Description
Taylor & Francis Ltd Handbook of Machine Learning for Computational Optimization Applications and Case Studies 2021 Edition by Vishal Jain, Sapna Juneja, Abhinav Juneja
Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers. Table of Contents : Chapter 1 Random Variables in Machine Learning [Piratla Srihari]Chapter 2 Analysis of EMG Signals using Extreme Learning Machinewith Nature Inspired Feature Selection Techniques [A. Anitha and A. Bakiya]Chapter 3 Detection of Breast Cancer by Using Various Machine Learningand Deep Learning Algorithms [Yogesh Jadhav and Harsh Mathur]Chapter 4 Assessing the Radial Efficiency Performance of Bus TransportSector Using Data Envelopment Analysis [Swati Goyal, Shivi Agarwal, Trilok Mathur, and Nirbhay Mathur]Chapter 5 Weight-Based Codes-A Binary Error Control CodingScheme-A Machine Learning Approach[Piratla Srihari]Chapter 6 Massive Data Classification of Brain Tumors Using DNN:Opportunity in Medical Healthcare 4.0 through Sensors[Rohit Rastogi, Akshit Rajan Rastogi, D.K. Chaturvedi,Sheelu Sagar, and Neeti Tandon]Chapter 7 Deep Learning Approach for Traffic Sign Recognition onEmbedded Systems [A. Shivankit, Gurminder Kaur, Sapna Juneja, and Abhinav Juneja]Chapter 8 Lung Cancer Risk Stratification Using ML and AI on Sensor-Based IoT: An Increasing Technological Trend for Health ofHumanity [Rohit Rastogi, Mukund Rastogi, D.K. Chaturvedi,Sheelu Sagar, and Neeti Tandon]Chapter 9 Statistical Feedback Evaluation System [Alok Kumar and Renu Jain]Chapter 10 Emission of Herbal Woods to Deal with Pollution and Diseases:Pandemic-Based Threats [Rohit Rastogi, Mamta Saxena, D. K. Chaturvedi,and Sheelu Sagar]Chapter 11 Artificial Neural Networks: A Comprehensive Review [Neelam Nehra, Pardeep Sangwan, and Divya Kumar]Chapter 12 A Case Study on Machine Learning to Predict the Students'Result in Higher Education [Tejashree U. Sawant and Urmila R. Pol]Chapter 13 Data Analytic Approach for Assessment Status of Awareness ofTuberculosis in Nigeria [Ishola Dada Muraina, Rafeeah Rufai Madaki, andAisha Umar Suleiman]Chapter 14 Active Learning from an Imbalanced Dataset: A StudyConducted on the Depression, Anxiety, and Stress Dataset [Umme Salma M. and Amala Ann K. A.]Chapter 15 Classification of the Magnetic Resonance Imaging of the BrainTumor Using the Residual Neural Network Framework [Tina and Sanjay Kumar Dubey]