Artificial Intelligence, Machine Learning and User Interface Design
Published on 13. January 2025
300 pages
978-981-5179-60-6 (ISBN)
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Edition
Digital original
Language
English
Place of publication
Basel/Berlin/Boston
Singapore
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
Product notice
Reflowable
File size
6,02 MB
ISBN-13
978-981-5179-60-6 (9789815179606)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Content
- Cover
- Title
- Copyright
- End User License Agreement
- Contents
- Preface
- List of Contributors
- Artificial Taste Perception of Tea Beverage Using Machine Learning
- Amruta Bajirao Patil1 and Mrinal Rahul Bachute1,*
- INTRODUCTION
- USER EXPERIENCE (UX) EVALUATION
- LITERATURE REVIEW
- Metal Oxide Semiconductor (MOS) Sensors
- Conducting Particle (CP) Sensors
- Acoustic Wave Sensors
- Potentiometric Sensor
- Voltammetric Sensor
- Commercial Solutions
- Color and Image Sensors
- PATENT REVIEW
- BIBLIOMETRIC REVIEW
- Tea Beverage
- Artificial Taste Perception
- Machine Learning (ML)
- IMPLEMENTATION
- Experiment Requirement
- Proportion Sample Sets
- Results
- CONCLUDING REMARKS
- REFERENCES
- Significance of Evolutionary Artificial Intelligence: A Detailed Overview of the Concepts, Techniques, and Applications
- Ashish Tripathi1,*, Rajnesh Singh1, Arun Kumar Singh2, Pragati Gupta1, Siddharth Vats3 and Manoj Singhal4
- INTRODUCTION
- ARTIFICIAL INTELLIGENCE
- Types of Artificial Intelligence
- Weak Artificial Intelligence
- Strong Artificial Intelligence
- Reactive Artificial Intelligence
- Limited Memory Artificial Intelligence
- Theory-of-Mind Artificial Intelligence
- Self-Aware Artificial Intelligence
- Applications of Artificial Intelligence
- Customer Service
- Speech Recognition
- Computer Vision
- Recommendation Engines
- Automated Stock Trading
- EVOLUTIONARY COMPUTATION
- STATE-OF-THE-ART DISCUSSION ON EVOLUTIONARY ARTIFICIAL INTELLIGENCE
- STATE-OF-THE-ART APPLICATIONS OF EVOLUTIONARY MACHINE LEARNING
- EVOLUTIONARY MACHINE LEARNING BASED CASE STUDIES
- Case Studies
- Case Studies in Companies
- Case Studies in Healthcare
- SIGNIFICANCE OF EVOLUTIONARY ARTIFICIAL INTELLIGENCE IN DECISION MAKING
- Limitations of Current AI in Decision-making
- Role of Evolutionary Computation to Overcome the Limitations of AI
- Evolutionary Computation with Artificial Intelligence
- Evolutionary Artificial Intelligence in Solving the Real World Problems
- Effective Web Interface Design
- Online Personalization Shopping
- Effective Marketing
- Surveillance System
- Agriculture and Food Security
- CURRENT ISSUES WITH EVOLUTIONARY MACHINE LEARNING
- CONCLUSION
- REFERENCES
- Impact of Deep Learning on Natural Language Processing
- Arun Kumar Singh1,*, Ashish Tripathi2, Sandeep Saxena2, Pushpa Choudhary2, Mahesh Kumar Singh3 and Arjun Singh1
- INTRODUCTION
- FUNDAMENTAL CONCEPTS OF A DEEP NEURAL NETWORK
- Concept of the Layers
- Input Layer (xi)
- Output Layer (Y)
- Hidden Layer (wixi)
- Neuron
- Deep Learning Background
- Convolutional Neural Networks
- Benefits of Employing CNNs
- Recurrent Neural Network
- Natural Language Processing
- Working Principle of NLP
- Lexical Analysis
- Syntactic Analysis/Syntax Analysis
- Semantic Analysis
- Discourse Integration
- Pragmatic Analysis
- Needs of NLP
- Application of NLP can Solve
- NLP LITERATURE REVIEW
- Sentiment Analysis
- Basic LSTM Model
- Challenges in THE NLP
- Syntactic Ambiguity Leads to Misunderstanding: Cases
- Latest Trends in Natural Language Processing-
- Future of Natural Language Processing (NLP)
- NLP Challenges
- Comparison with the New AI Models with NLP
- CONCLUSION
- REFERENCES
- A Review on Categorization of the Waste Using Transfer Learning
- Krantee M. Jamdaade1, Mrutunjay Biswal1,* and Yash Niranjan Pitre1
- INTRODUCTION
- RELATED WORKS
- Machine Learning Techniques
- Deep Learning Techniques
- Internet of Things
- Transfer Learning Techniques
- METHODOLOGY USED
- Survey
- Design and Creation
- VGG16
- Inceptionv3
- ResNet50
- MobileNET
- NASNetMobile
- Xception
- DATASET
- RESEARCH FINDINGS
- CONCLUSION
- ACKNOWLEDGEMENTS
- REFERENCES
- Automated Bird Species Identification using Audio Signal Processing and Neural Network
- Samruddhi Bhor1,*, Rutuja Ganage1, Hrushikesh Pathade1, Omkar Domb1 and Shilpa Khedkar1
- INTRODUCTION
- RELATED WORK
- BIRD CLASSIFICATION CHALLENGES
- MLSP 2013
- BirdCLEF 2016
- NIPS4B 2013
- PREVIOUS METHODOLOGIES
- MSE Approach
- Correlation Analysis
- Frequency Shift Correlation Analysis
- Shift in Frequency
- Symmetry-based Correlation Analysis
- MFCC Approach
- HMM-based Modelling of Bird Vocalisation Elements
- Segmentation and Estimation of Frequency Tracks
- BACKGROUND ON CONVOLUTIONAL NEURAL NETWORK
- Convolutional Layer
- Fully Connected Layer
- Dropout
- Dense Layer
- Activation Functions
- RelU
- Softmax Activation Function
- Categorical Cross Entropy
- Adam Optimizer
- Sequential Model
- ARCHITECTURE OF THE PROPOSED MODEL
- Dataset
- Preprocessing
- Feature Extraction
- Model Creation
- RESULTS
- CONCLUSION
- REFERENCES
- Powering User Interface Design of Tourism Recommendation System with AI and ML
- P.M. Shelke1, Suruchi Dedgaonkar1,* and R.N. Bhimanpallewar1
- INTRODUCTION
- THE EVOLUTION OF TRAVEL RECOMMENDER SYSTEMS
- The Collaborative Filtering (CF)
- The Content Based Filtering (CB)
- The Social Filtering (SF)
- Demographic Filtering (DE)
- Knowledge-based Filtering (KB)
- Utility-based (UB) Filtering
- Hybrid Recommendation (HR)
- CHALLENGES IN CURRENT TRS SYSTEM
- IMPORTANCE OF USER INTERFACE IN TRS
- HOW DO AI AND MACHINE LEARNING IMPROVE UX?
- Thin UI
- Task Automation
- Smart Systems
- Visual Effects
- Personalisation
- Choice Architecture
- Emotion Recognition
- Chatbots
- Recommendation Systems
- CASE STUDY
- Destination Recommendation System (DRS)
- Methodology
- UI/UX Implementation to Improve User Engagement
- AI/ML to Build the Recommendation System
- ChatBot
- Methodology
- Performance
- BENEFITS OF AI AND ML IN UX
- UI/UX AND AI/ML PRODUCTS
- UX Challenges for AI/ML Products
- Theme 1: Trust & Transparency
- Theme 2: User Feedback & Control
- Theme 3: Value Alignment
- Advancements by UI/UX and AI/ML Products
- CONCLUSION
- REFERENCES
- Exploring the Applications of Complex Adaptive Systems in the Real World: A Review
- Ajinkya Kunjir1,*
- INTRODUCTION
- BACKGROUND
- Emergence
- Adaptation
- Self-Organization
- Non-Linearity
- Aggregation
- Diversity
- CAS VS ABM
- Potential Applications of CAS
- Manufacturing and Assembly Systems
- Healthcare Organizations and Medical Service Delivery
- Conceptualizing CAS for Medical Service Delivery
- Military and Defense
- Distributed Systems (Peer-to-Peer)
- Internet of Things (IoT)
- TOOLS FOR CAS MODELLING
- Need for Visualization in CAS
- CONCLUSION
- REFERENCES
- Insights into Deep Learning and Non-Deep Learning Techniques for Code Clone Detection
- Ajinkya Kunjir1,*
- INTRODUCTION
- BACKGROUND
- Code Clones
- Existing Frameworks and Benchmarks for CCD Tools
- Target Functionality Selection
- Time Complexity
- COMPARATIVE STUDY OF CCD TECHNIQUES
- Text-based Techniques
- Token-based Techniques
- Tree-based Techniques
- Program Dependency Graph (PDG)
- Metrics-based Techniques
- CONCLUSION
- ACKNOWLEDGEMENTS
- REFERENCES
- Application Using Machine Learning to Predict Child's Health
- Saurabh Kolapate1,*, Tejal Jadhav1 and Nikhita Mangaonkar1
- INTRODUCTION
- SURVEY REPORT
- ALGORITHM
- Rule Based Algorithm
- Rules can be Accessed by Following Factors
- Properties of Rule-based Classifiers
- How to Create a Rule
- Features
- Disease Detection and Cure
- Vaccination Details
- Child Vaccination Reminder
- Daily Facts
- Daily Exercises
- BMI Calculator
- Healthy Tips
- SCREENSHOTS
- FUTURE SCOPE
- CONCLUSION
- REFERENCES
- Shifting from Red AI To Green AI
- Samruddhi Shetty1, Nirmala Joshi1,* and Abhijit Banubakode2,3
- INTRODUCTION
- METHODOLOGY
- Rationale
- Objective
- Hypothesis
- Hypothesis 1
- Hypothesis 2
- Hypothesis 3
- Conceptual Framework
- Artificial Intelligence AI-definition
- Types of Artificial Intelligence
- AI Adoption
- Red AI
- Green AI
- Sustainability SDGs Categories Bifurcation
- Sample Design
- SAMPLE RESULTS AND DISCUSSIONS
- FURTHER ANALYSIS
- CONCLUSION
- REFERENCES
- Knowledge Representation in Artificial Intelligence - A Practical Approach
- Vandana C. Bagal1,*, Archana L. Rane1, Debam Bhattacharya1, Abhijeet Banubakode2,3 and Vishwanath S. Mahalle3
- INTRODUCTION
- LITERATURE SURVEY
- INFERENCE RULE
- AI Knowledge Cycle
- Perception
- Learning
- Representation
- Reasoning
- Execution
- Connectives
- Methodology
- Rule 1
- Rule 2
- Rule 3
- Rule 4
- Rule 5
- Rule 6
- Rule 7
- KNOWLEDGE REPRESENTATION
- CONCLUSION
- REFERENCES
- File Content-based Malware Classification
- Mahendra Deore1,* and Chhaya S. Gosavi1
- INTRODUCTION
- Malware: A Threat to the Network
- MALWARE DETECTION
- MALWARE DATASET
- BLOCK DIAGRAM OF PROPOSED WORK
- MACHINE LEARNING
- Naive Bayes Classifier (NBC)
- Decision Tree
- Support Vector Machine (SVM)
- RESULTS
- CONCLUSION
- REFERENCES
- Enhancing Efficiency in Content-based Image Retrieval System Using Pre-trained Convolutional Neural Network Models
- Vishwanath S. Mahalle1,*, Narendra M. Kandoi1, Santosh B. Patil1, Abhijit Banubakode1,2 and Vandana C. Bagal3
- INTRODUCTION
- RELATED WORK
- PROPOSED CNN PRE-TRAINED MODEL FOR SIMILAR IMAGE RETRIEVAL
- Pre-processing
- Deep Features Extraction using ResNet Pre-trained CNN
- Similarity Calculation
- Full ResNet Architecture
- Loss Function
- EXPERIMENT
- Evaluation Matrics
- Datasets
- Experimental Configuration
- EXPERIMENTAL RESULTS
- CONCLUSION
- REFERENCES
- Role of Artificial Intelligence (AI) in Solid Waste Management: A Synopsis
- Pankaj Bhattacharjee1,* and Ashok B. More2
- INTRODUCTION
- PROBLEM STATEMENT
- Contribution of Artificial Intelligence (AI) and Machine Learning Algorithm in Solid Waste Management
- Intelligent Garbage Bins and Optimization of the Route for Transportation of Waste
- Sorting of Waste by the Internet of Things (IoT) and Machine Learning (ML)
- Implementation of Artificial Intelligence (AI) in Municipal Solid Waste Management (MSWM)
- Problem Identification
- Data Collection and Analysis
- Using Smart Waste Bins
- Route Optimisation
- Intelligent Sorting and Recycling
- Application of ML Algorithm in Municipal Solid Waste Management (MSWM)
- Machine Learning (ML) Algorithm Selection for Different Stages of Waste Management
- Types of Waste Classification Algorithms in Use
- Rule Based Algorithms
- Machine Learning Algorithms
- Deep Learning Algorithms
- Ensemble Methods
- Hybrid Approaches
- Algorithms for Landfill
- RELATED WORKS
- CONCLUSION
- REFERENCES
- Subject Index
- Back Cover
