<P>1. Data Acquisition and Preparation for Artificial Intelligence and Machine Learning Applications</P><I><P>Kallol Bosu Roy Choudhuri and Ramchandra S. Mangrulkar</P></I><B><P>2. Fundamental Models in Machine Learning and Deep Learning</P></B><I><P>Tatwadarshi P. Nagarhalli, Ashwini M. Save, and Narendra M. Shekokar</P></I><B><P>3. Research Aspects of Machine Learning: Issues, Challenges, and Future Scope</P></B><I><P>Reena Thakur, Mayur Tembhurney, and Dheeraj Rane</P></I><B><P>4. Comprehensive Analysis of Dimensionality Reduction Techniques for Machine Learning Applications</P></B><I><P>Archana Vasant Mire, Vinayak Elangovan, and Bharti Dhote</P></I><B><P>5. Application of Deep Learning in Counting WBCs, RBCs, and Blood Platelets Using Faster Region-Based Convolutional Neural Network</P></B><I><P>Nirav Jain, Shail Shah, Ramchandra S. Mangrulkar, and Pankaj Sonawane</P></I><B><P>6. Application of Neural Network and Machine Learning in Mental Health Diagnosis</P></B><I><P>Aniruddha Das, Enakshie Prasad, and Sindhu Nair</P></I><B><P>7. Application of Machine Learning in Cardiac Arrhythmia</P></B><I><P>Gresha S. Bhatia, Shefali Athavale, Yogita Bhatia, Tanya Mohanani, and Akanksha Mittal</P></I><B><P>8. Advances in Machine Learning and Deep Learning Approaches for Mammographic Breast Density Measurement for Breast Cancer Risk Prediction: An Overview</P></B><I><P>Shivaji D. Pawar, Kamal Kr. Sharma, and Suhas G. Sapate</P></I><B><P>9. Applications of Machine Learning in Psychology and the Lifestyle Disease Diabetes Mellitus</P></B><I><P>Ruhina Karani, Dharmik Patel, Akshay Chudasama, Dharmil Chhadva, and Gaurang Oza</P></I><B><P>10. Application of Machine Learning and Deep Learning in Thyroid Disease Prediction</P></B><I><P>Aditi Vora, Ramchandra S. Mangrulkar, Narendra M. Shekokar, and Meera Narvekar</P></I><B><P>11. Application of Machine Learning in Fake News Detection</P></B><I><P>Smita Bhoir, Jyoti Kundale, and Smita Bharne</P></I><B><P>12. Authentication of Broadcast News on Social Media Using Machine Learning</P></B><I><P>Smita Sanjay Ambarkar, Narendra M. Shekokar, Monika Mangla, and Rakhi Akhare</P></I><B><P>13. Application of Deep Learning in Facial Recognition</P></B><I><P>Jimit Gandhi, Aditya Jeswani, Fenil Doshi, Parth Doshi, and Ramchandra S. Mangrulkar</P></I><B><P>14. Application of Deep Learning in Deforestation Control and Prediction of Forest Fire Calamities</P></B><I><P>Muskan Goenka and Ramchandra S. Mangrulkar</P></I><B><P>15. Application of Convolutional Neural Network in Feather Classifications</P></B><I><P>Milind Shah, Keval Nagda, Anirudh Mukherjee, and Pratik Kanani</P></I><B><P>16. Application of Deep Learning Coupled with Thermal Imaging in Detecting Water Stress in Plants</P></B><I><P>Saiqa Khan, Meera Narvekar, Anam Khan, Aqdus Charolia, and Mushrifah Hasan</P></I><B><P>17. Machine Learning Techniques to Classify Breast Cancer</P></B><I><P>Drashti Shah and Ramchandra S. Mangrulkar</P></I><B><P>18. Application of Deep Learning in Cartography Using UNet and Generative Adversarial Network</P></B><I><P>Deep Gandhi, Govind Thakur, Pranit Bari, and Khushali Deulkar</P></I><B><P>19. Evaluation of Intrusion Detection System with Rule-Based Technique to Detect Malicious Web Spiders Using Machine Learning</P></B><I><P>Nilambari G. Narkar and Narendra M. Shekokar</P></I><B><P>20. Application of Machine Learning to Improve Tourism Industry</P></B><I><P>Krutibash Nayak and Saroj Kumar Panigrahy</P></I><B><P>21. Training Agents to Play 2D Games Using Reinforcement Learning</P></B><I><P>Harshil Jhaveri, Nishay Madhani, and Narendra M. Shekokar</P></I><B><P>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</P></B><I><P>Akash Shah, Romil Shah, Manan Gandhi, Rashmil Panchani, Govind Thakur, and Kriti Srivastava</P></I><B><P>23. Application of Deep Learning in Video Question Answering System</P></B><I><P>Mansi Pandya, Arnav Parekhji, Aniket Shahane, Palak V. Chavan, and Ramchandra S. Mangrulkar</P></I><B><P>24. Implementation and Analysis of Machine Learning and Deep Learning Algorithms</P></B><I><P>Samip Kalyani, Neel Vasani, and Ramchandra S. Mangrulkar</P></I><B><P>25. Comprehensive Study of Failed Machine Learning Applications Using a Novel 3C Approach</P></B><I><P>Neel Patel, Prem Bhajaj, Pratik Panchal, Tanmai Prabhune, Pankaj Sonawane, and Ramchandra S. Mangrulkar</P></I><B><P>Index</P></B>

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.

OCLC-licensed vendor bibliographic record.