Design of Intelligent Applications using Machine Learning and Deep Learning Techniques

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.

Table of Contents:
1. Data Acquisition and Preparation for Artificial Intelligence and Machine Learning Applications 2. Fundamental Models in Machine Learning and Deep Learning 3. Research Aspects of Machine Learning: Issues, Challenges, and Future Scope 4. Comprehensive Analysis of Dimensionality Reduction Techniques for Machine Learning Applications 5. Application of Deep Learning in Counting WBCs, RBCs, and Blood Platelets Using Faster Region-Based Convolutional Neural Network 6. Application of Neural Network and Machine Learning in Mental Health Diagnosis 7. Application of Machine Learning in Cardiac Arrhythmia 8. Advances in Machine Learning and Deep Learning Approaches for Mammographic Breast Density Measurement for Breast Cancer Risk Prediction: An Overview 9. Applications of Machine Learning in Psychology and the Lifestyle Disease Diabetes Mellitus 10. Application of Machine Learning and Deep Learning in Thyroid Disease Prediction 11. Application of Machine Learning in Fake News Detection 12. Authentication of Broadcast News on Social Media Using Machine Learning 13. Application of Deep Learning in Facial Recognition 14. Application of Deep Learning in Deforestation Control and Prediction of Forest Fire Calamities 15. Application of Convolutional Neural Network in Feather Classifications 16. Application of Deep Learning Coupled with Thermal Imaging in Detecting Water Stress in Plants 17. Machine Learning Techniques to Classify Breast Cancer 18. Application of Deep Learning in Cartography Using UNet and Generative Adversarial Network 19. Evaluation of Intrusion Detection System with Rule-Based Technique to Detect Malicious Web Spiders Using Machine Learning 20. Application of Machine Learning to Improve Tourism Industry 21. Training Agents to Play 2D Games Using Reinforcement Learning 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 23. Application of Deep Learning in Video Question Answering System 24. Implementation and Analysis of Machine Learning and Deep Learning Algorithms 25. Comprehensive Study of Failed Machine Learning Applications Using a Novel 3C Approach