Main description:

Focuses on new machine learning developments that can lead to newly developed applications
Uses a predictive and futuristic approach which makes Machine Learning a promising tool for business processes and sustainable solutions
Promotes newer algorithms which are more efficient and reliable for a new dimension in discovering certain latent domains of applications
Discusses the huge potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making
Offers many real-time case studies

Contents:

Chapter 1 Random Variables in Machine Learning Chapter 2 Analysis of EMG Signals using Extreme Learning Machine with Nature Inspired Feature Selection Techniques Chapter 3 Detection of Breast Cancer by Using Various Machine Learning and Deep Learning Algorithms Chapter 4 Assessing the Radial Efficiency Performance of Bus Transport Sector Using Data Envelopment Analysis Chapter 5 Weight-Based Codes-A Binary Error Control Coding Scheme-A Machine Learning Approach Chapter 6 Massive Data Classification of Brain Tumors Using DNN: Opportunity in Medical Healthcare 4.0 through Sensors Chapter 7 Deep Learning Approach for Traffic Sign Recognition on Embedded Systems Chapter 8 Lung Cancer Risk Stratification Using ML and AI on Sensor- Based IoT: An Increasing Technological Trend for Health of Humanity Chapter 9 Statistical Feedback Evaluation System Chapter 10 Emission of Herbal Woods to Deal with Pollution and Diseases: Pandemic-Based Threats Chapter 11 Artificial Neural Networks: A Comprehensive Review Chapter 12 A Case Study on Machine Learning to Predict the Students' Result in Higher Education Chapter 13 Data Analytic Approach for Assessment Status of Awareness of Tuberculosis in Nigeria Chapter 14 Active Learning from an Imbalanced Dataset: A Study Conducted on the Depression, Anxiety, and Stress Dataset Chapter 15 Classification of the Magnetic Resonance Imaging of the Brain Tumor Using the Residual Neural Network Framework