Advances in Artificial-Business Analytics and Quantum Machine Learning

Name: Advances in Artificial-Business Analytics and Quantum Machine Learning | Select Proceedings of the 3rd International Conference, Com-IT-Con 2023, Volume 2
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Select Proceedings of the 3rd International Conference, Com-IT-Con 2023, Volume 2

KC Santosh Poonam Nandal Sandeep Kumar Sood Hari Mohan Pandey(Editor)

Springer (Publisher)

1st Edition

Published on 18. October 2024

XVI, 330 pages

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978-981-97-4860-0 (ISBN)

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Prof. KC Santosh-a highly accomplished AI expert-is the Chair of the Department of Computer Science and the founding director of the Applied AI Research Lab at the University of South Dakota (USD). He also served the National Institutes of Health as a research fellow and LORIA Research Center as a postdoctoral research scientist, in collaboration with industrial partner, ITESOFT, France. He earned his Ph.D. in Computer Science-Artificial Intelligence from INRIA Nancy Grand East Research Center (France). With funding exceeding $8 million from sources like DOD, NSF, ED, and SDBOR, he has authored 10 books and more than 250 peer-reviewed research articles, including IEEE TPAMI. He serves as an associate editor for esteemed journals such as IEEE Transactions on AI, Int. J of Machine Learning & Cybernetics, and Int. J of Pattern Recognition & Artificial Intelligence.

To name a few, Prof. Santosh is the proud recipient of the Visionary Leadership Award (University of Derby - UK, 2023) Cutler Award for Teaching and Research Excellence (USD, 2021), the President's Research Excellence Award (USD, 2019), and the Ignite Award from the U.S. Department of Health & Human Services (HHS, 2014).

Effective from Spring 2024, he has joined the NIST's AI Safety Institute Consortium, with USD being the only institution representing the state of South Dakota in this consortium.

As the founder of AI programs at USD, he has taken significant strides to increase enrolment in the graduate program, resulting in over 3,000% growth in just three years. His leadership has helped build multiple inter-disciplinary AI/Data Science related academic programs, including collaborations with Biology, Physics, Biomedical Engineering, Sustainability and Business Analytics departments. Prof. Santosh is highly motivated in academic leadership, and his contributions have established USD as a pioneer in AI programs within the state of SD.

Dr. Poonam Nandal is an esteemed academic and researcher with an impressive career spanning over 17 years. Her academic journey reached its pinnacle with a Ph.D. in Computer Science & Engineering from YMCA University of Science & Technology in 2017. Complementing this, she holds a Master's degree in Computer Engineering from Maharishi Dayanand University, Rohtak, showcasing a comprehensive educational background. Dr. Nandal scholarly impact is evidenced by her substantial contributions, including 6 patents and more than 40 research papers published in esteemed conferences and peer-reviewed journals. These publications are indexed in renowned databases such as Scopus, WoS, EBSCO, and UGC Care, underscoring her dedication to advancing knowledge in her field. Beyond her research, she actively participates in advisory and editorial capacities for respected peer-reviewed journals.

Sandeep Kumar Sood received the Ph.D. degree in computer science and engineering from IIT Roorkee, Roorkee, India, in 2010. He is currently working as a Head and an Associate Professor in the Department of Computer Applications, N.I.T. Kurukshetra, Haryana, India. He has authored or coauthored more than 115 SCI/SCIE indexed research publications. According to Google Scholar, the citation number is, with an h-index equal to 34 and i10-index equal to 96. His research interests include network and information security, fog computing, cloud computing, IoT, and big data analytics.

Hari is in data science and artificial intelligence at the school of technology Bournemouth University, UK. He was featured in the World Ranking list of Top 2% scientists by Stanford University. He is specialized in Computer Science & Engineering. His research area includes artificial intelligence, soft computing techniques, natural language processing, language acquisition, machine learning, deep learning, and computer vision. Hari is the author of various books in computer science engineering (algorithms, programming and evolutionary algorithms). Hari has published over 150 scientific papers in reputed journals and conferences. He is serving in the editorial board of reputed journals as action editor, associate editor, and guest editor. He is a reviewer of top international conferences such as GECCO, CEC, IJCNN, BMVC, AAAI etc. Hari has delivered expert talks as keynote and invited speaker. He is a fellow of the HEA of the UK Professional Standards Framework (UKPSF) and has a rich teaching experience at the higher education level. He has been given the prestigious award "The Global Award for the Best Computer Science Faculty of the Year 2015", award for completing INDO-US project "GENTLE", award (Certificate of Exceptionalism) from the Prime Minister of India and award for developing innovative teaching and learning models for higher-education. In the past, he has worked as a sr. lecturer in the computer science department at Edge Hill University, UK. He also worked as a postdoctoral research fellow in machine learning at the school of technology at Middlesex University London where he worked on a European Commission project- DREAM4CARS

Content

Intro
Preface
Contents
Editors and Contributors
A Comprehensive Survey on Fake Review Detection System with Future Directions
1 Introduction
2 Literature Review
2.1 Review Based on Technique Used
2.2 Review Based on Identifying Features-Textual or Contextual
2.3 Review Based on Benchmark Datasets
3 Future Directions
4 Conclusion
References
Click Fraud Detection Using Ensemble Classifier
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 Feature Selection Using Recursive Feature Elimination
3.2 Hellinger Distance-Based Decision Tree
3.3 Ensemble Classifier
4 Experimental Dataset and Results
4.1 Dataset Description
4.2 Results and Analysis
5 Conclusion
References
Deep Learning-Based Brain Tumor Segmentation-An Overview
1 Introduction
2 Existing Surveys
2.1 Comparison Study on Previous Year Survey Papers
2.2 Categorization of Journals and Publication Trends
2.3 Classification of Brain Tumor
3 Techniques Used for Brain Tumor Segmentation
3.1 Magnetic Resonance Imaging
3.2 Computed Tomography Imaging
3.3 Deep Learning-Based Methods
4 Datasets
5 Conclusion
References
An Efficient Image Caption Generation Using GoogLeNet
1 Introduction
2 Methodology
2.1 GoogLeNet
3 Development
3.1 Dataset
4 Results
5 Conclusion
References
Brain Tumor Recognition Leveraging Machine Learning and CNN
1 Introduction
2 Related Work
3 Background
3.1 Magnetic Resonance Imaging
3.2 Supervised Machine Learning
3.3 Convolutional Neural Network
4 Proposed System
4.1 Preprocessing
4.2 Segmentation
4.3 Feature Extraction
4.4 Classification
5 Experimental Setup
6 Information Collection and Preprocessing
6.1 Demonstrate Architecture
6.2 Training and Validation
7 Result and Discussion
8 Conclusion and Future Work
References
Comparative Analysis of Fake Comments Posted in Twitter Using Different Machine Learning Models
1 Introduction
2 Literature Review
3 Research Gaps
4 Dataset Statistics
5 Proposed Methodology
6 Result and Discussion
7 Conclusion
References
Design and Development of IoT-Based Crop Growth Monitoring and Maintenance System for Hydroponic Indoor Vertical Farm
1 Introduction
2 Literature Survey
3 System and Design
3.1 Climate Control
3.2 Nutrient Solution Monitoring and Balancing
3.3 Other Major Components
4 Materials and Methods
5 Results
5.1 System Performance
6 Agronomic Analysis
7 Conclusion
References
Enhancing Smart Grid Reliability Prediction Through Improved Deep Learning Approach
1 Introduction
2 Materials and Techniques
2.1 Data Description
2.2 Optimized Deep Learning Models
3 Experimental Results
3.1 Experimental Setup
4 Modeling Definition and Adaptation
4.1 Performance Matrices
5 Results
5.1 Formal Correlation with State-of-the-Art Works
6 Conclusion
References
Analysis of Customer Transaction Along with Segmentation and Feedback Analysis
1 Introduction
2 Methodology
2.1 Dataset and Preprocessing
2.2 Algorithm
2.3 Classification
3 Result
4 Conclusion
References
Smart Pesticide Recommendation System Using Deep Learning Techniques
1 Introduction
2 Materials and Technology Used
2.1 Dataset
2.2 Image Preprocessing
2.3 Image Augmentation
2.4 Software Tools
3 Machine-Learning Algorithms
4 Proposed Methodology
4.1 The CNN Approach
4.2 Implementation Process
5 Conclusion
References
An Inferential Analysis of Customer Experience Transformation Through AR VR Immersive and Collaborative Environments
1 Introduction
2 Literature Review
3 Methods
3.1 Participants
3.2 Procedure
3.3 Measures
3.4 Analysis and Results
3.5 Discussions
4 Conclusions
References
Automated Diabetic Retinopathy Prediction System Using Inception V3
1 Introduction
1.1 Related Work
2 Proposed Methodology
2.1 Data Preprocessing
3 Proposed Model
4 Experimental Result Analysis
4.1 Dataset Preparation
4.2 Findings
5 Conclusion and Future Scope
References
Spatial Data Analytics for Regional Planning Using GIS and RS Technologies
1 Introduction
2 Literature Survey
3 Proposed Work
3.1 Methodology
4 Result and Analysis
5 Conclusion and Future Scope
References
TopK Movie Recommendation Using Matrix Factorization Methods
1 Introduction
2 Prelimanaries
2.1 SVD
2.2 SVD++
2.3 Non-Negative Matrix Factorization(NMF)
3 Related Work
4 Evaluation Metrics
4.1 RMSE
4.2 MAE
4.3 Precision@k
4.4 Recall@k
4.5 F1 Score
5 Data Set
6 Experimental Steps
7 Result Analysis
7.1 Number of Latent Factors Verus RMSE
7.2 Number of Latent Factors Verus MAE
7.3 Number of Latent Factors Versus Precision@k
7.4 Number of Latent Factors Versus Recall@k
7.5 Number of Latent Factors Verus F1 Score
8 Conclusions and Future Work
References
A Framework for Multi-technology Gateway Management Approach Based on Handover Decision for IoT
1 Introduction
2 Related Work
3 System Model
4 Proposed Framework For Gateway Management Using Handover
5 Conclusion
References
A Review on Deep Learning-Based Segmentation Techniques for Lung Nodules
1 Introduction
2 General CAD Framework for Lung Nodule Detection and Diagnosis
3 Datasets
3.1 LIDC/IDRI
3.2 LUNA16 (Lung Nodule Analysis)
4 Evaluation Metrics
4.1 Dice Coefficient
4.2 Intersection Over Union
4.3 Sensitivity (Recall)
4.4 ROC Curve
4.5 Free Response Open Characteristics
4.6 Area Under the Curve (AUC)
4.7 Precision
4.8 Accuracy
5 Literature Review
6 Challenges and Future Scope
7 Conclusion
References
Analysis of Machine-Learning Techniques for Emotion Detection from Text
1 Introduction
1.1 Categorical Approach
1.2 Dimensional Approach
2 Literature Review
3 Keyword Spotting Method
3.1 Lexical Affinity (LA) Method
3.2 Learning-Based Method
4 Data, Pre-processing, and Word Embedding
4.1 Dataset
4.2 Pre-processing
4.3 Word Embedding
5 Model Development
5.1 Naïve Bayes (NB)
5.2 Logistic Regression (LR)
5.3 Random Forest
5.4 Support Vector Machine (SVM)
6 Results and Discussion
6.1 Model Metrics Comparison
7 Conclusion
References
Role of ML in Cancer Prediction
1 Introduction
1.1 Background Information on Cancer and Its Diagnosis
1.2 The Importance of Early Cancer Detection
1.3 The Use of Machine Learning in Identification of Cancer
2 Machine-Learning Techniques Used in Cancer Prediction
3 Data Used in Cancer Prediction
4 Machine-Learning Applications in Cancer Prediction
5 Challenges and Limitations of ML in Cancer Prediction
5.1 Data Availability and Quality
5.2 Interpretability and Transparency
5.3 Generalizability and Bias
6 Future Directions and Conclusion
6.1 Emerging Technologies and Their Potential Impact
6.2 Importance of Collaboration Between Clinicians and Data Scientists
6.3 Conclusion and Future Outlook
References
Extending Minimum Prediction Deviation as a Defence Against Adversarial Attacks
1 Introduction
2 Literature Review
3 Background
3.1 Feature Importance Guided Attack (FIGA)
3.2 Projected Gradient Descent (PGD)
3.3 Minimum Prediction Deviation (MPD)
4 Methodology
4.1 Approach
4.2 Dataset Information
5 Experimental Set-Up
5.1 Adversarial Sample Generation
5.2 MPD Experimental Set-Up
6 Results and Analysis
7 Conclusion
References
Covid 19 Detection Using Advanced CNN
1 Introduction
1.1 SARS-CoV-Overview
1.2 Formulation of Problem
1.3 Solution Related to Problem
2 Literature Review
2.1 Inclusion/Exclusion Criteria
2.2 Quantum Computers and Simulations
2.3 Transfer Learning
2.4 Traditional Method of Machine Learning
2.5 Quantum Convolutional
2.6 Comparison
3 Data Set
4 Proposed Work
4.1 Deep Learning
4.2 Pre-Processing of CT Scan Images
4.3 Image Segmentation
4.4 Classification Using VGG-16 Model
4.5 Result
5 Discussion
5.1 Experimental Evaluation
6 Conclusion
References
Secure Image Retrieval in an Untrusted Cloud Environment Using Homomorphic and Attribute-Based Encryption
1 Introduction
2 Related Work
3 Problem Formulation
4 Proposed Solution
5 Experimental Details and Result Analysis
6 Limitations and Drawbacks
7 Conclusion and Future Scope
References
Data-Driven Approaches-Based Microwave Filter Tuning-A Review
1 Introduction
2 Challenges
3 Various Data-Driven Filter Tuning Strategies
3.1 Machine Learning
3.2 Fully Logic (FL)
3.3 Artificial Neural Networks (ANNs)
3.4 Filter Tuning Using a Neuro-Fuzzy System (NFS)
3.5 Filter Tuning Using Linear Matrix Operator
3.6 Filter Tuning Based on Linear Decomposition of Reflection Characteristics
3.7 Filter Tuning Using Support Vectors
4 Discussion
5 Conclusion
References
Licence Plate Detection and Recognition with OCR Using Machine Learning Techniques
1 Introduction
2 Literature Survey
3 Methodology
3.1 Detection and Recognition of Licence Plate
3.2 Character Recognition
3.3 Compilation of Feature Vector and Classification of Licence Plates
4 Results
5 Conclusion
References
Unleashing the Potential of Data
1 Introduction
2 Data Visualization
3 Data Analysis
3.1 Descriptive Analysis
3.2 Inferential Analysis
3.3 Predictive Analysis
4 Conclusion
References
Revamping Classroom Pedagogy with Educational Technology: Empowering Educators with TF-IDF Model
1 Introduction
2 Review of Related Literature
3 Grading Short-Answer Quiz Questions
4 Identifying Key Topics in Educational Textbooks
5 Recommender Systems
6 Retrieving Relevant Research Articles for a Literature Review
7 Grading Short Answer Questions
8 Sentiment Analysis
8.1 Analyzing Course Evaluations
8.2 Analyzing Student Sentiment in Course Evaluations
9 Limitations
10 Future Work and Way Forward
11 Conclusion
12 Disclosure Statement
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Advances in Artificial-Business Analytics and Quantum Machine Learning

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