AI methods allow us to discover patterns in data that can lead to experimental hypotheses and thus link data driven methods to new experiments and new understanding."
Watch the video: https://wp.me/p3RLHQ-kQi
Learn more: https://www.anl.gov/topic/science-technology/artificial-intelligence
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
June 2020: Most Downloaded Article in Soft Computing ijsc
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
1) The document discusses a semantics-based approach to machine perception that uses semantic web technologies to derive abstractions from sensor data using background knowledge on the web.
2) It addresses three primary issues: annotation of sensor data, developing a semantic sensor web, and enabling semantic perception intelligence at the edge on resource-constrained devices.
3) The approach represents background knowledge and sensor observations using ontologies, and uses deductive and abductive reasoning over these representations to interpret sensor data at multiple levels of abstraction.
Top downloaded article in academia 2020 - International Journal of Informatio...Zac Darcy
The International Journal of Information Technology, Modeling and Computing (IJITMC) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of Information Technology, Modeling and Computing. With the advances of Information Technology, there is an active multi-disciplinary research in the areas of IT, CSE, Modeling and Simulation with numerous applications in various fields. The International Journal of Information Technology, Modeling and Computing (IJITMC) is an abstracted and indexed international journal of high quality devoted to the publication of original research papers from IT, Modeling, CSE and Control Engineering with some emphasis on all areas and subareas of computer science, IT, scientific modeling, simulation, visualization and control systems and their broad range of applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This curriculum vitae summarizes the educational and professional background of Kamran Sartipi. He holds a PhD in Computer Science from the University of Waterloo and has published extensively. His research focuses on software engineering, information security, electronic health, and medical informatics. He has led several projects involving decision support systems, knowledge engineering, distributed systems, and standards-based health interoperability.
Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over four months from San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.
Complexity Neural Networks for Estimating Flood Process in Internet-of-Things...Dr. Amarjeet Singh
With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecastin gmodel based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyperparameters of the proposed CNN flood forecasting model.
Avinash Kumar is a PhD candidate in electrical and computer engineering at the University of Illinois. His research focuses on camera calibration, computational imaging, and computer vision applications to railroad monitoring. He has developed new models and analytical solutions for small field of view camera calibration. His other research includes developing an omnifocus imaging technique, extracting shadows obscured by scattering media, and structure from motion for indoor scenes. He has also worked on a machine vision system to analyze gaps between freight loads on trains.
- The document describes a method for understanding city traffic dynamics by utilizing sensor data that measures average speed and link travel time, as well as textual data from tweets and official traffic reports.
- It builds statistical models to learn normal traffic patterns from historical sensor data and identifies anomalies, then correlates anomalies with relevant traffic events extracted from tweets and reports.
- The method was evaluated on data collected for the San Francisco Bay Area, and it was able to scale to large real-world datasets by exploiting the problem structure and using Apache Spark for distributed processing. Events extracted from social media provided complementary information to sensor data for explaining traffic anomalies.
The document describes a study that used fuzzy C-means clustering and an adaptive neuro-fuzzy inference system to classify land use and land cover in Visakhapatnam, India from satellite images. Satellite images from 2012, 2014, and 2017 were collected and preprocessed using a hybrid directional lifting technique to remove noise. The preprocessed images were segmented using fuzzy C-means clustering. Local binary pattern and gray-level co-occurrence matrix features were then extracted from the segmented images. These features were input into an adaptive neuro-fuzzy inference system to classify the land use and land cover into four classes: water body, vegetation, settlement, and barren land. The proposed system achieved higher classification accuracy compared to existing methods.
Trends of machine learning in 2020 - International Journal of Artificial Inte...gerogepatton
This document discusses a hybrid machine learning algorithm to automatically classify measurement types from NASA's airborne measurement data archive. The goal is to develop an efficient and accurate algorithm that meets performance metrics. The proposed algorithm uses decision trees to select and weight features, then applies a weighted Naive Bayes classifier due to correlated inputs. It was deployed successfully at an industrial scale, balancing performance and resources required.
Industrial big data analytics for prediction of remaining useful life based o...nexgentechnology
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Trends in covolutional neural network in 2020 - International Journal of Arti...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
New Research Articles 2019 September Issue International Journal of Artificia...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications
TOP CITED UBICOMPUTING ARTICLES IN 2013 - International Journal of Ubiquitous...ijujournal
International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field
International Journal on Web Service Computing (IJWSC)ijwscjournal
Web Service Computing is a recent evolution in Distributed Computing series and it is an emerging and fast growing paradigm in the present scenario. Web Service Computing is a diversified discipline suite that related to the technologies of Business Process Integration and Management, Grid / Utility / Cloud Computing paradigms, autonomic computing, as well as the business and scientific applications. It applies the theories of Science and Technology for bridging the gap between Business Services and IT Services. Service oriented computing addresses how to enable the technology to help people to perform business processes more efficiently and effectively, ultimately resulting in creating WIN-WIN strategy between the business organizations and end users. The greatest significance of the web services is their interoperability, which allows businesses to dynamically publish, discover, and aggregate a range of Web services through the Internet to more easily create innovative products, business processes and value chains both from organization and end user points of views. Due to these, this cross discipline attracts the variety of researchers from various disciplines to conduct the versatile research and experiments in this area.
This document summarizes a book and 28 journal articles and conference papers authored or co-authored by C.G. Dethe. It includes the following key information:
- A book on performance evaluation of intelligent WLAN systems in multipath fading environments.
- 28 journal articles published in various international journals between 2009-2012 related to topics like UWB antenna design, resource allocation in MC-CDMA, medical image analysis, and video processing.
- 22 papers presented at international conferences between 2003-2013 on similar topics as the journal articles.
- 7 papers presented at national conferences in India between 2009-2014 related to wireless sensor networks, network traffic analysis, and intelligent transportation systems.
ADAPTIVE MODELING OF URBAN DYNAMICS DURING EPHEMERAL EVENT VIA MOBILE PHONE T...ieijjournal
The communication devices have produced digital traces for their users either voluntarily or not. This type
of collective data can give powerful indications that are affecting the urban systems design and
development. In this study mobile phone data during Armada event is investigated. Analyzing mobile
phone traces gives conceptual views about individuals densities and their mobility patterns in the urban
city. The geo-visualization and statistical techniques have been used for understanding human mobility
collectively and individually. The undertaken substantial parameters are inter-event times, travel distances
(displacements) and radius of gyration. They have been analyzed and simulated using computing platform
by integrating various applications for huge database management, visualization, analysis, and
simulation. Accordingly, the general population pattern law has been extracted. The study contribution
outcomes have revealed both the individuals densities in static perspective and individuals mobility in
dynamic perspective with multi levels of abstraction (macroscopic, mesoscopic, microscopic).
In this deck from the HPC User Forum, Rick Stevens from Argonne presents: AI for Science.
"Artificial Intelligence (AI) is making strides in transforming how we live. From the tech industry embracing AI as the most important technology for the 21st century to governments around the world growing efforts in AI, initiatives are rapidly emerging in the space. In sync with these emerging initiatives including U.S. Department of Energy efforts, Argonne has launched an “AI for Science” initiative aimed at accelerating the development and adoption of AI approaches in scientific and engineering domains with the goal to accelerate research and development breakthroughs in energy, basic science, medicine, and national security, especially where we have significant volumes of data and relatively less developed theory. AI methods allow us to discover patterns in data that can lead to experimental hypotheses and thus link data driven methods to new experiments and new understanding."
Watch the video: https://wp.me/p3RLHQ-kQi
Learn more: https://www.anl.gov/topic/science-technology/artificial-intelligence
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
June 2020: Most Downloaded Article in Soft Computing ijsc
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
1) The document discusses a semantics-based approach to machine perception that uses semantic web technologies to derive abstractions from sensor data using background knowledge on the web.
2) It addresses three primary issues: annotation of sensor data, developing a semantic sensor web, and enabling semantic perception intelligence at the edge on resource-constrained devices.
3) The approach represents background knowledge and sensor observations using ontologies, and uses deductive and abductive reasoning over these representations to interpret sensor data at multiple levels of abstraction.
Top downloaded article in academia 2020 - International Journal of Informatio...Zac Darcy
The International Journal of Information Technology, Modeling and Computing (IJITMC) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of Information Technology, Modeling and Computing. With the advances of Information Technology, there is an active multi-disciplinary research in the areas of IT, CSE, Modeling and Simulation with numerous applications in various fields. The International Journal of Information Technology, Modeling and Computing (IJITMC) is an abstracted and indexed international journal of high quality devoted to the publication of original research papers from IT, Modeling, CSE and Control Engineering with some emphasis on all areas and subareas of computer science, IT, scientific modeling, simulation, visualization and control systems and their broad range of applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This curriculum vitae summarizes the educational and professional background of Kamran Sartipi. He holds a PhD in Computer Science from the University of Waterloo and has published extensively. His research focuses on software engineering, information security, electronic health, and medical informatics. He has led several projects involving decision support systems, knowledge engineering, distributed systems, and standards-based health interoperability.
Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over four months from San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.
Complexity Neural Networks for Estimating Flood Process in Internet-of-Things...Dr. Amarjeet Singh
With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecastin gmodel based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyperparameters of the proposed CNN flood forecasting model.
Avinash Kumar is a PhD candidate in electrical and computer engineering at the University of Illinois. His research focuses on camera calibration, computational imaging, and computer vision applications to railroad monitoring. He has developed new models and analytical solutions for small field of view camera calibration. His other research includes developing an omnifocus imaging technique, extracting shadows obscured by scattering media, and structure from motion for indoor scenes. He has also worked on a machine vision system to analyze gaps between freight loads on trains.
- The document describes a method for understanding city traffic dynamics by utilizing sensor data that measures average speed and link travel time, as well as textual data from tweets and official traffic reports.
- It builds statistical models to learn normal traffic patterns from historical sensor data and identifies anomalies, then correlates anomalies with relevant traffic events extracted from tweets and reports.
- The method was evaluated on data collected for the San Francisco Bay Area, and it was able to scale to large real-world datasets by exploiting the problem structure and using Apache Spark for distributed processing. Events extracted from social media provided complementary information to sensor data for explaining traffic anomalies.
The document describes a study that used fuzzy C-means clustering and an adaptive neuro-fuzzy inference system to classify land use and land cover in Visakhapatnam, India from satellite images. Satellite images from 2012, 2014, and 2017 were collected and preprocessed using a hybrid directional lifting technique to remove noise. The preprocessed images were segmented using fuzzy C-means clustering. Local binary pattern and gray-level co-occurrence matrix features were then extracted from the segmented images. These features were input into an adaptive neuro-fuzzy inference system to classify the land use and land cover into four classes: water body, vegetation, settlement, and barren land. The proposed system achieved higher classification accuracy compared to existing methods.
Trends of machine learning in 2020 - International Journal of Artificial Inte...gerogepatton
This document discusses a hybrid machine learning algorithm to automatically classify measurement types from NASA's airborne measurement data archive. The goal is to develop an efficient and accurate algorithm that meets performance metrics. The proposed algorithm uses decision trees to select and weight features, then applies a weighted Naive Bayes classifier due to correlated inputs. It was deployed successfully at an industrial scale, balancing performance and resources required.
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Trends in covolutional neural network in 2020 - International Journal of Arti...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
New Research Articles 2019 September Issue International Journal of Artificia...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications
TOP CITED UBICOMPUTING ARTICLES IN 2013 - International Journal of Ubiquitous...ijujournal
International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
April 2023-Top Cited Articles in International Journal of Ubiquitous Computin...ijujournal
International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
May_2024 Top 10 Read Articles in Computer Networks & Communications.pdfIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Intention recognition for dynamic role exchange in hapticأحلام انصارى
1) The paper summarizes an experimental study that investigated the utility of a role exchange mechanism in human-computer collaboration through haptic interaction.
2) In this framework, a human dynamically interacts with a computer partner by communicating through the haptic channel to trade control levels on a shared task.
3) The study examined conditions of equal control, role exchange, and visuo-haptic cues, analyzing effects on task performance, efficiency, and subjective measures.
New Research Articles - 2018 November Issue-International Journal of Artifici...gerogepatton
This document summarizes several papers published in the November 2018 issue of the International Journal of Artificial Intelligence and Applications (IJAIA). The first paper proposes a movie genre recommendation system using machine learning algorithms to predict preferences from survey data with imbalanced responses and unequal classification costs. The second paper describes a hybrid fuzzy neural network and expert system to aid effort forecasting for software development projects based on complexity factors. It was tested on a real database and showed promising results.
Top cited articles 2020 - Advanced Computational Intelligence: An Internation...aciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
June 2020: Top Read Articles in Advanced Computational Intelligenceaciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
New research articles 2020 october issue international journal of multimedi...ijma
The International Journal of Multimedia & Its Applications (IJMA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Multimedia & its applications. The journal focuses on all technical and practical aspects of Multimedia and its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding recent developments this arena, and establishing new collaborations in these areas.
This paper proposes a new method for fingerprint classification based on orientation field features extracted using a pixel-wise gradient descent method. The orientation field is used to estimate the percentage of directional block classes in four dimensions, which along with singular point information forms a feature vector for classification. A support vector machine classifier is used and shown to achieve high accuracy compared to other spatial domain classifiers. The method extracts discriminative features from the orientation field to classify fingerprints into one of five classes.
Top Cited Papers In 2018 - International Journal of Network Security & Its Ap...IJNSA Journal
The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
The document summarizes a thesis defense presentation on contextual analysis for trust evaluations using ConTED. It discusses analyzing context from sources like location, identity, time and activity to evaluate trust. ConTED uses a hybrid context model and weighted context information expressed on context scales to make decentralized, context-aware decisions. It was tested on scenarios where the number of agents, information exchanged, and scenario size increased. Future work includes improving adaptive extraction, weights, and testing on larger scenarios.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
(Crestani et al., 2004) The proliferation of mobile devices and thMargaritoWhitt221
The document discusses several papers related to research in the field of mobile human-computer interaction (mobile HCI). The first paper discusses the International Workshop on Mobile and Ubiquitous Information Access that was held in 2003 in Italy and covered topics like interface design, interaction techniques, context-aware applications and implications of mobile computing. The second paper discusses a study that analyzed how often and for how long users look at their mobile devices on average. The third paper discusses the Mobile HCI 2004 conference that established mobile HCI as a central research area and impacted how the field is conducted today.
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
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International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
PhotoQR: A Novel ID Card with an Encoded Viewgerogepatton
There is an increasing interest in developing techniques to identify and assess data to allow an easy and
continuous access to resources, services or places that require thorough ID control. Usually, in order to
give access to these resources, different kinds of documents are mandatory. In order to avoid forgeries
without the need of extra credentials, a new system –named photoQR, is here proposed. This system is
based on a ID card having two objects: one person’s picture (pre-processed via blur and/or swirl
techniques) and one QR code containing embedded data related to the picture. The idea is that the picture
and the QR code can assess each other by a proper hash value in the QR. The QR without the picture
cannot be assessed and vice versa. An open source prototype of the photoQR system has been implemented
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12th International Conference of Artificial Intelligence and Fuzzy Logic (AI ...gerogepatton
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2024) provides a
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International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
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Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
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train and test our model. The results of our experiments show that our CNN-LSTM method is much better
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accuracy rate of 99.50%.
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10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
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May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)gerogepatton
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
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learning and OCR in automating label extraction and recognition.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
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International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Co...gerogepatton
Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster
recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on
Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function
adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the
advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic
algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian
function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster
recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in
Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution,
and allow bad solutions to be accepted during the search. The improved algorithm is applied to function
optimization, and the simulation results show that the calculation accuracy and stability of the algorithm
are improved, and the effectiveness of the improved algorithm is verified
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
10th International Conference on Artificial Intelligence and Soft Computing (...gerogepatton
10th International Conference on Artificial Intelligence and Soft Computing (AIS 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology, and
applications of Artificial Intelligence, Soft Computing. The Conference looks for significant
contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical
aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from
both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons.
As it has a negative impact on long-term growth objectives and workplace productivity, firms have
recognized it as a significant concern. To address this issue, organizations are increasingly turning to
machine-learning approaches to forecast employee attrition rates. This topic has gained significant
attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed
methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple
studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims
to fill this gap by providing an overview of research conducted on applying machine learning to predict
employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted,
summarized, and classified. Most studies agree on conducting comparative experiments with multiple
predictive models to determine the most effective one.From this literature survey, the RF algorithm and
XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms.
Additionally, the application of deep learning to employee attrition prediction issues also shows promise.
While there are discrepancies in the datasets used in previous studies, it is notable that the dataset
provided by IBM is the most widely utilized. This study serves as a concise review for new researchers,
facilitating their understanding of the primary techniques employed in predicting employee attrition and
highlighting recent research trends in this field. Furthermore, it provides organizations with insight into
the prominent factors affecting employee attrition, as identified by studies, enabling them to implement
solutions aimed at reducing attrition rates.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AIFU 2024) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
2. A HYBRID LEARNING ALGORITHM IN AUTOMATED TEXT
CATEGORIZATION OF LEGACY DATA
Dali Wang1
, Ying Bai2
, and David Hamblin1
1
Christopher Newport University, Newport News, VA, USA
2
Johnson C. Smith University, Charlotte, NC, USA
ABSTRACT
The goal of this research is to develop an algorithm to automatically classify measurement
types from NASA’s airborne measurement data archive. The product has to meet specific
metrics in term of accuracy, robustness and usability, as the initial decision-tree based
development has shown limited applicability due to its resource intensive characteristics. We
have developed an innovative solution that is much more efficient while offering comparable
performance. Similar to many industrial applications, the data available are noisy and
correlated; and there is a wide range of features that are associated with the type of
measurement to be identified. The proposed algorithm uses a decision tree to select features
and determine their weights. A weighted Naive Bayes is used due to the presence of highly
correlated inputs. The development has been successfully deployed in an industrial scale, and
the results show that the development is well-balanced in term of performance and resource
requirements.
KEYWORDS
Classification, machine learning, atmospheric measurement.
For More Details: http://aircconline.com/ijaia/V10N5/10519ijaia04.pdf
Volume Link: http://airccse.org/journal/ijaia/current2019.html
3. REFERENCES
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4. AN APPLICATION OF CONVOLUTIONAL NEURAL
NETWORKS ON HUMAN INTENTION PREDICTION
Lin Zhang1
, Shengchao Li2
, Hao Xiong2
, Xiumin Diao2
and Ou Ma1
1
Department of Aerospace Engineering and Engineering Mechanics, University of
Cincinnati, Cincinnati, Ohio, USA
2
School of Engineering Technology, Purdue University, West Lafayette, Indiana, USA
ABSTRACT
Due to the rapidly increasing need of human-robot interaction (HRI), more intelligent robots
are in demand. However, the vast majority of robots can only follow strict instructions, which
seriously restricts their flexibility and versatility. A critical fact that strongly negates the
experience of HRI is that robots cannot understand human intentions. This study aims at
improving the robotic intelligence by training it to understand human intentions. Different
from previous studies that recognizing human intentions from distinctive actions, this paper
introduces a method to predict human intentions before a single action is completed. The
experiment of throwing a ball towards designated targets are conducted to verify the
effectiveness of the method. The proposed deep learning based method proves the feasibility
of applying convolutional neural networks (CNN) under a novel circumstance. Experiment
results show that the proposed CNN-vote method out competes three traditional machine
learning techniques. In current context, the CNN-vote predictor achieves the highest testing
accuracy with relatively less data needed.
KEYWORDS
Human-robot Interaction; Intentions Prediction; Convolutional Neural Networks;
For More Details: http://aircconline.com/ijaia/V10N5/10519ijaia01.pdf
Volume Link: http://airccse.org/journal/ijaia/current2019.html
5. REFERENCES
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[3] M. J. Micire, “Evolution and field performance of a rescue robot,” J. F. Robot., vol. 25, no. 1–2,
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[4] F. Mondada et al., “The e-puck , a Robot Designed for Education in Engineering,” in
Robotics, 2009, vol. 1, no. 1, pp. 59–65.
[5] K. Wakita, J. Huang, P. Di, K. Sekiyama, and T. Fukuda, “Human-Walking-Intention-Based
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vol. 18, no. 1, pp. 285–296, Feb. 2013.
[6] K. Sakita, K. Ogawam, S. Murakami, K. Kawamura, and K. Ikeuchi, “Flexible cooperation
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[7] Z. Wang, A. Peer, and M. Buss, “An HMM approach to realistic haptic human-robot
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[8] S. Kim, Z. Yu, J. Kim, A. Ojha, and M. Lee, “Human-Robot Interaction Using Intention
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[9] D. Song et al., “Predicting human intention in visual observations of hand/object interactions,” in
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[16] P. Barros, C. Weber, and S. Wermter, “Emotional expression recognition with a cross-channel
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[17] A. H. Qureshi, Y. Nakamura, Y. Yoshikawa, and H. Ishiguro, “Show, attend and interact:
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[27] P. Munya, C. A. Ntuen, E. H. Park, and J. H. Kim, “A BAYESIAN ABDUCTION MODEL
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8. A SURVEY ON DIFFERENT MACHINE LEARNING
ALGORITHMS AND WEAK CLASSIFIERS BASED ON KDD
AND NSL-KDD DATASETS
Rama Devi Ravipati1
and Munther Abualkibash2
1
Graduate student in the Department of Computer Science, Eastern Michigan University,
Ypsilanti, Michigan
2
School of Information Security and Applied Computing, Eastern Michigan University,
Ypsilanti, Michigan
ABSTRACT
Network intrusion detection often finds a difficulty in creating classifiers that could handle
unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and
User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are
only 2% of overall datasets. So, these result in model not able to efficiently learn the
characteristics of rare categories and this will result in poor detection rates of rare attack
categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-
KDD datasets using different classifiers in WEKA.
KEYWORDS
KDD, NSL-KDD, WEKA, AdaBoost, KNN, Detection rate, False alarm rate.
For More Details: http://aircconline.com/ijaia/V10N3/10319ijaia01.pdf
Volume Link: http://airccse.org/journal/ijaia/current2019.html
9. REFERENCES
[1] Brandon Lokesak (December 4, 2008). "A Comparison Between Signature Based and Anomaly
Based Intrusion Detection Systems" (PPT). www.iup.edu. Douligeris, Christos; Serpanos,
Dimitrios N. (2007-02-09). Network Security: Current Status and Future Directions. John Wiley
& Sons. ISBN 9780470099735.
[2] Rowayda, A. Sadek; M Sami, Soliman; Hagar, S Elsayed (November 2013). "Effective
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[5] Shi-Jinn Horng, Ming-Yang Su, Yuan-Hsin Chen, TzongWann Kao, Rong-Jian Chen, Jui-Lin
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[6] Cheng Xiang, Png Chin Yong, Lim Swee Meng, Design of multiple-level hybrid classifier for
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[7] Weiming Hu, Steve Maybank, “AdaBoost-Based Algorithm for Network Intrusion Detection”.
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[12] Sandeep Gurung, Mirnal Kanti Ghose, Aroj Subedi,"Deep Learning Approach on Network
Intrusion Detection System using NSL-KDD Dataset", International Journal of Computer
Network and Information Security(IJCNIS), Vol.11, No.3, pp.8-14, 2019.DOI:
10.5815/ijcnis.2019.03.02.
[13] Jawhar, M. M. T., & Mehrotra, M. (2010). Design network intrusion detection system using
hybrid fuzzy neural network. International JournalofComputerScience and Security, 4(3), 285-
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Problems. International Journal of Applied Engineering Research, 13(5), 2985-2991.
11. ADAPTIVE LEARNING EXPERT SYSTEM FOR DIAGNOSIS
AND MANAGEMENT OF VIRAL HEPATITIS
Henok Yared Agizew
Department of Management Information System, Mettu University, Mettu, Ethiopia.
ABSTRACT
Viral hepatitis is the regularly found health problem throughout the world among other easily
transmitted diseases, such as tuberculosis, human immune virus, malaria and so on. Among all
hepatitis viruses, the uppermost numbers of deaths are result from the long-lasting hepatitis C
infection or long-lasting hepatitis B. In order to develop this system, the knowledge is acquired using
both structured and semi-structured interviews from internists of St.Paul Hospital. Once the
knowledge is acquired, it is modeled and represented using rule based reasoning techniques. Both
forward and backward chaining is used to infer the rules and provide appropriate advices in the
developed expert system. For the purpose of developing the prototype expert system SWI-prolog
editor also used. The proposed system has the ability to adapt with dynamic knowledge by
generalizing rules and discover new rules through learning the newly arrived knowledge from domain
experts adaptively without any help from the knowledge engineer.
KEYWORDS
Expert System, Diagnosis and Management of Viral Hepatitis, Adaptive Learning, Discovery and
Generalization Mechanism.
For More Details: http://aircconline.com/ijaia/V10N2/10219ijaia04.pdf
Volume Link: http://airccse.org/journal/ijaia/current2019.html
12. REFERENCES
[1] Amanuel, A. (2016).”Self Learning Computer Troubleshooting Expert System.”
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[13] Tesfamariam, M. A. (2015). Integrating Data Mining Results with the Knowledge Based for
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14. EFFICIENT POWER THEFT DETECTION FOR RESIDENTIAL
CONSUMERS USING MEAN SHIFT DATA MINING
KNOWLEDGE DISCOVERY PROCESS
Blazakis Konstantinos1
and Stavrakakis Georgios2
1
School of Electrical and Computer Engineering, Technical University of Crete, Greece
2
School of Electrical and Computer Engineering, Technical University of Crete, Greece
ABSTRACT
Energy theft constitutes an issue of great importance for electricity operators. The attempt to detect
and reduce non-technical losses is a challenging task due to insufficient inspection methods. With the
evolution of advanced metering infrastructure (AMI) in smart grids, a more complicated status quo in
energy theft has emerged and many new technologies are being adopted to solve the problem. In order
to identify illegal residential consumers, a computational method of analyzing and identifying
electricity consumption patterns of consumers based on data mining techniques has been presented.
Combining principal component analysis (PCA) with mean shift algorithm for different power theft
scenarios, we can now cope with the power theft detection problem sufficiently. The overall research
has shown encouraging results in residential consumers power theft detection that will help utilities to
improve the reliability, security and operation of power network.
KEYWORDS
Data mining, Mean Shift clustering algorithm, Principal Component Analysis (PCA), Density-Based
Spatial Clustering of Applications with Noise (DBSCAN), Non-Technical Losses (NTLs), power
theft, smart grid, smart electricity metering
For More Details: http://aircconline.com/ijaia/V10N1/10119ijaia06.pdf
Volume Link: http://airccse.org/journal/ijaia/current2019.html
15. REFERENCES
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AUTHORS
Blazakis Konstantinos: Received his BSc degree in Applied Mathematical and Physical
Sciences from N.T.U.A. (National Technical University of Athens) in 2010 and his MSc
degree in Electrical and Computer Engineering from Technical University of Crete, Chania,
Greece in 2015. Currently, he is a PhD candidate at Technical University of Crete. His areas
of research include data mining, machine learning, smart grids, distributed electricity
networks, renewable energy
Stavrakakis Georgios: Received his first degree Diploma in Electrical Engineering from the
N.T.U.A. (National Technical University of Athens), Athens, in 1980. His D.E.A. in
Automatic Control and Systems Engineering was obtained from I.N.S.A., Toulouse, in 1981
and his Ph.D. in the same area was obtained from “Paul Sabatier-Toulouse III” University,
Toulouse-France, in 1984. He has worked as a Research Fellow in the Robotics Laboratory of
N.T.U.A. (1985-1988), and as a Visiting Scientist at the Institute for Systems Engineering
and Informatics/Components Diagnostics & Reliability Sector of the Joint Research Center-
EEC at Ispra, Italy (1989-1990). He was Vice President of the Hellenic Center for Renewable
Energy Sources (CRES), Pikermi, Athens-Greece (2000-2002). He is currently a Full
Professor at the Technical University of Crete, Chania, Greece.