Artificial Intelligence, Machine Learning and User Interface Design

Name: Artificial Intelligence, Machine Learning and User Interface Design
Brand: Bentham Science Publishers
Price: 54.41 EUR
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Abhijit Banubakode Sunita Dhotre Chhaya S. Gosavi(Herausgeber*in)

Bentham Science Publishers

1. Auflage

Erschienen am 10. Mai 2024

300 Seiten

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978-981-5179-60-6 (ISBN)

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Inhalt

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

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