Proceedings of the 12th International Conference on Soft Computing for Problem Solving : SocProS 2023, Volume 2 / edited by Millie Pant, Kusum Deep, Atulya Nagar.

Format:

Book

Author/Creator:

Pant, Millie.

Contributor:

Deep, Kusum.

Nagar, Atulya.

Series:

Lecture Notes in Networks and Systems, 2367-3389 ; 995

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Algorithms.

Computational Intelligence.

Artificial Intelligence.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Algorithms.

Physical Description:

1 online resource (922 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

This book provides an insight into 12th International Conference on Soft Computing for Problem Solving (SocProS 2023), organized by The Department of Applied Mathematics and Scientific Computing, Saharanpur Campus of Indian Institute of Technology, Roorkee, India, in conjunction with Continuing Education Center during 11–13 August 2023. This book presents the latest achievements and innovations in the interdisciplinary areas of soft computing, machine learning, and data science. It covers original research papers in the areas of algorithms (artificial neural network, deep learning, statistical methods, genetic algorithm, and particle swarm optimization) and applications (data mining and clustering, computer vision, medical and health care, finance, data envelopment analysis, business, and forecasting applications). This book is beneficial for young as well as experienced researchers dealing across complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.

Contents:

Preface

Contents

Editors and Contributors

Using Opinionated-Objective Terms to Improve Lexicon-Based Sentiment Analysis

1 Introduction

2 Related Work

3 Methodology

3.1 Dataset

3.2 Data Pre-processing

3.3 Lexicon Generation

3.4 Corpus Statistics

3.5 Evaluation Approach and Metrics

4 Results

5 Conclusion

5.1 Theoretical and Practical Implications

5.2 Limitation and Future Work

References

Explaining the Artificial Neural Network Using Evolutionary Fuzzy Association Rule Mining (EFARM)

3 Background

3.1 FARM (Fuzzy Association Rule Mining)

3.2 NSGA-II

4 Proposed Methodology

4.1 Data Transformation and Training of the ANN Model

4.2 Rule Extraction

4.3 Designing of NSGA-II

5 Result and Analysis

6 Conclusion

References Generated by AI.

Notes:

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Other Format:

Print version: Pant, Millie Proceedings of the 12th International Conference on Soft Computing for Problem Solving

ISBN:

9789819732920

OCLC:

1443939890