IEEE Organizations related to Knowledge Discovery

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Conferences related to Knowledge Discovery

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

All fields of satellite, airborne and ground remote sensing.


2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC)

The conference focuses on networking, sensing and control three areas, but also opens to some emerging subjects in information technology, such as data science and machine learning.


2019 IEEE 21st International Conference on Business Informatics (CBI)

The IEEE CBI series encourages a broad understanding of Business Informatics research, and intends to further its many different facets, theoretical foundations and experiential body of knowledge.


2018 11th International Symposium on Computational Intelligence and Design (ISCID)

Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models. The application of computational intelligence techniquesinto industrial design, interactive design, media design, and engineering design are also within the scope.

  • 2017 10th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models. The application of computational intelligence techniques into industrial design, interactive design, media design, and engineering design are also within the scope.

  • 2016 9th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models.

  • 2015 8th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation,Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems andother similar computational models.

  • 2014 7th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation,Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems andother similar computational models.

  • 2013 6th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models.

  • 2012 5th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models.

  • 2011 4th International Symposium on Computational Intelligence and Design (ISCID)

    Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models. Computational Intelligence constitutes an umbrella of techniques, has proven to be flexible in decision making in dynamic environment.

  • 2010 3rd International Symposium on Computational Intelligence and Design (ISCID)

    ISCID 2010 will be held at Hangzhou, China in 29-31, October 2010. It provides researchers and practitioners interested in new information technologies an opportunity to highlight innovative research directions, novel applications, and a growing number of relationships between rough sets and such are as computational intelligence, knowledge discovery and design.

  • 2009 2nd International Symposium on Computational Intelligence and Design (ISCID)

    This symposium provide researchers and practitioners interested in new information technologies an opportunity to highlight innovative research directions, novel applications, and a growing number of relationships between rough sets and such areas as computational intelligence, knowledge discovery and data mining, non-conventional models of computation and design.

  • 2008 International Symposium on Computational Intelligence and Design (ISCID)

    computational intelligence, knowledge discovery and data mining, intelligent information systems, web mining, synthesis and analysis of complex objects , non-conventional models of computation and Industrial Design.


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Periodicals related to Knowledge Discovery

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No periodicals are currently tagged "Knowledge Discovery"


Most published Xplore authors for Knowledge Discovery

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Xplore Articles related to Knowledge Discovery

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Building a Concept-Level Sentiment Dictionary Based on Commonsense Knowledge

IEEE Intelligent Systems, 2013

Sentiment dictionaries are essential for research in the sentiment analysis field. A two-step method integrates iterative regression and random walk with in-link normalization to build a concept-level sentiment dictionary. The approach uses ConceptNet as a framework to propagate sentiment values, based on the assumption that semantically related concepts share a common sentiment.


Minitrack Introduction

Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), 2008

This minitrack consists of five papers involving knowledge discovery for managerial decision support. These five papers illustrate a diverse set of approaches, demonstrating the variety of ways in which modern information technologies can be applied to today's complex decision problems.


Question classification for medical domain Question Answering system

2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), 2016

Question classification plays an important role in question answering system. It helps in finding or constructing accurate answers and hence improves the quality of Question Answering systems. The question classification approaches generally used are: Rule based, Machine learning and Hybrid. This paper presents our research work on question classification through rule based approach. The question processing module helps in assigning ...


K-Extractor: Automatic Knowledge Extraction for Hybrid Question Answering

2016 IEEE Tenth International Conference on Semantic Computing (ICSC), 2016

This paper describes an approach to integrate unstructured and structured data and provide a natural language query interface to the consolidated knowledge base. The approach is based on deep semantic representation expressed in RDF triples. Natural language questions are automatically converted into SPARQL queries executed against the RDF store. The approach is implemented in K-Extractor platform. Two domain collections are ...


A segmentation technology for multivariate contextual time series

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2017

A time series is a series of data points indexed in time order, mining multivariate contextual time series (MCTS) should pay more attention to time order. This paper proposes a new method for splitting the MCTS into a number of segments, uses the concept of scenarios and themes to represent MCTS instead of data points and extracts important contextual features ...


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Educational Resources on Knowledge Discovery

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IEEE-USA E-Books

  • Building a Concept-Level Sentiment Dictionary Based on Commonsense Knowledge

    Sentiment dictionaries are essential for research in the sentiment analysis field. A two-step method integrates iterative regression and random walk with in-link normalization to build a concept-level sentiment dictionary. The approach uses ConceptNet as a framework to propagate sentiment values, based on the assumption that semantically related concepts share a common sentiment.

  • Minitrack Introduction

    This minitrack consists of five papers involving knowledge discovery for managerial decision support. These five papers illustrate a diverse set of approaches, demonstrating the variety of ways in which modern information technologies can be applied to today's complex decision problems.

  • Question classification for medical domain Question Answering system

    Question classification plays an important role in question answering system. It helps in finding or constructing accurate answers and hence improves the quality of Question Answering systems. The question classification approaches generally used are: Rule based, Machine learning and Hybrid. This paper presents our research work on question classification through rule based approach. The question processing module helps in assigning a suitable question category and identifying the keywords from the given input question. A prototype system based on the proposed method has been constructed and the experiment on 500 medical questions collected from patients and doctors has been carried out. Using the two layered taxonomy of 6 course grain and 50 fine grained categories developed by Li and Roth, we have classified the questions into various categories. We have also studied the syntactic structure of the question and suggest the syntactic patterns for particular category of questions. Using these question patterns we have classified the question into particular category. In this paper we have proposed a compact and effective method for question classification. The experimental output shows that even with small set of question categories we can classify the questions with more satisfactory and better result.

  • K-Extractor: Automatic Knowledge Extraction for Hybrid Question Answering

    This paper describes an approach to integrate unstructured and structured data and provide a natural language query interface to the consolidated knowledge base. The approach is based on deep semantic representation expressed in RDF triples. Natural language questions are automatically converted into SPARQL queries executed against the RDF store. The approach is implemented in K-Extractor platform. Two domain collections are shown: antibiotic resistant bacteria and illicit drugs.

  • A segmentation technology for multivariate contextual time series

    A time series is a series of data points indexed in time order, mining multivariate contextual time series (MCTS) should pay more attention to time order. This paper proposes a new method for splitting the MCTS into a number of segments, uses the concept of scenarios and themes to represent MCTS instead of data points and extracts important contextual features to carry out the multidimensional fitting for MCTS.

  • Precision Health Care for Sustainable Patient Centric Solutions

    The quest for sustainable healthcare amidst the rising cost and shrinking resources has led to the industrial uptake of essential technology that has impacted on the provision of healthcare services, subsequently triggering the mobility of patients seeking specialized healthcare services. Precision Health Care (PHC) seeks to harness technology in its delivery of bespoke patient centered diagnosis and treatment. This entails utilizing an individual's medical history and advanced decision support systems to tailor treatment prescriptions. With the increasing availability of healthcare data, PHC has the potential to break down the walls in realizing substantial benefits to all its stakeholders by providing valuable information integral for the delivery of personalized health support services to the patients and improved clinical decision support for the service provider. Understanding the role of a precise healthcare system paves way for a more intelligent healthcare ecosystem that's focused on prevention, early disease detection and personalized treatments through health data integration. This paper seeks to deconstruct the viability PHC in the context of knowledge discovery, privacy, data re-use and governance in the context of patient centric treatment solutions.

  • A Pay-As-You-Go Methodology for Ontology-Based Data Access

    A successfully repeated use case for Semantic Web technologies is Ontology- Based Data Access for data integration. In this approach, an ontology serves as a uniform conceptual federating model, which is accessible to both IT developers and business users. Here, two challenges for developing an OBDA system are considered: ontology and mapping engineering, along with a pay-as- you-go methodology that addresses these challenges and enables agility.

  • Automated question answering system using ontology and semantic role

    Semantic similarity is an essential part for question answering, it is used various fields such as Artificial Intelligence, Natural Language Processing, information retrieval, Document Retrieval and Automatic evaluations. This paper mainly focuses on similarity measure based on the posted query, and finding the appropriate meaning between the words. Accessing an accurate answer from the web document is challenging task. The proposed approach is used to analyze and measuring the similarity between the words. It presents the Web And semantic knowledge-Driven automatic question answering system (WAD). It encompasses three phases to enhance the performance of QA system using the web as well as the semantic knowledge. Initially, the WAD approach determines the user query, query expansion technique and entity linking method. The ontology based information is used in WAD to rank the answers and experimental results provide the result with high accuracy than the baseline method.

  • Automatic question-answering using a deep similarity neural network

    Automatic question-answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does. In this work, we propose a deep learning based model for automatic question-answering. First the questions and answers are embedded using neural probabilistic modeling. Then a deep similarity neural network is trained to find the similarity score of a pair of answer and question. Then for each question, the best answer is found as the one with the highest similarity score. We first train this model on a large- scale public question-answering database, and then fine-tune it to transfer to the customer-care chat data. We have also tested our framework on a public question-answering database and achieved very good performance.

  • An efficient skyline maintenance method for data modification

    Skyline queries are useful in many applications such as multicriteria decision making, data mining, and user-preference queries. However, the data points may be modified as time goes by. The price of a certain flight ticket or the performances of the players are examples. Generally, in previous studies, two simple ways are used to maintain the skyline for this situation. That is, deleting the data that need to be modified first and then inserting the updated object as a new one or inserting the updated object first and then deleting the old one. However, data deletion breaks the dominance relationship between data points, and generates a lot of temporary skyline points. Moreover, if data modification is treated as data deletion/insertion, we need to perform dominance test to all data points twice which is inefficient for skyline maintaining. In this paper, by considering the dominance relationship between the data before and after modification, an efficient method for maintaining skyline is proposed. Moreover, a set of simulation is performed to show the benefit of the approach.



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