1st Edition

Deep Learning Concepts in Operations Research

    344 Pages 75 B/W Illustrations
    by Auerbach Publications

    The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and machine learning as well. Among a variety of topics, the book examines:

    • An overview of applications and computing devices
    • Deep learning impacts in the field of AI
    • Deep learning as state-of-the-art approach to AI
    • Exploring deep learning architecture for cutting-edge AI solutions

    Operations research is the branch of mathematics for performing so many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how the proper decision depends on a number of factors, the book examines how AI and ML can be used to model equations and define constraints to solve more easily problems and discover proper and valid solutions. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

    1. Deep Learning: Overview, Applications and Computing Devices
    N. Varatharajan, S. Lavanya, A. Suganya, and R. Vikkram

    2. Deep Learning Impacts in the Field of Artificial Intelligence
    Wasswa Shafik

    3. Deep Learning is a State-of-the-Art Approach to Artificial Intelligence
    Soumik Kumar Mohanta, Ambarish G. Mohapatra, Anita Mohanty, and Sasmita Nayak

    4. Unleashing the Power: Exploring Deep Learning Architecture for Cutting-Edge AI Solutions
    Suman Patra

    5. Deep Learning for ECG Classification: Techniques, Applications, and Challenges
    Sucharita Mitra

    6. Social Distancing Detection System Using Single Shot Detection (SSD) and Neural Networks
    Dipti Jadhav, Gokarna Patil, Purva Tekade, and Shubhada Tambe

    7. Recognition of Voice and Speech Using NLP Techniques
    Dipti Jadhav, Chinmay Shirsath, Siddharth Sahasrabuddhe, and Prabhatkumar Singh

    8. Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment Analysis
    Santanu Modak and Subhasmita Ghosh

    9. Machine Learning for Traffic Flow Prediction Addressing Congestion Challenges
    Jogendra Kumar,Divyanshu Semwal, Mayank Mehra, Harshita Rana, and Yash Bhardwaj

    10. Enhancing Autistic Spectrum Disorder Diagnosis Using ML Techniques: A Study on Deep Neural Network and Drop-out Deep Neural Network
    Sanat Kumar Sahu and Sushil Kumar Sahu

    11. Deep Learning: A State-of-the-Art Approach to Artificial Intelligence
    Sangeet Vashishtha and Pooja Sharma

    12. An Approach through Different Mathematical Models to Enhance the Utility in Different Areas of Machine Learning
    Pooja Swaroop Saxena

    13. Study of Different Regression Methods, Models and Application in Deep Learning Paradigm
    Arpita Shome, Gunjan Mukherjee, Arpitam Chatterjee and Bipan Tudu

    14. Deep Learning Impacts in the Field of Artificial Intelligence
    Reshma Gulwani and Minal Aggarwal

    15. Stock Prices Prediction of the FMCG Sector in NSE India: An Artificial Intelligence Approach
    Subrata Jana, Anirban Sarkar, Bhaskar Nandi, Arpan Ghoshal, Binay Maji, and Biswadip Basu Mallik

    16. Multi-Attribute Decision Modelling
    Gurjapna Anand, Priyanka Vashisht, and Simar Preet Singh

    17. Regression Methods and Models
    Parthiban Krishna Moorthy, Nimish Goel, and Shivam Baghel

    18. The Machine Learning Pipeline: Algorithms, Applications, and Managerial Implications
    Anjali Munde

    19. Role of Fertamean Neutrosophic Sets for Decision Making Modelling in Machine Learning
    R. Narmada Devi, Regan Murugesan, Nagadevi Bala Nagaram, Kala Raja Mohan, and Sathish Kumar Kumaravel

    20. Performance Evaluation of Machine Learning Algorithms in the Field of Security-Malware Detection
    Aswathy K Cherian, E Poovammal, and M. Vaidhehi


    Biswadip Basu Mallik is a Senior Assistant Professor of Mathematics in the Department of Basic Sciences & Humanities at Institute of Engineering & Management, Kolkata, India.
    Gunjan Mukherjee is an Assistant professor in the Department of Computational Science, Brainware University, Barasat, India.
    Rahul Kar holds a master's degree in mathematics from Burdwan University and is currently working as a SACT-II Mathematics faculty of Kalyani Mahavidyalaya, Kalyani, Nadia, West Bengal.
    Aryan Chaudhary is the Chief Scientific Advisor at BioTech Sphere Research, India, and a recognized researcher of healthcare and technology.