Table of Contents
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PREFACE
List of Contributors
Redefining Human-AI Interactions: Unveiling ChatGPT's Profound Emotional Understanding
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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)
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Emerging Trends in Computation Intelligence and Disruptive Technologies
(Volume 1)
A Practitioner's Approach to Problem-Solving using AI
Edited by
Satvik Vats
Department of Computer Science and Engineering
Graphic Era Hill University
Dehradun, Uttarakhand, India
Vikrant Sharma
Department of Computer Science and Engineering
Graphic Era Hill University
Dehradun, Uttarakhand, India
Dibyahash Bordoloi
Department of Computer Science and Engineering
Graphic Era Hill University
Dehradun, Uttarakhand, India
&
Satya Prakash Yadav
School of Computer Science Engineering and Technology (SCSET)
Bennett University
Greater Noida, Uttar Pradesh, India
BENTHAM SCIENCE PUBLISHERS LTD.
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PREFACE
Artificial intelligence (AI) and machine learning (ML) are revolutionizing the way we approach healthcare and various industries. AI and ML are being used to improve patient outcomes, reduce costs, and increase efficiency in the healthcare industry. AI is also being used in medical devices to predict and identify diseases, classify data for disease outbreaks, and optimize medical therapy.
In this book, we explore the role of neural networks in AI and ML in the medical and health sectors. Neural networks are being used in oncology to train algorithms that can identify cancerous tissues at the microscopic level with the same accuracy as trained physicians. Various rare diseases may manifest in physical characteristics that can be identified in their premature stages by facial analysis of patient photos.
The book also explores the role of AI and ML in various industries such as finance, retail, manufacturing, and more. AI is being used to improve customer experience by providing personalized recommendations based on customer data. In manufacturing, AI is being used to optimize supply chain management by predicting demand and reducing waste.
This book is a comprehensive guide for anyone interested in learning about the role of AI and ML in medical, health sectors, and various industries.
Satvik Vats
Department of Computer Science and Engineering
Graphic Era Hill University
Dehradun, Uttarakhand, IndiaVikrant Sharma
Department of Computer Science and Engineering
Graphic Era Hill University
Dehradun, Uttarakhand, IndiaDibyahash Bordoloi
Department of Computer Science and Engineering
Graphic Era Hill University
Dehradun, Uttarakhand, India
&Satya Prakash Yadav
List of Contributors
Amit GuptaDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, IndiaAditya VermaDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, IndiaAshish GargDepartment of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, IndiaAarti ChaudharyDepartment of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, IndiaAlok BarddhanJIMS Engineering Management Technical Campus (JEMTEC), Greater Noida, Uttar Pradesh, IndiaAshish DixitDepartment of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, IndiaAbha Kiran RajpootDepartment of Computer Science and Engineering (AI & ML), KIET Group of Institutions, Delhi-NCR, Ghaziabad, IndiaDevesh TiwariGraphic Era Hill University, Dehradun, Uttarakhand, IndiaDeepti SahuDepartment of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, IndiaDibyahash BordoloiDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, IndiaGunajn AggarwalDepartment of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, IndiaKamna SinghDepartment of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, IndiaKaran PurohitDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, IndiaManish ChhabraDepartment of Artificial Intelligence and Machine Learning, Vardhaman College of Engineering, Hyderabad, Telangana, IndiaMadhvan BajajDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, IndiaMahaveer Singh NarukaDepartment of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Greater Noida, IndiaNilotpal PathakDepartment of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Greater Noida, IndiaNeha GargDepartment of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, IndiaNavin GargDepartment of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, IndiaOwais Ahmad ShahSchool of Engineering and Technology, K. R. Mangalam University, Gurugram, Haryana, IndiaPrernaDepartment of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, IndiaPriyanshu RawatDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, IndiaPrashant UpadhyayDepartment of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, IndiaPawan Kumar SinghDepartment of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Greater Noida, IndiaRajesh PokhariyalDepartment of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, IndiaRishabh SaklaniDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, IndiaRicha GuptaDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, IndiaRishika YadavDepartment of Computer Science, Graphic Era Hill University, Dehradun, IndiaShashank AwasthiDepartment of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Greater Noida, India
School of Computer Science Engineering and Technology (SCSET), Bennett University, Greater Noida, Uttar Pradesh, IndiaSheradha JauhariDepartment of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, IndiaSansar Singh ChauhanDepartment of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Greater Noida, IndiaSachin JainDepartment of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, IndiaSushant ChamoliDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, IndiaSatvik VatsDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, IndiaShreshtha MehtaDepartment of Biotechnology, Graphic Era Deemed to be University, Dehradun, IndiaSatya Prakash YadavSchool of Computer Science Engineering and Technology (SCSET), Bennett University, Greater Noida, Uttar Pradesh, IndiaShreshtha MehtaDepartment of Biotechnology, Graphic Era Deemed to be University, Dehradun, IndiaSandeep KumarDepartment of Computer Science and Engineering (AI), ABES Institute of Technology, Ghaziabad, IndiaSantosh Kumar UpadhyayDepartment of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, IndiaShikha AgarwalDepartment of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, IndiaUpendra Singh AswalDepartment of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, IndiaVikrant SharmaDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, IndiaVeena BhartiDepartment of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India
Redefining Human-AI Interactions: Unveiling ChatGPT's Profound Emotional Understanding
Priyanshu Rawat1,*,Madhvan Bajaj1,Satvik Vats1,Vikrant Sharma1
1 Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India
Abstract
The AI-powered conversational agent known as ChatGPT has received significant attention due to its exceptional performance in natural language processing tasks and its exponential growth in user base. While ChatGPT has demonstrated its ability to generate knowledge across various domains, its proficiency in identifying and expressing emotions remains uncertain. Recognizing and understanding emotional states, both in oneself and others, is widely acknowledged as a crucial aspect of mental health, referred to as emotional awareness (EA). The present study employed the Levels of Emotional Awareness Scale (LEAS) as a standardized and task-oriented metric to assess the efficacy of ChatGPT in addressing twenty distinct scenarios. The present investigation sought to conduct a comparative analysis of ChatGPT's proficiency in emotional awareness (EA) vis-à-vis the general populace, ascertained through prior scholarly inquiry. A follow-up evaluation was conducted one month later to assess potential improvements in ChatGPT's emotional intelligence algorithm over time. Additionally, licensed psychologists independently evaluated the appropriateness of ChatGPT's EA responses in the given context. The preliminary evaluation indicates that ChatGPT exhibits a considerably greater level of proficiency in all aspects of the LEAS in comparison to the general populace, as evidenced by a Z score of 2.79. The post-evaluation analysis revealed a significant enhancement in the operational efficiency of ChatGPT, exhibiting a close proximity to the highest achievable LEAS score, Z score = 4.15. Furthermore, ChatGPT exhibited a statistically significant level of precision, achieving a score of 9.7 out of 10. These findings suggest that ChatGPT exhibits a high level of proficiency in generating appropriate responses for EA, and its effectiveness may significantly improve over time. The results have important implications in both theoretical and practical contexts. Integrating ChatGPT into cognitive training programs could hold potential for addressing executive attention deficits in clinical populations. Moreover, ChatGPT's EA-like capabilities can aid in the assessment and diagnosis of psychiatric disorders, as well as advancing our understanding of emotional language. Additional investigation is required to comprehensively scrutinize the potential advantages and disadvantages of ChatGPT and optimize its application for advancing psychological well-being.
Keywords: ChatGPT, Conversational agent, Emotional awareness (EA), LEAS, Natural language processing.
*Corresponding author Priyanshu Rawat: Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India; E-mail:
[email protected]References
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