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 proces...

Ausführliche Beschreibung

Bibliographische Detailangaben

Weitere Verfasser:	Jain, Vishal 1983- (HerausgeberIn), Juneja, Sapna (HerausgeberIn), Juneja, Abhinav (HerausgeberIn), Ramani Kannan (HerausgeberIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2022
Ausgabe:	first edition
Schriftenreihe:	Demystifying technologies for computational excellence: Moving towards society 5.0
Schlagworte:	Optimierung Maschinelles Lernen Soft Computing
Zusammenfassung:	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 which 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
Beschreibung:	Literaturangaben 1. Random Variables in Machine Learning. 2. Analysis of EMG Signals using Extreme Learning Machine with Nature Inspired Feature Selection Techniques. 3. Detection of Breast Cancer by Using Various Machine Learning and Deep Learning algorithms. 4. Assessing the Radial Efficiency Performance of Bus Transport Sector Using Data Envelopment Analysis. 5. Weight Based Codes-A Binary Error Control Coding Scheme-A Machine Learning Approach. 6. Massive Data Classification of Brain Tumors using DNN: Opportunity in Medical Healthcare 4.0 through Sensors. 7. Deep Learning Approach For Traffic Sign Recognition on Embedded Systems. 8. Lung Cancer Risk Stratification Using ML and AI on Sensor Based IoT: An Increasing Technological Trend for Health of Humanity. 9. Statistical Feedback Evaluation System. 10. Herbal Woods Emission to Deal with Pollution and Diseases: Pandemic Based Threat. 11. Artificial Neural Networks: A Comprehensive Review. 12. A Case Study on Machine Learning to predict Student Result in higher education. 13. Data Analytic Approach for Assessment Status of Awareness of Tuberculosis in Nigeria. 14. Analyzing Toxicity in Online Gaming Communities. 15. Active Learning from Imbalanced Dataset: A Study Conducted on Depression, Anxiety and Stress Dataset. 16. Classification of Brain Tumor MRI Imaging using Resnet Framework
Beschreibung:	xiv, 280 Seiten Illustrationen, Diagramme 235 mm
ISBN:	9780367685423 9780367685454