A Practitioner's Approach for Problem-Solving using AI
1st Edition
Published on 18. October 2024
300 pages
978-981-5305-36-4 (ISBN)
Description
This book demonstrates several use cases of how artificial intelligence (AI) and machine learning (ML) are revolutionizing problem-solving across various industries. The book presents 18 edited chapters beginning with the latest advancements in human-AI interactions and neuromorphic computing, setting the stage for practical applications.
Chapters focus on AI and ML applications such as fingerprint recognition, glaucoma detection, and lung cancer identification using image processing. The book also explores the role of AI in professional operations such as UX design, event detection, and content analysis. Additionally, the book includes content that examines AI's impact on technical operations wireless communication, VLSI systems, and advanced manufacturing processes. Each chapter contains summaries and references for addressing the needs of beginner and advanced readers.
This comprehensive guide is an essential resource for anyone seeking to understand AI's transformative role in modern problem-solving in professional industries.
Readership
Tech professionals, enthusiasts, computer science students, trainers, and instructors.
Series Intro
Emerging Trends in Computation Intelligence and Disruptive Technologies is a an informativce series of edited volumes that explores the latest advancements and innovations in the fields of computational intelligence and disruptive technologies. Each volume delves into cutting-edge research, applications, and theoretical developments across a broad range of topics, including artificial intelligence, machine learning, robotics, nanotechnology, and more. The series brings together contributions from leading experts, offering comprehensive insights into how these technologies are shaping industries, driving innovation, and addressing complex challenges. This series serves as an essential resource for researchers, professionals, and students looking to stay ahead in the rapidly evolving landscape of computation and technology.
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Content
- Cover
- Title
- Copyright
- End User License Agreement
- Contents
- Preface
- List of Contributors
- Redefining Human-AI Interactions: Unveiling ChatGPT's Profound Emotional Understanding
- Priyanshu Rawat1,*, Madhvan Bajaj1, Satvik Vats1 and Vikrant Sharma1
- 1. INTRODUCTION
- 2. THE POTENTIAL IMPACT OF ARTIFICIAL INTELLIGENCE (AI) ON MENTAL HEALTH
- 3. EMOTIONAL AWARENESS AND THE LEVELS OF EMOTIONAL AWARENESS SCALE: ASSESSING CHATGPT'S PERFORMANCE AND POTENTIAL ENHANCEMENT
- 4. METHODOLOGY
- 4.1. Evaluation Procedure
- 4.2. Experimental Protocol
- 4.3. Evaluation
- 4.4. Statistical Analysis
- 5. RESULTS
- CONCLUSION
- REFERENCES
- Neuromorphic Computing: Forging a Link between Artificial Intelligence and Neurological Models
- Madhvan Bajaj1,*, Priyanshu Rawat1, Vikrant Sharma1 and Satvik Vats1
- 1. INTRODUCTION
- 1.1. Gap between AI and Brain-Inspired Systems
- 2. THE BRAIN'S INSPIRATION
- 2.1. The Remarkable Capabilities of the Human Brain
- 2.2. Cognitive Processes and Neural Networks
- 2.3. Intricacies of Neurons and Synapses
- 3. MIMICKING THE BRAIN
- 3.1. Neuromorphic Computing
- 3.2. Designing and Developing Neuromorphic Chips
- 3.3. Replicating the Behavior of Neurons and Synapses
- 3.4. Achieving Computational Efficiency and Energy Savings
- 4. APPLICATIONS AND IMPACT
- 4.1. Robotics and The Integration of Neuromorphic Computing
- 4.2. Sensory Processing and the Potential for Real-Time Analysis
- 4.3. Pattern Recognition and Enhanced Capabilities
- 4.4. Potential Advancements in ML Algorithms
- 5. SENSORY PROCESSING AND THE POTENTIAL FOR REAL-TIME ANALYSIS
- 5.1. Ongoing R&D in Neuromorphic Computing
- 5.2. Unravelling the Mysteries of the Brain
- 5.3. Refining Neuromorphic Architectures
- 5.4. Transformative Impact on the Field of AI
- CONCLUSION
- REFERENCES
- Fingerprint Recognition System Study
- Prerna1,*, Rishika Yadav2, Shashank Awasthi3, Satya Prakash Yadav3 and Prashant Upadhyay4
- 1. INTRODUCTION
- 2. FINGERPRINT
- 3. IDENTIFICATION OF FINGERPRINTS
- 4. SYSTEM LEVEL DESIGN FOR FINGERPRINT
- 5. HISTOGRAM EQUIVALENCE FOR FINGERPRINT
- 6. FOURIER TRANSFORM FOR FINGERPRINT
- CONCLUSION
- REFERENCES
- Glaucoma Detection with Retinal Fundus Images
- Shreshtha Mehta1,*, Amit Gupta2, Deepti Sahu3, Pawan Kumar Singh5 and Satya Prakash Yadav4
- 1. INTRODUCTION
- 2. DETECTION OF GLAUCOMA
- 3. OPTIC DISC AND OPTIC CUP SEGMENTATION IMAGES
- 4. CLASSIFICATION
- CONCLUSION
- REFERENCES
- Detection of Lung Cancer using Image Processing Methods
- Shreshtha Mehta1,*, Dibyahash Bordoloi2, Satya Prakash Yadav3, Pawan Kumar Singh5 and Prashant Upadhyay4
- 1. INTRODUCTION
- 2. COMPONENTS AND METHOD
- 3. IMAGE ENHANCEMENT
- 4. DETECTION OF ENHANCED IMAGE BY FAST FOURIER TRANSFORM
- 5. IMAGE SEGMENTATION OF DETECTION OF LUNGS CANCER
- 6. MASKING TECHNIQUE
- CONCLUSION
- REFERENCES
- Web User Access Path Prediction using Recognition with Recurrent Neural Network
- Prerna1, Sushant Chamoli2,*, Pawan Kumar Singh3, Sansar Singh Chauhan3 and Satya Prakash Yadav4
- 1. INTRODUCTION
- 2. RELATED WORKS
- 2.1. Impact of Redundant Information and Information Overload on Information Retrieval Efficiency
- 2.2. User Confusion due to Complicated Website Architectures and Excessive Redirected Links
- 3. RESEARCH METHODOLOGY
- 4. EXPERIMENT & RESULTS
- 4.1. Experimental Validation
- 4.2. Impact of Noisy Data on Path Predictability Rate
- CONCLUSION
- REFERENCES
- News Event Detection Methods Based on Big Data Processing Techniques
- Karan Purohit1,*, Rishabh Saklani1, Veena Bharti2, Mahaveer Singh Naruka3, Satya Prakash Yadav5 and Upendra Singh Aswal4
- 1. INTRODUCTION
- 2. RELATED WORKS
- 3. RESEARCH METHODOLOGY
- 4. RESEARCH SETUP
- 5. EXPERIMENT & RESULTS
- CONCLUSION
- REFERENCES
- Rolling-Type Collaborative Training for False Comment Identification: Enhancing Accuracy through Multi-Characteristic Fusion
- Sandeep Kumar1,*, Shashank Awasthi2, Nilotpal Pathak2, Amit Gupta3 and Rajesh Pokhariyal4
- 1. INTRODUCTION
- 2. RELATED WORKS
- 3. RESEARCH METHODOLOGY
- 4. EXPERIMENT & RESULTS
- 4.1. Data Source
- 4.2. Experiment Platform
- 4.3. Analysis of Experimental Processes and Results
- CONCLUSION
- REFERENCES
- A Neural Network Study of Face Recognition
- Rishabh Saklani1,*, Karan Purohit1, Santosh Kumar Upadhyay2, Prashant Upadhyay3, Satya Prakash Yadav4, Aditya Verma1 and Ashish Garg5
- 1. INTRODUCTION
- 2. FACE RECOGNITION
- 3. ANN AND ADABOOST FOR FACE DETECTION
- 4. FACE ALIGNMENT USING LOCAL TEXTURE CLASSIFIERS BASED ON MULTILAYER PERCEPTRONS
- 5. VECTORS WITH GEOMETRIC-FACE COMPONENTS
- 6. IMAGE PROCESSING OF FACES
- 7. COMPRESSION OF 2D-DCT IMAGES
- 8. HEAD POSITIONS
- CONCLUSION
- REFERENCES
- Time Sequence Data Monitoring Method Based on Auto-Aligning Bidirectional Long and Short-Term Memory Network
- Abha Kiran Rajpoot1,*, Shashank Awasthi2, Mahaveer Singh Naruka2, Dibyahash Bordoloi3 and Neha Garg4
- 1. INTRODUCTION
- 2. RELATED WORKS
- 3. RESEARCH METHODOLOGY
- 4. EXPERIMENT & RESULTS
- 5. ADVANTAGES OF THE RESEARCH
- 6. FUTURE WORK
- CONCLUSION
- REFERENCES
- Performance Evaluation of Wireless Communi- cation System MIMO Detection Algorithms
- Shikha Agarwal1,*, Aarti Chaudhary1, Alok Barddhan2, Sushant Chamoli3 and Upendra Singh Aswal4
- 1. INTRODUCTION
- 2. SISO, SIMO, MISO, AND MIMO TERMINOLOGY
- 2.1. SISO Systems
- 2.2. SIMO Systems
- 2.3. MISO Systems
- 2.4. MIMO Systems
- 3. OVERVIEW OF MIMO
- 4. MIMO DETECTION ALGORITHMS
- CONCLUSION
- REFERENCES
- Design and Implementation of a Clock Generator Based on All Digital PLL (ADPLL)
- Shashank Awasthi1,*, Satya Prakash Yadav5, Manish Chhabra2, Richa Gupta3 and Rajesh Pokhariyal4
- 1. INTRODUCTION
- 2. ELECTRIC LOOP FILTER
- 3. DIGITAL OSCILLATOR CONTROLLER
- 4. FREQUENCY MULTIPLIER
- 5. ORGANIZING THE WORK
- 6. DEVELOPMENT STATE
- CONCLUSION
- REFERENCES
- Three-Dimensional Point Cloud Initial Enrollment Algorithm Based on Centre-of-mass and Centering
- Mahaveer Singh Naruka1,*, Pawan Kumar Singh1, Manish Chhabra2, Rishika Yadav3 and Neha Garg4
- 1. INTRODUCTION
- 2. RELATED WORKS
- 3. RESEARCH METHODOLOGY: A THREE-DIMENSIONAL POINT CLOUD INITIAL ENROLLMENT ALGORITHM
- 3.1. Algorithm Description
- 3.2. Cloud Filtering Processing
- 3.3. Original Rotational Conversion Model
- 3.3.1. Calculation of Centre-of-mass and Mass Center
- 3.3.2. Calculation of Vector Formation
- 3.3.3. Calculation of Rotational Transformation Matrix
- 4. ITERATION ANGULAR SHIFT MODEL
- 4.1. Calculation of Angular Shift
- 4.2. Iteration Process
- 5. EXPERIMENT & RESULTS
- 5.1. Experimental Setup
- 5.2. Experimental Procedure
- 5.3. Results
- CONCLUSION
- REFERENCES
- Multi-Resolution Image Similarity Learning: A Method for Extracting Comprehensive Image Features
- Sheradha Jauhari1,*, Sansar Singh Chauhan2, Gunajn Aggarwal3, Amit Gupta4 and Navin Garg5
- 1. INTRODUCTION
- 2. RELATED WORKS
- 3. RESEARCH METHODOLOGY
- 4. EXPERIMENT & RESULTS
- 5. ADVANTAGEOUS EFFECTS OF THE INVENTION
- CONCLUSION
- REFERENCES
- Tensor Singular Value Decomposition-Based Multiple View Spectral Segmentation
- Ashish Dixit1,*, Pawan Kumar Singh2, Satya Prakash Yadav5, Dibyahash Bordoloi3 and Upendra Singh Aswal4
- 1. INTRODUCTION
- 2. RELATED WORKS
- 3. PROPOSED MULTIPLE VIEW SPECTRAL SEGMENTATION BASED ON TENSOR SINGULAR VALUE DECOMPOSITION ALGORITHM
- 4. EXPERIMENT SETUP
- 5. RESULT
- CONCLUSION
- REFERENCES
- Enhanced CNN-Based Failure Integrated Assessment Procedure for Energy Accumulator Packs
- Sachin Jain1,*, Kamna Singh1, Prashant Upadhyay2, Richa Gupta3 and Ashish Garg4
- 1. INTRODUCTION
- 2. RELATED WORKS
- 3. PROPOSED ENHANCED CNN-BASED FAILURE INTEGRATED ASSESSMENT PROCEDURE FOR ENERGY ACCUMULATOR PACKS
- 4. EXPERIMENT SETUP
- 5. RESULT
- CONCLUSION
- REFERENCES
- Fine Granularity Conceptual Model for Bilinearity Fusion Features and Learning Methods in Multilayer Feature Extraction
- Satya Prakash Yadav5,*, Mahaveer Singh Naruka1, Prashant Upadhyay2, Sushant Chamoli3 and Rajesh Pokhariyal4
- 1. INTRODUCTION
- 2. RELATED WORKS
- 3. RESEARCH METHODOLOGY
- 3.1. Data Preprocessing and Enhancement
- 3.2. Bilinearity Fine Granularity Conceptual Model
- 3.3. Feature Fusion
- 3.4. Classification and Training
- 3.5. Bilinear Model and Operations
- 3.6. Bilinearity Feature Extraction
- 3.7. Pond Processing for Feature Extraction
- 4. EXPERIMENTAL VERIFICATION
- 4.1. Experimental Results
- 4.2. Experimental Results on CUB-200-2011 Dataset
- CONCLUSION
- REFERENCES
- From Chips to Systems: Exploring Disruptive VLSI Ecosystems
- Owais Ahmad Shah1,* and Devesh Tiwari2
- 1. INTRODUCTION
- 2. THE EVOLUTION OF VLSI: A BRIEF OVERVIEW
- 3. VLSI ECOSYSTEM: A HOLISTIC VIEW
- 3.1. Hardware-Software Co-design
- 3.1.1. Collaborative Design Approach
- 3.1.2. Co-simulation and Co-verification
- 3.2. Embedded Systems Integration
- 3.2.1. Rise of Embedded Systems
- 3.2.2. IoT and Edge Computing
- 3.3. Neuromorphic Systems
- 3.3.1. Mimicking Brain Functionality
- 3.3.2. Spiking Neural Networks
- 3.4. Interconnectivity and Networking
- 3.4.1. Interconnecting VLSI Chips
- 3.4.2. Communication Protocols and Standards
- 4. APPLICATIONS OF DISRUPTIVE VLSI ECOSYSTEMS
- 4.1. Artificial Intelligence (AI) and Machine Learning (ML)
- 4.1.1. Neural Network Acceleration
- 4.1.2. Edge AI and IoT Devices
- 4.2. Healthcare and Biotechnology
- 4.2.1. Medical Imaging Devices
- 4.2.2. Wearable Health Monitoring Devices
- 4.3. Automotive and Transportation
- 4.3.1. Advanced Driver Assistance Systems (ADAS)
- 4.3.2. Electric and Autonomous Vehicles
- 4.4. Consumer Electronics
- 4.4.1. High-Performance Computing
- 4.4.2. Consumer IoT Devices
- 5. CHALLENGES AND FUTURE PROSPECTS
- 5.1. Technological Challenges
- 5.1.1. Scaling Limitations
- 5.1.2. Power Dissipation
- 5.2. Design Complexity and Verification
- 5.2.1. Design Productivity
- 5.2.2. Verification Complexity
- 5.3. Heterogeneous Integration
- 5.3.1. Integration Challenges
- 5.3.2. Interconnect Bottlenecks
- 6. FUTURE PROSPECTS
- 6.1. Beyond von Neumann Architecture
- 6.2. Advanced Manufacturing Technologies
- 6.3. AI-Driven Design
- 6.4. Ethical and Security Considerations
- CONCLUSION
- REFERENCES
- Subject Index
