Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining

Augmented intelligence is an alternate approach of artificial intelligence (AI), which emphasizes AI’s assistive role. Augmented intelligence enhances human skills of reasoning in a robotic system or software by simulating expectancy, educational mining, problem solving, recollection, sequencing, and decision-making capabilities. It is based on a combination of techniques such as machine learning, deep learning and cognitive computing. This book explains artificial intelligence models that support assistive processes in different situations. The contributors aim to provide information to a diverse audience with groundbreaking developments in mathematical computing. The book presents 8 chapters on these topics: - Educational data mining in augmented reality virtual learning environment - Brain and computer interfaces - Tree-based tools for chemometric analysis of infrared spectra - Applications of deep learning in medical engineering - Bankruptcy prediction model using an enhanced boosting classifier - Reputation systems for mobile agent security - The crow search algorithm - COVID-19 diagnosis and treatment The contents attempt to integrate various facets of augmented Intelligence, by describing recent research developments and advanced topics of interest to academicians and researchers working on machine learning problems and AI. Augmented intelligence is an alternate approach of artificial intelligence (AI), which emphasizes AI s assistive role. Augmented intelligence enhances human skills of reasoning in a robotic system or software by simulating expectancy, educational mining, problem solving, recollection, sequencing, and decision-making capabilities. It is based on a combination of techniques such as machine learning, deep learning and cognitive computing.This book explains artificial intelligence models that support assistive processes in different situations.The contributors aim to provide information to a diverse audience with groundbreaking developments in mathematical computing.The book presents 8 chapters on these topics:- Educational data mining in augmented reality virtual learning environment- Brain and computer interfaces- Tree-based tools for chemometric analysis of infrared spectra- Applications of deep learning in medical engineering- Bankruptcy prediction model using an enhanced boosting classifier- Reputation systems for mobile agent security- The crow search algorithm- COVID-19 diagnosis and treatmentThe contents attempt to integrate various facets of augmented Intelligence, by describing recent research developments and advanced topics of interest to academicians and researchers working on machine learning problems and AI. Cover Half Title Title Page Copyright Page About the Editors Table of Contents Contributors Abbreviations Preface 1. Evolution of 1-D, 2-D, and 3-D Lifting Discrete Wavelet Transform VLSI Architecture 2. Execution of Lifting-Scheme Discrete Wavelet Transform by Canonical Signed Digit Multiplier 3. Radix-8 Booth Multiplier in Terms of Power and Area Efficient for Application in Field of 2D DWT Architecture 4. Design and Performance Evaluation of Energy Efficient 8-Bit ALU at Ultra-Low Supply Voltages Using FinFET with 20 nm Technology 5. Design and Statistical Analysis of Strong Arbiter PUFs for Device Authentication and Identification 6. An Impact of Aging on Arbiter Physical Unclonable Functions 7. Advanced Power Management Methodology for SoCs Using UPF 8. Architecture Design: Network-on-Chip 9. Routing Strategy: Network-on-Chip Architectures 10. Self-Driven Clock Gating Technique for Dynamic Power Reduction of High-Speed Complex Systems 11. Optimization of SOC Sub-Circuits Using Mathematical Modeling 12. An Efficient Design of D Flip Flop in Quantum-Dot Cellular Automata (QCA) for Sequential Circuits 13. Design and Performance Analysis of Digitally Controlled DC-DC Converter Index