Applications of Soft Computing for the Web

Applications of Soft Computing for the Web

Author: Rashid Ali

Publisher: Springer

Published: 2018-01-08

Total Pages: 284

ISBN-13: 9811070989

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This book discusses the applications of different soft computing techniques for the web-based systems and services. The respective chapters highlight recent developments in the field of soft computing applications, from web-based information retrieval to online marketing and online healthcare. In each chapter author endeavor to explain the basic ideas behind the proposed applications in an accessible format for readers who may not possess a background in these fields. This carefully edited book covers a wide range of new applications of soft computing techniques in Web recommender systems, Online documents classification, Online documents summarization, Online document clustering, Online market intelligence, Web usage profiling, Web data extraction, Social network extraction, Question answering systems, Online health care, Web knowledge management, Multimedia information retrieval, Navigation guides, User profiles extraction, Web-based distributed information systems, Web security applications, Internet of Things Applications and so on. The book is aimed for researchers and practitioner who are engaged in developing and applying intelligent systems principles for solving real-life problems. Further, it has been structured so that each chapter can be read independently of the others.


A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis

A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis

Author: Kritika Mishra

Publisher: Infinite Study

Published: 2020-10-18

Total Pages: 22

ISBN-13:

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We have proposed a novel framework that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentiment analysis on the text files of those audio.


Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics

Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics

Author: Florentin Smarandache

Publisher: Elsevier

Published: 2023-02-11

Total Pages: 495

ISBN-13: 0323994571

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Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics investigates and presents the many applications that have arisen in the last ten years using neutrosophic statistics in bioinformatics, medicine, agriculture and cognitive science. This book will be very useful to the scientific community, appealing to audiences interested in fuzzy, vague concepts from which uncertain data are collected, including academic researchers, practicing engineers and graduate students. Neutrosophic statistics is a generalization of classical statistics. In classical statistics, the data is known, formed by crisp numbers. In comparison, data in neutrosophic statistics has some indeterminacy. This data may be ambiguous, vague, imprecise, incomplete, and even unknown. Neutrosophic statistics refers to a set of data, such that the data or a part of it are indeterminate in some degree, and to methods used to analyze the data. Introduces the field of neutrosophic statistics and how it can solve problems working with indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data Presents various applications of neutrosophic statistics in the fields of bioinformatics, medicine, cognitive science and agriculture Provides practical examples and definitions of neutrosophic statistics in relation to the various types of indeterminacies


Comparison of neutrosophic approach to various deep learning models for sentiment analysis

Comparison of neutrosophic approach to various deep learning models for sentiment analysis

Author: Mayukh Sharma

Publisher: Infinite Study

Published:

Total Pages: 14

ISBN-13:

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Deep learning has been widely used in numerous real-world engineering applications and for classification problems. Real-world data is present with neutrality and indeterminacy, which neutrosophic theory captures clearly. Though both are currently developing research areas, there has been little study on their interlinking. We have proposed a novel framework to implement neutrosophy in deep learning models. Instead of just predicting a single class as output, we have quantified the sentiments using three membership functions to understand them better. Our proposed model consists of two blocks, feature extraction, and feature classification.


Neutrosophic Graphs: A New Dimension to Graph Theory

Neutrosophic Graphs: A New Dimension to Graph Theory

Author: Vasantha Kandasamy

Publisher: Infinite Study

Published: 2015

Total Pages: 127

ISBN-13: 1599733625

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Studies to neutrosophic graphs happens to be not only innovative and interesting, but gives a new dimension to graph theory. The classic coloring of edge problem happens to give various results. Neutrosophic tree will certainly find lots of applications in data mining when certain levels of indeterminacy is involved in the problem. Several open problems are suggested.


Algorithms for single-valued neutrosophic decision making based on TOPSIS and clustering methods with new distance measure

Algorithms for single-valued neutrosophic decision making based on TOPSIS and clustering methods with new distance measure

Author: Harish Garg

Publisher: Infinite Study

Published:

Total Pages: 23

ISBN-13:

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Single-valued neutrosophic set (SVNS) is an important contrivance for directing the decision-making queries with unknown and indeterminant data by employing a degree of “acceptance”, “indeterminacy”, and “non-acceptance” in quantitative terms. Under this set, the objective of this paper is to propose some new distance measures to find discrimination between the SVNSs. The basic axioms of the measures have been highlighted and examined their properties. Furthermore, to examine the relevance of proposed measures, an extended TOPSIS (“technique for order preference by similarity to ideal solution”) method is introduced to solve the group decision-making problems. Additionally, a new clustering technique is proposed based on the stated measures to classify the objects. The advantages, comparative analysis as well as superiority analysis is given to shows its influence over existing approaches.


Interval Neutrosophic Sets and Logic: Theory and Applications in Computing

Interval Neutrosophic Sets and Logic: Theory and Applications in Computing

Author: Haibin Wang

Publisher: Infinite Study

Published: 2005

Total Pages: 99

ISBN-13: 1931233942

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This book presents the advancements and applications of neutrosophics, which are generalizations of fuzzy logic, fuzzy set, and imprecise probability. The neutrosophic logic, neutrosophic set, neutrosophic probability, and neutrosophic statistics are increasingly used in engineering applications (especially for software and information fusion), medicine, military, cybernetics, physics.In the last chapter a soft semantic Web Services agent framework is proposed to facilitate the registration and discovery of high quality semantic Web Services agent. The intelligent inference engine module of soft semantic Web Services agent is implemented using interval neutrosophic logic.


Neutrosophic Clustering Algorithm Based on Sparse Regular Term Constraint

Neutrosophic Clustering Algorithm Based on Sparse Regular Term Constraint

Author: Dan Zhang

Publisher: Infinite Study

Published:

Total Pages: 12

ISBN-13:

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Clustering algorithm is one of the important research topics in the field of machine learning. Neutrosophic clustering is the generalization of fuzzy clustering and has been applied to many fields. this paper presents a new neutrosophic clustering algorithm with the help of regularization. Firstly, the regularization term is introduced into the FC-PFS algorithm to generate sparsity, which can reduce the complexity of the algorithm on large data sets. Secondly, we propose a method to simplify the process of determining regularization parameters. Finally, experiments show that the clustering results of this algorithm on artificial data sets and real data sets are mostly better than other clustering algorithms. Our clustering algorithm is effective in most cases.