Design of intelligent applications using machine learning and deep learning techniques / edited by Ramchandra Sharad Mangrulkar, Antonis Michalas, Narendra Shekokar, Meera Narvekar, Pallavi Vijay Chavan. - 1st. - 1 online resource : illustrations (black and white, and colour)

1. Data Acquisition and Preparation for Artificial Intelligence and Machine Learning Applications

Kallol Bosu Roy Choudhuri and Ramchandra S. Mangrulkar
2. Fundamental Models in Machine Learning and Deep Learning
Tatwadarshi P. Nagarhalli, Ashwini M. Save, and Narendra M. Shekokar
3. Research Aspects of Machine Learning: Issues, Challenges, and Future Scope
Reena Thakur, Mayur Tembhurney, and Dheeraj Rane
4. Comprehensive Analysis of Dimensionality Reduction Techniques for Machine Learning Applications
Archana Vasant Mire, Vinayak Elangovan, and Bharti Dhote
5. Application of Deep Learning in Counting WBCs, RBCs, and Blood Platelets Using Faster Region-Based Convolutional Neural Network
Nirav Jain, Shail Shah, Ramchandra S. Mangrulkar, and Pankaj Sonawane
6. Application of Neural Network and Machine Learning in Mental Health Diagnosis
Aniruddha Das, Enakshie Prasad, and Sindhu Nair
7. Application of Machine Learning in Cardiac Arrhythmia
Gresha S. Bhatia, Shefali Athavale, Yogita Bhatia, Tanya Mohanani, and Akanksha Mittal
8. Advances in Machine Learning and Deep Learning Approaches for Mammographic Breast Density Measurement for Breast Cancer Risk Prediction: An Overview
Shivaji D. Pawar, Kamal Kr. Sharma, and Suhas G. Sapate
9. Applications of Machine Learning in Psychology and the Lifestyle Disease Diabetes Mellitus
Ruhina Karani, Dharmik Patel, Akshay Chudasama, Dharmil Chhadva, and Gaurang Oza
10. Application of Machine Learning and Deep Learning in Thyroid Disease Prediction
Aditi Vora, Ramchandra S. Mangrulkar, Narendra M. Shekokar, and Meera Narvekar
11. Application of Machine Learning in Fake News Detection
Smita Bhoir, Jyoti Kundale, and Smita Bharne
12. Authentication of Broadcast News on Social Media Using Machine Learning
Smita Sanjay Ambarkar, Narendra M. Shekokar, Monika Mangla, and Rakhi Akhare
13. Application of Deep Learning in Facial Recognition
Jimit Gandhi, Aditya Jeswani, Fenil Doshi, Parth Doshi, and Ramchandra S. Mangrulkar
14. Application of Deep Learning in Deforestation Control and Prediction of Forest Fire Calamities
Muskan Goenka and Ramchandra S. Mangrulkar
15. Application of Convolutional Neural Network in Feather Classifications
Milind Shah, Keval Nagda, Anirudh Mukherjee, and Pratik Kanani
16. Application of Deep Learning Coupled with Thermal Imaging in Detecting Water Stress in Plants
Saiqa Khan, Meera Narvekar, Anam Khan, Aqdus Charolia, and Mushrifah Hasan
17. Machine Learning Techniques to Classify Breast Cancer
Drashti Shah and Ramchandra S. Mangrulkar
18. Application of Deep Learning in Cartography Using UNet and Generative Adversarial Network
Deep Gandhi, Govind Thakur, Pranit Bari, and Khushali Deulkar
19. Evaluation of Intrusion Detection System with Rule-Based Technique to Detect Malicious Web Spiders Using Machine Learning
Nilambari G. Narkar and Narendra M. Shekokar
20. Application of Machine Learning to Improve Tourism Industry
Krutibash Nayak and Saroj Kumar Panigrahy
21. Training Agents to Play 2D Games Using Reinforcement Learning
Harshil Jhaveri, Nishay Madhani, and Narendra M. Shekokar
22. Analysis of the Effectiveness of the Non-Vaccine Countermeasures Taken by the Indian Government against COVID-19 and Forecasting Using Machine Learning and Deep Learning
Akash Shah, Romil Shah, Manan Gandhi, Rashmil Panchani, Govind Thakur, and Kriti Srivastava
23. Application of Deep Learning in Video Question Answering System
Mansi Pandya, Arnav Parekhji, Aniket Shahane, Palak V. Chavan, and Ramchandra S. Mangrulkar
24. Implementation and Analysis of Machine Learning and Deep Learning Algorithms
Samip Kalyani, Neel Vasani, and Ramchandra S. Mangrulkar
25. Comprehensive Study of Failed Machine Learning Applications Using a Novel 3C Approach
Neel Patel, Prem Bhajaj, Pratik Panchal, Tanmai Prabhune, Pankaj Sonawane, and Ramchandra S. Mangrulkar
Index

Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated - bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: Fundamental models, issues and challenges in ML and DL. Comprehensive analyses and probabilistic approaches for ML and DL. Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia. Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals. This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.

ISBN: 9781000423884 1000423883 9781000423839 1000423832 9781003133681 1003133681

Standard No.: 10.1201/9781003133681 doi

Subjects--Topical Terms:
Machine learning--Industrial applications.
COMPUTERS / Artificial Intelligence
SCIENCE / System Theory
TECHNOLOGY / Machinery

LC Class. No.: Q325.5

Dewey Class. No.: 006.31