Ontology matching finds correspondences between similar entities of different ontologies. Two ontologies may be similar in some aspects such as structure, semantic etc. Most ontology matching systems integrate multiple matchers to extract all the similarities that two ontologies may have. Thus, we face a major problem to aggregate different similarities.
Some matching systems use experimental weights for aggregation of similarities among different matchers while others use machine learning approaches and optimization algorithms to find optimal weights to assign to different matchers. However, both approaches have their own deficiencies.
Symbol Emergence in Robotics: Language Acquisition via Real-world Sensorimoto...Tadahiro Taniguchi
Invited talk at Gatsby-Kakenhi Joint Workshop on AI and Neuroscience, London, 12th May, 2017
This talk is delivered as a part of the session about artificial general intelligence.
Symbol emergence in robotics can be regarded as a research activity contributing to develop an AGI.
A Semi-Automatic Ontology Extension Method for Semantic Web ServicesIDES Editor
this paper provides a novel semi-automatic ontology
extension method for Semantic Web Services (SWS). This is
significant since ontology extension methods those existing
in literature mostly deal with semantic description of static
Web resources such as text documents. Hence, there is a need
for methods that can serve dynamic Web resources such as
SWS. The developed method in this paper avoids redundancy
and respects consistency so as to assure high quality of the
resulting shared ontologies.
it's our presentation during the third international conference of information systems and technologies ICIST 2013 held at Tangier, Morocco in which we propose a new approach for human assessment of ontologies using an online questionnaire.
Ontology matching finds correspondences between similar entities of different ontologies. Two ontologies may be similar in some aspects such as structure, semantic etc. Most ontology matching systems integrate multiple matchers to extract all the similarities that two ontologies may have. Thus, we face a major problem to aggregate different similarities.
Some matching systems use experimental weights for aggregation of similarities among different matchers while others use machine learning approaches and optimization algorithms to find optimal weights to assign to different matchers. However, both approaches have their own deficiencies.
Symbol Emergence in Robotics: Language Acquisition via Real-world Sensorimoto...Tadahiro Taniguchi
Invited talk at Gatsby-Kakenhi Joint Workshop on AI and Neuroscience, London, 12th May, 2017
This talk is delivered as a part of the session about artificial general intelligence.
Symbol emergence in robotics can be regarded as a research activity contributing to develop an AGI.
A Semi-Automatic Ontology Extension Method for Semantic Web ServicesIDES Editor
this paper provides a novel semi-automatic ontology
extension method for Semantic Web Services (SWS). This is
significant since ontology extension methods those existing
in literature mostly deal with semantic description of static
Web resources such as text documents. Hence, there is a need
for methods that can serve dynamic Web resources such as
SWS. The developed method in this paper avoids redundancy
and respects consistency so as to assure high quality of the
resulting shared ontologies.
it's our presentation during the third international conference of information systems and technologies ICIST 2013 held at Tangier, Morocco in which we propose a new approach for human assessment of ontologies using an online questionnaire.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
Computer Aided Development of Fuzzy, Neural and Neuro-Fuzzy SystemsIJEACS
Development of an expert system is difficult because of two challenges involve in it. The first one is the expert system itself is high level system and deals with knowledge, which make is difficult to handle. Second, the systems development is more art and less science; hence there are little guidelines available about the development. This paper describes computer aided development of intelligent systems using modem artificial intelligence technology. The paper illustrates a design of a reusable generic framework to support friendly development of fuzzy, neural network and hybrid systems such as neuro-fuzzy system. The reusable component libraries for fuzzy logic based systems, neural network based system and hybrid system such as neuro-fuzzy system are developed and accommodated in this framework. The paper demonstrates code snippets, interface screens and class libraries overview with necessary technical details.
Phrase Structure Identification and Classification of Sentences using Deep Le...ijtsrd
Phrase structure is the arrangement of words in a specific order based on the constraints of a specified language. This arrangement is based on some phrase structure rules which are according to the productions in context free grammar. The identification of the phrase structure can be done by breaking the specified natural language sentence into its constituents that may be lexical and phrasal categories. These phrase structures can be identified using parsing of the sentences which is nothing but syntactic analysis. The proposed system deals with this problem using Deep Learning strategy. Instead of using Rule Based technique, supervised learning with sequence labelling is done using IOB labelling. This is a sequence classification problem which has been trained and modeled using RNN LSTM. The proposed work has shown a considerable result and can be applied in many applications of NLP. Hashi Haris | Misha Ravi ""Phrase Structure Identification and Classification of Sentences using Deep Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23841.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23841/phrase-structure-identification-and-classification-of-sentences-using-deep-learning/hashi-haris
This is a slide for the invited talk at The 4th Workshop on Naturalistic Driving Data Analytics,
IEEE IV2017, Los Angeles, 11th June, 2017.
This talk summarizes a series of work on a symbolization approach toward naturalistic driving behavior data.
Most of the works are conducted by collaboration between DESNO co. and Ritsumeikan university
Ontology Construction from Text: Challenges and TrendsCSCJournals
Ontology is one of the most popular representation model used for knowledge representation, sharing and reusing. In light of the importance of ontology, different methodologies for building ontologies have been proposed. Ontology construction is a difficult and time-consuming process. Many efforts have been made to help ontology engineers to construct ontologies and to overcome the bottleneck of knowledge acquisition. The aim of this paper is to give a brief overview of ontology learning approaches and to review some of ontology extraction systems and tools followed by a summarizing comparison of them. Also some of the current issues and main trends of ontology construction from texts will be discussed.
Presentation of the Defence of the PhD Dissertation "Towards a personalised virtual library: indications from navigational and personal information behaviour of e-learning students".
Failed queries: a morpho-syntactic analysis based on transaction log filesGiannis Tsakonas
Presentation in the First Workshop on Digital Information Management. The workshop is organized by the Laboratory on Digital Libraries and Electronic Publication, Department of Archives and Library Sciences, Ionian University, Greece and aims to create a venue for unfolding research activity on the general field of Information Science. The workshop features sessions for the dissemination of the research results of the Laboratory members, as well as tutorial sessions on interesting issues.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
Nonparametric Bayesian Word Discovery for Symbol Emergence in RoboticsTadahiro Taniguchi
This is a material for invited talk in the workshop on Machine Learning Methods for High-
Level Cognitive Capabilities in Robotics 2016 (ML-HLCR2016) held in IROS2016, Korea.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
Computer Aided Development of Fuzzy, Neural and Neuro-Fuzzy SystemsIJEACS
Development of an expert system is difficult because of two challenges involve in it. The first one is the expert system itself is high level system and deals with knowledge, which make is difficult to handle. Second, the systems development is more art and less science; hence there are little guidelines available about the development. This paper describes computer aided development of intelligent systems using modem artificial intelligence technology. The paper illustrates a design of a reusable generic framework to support friendly development of fuzzy, neural network and hybrid systems such as neuro-fuzzy system. The reusable component libraries for fuzzy logic based systems, neural network based system and hybrid system such as neuro-fuzzy system are developed and accommodated in this framework. The paper demonstrates code snippets, interface screens and class libraries overview with necessary technical details.
Phrase Structure Identification and Classification of Sentences using Deep Le...ijtsrd
Phrase structure is the arrangement of words in a specific order based on the constraints of a specified language. This arrangement is based on some phrase structure rules which are according to the productions in context free grammar. The identification of the phrase structure can be done by breaking the specified natural language sentence into its constituents that may be lexical and phrasal categories. These phrase structures can be identified using parsing of the sentences which is nothing but syntactic analysis. The proposed system deals with this problem using Deep Learning strategy. Instead of using Rule Based technique, supervised learning with sequence labelling is done using IOB labelling. This is a sequence classification problem which has been trained and modeled using RNN LSTM. The proposed work has shown a considerable result and can be applied in many applications of NLP. Hashi Haris | Misha Ravi ""Phrase Structure Identification and Classification of Sentences using Deep Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23841.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23841/phrase-structure-identification-and-classification-of-sentences-using-deep-learning/hashi-haris
This is a slide for the invited talk at The 4th Workshop on Naturalistic Driving Data Analytics,
IEEE IV2017, Los Angeles, 11th June, 2017.
This talk summarizes a series of work on a symbolization approach toward naturalistic driving behavior data.
Most of the works are conducted by collaboration between DESNO co. and Ritsumeikan university
Ontology Construction from Text: Challenges and TrendsCSCJournals
Ontology is one of the most popular representation model used for knowledge representation, sharing and reusing. In light of the importance of ontology, different methodologies for building ontologies have been proposed. Ontology construction is a difficult and time-consuming process. Many efforts have been made to help ontology engineers to construct ontologies and to overcome the bottleneck of knowledge acquisition. The aim of this paper is to give a brief overview of ontology learning approaches and to review some of ontology extraction systems and tools followed by a summarizing comparison of them. Also some of the current issues and main trends of ontology construction from texts will be discussed.
Presentation of the Defence of the PhD Dissertation "Towards a personalised virtual library: indications from navigational and personal information behaviour of e-learning students".
Failed queries: a morpho-syntactic analysis based on transaction log filesGiannis Tsakonas
Presentation in the First Workshop on Digital Information Management. The workshop is organized by the Laboratory on Digital Libraries and Electronic Publication, Department of Archives and Library Sciences, Ionian University, Greece and aims to create a venue for unfolding research activity on the general field of Information Science. The workshop features sessions for the dissemination of the research results of the Laboratory members, as well as tutorial sessions on interesting issues.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
Nonparametric Bayesian Word Discovery for Symbol Emergence in RoboticsTadahiro Taniguchi
This is a material for invited talk in the workshop on Machine Learning Methods for High-
Level Cognitive Capabilities in Robotics 2016 (ML-HLCR2016) held in IROS2016, Korea.
The Internet is a global system of interconnected computer networks that link several billion devices worldwide.
Any device can communicate with any other device.
Through a series of tubes!
Leadership Training as Value Creation Process in Indonesia: Strengthening Cur...Tri Widodo W. UTOMO
Presented in Panel Discussion on “Revolusi Mental: Creating Shared Value in Public Sector for Sustainable Innovation and Development”, held by NIPA in cooperation with United In Diversity (UID) and Australia-Indonesia Partnership in Economic Governance (AIPEG)
Dr. Tri Widodo W. Utomo, MA
Deputy Chairman for Innovation
National Institute of Public Administration (NIPA)
Republic of Indonesia
Der Beitrag bietet eine Grundlage zur Entwicklung neuer Geschäftsmodelle und Marktchancen für VR. Dazu werden veränderte Voraussetzungen in Form neuer Technologien und der Verfügbarkeit von 3D-Daten vorgestellt. Grundsätzliche VR- und AR-Awendungen werden erläutert. Der Beitrag nennt die heutigen Haupteinsatzhemmnisse von VR und AR in der Industrie.
Presentation held by K.U. Danyaro, J. Jaafar, and M.S. Liew at the Agricultural Ontology Service (AOS) Workshop 2012 in Kutching, Sarawak, Malaysia from September 3 - 4, 2012
Karolina Szymańska, założycielka i CEO agencji OWL PR, opowiedziała jak agencja komunikacji działa przy projektach zaangażowanych społecznie i wydarzeniach kulturalnych.
Digital workplaces - skills for technologistsDorje McKinnon
An interactive talk for technologists and software developers to learn about : needs analysis, personas, user stories, paper prototyping.
This talk was given at #CodeCamp Auckland on 3 October 2015 with the goal of introducing technologists to ways they can improve the products they're developing using personas and needs analysis.
This is an improved version of the talk I gave to #codecamp in Christchurch a few months ago.
IBP Insight:
Digitalizing the Automotive Customer Relationship – Changing Dynamics in Customer Communication
Im Bord Display wird dem Fahrer des Wagens eine Nachricht angezeigt:
„Sehr geehrter Herr Müller, die Wettervorhersagen für die nächste Woche sagen Frost an. Wir empfehlen den Wechsel auf Winterreifen. Ihr Autohändler Schmidt bietet Ihnen das Reifenwechsel-Komplettpaket für 60 Euro inkl. Auto-Wintercheck an. Bestätigen Sie diese Nachricht mit „OK“, um direkt telefonisch mit dem Autohändler zur Terminvereinbarung verbunden zu werden.“
Das Beispiel zeigt: Die digitalisierte Welt und Big Data eröffnen den Automobilherstellern und ihren Händlern eine Vielzahl neuer Möglichkeiten, um in einem kontinuierlichen Dialog mit ihren Kunden zu treten. Datenquellen wie das Connected Car oder Webportale zur Verwaltung des eigenen Fahrzeugs ermöglichen es, dem Kunden zielgerichtete Angebote zu machen. Traditionell war das Kundenbeziehungsmanagement fest bei den Autohändlern verankert. Hersteller waren zwar unterstützend durch Kommunikationsvorlagen oder die Analyse von Kundendaten involviert, sie hatten allerdings wenig Transparenz hinsichtlich der konkreten Kundeninteraktion. Die Kommunikation war weitestgehend standardisiert und ausgelegt für eine große Masse an Kunden auf festgelegten Zeitpunkten.
Neue Datenquellen wie Connected Car, Webportalen oder Social Media werden weitestgehend zentral von den Herstellern verwaltet. Um deren volles Potenzial nutzen zu können, müssen sie mit den Kundendaten beim Händler verknüpft werden. Nur so ist eine gezielte und maßgeschneiderte Kontaktaufnahme möglich.
Dabei gilt es jedoch, Herausforderungen zu überwinden. Automobilhersteller und Händler müssen enger zusammenarbeiten, um die Kundeninformationen an zentraler Stelle zu sammeln, sie zu analysieren und so eine ganzheitliche Kundenkommunikation zu schaffen. Zudem müssen auch die Kunden einen klaren Mehrwert in der Kommunikation sehen, um ihr Einverständnis für die Nutzung und Verknüpfung ihrer persönlichen Daten zu einem 360-Grad-Kundenprofil zu geben.
Die Experten der Unternehmensberatung Iskander Business Partner besitzen jahrelange Expertise im Bereich Customer Relationship Management (CRM) und haben die Chancen und Risiken eines digitalen Kundenbeziehungsmanagements im Automobilbereich analysiert. Wie werden Hersteller und Händler in der Zukunft mit den Kunden in Kontakt treten? Welche Voraussetzungen müssen erfüllt werden? Und wie müssen Hersteller und Händler zusammenarbeiten?
Recruitment Based On Ontology with Enhanced Security Featurestheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Ontology Evaluation Methods and Metrics - This is work I did while I was at The MITRE Corporation. I came up with a framework to support ontology evaluation for reuse that could also be used for ontology construction. I was the sole author of the approach, which was intended to begin a research program and a community of practice around it. It's been on hold and would like that to change. I'm now at the Tetherless World Constellation at Rensselaer Polytechnic Institute, if interested contact me there.
An Ontology Model for Knowledge Representation over User ProfilesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Automatic Classification of Springer Nature Proceedings with Smart Topic MinerFrancesco Osborne
The process of classifying scholarly outputs is crucial to ensure timely access to knowledge. However, this process is typically carried out manually by expert editors, leading to high costs and slow throughput. In this paper we present Smart Topic Miner (STM), a novel solution which uses semantic web technologies to classify scholarly publications on the basis of a very large automatically generated ontology of research areas. STM was developed to support the Springer Nature Computer Science editorial team in classifying proceedings in the LNCS family. It analyses in real time a set of publications provided by an editor and produces a structured set of topics and a number of Springer Nature classification tags, which best characterise the given input. In this paper we present the architecture of the system and report on an evaluation study conducted with a team of Springer Nature editors. The results of the evaluation, which showed that STM classifies publications with a high degree of accuracy, are very encouraging and as a result we are currently discussing the required next steps to ensure large-scale deployment within the company.
A study on the approaches of developing a named entity recognition tooleSAT 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
Ontology learning techniques and applications computer science thesis writing...Tutors India
At Tutors India, we offer Computer science and Information Technology Research Guidance services – We deliver exceptional work where your dissertation will deserve publication without significant reworking or alternation.
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. Images have an irrefutably central role in scientific discovery and discourse.
However, the issues associated with knowledge management and utility operations
unique to image data are only recently gaining recognition. In our previous
work, we have developed Yale Image finder (YIF), which is a novel Biomedical image
search engine that indexes around two million biomedical image data, along with
associated metadata. While YIF is considered to be a veritable source of easily accessible
biomedical images, there are still a number of usability and interoperability challenges
that have yet to be addressed. To overcome these issues and to accelerate the
adoption of the YIF for next generation biomedical applications, we have developed a
publically accessible semantic API for biomedical images with multiple modalities.
The core API called iCyrus is powered by a dedicated semantic architecture that exposes
the YIF content as linked data, permitting integration with related information
resources and consumption by linked data-aware data services. To facilitate the adhoc
integration of image data with other online data resources, we also built semantic
web services for iCyrus, such that it is compatible with the SADI semantic web service
framework. The utility of the combined infrastructure is illustrated with a number
of compelling use cases and further extended through the incorporation of Domeo, a
well known tool for open annotation. Domeo facilitates enhanced search over the
images using annotations provided through crowdsourcing. The iCyrus triplestore
currently holds more than thirty-five million triples and can be accessed and operated
through syntactic or semantic query interfaces. Core features of the iCyrus API,
namely: data reusability, system interoperability, semantic image search, automatic
update and dedicated semantic infrastructure make iCyrus a state of the art resource
for image data discovery and retrieval
ABSTRACT
Scientific publications are considered as the most up-to-date resource of ongoing research
activities and scientific knowledge. Efficient practices for accessing biomedical
publications are key to allowing a timely transfer of information from the scientific
research community to peer investigators and other healthcare practitioners. Biomedical
sequence images published within the literature play a central role in life science
discoveries. Whereas advanced text-mining pipelines for information retrieval and
knowledge extraction are now commonplace methodologies for processing documents,
the ongoing challenges associated with knowledge management and utility operations
unique to biomedical image data are only recently gaining recognition. Sequence images
depicting key findings of research papers contain rich information derived from a wide
range of biomedical experiments. Searching for relevant sequence images is however error
prone as images are still opaque to information retrieval and knowledge extraction
engines. Specifically, there is no explicit description or annotation of the sequence image
content. Moreover, traditional biomedical search engines, which search image captions
for relevant keywords only, offer syntactic search mechanisms without regard for the
exact meaning of the query. As proposed in this thesis, semantic enrichment of biomedical
sequence images is a solution which adopts a combination of technologies to harness the
comprehensive information associated with, and contained in, biomedical sequence
images. Extracted information from sequence images is used as seed data to aggregate and
iii
harvest new annotations from heterogeneous online biomedical resources. Comprehensive
semantic enrichment of biomedical images incorporates a variety of knowledge
infrastructure components and services including image feature extraction, semantic web
data services, linked open data and crowd annotation.
Together, these resources make it possible to automatically and/or semi-automatically
discover and semantically interlink new information in a way that supports semantic
search for sequence images. The resulting enriched sequence images are readily reusable
based on their semantic annotations and can be made available for use in ad-hoc data
integration activities. Furthermore, to support image reuse this thesis introduces a
mechanism for identifying similar sequence images based on fuzzy inference and cosine
similarity techniques that can retrieve and classify the related sequence images based on
their semantic annotations. The outcomes of this research work will be relevant to a variety
of user groups ranging from clinicians and researchers searching with sequence image
data.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
1. A smart coupling of type-2 fuzzy ontology (T2FO) with a multi-agent
system: A novel mechanism to automate the personalized itinerary
Student Name: Syed Ahmad Chan Bukhari
Student Id: 2010214029
Lab: Artificial Intelligence Lab
Supervised by: Prof. Yong-Gi Kim
Department of Computer Science, Gyeongsang National University, Jinju Korea 1
2. Contents
• Background and motivation
• Past research work
• Proposed solution
• ST2FO-MAS to automate personalized Itinerary (Problem Intro.)
• Secure Type-2 Fuzzy Ontology
– Secure Type-2 Fuzzy Ontology (A quick review of terminologies)
– Type-1 Fuzzy system
– Type-2 Fuzzy system
• Secure Type-2 Fuzzy Ontology Development
– Crisp ontology development
– Type-1 Fuzzy ontology Development
– Type-2 Fuzzy ontology development
• Multi-Agent System
– Terminology, Role, Integration and usage
– Architecture and working
• Architecture of STFO-MAS and its Application to automate the personalized itinerary
– inside decision supported multi-agent pool
– Inside the Natural language query processing
• Experiments and results
– Ontology Evaluation
– Overall system evaluation
– Extracted results
– Graphical efficiency comparison
ST2FO-MAS to automate personlized
2
itinerary
3. Background and Motivation
• As the internet grows rapidly, millions of web pages are being
added on a daily basis
• Personalized information extraction and intelligent decision making
on it behalf are challenging issues nowadays
• Explosive internet heterogeneity making relevant Info. Extraction
and intelligent decision making more challenging
• Search engines are used commonly to find information
• Conventional mechanism of searching: keywords and directory
structure
• Most of the data on internet is in imprecise, uncertain
• Optimal searching not possible by using conventional ways
• Currently users spend hours and hours to find desired information
from internet
• Any solution?
ST2FO-MAS to automate personlized
3
itinerary
4. Past research work
Researchers Research area/ Domain Year Tools and technologies
Yi et al. To represent the Chinese medicines 2010 Ontology, Fuzzy system
Zhai et al. SCM 2009 Ontology
A. Segev et al. Patent search 2010 Ontology
Huiying et al. Enterprise information-retrieval model 2009 NLP, Ontology, AI
Noy et al. FOGA 2001 Fuzzy system, ANN, Ontology
Zhai et al. E-commerce domain 2008 Fuzzy Ontology
hang Shing et al. Diet recommendations for diabetic patient 2011 FML, ONTOLOGY
C.S. Lee To present the computer Go knowledge 2010 Fuzzy system, ontology
Wang, M et al. Automate meetings scheduling 2010 FNL, Ontology, AI
Jaber et al. Customized learning paths in an e-learning platform 2010 MAS, Ontology
S. Yang E-health 2010 MAS, Ontology
Szu-Yin, L et al. Corporate tacit knowledge 2005 Ontology, AI
Jung et al. Indirect alignment between multiple language ontologies. 2011 MAS, Ontology, AI
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5. Proposed solution
• Researchers proposed several solution but mostly failed with time, due to
diverse and fatally vague nature of web data
• Some solutions found working but with low precision rate and with high
cost
• We provide an end-to-end solution to automate the optimal information
extraction and decision making
• Our system based on: Secure Type-2 Fuzzy Ontology MAS
– Why Type-2 fuzzy system used?
– Why incorporated Type-2 fuzzy system with ontology?
– Why information security important?
– What is Ontology and how can we exploit it?
– What is the co-relation of MAS, NLP with T2FO and optimal information
extraction and decision making?
• Domain of application: Personalized itinerary booking (Why use this
domain???)
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6. ST2FO-MAS to automate personalized Itinerary
(Problem Intro.)
• Manual air ticket booking : time consuming and laborious
• Easiness of web technology provides opportunity to travel companies to
online their portals
• Thousands of solutions available now
• Passengers spend hours to find acceptable fare
• Travelers are anxiously waiting for solution with personalized outcomes
Problems Proposed Solution
Intensively blurred information Type-2 Fuzzy system
Scattered information resources T2FO and MAS
Personalized constraints Type-2 Fuzzy ontology
Tour’s operator limitations
Information security based on XML
Increasing Information security
challenges (hacking risks) Type-2 Fuzzy Ontology
Limitation of Fuzzy Information NLP
acquisition techniques MAS,NLP and T2FO
Usability issues Ontology with MAS
Process Automation
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7. Secure Type-2 Fuzzy Ontology
A quick review of terminologies
Ontology:
The term ontology has its origin in philosophy and has been applied in many
different ways.
1. “An ontology formally represents knowledge as a set of concepts within
a domain, and the relationships between those concepts.”
2. “Formal, explicit specification of a shared conceptualization“
Main Components of Ontology
Individuals: instances or objects (the basic or "ground level" objects)
Classes: sets, collections, concepts, types of objects, or kinds of things.
Attributes: aspects, properties, features, characteristics, or parameters that
objects (and classes) can have
Relations: ways in which classes and individuals can be related to one another
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8. Secure Type-2 Fuzzy Ontology
(A quick review of terminologies)
Common definitions and concepts about type-1 Fuzzy set and type-2
Type-1 Fuzzy system
• The fuzzy set theory was introduced by Lotfi Zadeh in 1965 to deal with vague
and imprecise concepts.
• In classical set theory, elements either belong to a particular set or they don’t
belong.
• However, in fuzzy set theory the association of an element with a particular set
lies between ‘0’ and ‘1’ which is called degree of association or membership
degree. A fuzzy set can be defined as:
Definition 1: A fuzzy set ‘s’ over universe of discourse ‘X’ can be defined by its
membership function µ_s which maps element ‘x’ to values between [0,1].
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9. Secure Type-2 Fuzzy Ontology
(A quick review of terminologies)
Type-2 Fuzzy System
• Type-1 or conventional fuzzy logic can handle the uncertainty at certain
level.
Some Fact
• vagueness are the vital parts of any real-time system
• Uncertainty and vagueness is increasing continuously due to heterogeneity.
How to handle the extensive blurred information?
Solution: Type-2 Fuzzy logic
• Type-2 fuzzy logic is the extended version of classical fuzzy set theory.
• In type-1 fuzzy set theory, the membership values are crisp, while type-2
fuzzy systems have fuzzy membership values.
9
10. Secure Type-2 Fuzzy Ontology
(Ontology Development)
OUR Proposed formation of Type-2 Fuzzy Ontology Building
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11. Development of Secure type-2 fuzzy ontology
The anatomy of Type-2 Secured Fuzzy Ontology (Layered
Domain Ontology Development steps
Architecture)
1. Determine the domain
and scope of the
ontology.
2. Consider reusing
existing ontologies.
3. Enumerate important
terms in the ontology.
4. Define the classes and
the class hierarchy.
5. Define the properties of
the classes.
6. Define the facets of the
slots.
7. Create instances.
Language: OWL-2 , RDF and Protégé
Reasoner: Pellet, DeLorean
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12. Development of Secure type-2 fuzzy ontology
• Fuzzy ontology can be defined in the form of fuzzy sets.
• Let be fuzzy class in universe of discourse µ then
and the relationship between two ontology classes are fuzzy relation
• Annotation feature of protégé is used to define fuzzy concept in fuzzy ontology
• Manual process of annotation adding is a complex and error pruning
• Protégé fuzzy OWL tab helps us to make this process handy
• A class of cheap ticket can be described in to fuzzy form as:
•Similarly very cheap ticket can be expressed as:
12
13. Development of Secure type-2 fuzzy ontology
13
Secure Type-2 Fuzzy Ontology of Ticket Booking Domain
14. Secure Type-2 Fuzzy Ontology
(Information security)
Why information security important?
• Information is the most valuable assets of any organization.
• Nowadays, secure information has become a strategic issue for online
businesses.
• In ontology, all kind of information is shared in plain text format.
• This raises the issues of information leakage, altering and deletion of
information contents
Possible Information security Challenges
• DOS attack on server
• XML content exploit attack (data holders: CDATA,PCDATA, NUMBER)
• X-Path altering attack (also known as XML bomb)
Light Weight solution for content security
• XML security recommendations developed by W3C
•XML digital signature
• XML encryption
• XML key management specification (XKMS)
•security assertion markup language
• XML access control markup language XACML) 14
16. Multi-agent system ( Terminology, Role, Integration and usage)
• Diversity and complexity factors are increasing day by day in modern
software applications.
• The multi-Agent system is considered an efficient technology in the
development of distributed systems.
• A multi-Agent system is basically the group of interconnected agents, in
which each agent works autonomously while sharing information.
• An agent is a bunch of code which is designed to perform a specific task
on the behalf of its user.
Why we used MAS?
Our domain is diverse
Complex and unstructured
For automatic information extraction
For intelligent decision making
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17. Multi-agent system ( Architecture and working)
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18. A graphical architecture of STFO-MAS and its Application to automate the
personalized itinerary
2 3
1 4
5
6
7
8
18
19. What's inside decision supported multi-agent pool?
Agent Name Agent Acronym Funcationality
Query Processing Agent QPA Natural language to query building
Query Processing
Query Optimization
Personal Preferences and PPSA Interaction with personal ontology
Schedule Maintaining Agent Communication with other agents to rovide the
personal preferences information
Monitoring the information process and
implementations of user constraints.
Type-2 Fuzzy Inference T2FIA Crtical decision making based on information
Engine Agent Remain in touch all the time with PPSA and SBTA
Responsible for making underlying connection with
fuzzy ontology
Secured Bank Transaction SBTA Receiving requests for transaction.
Agent Authentication
Resorce allocation
Transaction processing
Log generation
Ticket reservation Agent TRA Making connection with travel agency databases
Finding and reserving of the optimal ticket
Keep in touch with T2FIA AND PPSA
Multi-agent system schema
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20. What's Inside the Natural language query processing agent
(QPA)?
I(noun) want to(preposition) go(verb)
from(preposition) Seoul(noun) to
London(noun) to attend(preposition) a
meeting(verb) . The meeting will be
held afternoon (noun, adjective), so I
want to take (verb) vegetables (noun)
in lunch (noun). Please book (verb) a
ticket (noun) of economy class (noun+
adjective) with cheap rate (noun+
adjective) and minimum delay (noun+
adjective).”
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21. Experiments and results
Ontology Evaluation
• We evaluated the ontology after completion of each phase of T2FO development to
measure the efficiency
•We used Manchester OWL-2 syntax of DL-query to evaluate the efficiency of ontology
Some queries results are:
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22. Experiments and results
System security Evaluator
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23. Experiments and results
Overall system evaluation
•Information system can be categorized on the basis of
its effectiveness.
•There are some known ways to define the efficiency of an
information system, such as the precision, recall and time
• To exact judge the performance, we requested five volunteer to
help us in experiments.
•The volunteers enquired from the system by using crisp ontology
and Type-2 fuzzy ontology.
• we noted the time, precision and recall in each mode
• Mathematically, the precision and recall can be expressed as the following:
here ‘ce’ is the total number of records that are extracted from the internet,
and ‘te’ and ‘fe’ represent the true and false elements in the extracted records.
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24. Experiments and results (Extracted results)
Overall system performance results recoded in the case of the secured type-1 fuzzy ontology.
Total Number of No of False Precision Recall Job
Number of True Elements (fe) Percentage Percentage Completion
Resource Elements (te) (PP) (%) (RC) (%) Time (JCT)
Extracted (Seconds)
Corpus 1 (ce)
Volunteer 1 569 191 378 61.1 74.8 180
Volunteer 2 479 146 333 58.9 76.6 234
Volunteer 3 587 275 312 58.1 68.1 156
Volunteer 4 389 87 302 94.8 81.8 132
Volunteer 5 495 198 297 62.5 71.5 210
Overall system performance results recoded in the case of the secured type-1 fuzzy
ontology.
Total Number Number of No of False Precision Recall Job Completion
of Resource True Elements Elements (fe) Percentage (PP) Percentage (RC) Time (JCT)
Extracted (te) (%) (%) (Seconds)
Corpus 1 (ce)
Volunteer 1 569 311 258 68.8 71.2 228
Volunteer 2 479 292 187 71.9 67.3 258
Volunteer 3 587 496 91 86.5 54.2 286
Volunteer 4 389 278 111 77.9 58.3 305
Volunteer 5 495 267 228 68.5 64.9 315
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25. Experiments and results
Overall system performance results recoded in the case of the secured type-2
fuzzy ontology.
Total Number of No of False Precision Recall Job
Number of True Elements (fe) Percentage Percentage Completion
Resource Elements (te) (PR) (%) (RC) (%) Time (JCT)
Extracted (Seconds)
Corpus 1 (ce)
Volunteer 1 569 437 159 78.2 56.5 336
Volunteer 2 479 337 142 77.2 58.8 319
Volunteer 3 587 530 57 91.1 52.55 422
Volunteer 4 389 279 110 77.9 58.23 357
Volunteer 5 495 391 104 82.6 55.3 467
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26. Experiments and results (Efficiency Comparison)
Crisp ontology Case
Fuzzy ontology Case
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27. Experiments and results (Efficiency Comparison)
Type-2 Fuzzy ontology
Case
Combine Efficiency Analysis
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31. Many Thanks for your Kind attention!
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