Deep Learning Concepts in Operations Research (eBook, PDF)

Redaktion: Basu Mallik, Biswadip; Chaudhary, Aryan; Kar, Rahul; Mukherjee, Gunjan

• Format: PDF

Produktbeschreibung

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the 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 ML 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 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 a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 276
• Erscheinungstermin: 30. August 2024
• Englisch
• ISBN-13: 9781040102367
• Artikelnr.: 72271165

Autorenporträt

Dr. Biswadip Basu Mallik is an associate professor of Mathematics in the Department of Basic Science & Humanities at Institute of Engineering & Management, University of Engineering & Management, Kolkata, India. Dr. Gunjan Mukherjee is an associate 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.

Inhaltsangabe

1. Deep Learning: Overview, Applications and Computing Devices 2. Deep
Learning Impacts in the Field of Artificial Intelligence 3. Deep Learning
is a State-of-the-Art Approach to Artificial Intelligence 4. Unleashing the
Power: Exploring Deep Learning Architecture for Cutting-Edge AI Solutions
5. Deep Learning for ECG Classification: Techniques, Applications, and
Challenges 6. Social Distancing Detection System Using Single Shot
Detection (SSD) and Neural Networks 7. Recognition of Voice and Speech
Using NLP Techniques 8. Transfer Learning with Joint Fine-Tuning for
Multimodal Sentiment Analysis 9. Machine Learning for Traffic Flow
Prediction Addressing Congestion Challenges 10. Enhancing Autistic Spectrum
Disorder Diagnosis Using ML Techniques: A Study on Deep Neural Network and
Drop-out Deep Neural Network 11. Deep Learning: A State-of-the-Art Approach
to Artificial Intelligence 12. An Approach through Different Mathematical
Models to Enhance the Utility in Different Areas of Machine Learning 13.
Study of Different Regression Methods, Models and Application in Deep
Learning Paradigm 14. Deep Learning Impacts in the Field of Artificial
Intelligence 15. Stock Prices Prediction of the FMCG Sector in NSE India:
An Artificial Intelligence Approach 16. Multi-Attribute Decision Modelling
17. Regression Methods and Models 18. The Machine Learning Pipeline:
Algorithms, Applications, and Managerial Implications 19. Role of Fertamean
Neutrosophic Sets for Decision Making Modelling in Machine Learning 20.
Performance Evaluation of Machine Learning Algorithms in the Field of
Security-Malware Detection