Machine Learning Paradigms

<p>Chapter 1: Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems.- Chapter 2: A Comparison of Machine Learning Techniques to Predict the Risk of Heart Failure.- Chapter 3: Differential gene Expression Analysis of RNA-seq Data Using Machine Learning for Cancer Research.- Chapter 4: Machine Learning Approaches for Pap-Smear Diagnosis: An Overview.- Chapter 5:&nbsp;Multi-Kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systems.- Chapter 6:&nbsp;Conceptualizing and Measuring Energy Security: Geopolitical Dimensions, Data Availability, Quantitative and Qualitative Methods.- Chapter 7:&nbsp;Automated Stock Price Motion Prediction Using Technical Analysis Datasets and Machine Learning.- Chapter 8:&nbsp;Airport Data Analysis Using Common Statistical Methods and Knowledge-Based Techniques.- Chapter 9:&nbsp;A Taxonomy and Review of the Network Data Envelopment Analysis Literature.- Chapter 10: Applying Advanced Data Analytics and Machine&nbsp;Learning to Enhance the Safety Control of Dams.- Chapter 11:&nbsp;Analytics and Evolving Landscape of Machine&nbsp;Learning for Emergency Response.- Chapter 12:&nbsp;Social Media Analytics, Types and Methodology.- Chapter 13: Machine Learning Methods for Opinion Mining in Text: The Past and the Future.- Chapter 14:&nbsp;Ship Detection Using Machine Learning and&nbsp;Optical Imagery in the Maritime Environment.- Chapter 15:&nbsp;Video Analytics for Visual Surveillance and Applications: An Overview and Survey.- Chapter 16:&nbsp;Machine Learning in Alternate Testing of&nbsp;Integrated Circuits</p><br>