Artificial Intelligence, Machine Learning and User Interface Design is a forward-thinking compilation of reviews that explores the intersection of Artificial Intelligence (AI), Machine Learning (ML) and User Interface (UI) design. The book showcases recent advancements, emerging trends and the transformative impact of these technologies on digital experiences and technologies.
The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains.
Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing, code clone detection, knowledge representation, tourism recommendation systems and solid waste management.
Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.
The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains.
Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing, code clone detection, knowledge representation, tourism recommendation systems and solid waste management.
Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.
Readership
Computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.Table of Contents
Chapter 1 Artificial Taste Perception of Tea Beverage Using Machine- Learning
- Amruta Bajirao Patil and Mrinal Rahul Bachute
- 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
- Detailed Overview of the Concepts, Techniques, and Applications
- Ashish Tripathi, Rajnesh Singh, Arun Kumar Singh, Pragati Gupta, Siddharth Vats
- And Manoj Singhal
- 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 39
- 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
- Arun Kumar Singh, Ashish Tripathi, Sandeep Saxena, Pushpa Choudhary, Mahesh
- Kumar Singh and Arjun Singh
- 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
- Learning
- Krantee M. Jamdaade, Mrutunjay Biswal and Yash Niranjan Pitre
- 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
- Processing and Neural Network
- Samruddhi Bhor, Rutuja Ganage, Hrushikesh Pathade, Omkar Domb and Shilpa
- Khedkar
- 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
- Recommendation System With Ai and Ml
- P.M. Shelke, Suruchi Dedgaonkar and R.N. Bhimanpallewar
- 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
Author
- Abhijit Banubakode
- Sunita Dhotre
- Chhaya S. Gosavi
- G. S. Mate
