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Computational Intelligence for Modern Business Systems

Emerging Applications and Strategies

  • Book
  • © 2024

Overview

Editors:
  1. Sandeep Kautish

    1. LBEF Campus, Kathmandu, Nepal

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  2. Prasenjit Chatterjee

    1. Department of Mechanical Engineering, MCKV Institute of Engineering, Howrah, India

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  3. Dragan Pamucar

    1. Faculty of Organizational Sciences, Department of Operations Research and Statistics, University of Belgrade, Belgrade, Serbia

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  4. N. Pradeep

    1. Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India

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  5. Deepmala Singh

    1. Symbiosis Centre for Management Studies, Symbiosis International (Deemed University), Pune, India

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  • Serves as a strong technological convergence solution for industry professionals, academicians, and researchers
  • Includes information on the integrated platform of business applications and computational intelligence techniques
  • Provides insights, research gaps, open challenges, and unsolved problems

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Table of contents (27 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Computational Intelligence for Business Finance Applications

    1. Front Matter

      Pages 1-1
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    2. Artificial Intelligence and Machine Learning in Financial Services to Improve the Business System

      • Komalpreet Kaur, Yogesh Kumar, Sukhpreet Kaur
      Pages 3-30

    3. Covid-19 Related Ramifications on Financial Market: A Qualitative Study of the Pandemic’s Effects on the Stock Exchange of Big Technology Companies

      • Pragya Gupta, Drishti Jain, B. Ida Seraphim, Rashima Mahajan
      Pages 31-45

    4. Computational Intelligence Techniques for Behavioral Research on the Analysis of Investment Decisions in the Commercial Realty Market

      • S. Siva Venkata Ramana, T. Mydhili, Ponduri Siddardha, Gomatam Mohana Charyulu, K. Saikumar
      Pages 47-62

    5. Trust the Machine and Embrace Artificial Intelligence (AI) to Combat Money Laundering Activities

      • Guneet Kaur
      Pages 63-81

    6. Predictive Analysis of Crowdfunding Projects

      • Aashay Shah, Prithvi Shah, Umang Savla, Yash Rathod, Nirmala Baloorkar
      Pages 83-95

    7. Stock Prediction Using Multi Deep Learning Algorithms

      • Bui Thanh Hung, Prasun Chakrabarti, Prasenjit Chatterjee
      Pages 97-113

    8. House Price Prediction by Machine Learning Technique—An Empirical Study

      • Suriya Begum
      Pages 115-133

  3. Computational Intelligence for Marketing, Business Process and Human Resource Applications

    1. Front Matter

      Pages 135-135
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    2. SDN-Based Network Resource Management

      • João Carlos Marques Silva, José André Moura, Nuno Manuel Branco Souto
      Pages 137-156

    3. The Future of Digital Marketing: How Would Artificial Intelligence Change the Directions?

      • Khan Md. Raziuddin Taufique, Md. Mahiuddin Sabbir
      Pages 157-183

    4. Business Process Reengineering in Public Sector: A Case Study of World Book Fair

      • M. A. Sikandar, M. Razaulla Khan, Anita Sikandar
      Pages 185-213

    5. Improved Machine Learning Prediction Framework for Employees with a Focus on Function Selection

      • Kamal Gulati, T. S. Ragesh, K. Bhavana Raj, Bhimraj Basumatary, Ashutosh Gaur, Gaurav Dhiman et al.
      Pages 215-226

    6. Applications of Data Science and Artificial Intelligence Methodologies in Customer Relationship Management

      • E. Fantin Irudaya Raj
      Pages 227-242

    7. AI Integrated Human Resource Management for Smart Decision in an Organization

      • S. B. Goyal, Pradeep Bedi, Anand Singh Rajawat, Deepmala Singh, Prasenjit Chatterjee
      Pages 243-253

    8. A q-ROF Based Intelligent Framework for Exploring the Interface Among the Variables of Culture Shock and Adoption Toward Organizational Effectiveness

      • Sanjib Biswas, Dragan Pamucar, Poushali Dey, Shreya Chatterjee, Shuvendu Majumder
      Pages 255-293

    9. Personality Prediction System to Improve Employee Recruitment

      • Mihir Satra, Faisal Mungi, Jinit Punamiya, Kavita Kelkar
      Pages 295-308

  4. Computational Intelligence for Operational Excellence, Supply Chain and Project Management

    1. Front Matter

      Pages 295-295
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    2. Towards Operation Excellence in Automobile Assembly Analysis Using Hybrid Image Processing

      • E. Sandeep Kumar, Gohad Atul
      Pages 311-320
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Keywords

About this book

This book covers the applications of computational intelligence techniques in business systems and advocates how these techniques are useful in modern business operations. The book redefines the computational intelligence foundations, the three pillars - neural networks, evolutionary computation, and fuzzy systems. It also discusses emerging areas such as swarm intelligence, artificial immune systems (AIS), support vector machines, rough sets, and chaotic systems. The other areas have also been demystified in the book to strengthen the range of computational intelligence techniques such as expert systems, knowledge-based systems, and genetic algorithms. Therefore, this book will redefine the role of computational intelligence techniques in modern business system operations such as marketing, finance & accounts, operations, personnel management, supply chain management, and logistics. Besides, this book guides the readers through using them to model, discover, and interpret new patterns that cannot be found through statistical methods alone in various business system operations. This book reveals how computational intelligence can inform the design and integration of services, architecture, brand identity, and product portfolio across the entire enterprise. The book will provide insights into research gaps, open challenges, and unsolved computational intelligence problems. The book will act as a premier reference and instant material for all the users who are contributing/practicing the adaptation of computational intelligence modern techniques in business systems.

Editors and Affiliations

  • LBEF Campus, Kathmandu, Nepal

    Sandeep Kautish

  • Department of Mechanical Engineering, MCKV Institute of Engineering, Howrah, India

    Prasenjit Chatterjee

  • Faculty of Organizational Sciences, Department of Operations Research and Statistics, University of Belgrade, Belgrade, Serbia

    Dragan Pamucar

  • Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India

    N. Pradeep

  • Symbiosis Centre for Management Studies, Symbiosis International (Deemed University), Pune, India

    Deepmala Singh

About the editors


Prasenjit Chatterjee is currently the Dean of Research and Consultancy at MCKV Institute of Engineering, West Bengal, India. He has over 90 research papers in various international journals and peer-reviewed conferences. He has been the Guest Editor of several special issues of SCI, SCIE, Scopus, and ESCI-indexed journals. He has authored and edited several books on decision-making approaches, supply chains, and sustainability modeling. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI).

 

Dragan Pamucar is an Associate Professor at the University of Defence in Belgrade, the Department of Logistics, Serbia. Dr. Pamucar obtained his MSc at the Faculty of Transport and Traffic Engineering in Belgrade in 2009, and his Ph.D. degree in Applied Mathematics with specialization in multi-criteria modeling and soft computing techniques at the University of Defence in Belgrade, Serbia in 2013. His research interests include the fields of computational intelligence, multi-criteria decision-making problems, neuro-fuzzy systems, fuzzy, rough, and intuitionistic fuzzy set theory, and neutrosophic theory, with applications in a wide range of logistics problems. Dr. Pamucar has authored/co-authored over 120 papers published in International journals and has been the guest editor of numerous special issues of Scopus and SCI-indexed journals. He has authored and edited books on decision-making approaches, optimization, and logistics.

 

Pradeep N. is an Associate Professor in Computer Science and Engineering at Bapuji Institute of Engineering and Technology, Karnataka, India. He has 18 years of teaching and research experience. His research areas are machine learning, pattern recognition, medical imageanalysis, knowledge discovery techniques, and data analytics. He has published over 20 research articles published in refereed journals, authored six book chapters, and edited several books. His one Indian patent application is published and one Australian patent is granted. 

 

Deepmala Singh is an Assistant Professor at Symbiosis International University, SCMS Nagpur. She completed her Ph.D. from Banaras Hindu University in 2016. Her research focused on the digital initiatives of human resource development practices in BHEL. Before joining Symbiosis she was associated with reputed universities like Asia Pacific University Malaysia, MNNIT Allahabad, etc. Besides, she also served as a project fellow in a major research project funded by UGC in 2011. She has over 26 journal publications and 3 edited books with international publishers to her credit.



Bibliographic Information

  • Book Title: Computational Intelligence for Modern Business Systems

  • Book Subtitle: Emerging Applications and Strategies

  • Editors: Sandeep Kautish, Prasenjit Chatterjee, Dragan Pamucar, N. Pradeep, Deepmala Singh

  • Series Title: Disruptive Technologies and Digital Transformations for Society 5.0

  • DOI: https://doi.org/10.1007/978-981-99-5354-7

  • Publisher: Springer Singapore

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

  • Hardcover ISBN: 978-981-99-5353-0Published: 04 November 2023

  • Softcover ISBN: 978-981-99-5356-1Due: 05 December 2023

  • eBook ISBN: 978-981-99-5354-7Published: 03 November 2023

  • Series ISSN: 2730-9061

  • Series E-ISSN: 2730-907X

  • Edition Number: 1

  • Number of Pages: XXI, 522

  • Number of Illustrations: 34 b/w illustrations, 120 illustrations in colour

  • Topics: Communications Engineering, Networks, Artificial Intelligence, Data Structures and Information Theory

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