A Practitioner's Approach for Problem-Solving using AI -  - E-Book

A Practitioner's Approach for Problem-Solving using AI E-Book

0,0
54,85 €

-100%
Sammeln Sie Punkte in unserem Gutscheinprogramm und kaufen Sie E-Books und Hörbücher mit bis zu 100% Rabatt.
Mehr erfahren.
Beschreibung

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.

Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:

EPUB
MOBI

Seitenzahl: 520

Veröffentlichungsjahr: 2024

Bewertungen
0,0
0
0
0
0
0
Mehr Informationen
Mehr Informationen
Legimi prüft nicht, ob Rezensionen von Nutzern stammen, die den betreffenden Titel tatsächlich gekauft oder gelesen/gehört haben. Wir entfernen aber gefälschte Rezensionen.



Table of Contents
BENTHAM SCIENCE PUBLISHERS LTD.
End User License Agreement (for non-institutional, personal use)
Usage Rules:
Disclaimer:
Limitation of Liability:
General:
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.

End User License Agreement (for non-institutional, personal use)

This is an agreement between you and Bentham Science Publishers Ltd. Please read this License Agreement carefully before using the ebook/echapter/ejournal (“Work”). Your use of the Work constitutes your agreement to the terms and conditions set forth in this License Agreement. If you do not agree to these terms and conditions then you should not use the Work.

Bentham Science Publishers agrees to grant you a non-exclusive, non-transferable limited license to use the Work subject to and in accordance with the following terms and conditions. This License Agreement is for non-library, personal use only. For a library / institutional / multi user license in respect of the Work, please contact: [email protected].

Usage Rules:

All rights reserved: The Work is the subject of copyright and Bentham Science Publishers either owns the Work (and the copyright in it) or is licensed to distribute the Work. You shall not copy, reproduce, modify, remove, delete, augment, add to, publish, transmit, sell, resell, create derivative works from, or in any way exploit the Work or make the Work available for others to do any of the same, in any form or by any means, in whole or in part, in each case without the prior written permission of Bentham Science Publishers, unless stated otherwise in this License Agreement.You may download a copy of the Work on one occasion to one personal computer (including tablet, laptop, desktop, or other such devices). You may make one back-up copy of the Work to avoid losing it.The unauthorised use or distribution of copyrighted or other proprietary content is illegal and could subject you to liability for substantial money damages. You will be liable for any damage resulting from your misuse of the Work or any violation of this License Agreement, including any infringement by you of copyrights or proprietary rights.

Disclaimer:

Bentham Science Publishers does not guarantee that the information in the Work is error-free, or warrant that it will meet your requirements or that access to the Work will be uninterrupted or error-free. The Work is provided "as is" without warranty of any kind, either express or implied or statutory, including, without limitation, implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the results and performance of the Work is assumed by you. No responsibility is assumed by Bentham Science Publishers, its staff, editors and/or authors for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products instruction, advertisements or ideas contained in the Work.

Limitation of Liability:

In no event will Bentham Science Publishers, its staff, editors and/or authors, be liable for any damages, including, without limitation, special, incidental and/or consequential damages and/or damages for lost data and/or profits arising out of (whether directly or indirectly) the use or inability to use the Work. The entire liability of Bentham Science Publishers shall be limited to the amount actually paid by you for the Work.

General:

Any dispute or claim arising out of or in connection with this License Agreement or the Work (including non-contractual disputes or claims) will be governed by and construed in accordance with the laws of Singapore. Each party agrees that the courts of the state of Singapore shall have exclusive jurisdiction to settle any dispute or claim arising out of or in connection with this License Agreement or the Work (including non-contractual disputes or claims).Your rights under this License Agreement will automatically terminate without notice and without the need for a court order if at any point you breach any terms of this License Agreement. In no event will any delay or failure by Bentham Science Publishers in enforcing your compliance with this License Agreement constitute a waiver of any of its rights.You acknowledge that you have read this License Agreement, and agree to be bound by its terms and conditions. To the extent that any other terms and conditions presented on any website of Bentham Science Publishers conflict with, or are inconsistent with, the terms and conditions set out in this License Agreement, you acknowledge that the terms and conditions set out in this License Agreement shall prevail.

Bentham Science Publishers Pte. Ltd. 80 Robinson Road #02-00 Singapore 068898 Singapore Email: [email protected]

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

[1]Agosta L.. , . Theory and Practice of Online Therapy. New York, NY: Routledge; 2018. Empathy in cyberspace.; p. 34.-46.[2]Agarwal A., Vats S., Agarwal R., Ratra A., Sharma V., Gopal L.. Sentiment Analysis in Stock Price Prediction: A Comparative Study of Algorithms., 10th International Conference on Computing for Sustainable Global Development (INDIACom).2023: 1403-1407. [CrossRef][3]Agarwal A., Vats S., Agarwal R., Ratra A., Sharma V., Jain A.. Efficient NetB3 for Automated Pest Detection in Agriculture., 10th International Conference on Computing for Sustainable Global Development (INDIACom).2023; (): 1408-1413. [CrossRef][4]Bagby R.M., Parker J.D.A., Taylor G.J.. The twenty-item Toronto Alexithymia scale—I. Item selection and cross-validation of the factor structure., J. Psychosom. Res..1994; 38(1): 23-32. [CrossRef] [PubMed][5]Baron-Cohen S., Wheelwright S., Hill J., Raste Y., Plumb I.. The “Reading the Mind in the Eyes” Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism., J. Child Psychol. Psychiatry.2001; 42(2): 241-251. [CrossRef] [PubMed][6]Bar-On R.. , . The Handbook of Emotional Intelligence: Theory, Development, Assessment, and Application at Home, School, and in the Workplace. Hoboken, NJ: Jossey-Bass/Wiley; 2000. Emotional and social intelligence: insights from the emotional quotient inventory.; p. 363.-388.[7]Baslet G., Termini L., Herbener E.. Deficits in emotional awareness in schizophrenia and their relationship with other measures of functioning., J. Nerv. Ment. Dis..2009; 197(9): 655-660. [CrossRef] [PubMed][8]Bajaj M., Rawat P., Bhatt A., Sharma V., Jain A., Kumar N.. Classification And Prediction of Brain Tumors and its Types using Deep Learning., International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). : 705-710.2023[CrossRef][9]Bajaj M., Rawat P., Bhatt C., Chauhan R., Singh T.. Heart Disease Prediction using Ensemble ML., International Conference on Sustainable Computing and Data Communication Systems (ICSCDS).2023: 680-685. [CrossRef][10]Bajaj M., Rawat P., Diksha Vats S.. Vats, V. Sharma, and L. Gopal, “Prediction of Mental Health Treatment Adherence using Machine Learning Algorithms., International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). : 716-720.2023[CrossRef][11]Bhatia M., Sharma V., Singh P., Masud M.. Multi-Level P2P Traffic Classification Using Heuristic and Statistical-Based Techniques: A Hybrid Approach., Symmetry (Basel).2020; 12(12): 2117. [CrossRef][12]Birch S.A.J., Bloom P.. The curse of knowledge in reasoning about false beliefs., Psychol. Sci..2007; 18(5): 382-386. [CrossRef] [PubMed][13]Benjamini Y., Hochberg Y.. Controlling the false discovery rate: a practical and powerful approach to multiple testing., J. R. Stat. Soc. Series B Stat. Methodol..1995; 57(1): 289-300. [CrossRef][14]Burger A.J., Lumley M.A., Carty J.N., Latsch D.V., Thakur E.R., Hyde-Nolan M.E., Hijazi A.M., Schubiner H.. The effects of a novel psychological attribution and emotional awareness and expression therapy for chronic musculoskeletal pain: A preliminary, uncontrolled trial., J. Psychosom. Res..2016; 81: 1-8. [CrossRef] [PubMed][15]Bydlowski S., Corcos M., Jeammet P., Paterniti S., Berthoz S., Laurier C., Chambry J., Consoli S.M.. Emotion-processing deficits in eating disorders., Int. J. Eat. Disord..2005; 37(4): 321-329. [CrossRef] [PubMed][16]Bzdok D., Meyer-Lindenberg A.. Machine learning for precision psychiatry: opportunities and challenges., Biol. Psychiatry Cogn. Neurosci. Neuroimaging.2018; 3(3): 223-230. [CrossRef] [PubMed][17]Chhatwal J., Lane R.D.. A cognitive-developmental model of emotional awareness and its application to the practice of psychotherapy., Psychodyn. Psychiatry.2016; 44(2): 305-325. [CrossRef] [PubMed][18]IBM Corp, "IBM SPSS statistics for Windows (Version 28.0)" Armonk, NY: IBM Corp, 2021.[19]Cicchetti D.V.. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology., Psychol. Assess..1994; 6(4): 284-290. [CrossRef][20]Danieli M., Ciulli T., Mousavi S.M., Silvestri G., Barbato S., Di Natale L., Riccardi G.. Assessing the impact of conversational artificial intelligence in the treatment of stress and anxiety in aging adults: Randomized controlled trial., JMIR Ment. Health.2022; 9(9): e38067. [CrossRef] [PubMed][21][CrossRef] M. H. Davis, "Interpersonal reactivity index (IRI) [Database record]," APA PsycTests, 1980.[22]Dolli P.. Rawat, M. Bajaj, S. Vats, and V. Sharma, “An Analysis of Crop Recommendation Systems Employing Diverse Machine Learning Methodologies., International Conference on Device Intelligence, Computing and Communication Technologies (DICCT). : 619-624.2023[CrossRef][23]Donges U.S., Kersting A., Dannlowski U., Lalee-Mentzel J., Arolt V., Suslow T.. Reduced awareness of others’ emotions in unipolar depressed patients., J. Nerv. Ment. Dis..2005; 193(5): 331-337. [CrossRef] [PubMed][24]Fitzpatrick K.K., Darcy A., Vierhile M., Darcy A.. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial., JMIR Ment. Health.2017; 4(2): e19-e11. [CrossRef] [PubMed][25]Frewen P., Lane R.D., Neufeld R.W.J., Densmore M., Stevens T., Lanius R.. Neural correlates of levels of emotional awareness during trauma script-imagery in posttraumatic stress disorder., Psychosom. Med..2008; 70(1): 27-31. [CrossRef] [PubMed][26]K. Hu, "ChatGPT sets record for fastest-growing user base—analyst note". Available from: https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ 2023.[27]Happé F.G.E.. An advanced test of theory of mind: Understanding of story characters’ thoughts and feelings by able autistic, mentally handicapped, and normal children and adults., J. Autism Dev. Disord..1994; 24(2): 129-154. [CrossRef] [PubMed][28]Ji Z., Liu Z., Lee N., Yu T., Wilie B., Zeng M., et al. RHO (ρ): reducing hallucination in open-domain dialogues with knowledge grounding, arXiv Preprint, 2022.[29]Kung T.H., Cheatham M., Medenilla A., Sillos C., De Leon L., Elepaño C., Madriaga M., Aggabao R., Diaz-Candido G., Maningo J., Tseng V.. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models., PLOS Digital Health.2023; 2(2): e0000198. [CrossRef] [PubMed][30]Lane R.D., Quinlan D.M., Schwartz G.E., Walker P.A., Zeitlin S.B.. The Levels of Emotional Awareness Scale: a cognitive-developmental measure of emotion., J. Pers. Assess..1990; 55(1-2): 124-134. [CrossRef] [PubMed][31]Lane R.D., Smith R.. Levels of emotional awareness: theory and measurement of a socio-emotional skill., J. Intell..2021; 9(3): 42. [CrossRef] [PubMed][32]Levine D., Marziali E., Hood J.. Emotion processing in borderline personality disorders., J. Nerv. Ment. Dis..1997; 185(4): 240-246. [CrossRef] [PubMed][33]Montag C., Haase L., Seidel D., Bayerl M., Gallinat J., Herrmann U., Dannecker K.. A pilot RCT of psychodynamic group art therapy for patients in acute psychotic episodes: feasibility, impact on symptoms and mentalising capacity., PLoS One.2014; 9(11): e112348. [CrossRef] [PubMed][34]Moor J.H.. An analysis of the turing test., Philos. Stud..1976; 30(4): 249-257. [CrossRef][35]Nandrino J.L., Baracca M., Antoine P., Paget V., Bydlowski S., Carton S.. Level of emotional awareness in the general French population: Effects of gender, age, and education level., Int. J. Psychol..2013; 48(6): 1072-1079. [CrossRef] [PubMed][36]Neumann D., Malec J.F., Hammond F.M.. Reductions in alexithymia and emotion dysregulation after training emotional self-awareness following traumatic brain injury: a phase I trial., J. Head Trauma Rehabil..2017; 32(5): 286-295. [CrossRef] [PubMed][37]Prakash Yadav S., Yadav S.. Fusion of Medical Images in Wavelet Domain: A Discrete Mathematical Model., Ingeniería Solidaria.2018; 14(25): 1-11. [CrossRef][38]Uniyal S., Dhoundiyal P., Sharma V., Vats S.. , . 2nd International Conference on Disruptive Technologies (ICDT),. Greater Noida, India; 2024. An intelligent approach to grape leaf disease diagnosis through machine learning.; p. 284.-288.[39]Prakash Yadav S., Yadav S.. Fusion of Medical Images in Wavelet Domain: A Hybrid Implementation., Comput. Model. Eng. Sci..2020; 122(1): 303-321. [CrossRef][40]Pham K.T., Nabizadeh A., Selek S.. Artificial intelligence and chatbots in psychiatry., Psychiatr. Q..2022; 93(1): 249-253. [CrossRef] [PubMed][41]Radice-Neumann D., Zupan B., Tomita M., Willer B.. Training emotional processing in persons with brain injury., J. Head Trauma Rehabil..2009; 24(5): 313-323. [CrossRef] [PubMed][42]Rawat P., Bajaj M., Mehta S., Sharma V., Jain A., Manjul M.. Cancer Malignancy Prediction Using Machine Learning: A Cross-Dataset Comparative Study., International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). : 699-704.2023[CrossRef][43]Rawat P., Bajaj M., Mehta S., Sharma V., Vats S.. A Study on Cervical Cancer Prediction using Various Machine Learning Approaches., International Conference on Innovative Data Communication Technologies and Application (ICIDCA). : 1101-1107.2023[CrossRef][44]Rawat P., Bajaj M., Prerna P., Vats S., Sharma V., Das P.. A Study on Liver Disease Using Different Machine Learning Algorithms., International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). : 721-727.2023[CrossRef][45]Rawat P., Bajaj M., Sharma V., Vats S.. A Comprehensive Analysis of the Effectiveness of Machine Learning Algorithms for Predicting Water Quality., International Conference on Innovative Data Communication Technologies and Application (ICIDCA). : 1108-1114.2023[CrossRef][46]Rawat P., Bajaj M., Vats S., Sharma V.. ASD Diagnosis in Children, Adults, and Adolescents using Various Machine Learning Techniques., International Conference on Device Intelligence, Computing and Communication Technologies (DICCT). : 625-630.2023[CrossRef][47]Rawat P., Bajaj M., Vats S., Sharma V., Gopal L., Kumar R.. Optimizing Hypothyroid Diagnosis with Physician-Supervised Feature Reduction using Machine Learning Techniques., International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). : 711-715.2023[CrossRef][48]Rudolph J., Tan S., Tan S.. ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?, Journal of Applied Learning & Teaching.2023; 6(1): 1-22. [CrossRef][49]Sharma V., Patel R.B., Bhadauria H.S., Prasad D.. Deployment schemes in wireless sensor network to achieve blanket coverage in large-scale open area: A review., Egyptian Informatics Journal.2016; 17(1): 45-56. [CrossRef][50]Negi A., Vats S., Khatri M., Sharma V., Narang H., Kukreja V.. , . 2nd International Conference on Disruptive Technologies (ICDT),. Greater Noida, India; 2024. Automated medical analysis for pneumonia diagnosis.; p. 577.-581.[51]Sharma V., Vats S., Arora D., Singh K., Prabuwono A.S., Alzaidi M.S., Ahmadian A.. OGAS: Omni-directional Glider Assisted Scheme for autonomous deployment of sensor nodes in open area wireless sensor network., ISA Trans..2023; 132: 131-145. [CrossRef] [PubMed][52]Subic-Wrana C., Bruder S., Thomas W., Lane R.D., Köhle K.. Emotional awareness deficits in inpatients of a psychosomatic ward: a comparison of two different measures of alexithymia., Psychosom. Med..2005; 67(3): 483-489. [CrossRef] [PubMed][53]Topol E.. , . Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York City, NY: Basic Books; 2019.[54]Vats S., Sharma V., Singh K., Katti A., Ariffin M.M., Ahmad M.N., Ahmadian A., Salahshour S.. Incremental Learning-Based Cascaded Model for Detection and Localization of Tuberculosis from Chest X-Ray Images., Expert Systems with Applications.2023; 2023: 122129. [CrossRef][55]