Applied Intelligence in Human-Computer Interaction

The text comprehensively discusses the fundamental aspects of human– computer interaction, and applications of artificial intelligence in diverse areas including disaster management, smart infrastructures, and healthcare. It employs a solution-based approach in which recent methods and algorithms are used for identifying solutions to real-life problems. This book • Discusses the application of artificial intelligence in the areas of user interface development, computing power analysis, and data management. • Uses recent methods/algorithms to present solution-based approaches to real-life problems in different sectors. • Showcases the applications of artificial intelligence and automation techniques to respond to disaster situations. • Covers important topics such as smart intelligence learning, interactive multimedia systems, and modern communication systems. • Highlights the importance of artificial intelligence for smart industrial automation and systems intelligence. The book elaborates on the application of artificial intelligence in user interface development, computing power analysis, and data management. It explores the use of human–computer interaction for intelligence signal and image processing techniques. The text covers important concepts such as modern communication systems, smart industrial automation, interactive multimedia systems, and machine learning interface for the internet of things. It will serve as an ideal text for senior undergraduates, and graduate students in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology. Cover Half Title Title Page Copyright Page Table of Contents Editors Contributors Chapter 1: Prediction of extreme rainfall events in Rajasthan 1.1 Introduction 1.1.1 The area of work 1.1.2 Problem addressed 1.1.3 Literature survey 1.1.4 Dataset creation 1.1.5 Feature importance 1.1.6 Random Forest Regressor 1.1.7 Multi-layer perceptron 1.1.8 Basic RNN 1.1.9 LSTM 1.1.10 Conclusion and future work Bibliography Chapter 2: Diagnostic model for wheat leaf rust disease using image segmentation 2.1 Introduction 2.1.1 Image segmentation 2.1.2 Properties of segmentation 2.1.3 Image segmentation process 2.1.4 Object detection, classification, and segmentation 2.1.4.1 Semantic and instance segmentation 2.2 Related work 2.3 Materials and methods 2.3.1 Dataset 2.3.2 Segmentation techniques 2.3.2.1 Pixel-based segmentation 2.3.2.2 Area-based segmentation 2.3.2.3 Edge-based segmentation 2.3.2.4 Physics-based segmentation 2.4 Proposed approach 2.5 Performance parameters 2.6 Experimental setup 2.7 Experimental results 2.8 Performance analysis 2.9 Conclusion References Chapter 3: A comparative study of traditional machine learning and deep learning approaches for plant leaf disease classification 3.1 Introduction 3.2 Materials and methods 3.2.1 Dataset used 3.2.2 Log-Gabor transform 3.2.3 Convolutional neural networks 3.2.4 Performance measures 3.2.4.1 Accuracy 3.2.4.2 Precision 3.2.4.3 Recall 3.2.4.4 F1 score 3.3 Experimental results and discussion 3.4 Conclusion and future work References Chapter 4: Application of artificial intelligence and automation techniques to health service improvements 4.1 Introduction 4.2 The advance of artificial intelligence and machine learning 4.2.1 Machine learning 4.2.2 Deep learning 4.2.3 Supervised learning 4.2.4 Unsupervised learning 4.2.5 Reinforcement learning 4.3 The use of AI in the healthcare decision-making process 4.4 The potential impact of AI on the healthcare workforce and clinical care 4.5 Creating a supportive environment for the use of AI in health systems 4.6 Organization of data 4.7 Trust and data management 4.8 Working with the technology industry 4.9 Accountability 4.10 Managing strategic transformation capability 4.11 AI-based biometric authentication 4.12 Results 4.12.1 Application of AI in diabetic detection using machine learning 4.12.2 Detection of glaucoma using machine learning 4.12.3 Deep learning for glaucoma detection 4.12.4 Deep learning techniques for glaucoma detection using OCT images 4.12.5 Conclusions References Chapter 5: Artificial intelligence in disaster prediction and risk reduction 5.1 Introduction 5.2 Discovering the un-predicting 5.2.1 Predicting shaking 5.2.2 AI in flood prediction 5.3 Disaster information processing using AI 5.3.1 Artificial intelligence in early warning system monitoring and disaster prediction 5.3.1.1 Example 5.3.2 Social media, artificial intelligence information extraction, and situational awareness 5.4 Public issues 5.5 Artificial intelligence in disaster management: A quick overview 5.6 Conclusion References Chapter 6: IoT-based improved mechanical design for elevator cart 6.1 Introduction 6.2 A closer look at the elevator 6.3 IoT and devices 6.4 The tech behind an elevator 6.5 Design of the new model 6.5.1 Temperature modulation and user interface 6.6 Cellphone reception 6.6.1 Current in-cart cellphone coverage models in elevators 6.6.2 Proposed solution to the problem of network coverage 6.7 Results and findings 6.8 Conclusion Acknowledgment References Chapter 7: Wearable IoT using MBANs 7.1 Introduction: background and driving forces 7.2 The IEEE 802.15.6 standard 7.3 Overview of the 802.15.6 standard 7.4 Channel communication modes for MBAN 7.5 Resource allocation 7.5.1 Slotted Aloha access 7.5.2 CSMA/CA access 7.6 Research and development for MBAN 7.7 Conclusions References Chapter 8: Simultaneous encryption and compression for securing large data transmission over a heterogeneous network 8.1 Introduction 8.1.1 Research scope 8.1.2 Research objectives 8.1.3 Organization of the paper 8.2 Literature review 8.2.1 Research gap analysis 8.3 Proposed technique 8.4 Assessment platform 8.4.1 Experiment setup 8.4.1.1 System requirement 8.4.1.2 Data preparation 8.4.2 Assessment parameters 8.4.2.1 Randomness analysis using the NIST 8.4.2.2 Throughput 8.4.2.3 Percentage of space saving 8.5 Result analysis 8.6 Conclusion and future work References Chapter 9: 2D network on chip 9.1 Introduction 9.2 Literature survey 9.3 Methodology 9.3.1 Router 9.3.2 Routing 9.3.3 Pseudo code 9.4 Result 9.5 Conclusion References Chapter 10: Artificial intelligence-based techniques for operations research and optimization 10.1 Introduction 10.2 Problem formulation 10.3 AI-based metaheuristics/optimization methods 10.3.1 Evolutionary algorithms 10.3.1.1 Genetic algorithms (GA) 10.3.1.2 Evolution strategies (ES) 10.3.1.3 Evolutionary programming (EP) 10.3.1.4 Differential evolution (DE) 10.3.2 Other nature-inspired algorithms 10.3.2.1 Particle swarm optimization 10.3.2.2 Ant colony optimization (ACO) 10.3.2.3 Cuckoo search 10.3.3 Artificial neural networks 10.3.3.1 Feed-forward network for objective function simplification 10.3.3.2 Hopfield networks for combinatorial optimization problems 10.4 Conclusion References Chapter 11: Heuristic strategies for warehouse location with store incompatibilities in supply chains 11.1 Introduction 11.2 Capacitated facility location with store incompatibilities 11.3 Heuristics based on Vogel’s Approximation Method (VAM) 11.3.1 Modified VAM heuristic (MVH1) for incompatible store pairs 11.3.2 Modified VAM heuristic (MVH2) using minimum product for incompatible pairs 11.3.3 Modified VAM heuristic (MVH3) using minimum supply costs 11.4 Greedy heuristics 11.4.1 Greedy Heuristic based on Supply Costs and Demand Product (GDH1) 11.4.2 Greedy heuristic based on supply costs-to-demand ratio (GDH2) 11.4.3 Randomized greedy heuristics 11.5 Experimental work 11.6 Conclusions References Chapter 12: Novel scheduling heuristics for a truck-and-drone parcel delivery problem 12.1 Introduction: Background and driving forces 12.2 Traveling salesman problem (TSP)-based truck-drone parcel delivery problems 12.3 Constrained single truck-and-drone parcel delivery problem (CSTDP) 12.4 Modified TSP heuristics for the CSTDP 12.4.1 Modified nearest neighbor (mNN) heuristic 12.4.2 Modified nearest insertion (mNI) heuristic 12.4.3 Modified minimum spanning tree (mMST) heuristic 12.5 Experimental work 12.6 Conclusions References Chapter 13: A reliable click-fraud detection system for the investigation of fraudulent publishers in online advertising 13.1 Introduction 13.2 Related work 13.3 Methods and materials 13.3.1 Dataset details 13.3.2 Data pre-processing 13.3.3 Feature engineering 13.3.4 Data re-sampling using data-level sampling strategies 13.3.4.1 SMOTE: synthetic minority oversampling technique 13.3.4.2 RUSBoost 13.3.5 Proposed feature selection approach: hybrid-manifold feature subset selection 13.4 Classification approaches 13.4.1 Gradient boosting machine 13.5 Validation using 10-fold cross-validation 13.6 Classification matrix and assessment methods for multi-class classification 13.7 Results and discussion 13.7.1 Impact of sampling 13.7.2 Impact of feature selection 13.7.3 Performance comparison 13.8 Conclusion Abbreviations References Chapter 14: Crypto-currency analytics and price prediction: A survey 14.1 Introduction 14.2 Bitcoin 14.3 Blockchain 14.4 Twitter 14.5 Major algorithms and ways to predict crypto-prices 14.5.1 Sentiment analysis 14.5.2 Naїve Bayes 14.5.3 Support vector machine 14.5.4 Decision tree 14.5.5 Bayesian regression 14.5.6 ARIMA 14.6 Advantages of different algorithms 14.7 Disadvantages of different algorithms 14.8 Technology or experience? 14.9 Accuracy 14.10 Ethics 14.11 Why data analysis 14.12 Conclusion References Chapter 15: Interactive remote control interface design for a cyber-physical system 15.1 Introduction 15.2 Related works 15.3 Research methodology 15.4 Results and discussion 15.5 Deep learning applications in CPS 15.6 Conclusion and future work Acknowledgment References Chapter 16: Collateral-based system for lending and renting of NFTs 16.1 Background 16.1.1 Blockchain [ 1 ] 16.1.2 How do we solve the problem of centralization? 16.1.3 Blockchain applications 16.1.4 Non-fungible tokens (NFTs) 16.1.5 Trends in the NFT market 16.2 Collateral-based system in traditional banks [ 6 ] 16.3 Overview 16.3.1 Problem statement 16.3.2 Proposed solution 16.4 NFT lifecycle 16.5 Contract overview 16.5.1 Getters 16.5.1.1 Get NFT details 16.5.1.2 Get NFT list available for rent 16.5.1.3 Get lend NFT details 16.5.1.4 Get rent NFT details 16.5.1.5 Get user lend NFT details 16.5.1.6 Get user rent NFT details 16.5.2 Algorithms 16.5.2.1 Add user 16.5.2.2 Lend NFT 16.5.2.3 Rent NFT 16.5.2.4 Stop lending 16.5.2.5 Claim collateral 16.5.2.6 Return NFT 16.6 Gas graphs 16.7 Revenue model 16.7.1 Lending charge 16.7.2 Late fee 16.8 Future scope 16.8.1 Credit score 16.8.2 Capped collateral 16.8.3 Buy recommendation 16.8.4 Rent bidding 16.9 Conclusion References Index