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Table of Contents: “...Front Cover -- Deep Learning in Personalized Healthcare and Decision Support -- Deep Learning in Personalized Healthcare and Decision Support -- Copyright -- Contents -- Contributors -- Preface -- Acknowledgments -- 1 -- The future of health diagnosis and treatment: an exploration of deep learning frameworks and innovative applica ... -- 1. ...”
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Table of Contents: “...-Obtaining Difference Equations for Glucose Prediction by Structured Grammatical Evolution and sparse identification -- Model-Based System Design, Verification and Simulation -- Modeling Approaches for Cyber Attacks on Energy Infrastructure -- Simulation setup for a closed-loop regulation of neuro-muscular blockade -- Textile In The Loop as Automated Verification Tool for Smart Textiles Applications -- Orchestrating Digital Twins for Distributed Manufacturing Execution Systems -- Automata with Bounded Repetition in RE2 -- Integrating OSLC Services into Eclipse -- Developing an Application in the Forest for New Tourism Post COVID-19 -- GPU-Accelerated Synthesis of Probabilistic Programs -- Static Deadlock Detection in Low-Level C Code -- Applications of Signal Processing Technology -- 3D Ultrasound Fingertip Tracking -- An Artificial Skin from Conductive Rubber -- Neural Network Based Single-Carrier Frequency Domain Equalization -- Smooth Step Detection -- Optical Preprocessing and Digital Signal Processing for the Measurement of Strain in Thin Specimen -- Lower Limbs Gesture Recognition Approach to Control a Medical Treatment Bed -- Artificial Intelligence and Data Mining for Intelligent Transportation Systems and Smart Mobility -- JKU-ITS Automobile for Research on Autonomous Vehicles -- Development of a ROS-based Architecture for Intelligent Autonomous on Demand Last Mile Delivery -- Contrastive Learning for Simulation-to-Real Domain Adaptation of LiDAR data -- Deep Learning Data Association Applied to Multi-Object Tracking Systems -- A Methodology to Consider Explicitly Emissions in Dynamic User Equilibrium Assignment -- Sensitivity Analysis for A Cooperative Adaptive Cruise Control Car Following Model: Preliminary Findings -- On Smart Mobility and Data Stream Mining -- Smart Vehicle Inspection -- Computer Vision, Machine Learning for Image Analysis and Applications -- Impact of the Region of Analysis on the Performance of the Automatic Epiretinal Membrane Segmentation in OCT Images -- Performance Analysis of GAN approaches in the Portable Chest X-ray synthetic image generation for COVID-19 screening -- Clinical Decision Support tool for the Identification of Pathological Structures Associated with Age-related Macular Degeneration -- Deep Features-based approaches for Phytoplankton Classification in Microscopy Images -- Robust Deep Learning-based Approach for Retinal layer Segmentation in Optical Coherence Tomography Images -- Impact of increased centerline weight on the Joint segmentation and classification of arteries and veins in color fundus images -- Rating the Severity of Diabetic Retinopathy on a Highly Imbalanced Dataset -- Gait Recognition using 3D View-Transformation Model -- Segmentation and Multi-Facet Classification of Individual Logs in Wooden Piles -- Drone Detection Using Deep Learning: A Benchmark Study -- Computer and Systems Based Methods and Electronic Technologies in Medicine -- Continuous Time Normalized Signal Trains for a Better Classification of Myoelectric Signals -- A Comparison of Covariate Shift Detection Methods on Medical Datasets -- Towards a Method to Provide Tactile Feedback in Minimally Invasive Robotic Surgery -- Reference Datasets for Analysis of Traditional Japanese and German Martial Arts -- A Novel Approach to Continuous Heart Rhythm Monitoring for Arrhythmia Detection -- Indoor Positioning Framework for Training Rescue Operations Procedures at the Site of a Mass Incident or Disaster -- Designing sightseeing support system in Oku-Nikko using BLE beacon -- Systems in Industrial Robotics, Automation and IoT -- Mixed Reality HMI for Collaborative Robots -- A Digital Twin Demonstrator for Research and Teaching in Universities -- Robot System as a Testbed for AI Optimizations -- An Architecture for Deploying Reinforcement Learning in Industrial Environments -- Ck-continuous Spline Approximation with TensorFlow Gradient Descent Optimizers -- Stepwise Sample Generation -- Optimising Manufacturing Process with Bayesian Learning and Knowledge Graphs -- Representing Technical Standards as Knowledge Graph to Guide the Design of Industrial Systems -- Improvements for mlrose Applied to the Traveling Salesperson Problem -- Survey on Radar Odometry -- Systems Thinking. ...”
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Table of Contents: “...Computer Media Wiping /...”
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Table of Contents: “...Personal information managementImplications of digital hoarding; Our research; Implications of digital hoarding behaviours; Strategies for digital decluttering; Directions for future work; References; 6 -- A review of security awareness approaches: towards achieving communal awareness; Introduction; Designing an effective approach to increasing security awareness; Program content and delivery method; Underlying theory; Methodology; Search process; Search terms; Findings and discussions; Overview of theories used...”
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Table of Contents: “...Machine Unlearning 26 5.2 The Importance of Adaptability in AI 27 5.3 Strategies for Implementing Machine Unlearning 27 5.3.1 Regularization techniques 28 5.3.2 Dynamic memory allocation 28 5.3.3 Memory networks and attention mechanisms 28 5.3.4 Incremental learning and lifelong adaptation 28 5.4 Applications of Machine Unlearning 28 5.4.1 Copyright compliance 29 5.4.2 Personalized recommendations and content delivery 29 5.4.3 Healthcare and medical diagnosis 30 5.4.4 Autonomous vehicles and robotics 30 5.4.5 Ethical considerations and bias mitigation 30 5.5 Ethical Implications and Considerations 30 5.5.1 Transparency and accountability 30 5.5.2 Privacy and data retention 30 5.5.3 Unintended consequences 31 5.5.4 Bias amplification 31 5.6 The Road Ahead: Challenges and Future Directions 31 5.6.1 Developing effective algorithms 31 5.6.2 Granularity and context 31 5.6.3 Dynamic and contextual adaptability 31 5.6.4 Ethical frameworks 31 5.7 The Future 32 6 Programming Languages Used in AI Development 33 6.1 Python: The Lingua Franca of AI 34 6.1.1 Natural language processing (NLP) with Python and NLTK 34 6.1.2 Computer vision with OpenCV and Python 35 6.1.3 Machine learning classification with scikit-learn 35 6.1.4 Reinforcement learning with OpenAI Gym and Python 36 6.2 Java: Scalability and Performance 37 6.3 R: Statistical Computing for AI Research 37 6.4 TensorFlow (JavaScript): Bringing AI to the Browser 38 7 Unraveling the Challenges: Navigating the Barriers to Generative AI Success 41 7.1 Data Quality and Quantity: The Cornerstone Challenge 42 7.2 Computational Power: The Hunger for Resources 43 7.3 Explainability and Interpretability: Deciphering the Black Box 43 7.4 Ethical Concerns: Navigating the Moral Landscape 44 7.5 Adversarial Attacks: Testing the Robustness 44 7.6 Transferability and Generalization: Beyond Training Data 44 7.7 Legal and Regulatory Challenges: Navigating the Legal Landscape 45 8 Exploring the Challenges and Progress in AI Alignment 47 8.1 Understanding AI Alignment 48 8.1.1 The alignment problem 48 8.1.2 Types of AI alignment 49 8.2 Challenges in AI Alignment 49 8.2.1 Ambiguity in human values 49 8.2.2 Value drift 49 8.2.3 Scalability 49 8.2.4 Adversarial manipulation 50 8.3 Approaches to AI Alignment 50 8.3.1 Value learning 50 8.3.2 Inverse reinforcement learning 50 8.3.3 Cooperative inverse reinforcement learning 50 8.3.4 Formal verification 50 8.4 Progress in AI Alignment 51 8.4.1 Research initiatives 51 8.4.2 Collaborative efforts 51 8.4.3 Ethical guidelines 51 8.4.4 Public awareness and engagement 51 8.5 Future Directions and Considerations 51 8.5.1 Continued research and innovation 52 8.5.2 Ethical governance 52 8.5.3 Human⁰́₃AI collaboration 52 8.5.4 Education and awareness 52 9 Creating AI Models: From Data to Deployment 53 9.1 Building AI Models 53 9.1.1 Step 1: Data collection and preprocessing 53 9.1.2 Step 2: Model selection and architecture design 54 9.1.3 Step 3: Model training 55 9.1.4 Step 4: Model evaluation and tuning 56 9.1.5 Step 5: Model deployment and integration 56 9.1.6 Ethical considerations 57 9.2 Putting It All Together: An End-to-End Example 58 9.2.1 Step 1: Data collection and preprocessing 58 9.2.2 Step 2: Model selection and architecture design 58 9.2.3 Step 3: Model training 58 9.2.4 Step 4: Model evaluation and tuning 58 9.2.5 Step 5: Model deployment and integration 59 10 Large Language Models as Data Compression Engines 61 10.1 Data Compression Fundamentals 62 10.1.1 Information theory principles 62 10.1.2 Traditional data compression techniques 62 10.2 Large Language Models Unveiled (Figure 10.1) 62 10.2.1 Neural networks and transformers 62 10.2.2 Pre-training and fine-tuning 63 10.3 LLMs as Data Compressors (Figure 10.2) 63 10.3.1 Pattern extraction 63 10.3.2 Semantic encoding 64 10.3.3 Contextual analysis 64 10.3.4 Data compression efficiency 64 10.3.5 Efficient parameterization 64 10.3.6 Adaptive compression 65 10.3.7 Contextual optimization 65 10.3.8 Comparative efficiency 65 10.4 The LLM as an Information-theoretic Compressor (Figure 10.3) 66 10.4.1 Entropy and information gain 66 10.4.2 Compression ratios and efficiency 66 10.5 Applications and Implications 66 10.5.1 Real-world applications 66 10.5.2 Ethical considerations 66 10.6 Future Directions and Challenges 67 Part: II AI Applications 69 11 Can We Stop Robots from Replacing Humans 71 11.1 How Humans Can Secure Their Jobs in the Age of Advancing AI 73 11.2 ⁰́₋Self-Replicating Robots⁰́₊ 75 11.3 ⁰́₋Kill Switch⁰́₊ 76 12 Green Artificial Intelligence 81 12.1 Factors Contributing to Carbon Emissions 82 12.2 Mitigation Strategies for a Greener AI Future (Figure 12.1) 82 13 Artificial Intelligence and Natural Disasters 85 13.1 Understanding Natural Disasters 85 13.2 The Need for Prevention 86 13.3 AI in Disaster Prevention (Figure 13.1) 86 13.3.1 Early warning systems 87 13.3.2 Seismic activity prediction 87 13.3.3 Forest fire prevention 87 13.3.4 Flood prediction and management 87 13.3.5 Landslide detection 87 13.3.6 Climate change mitigation 88 13.3.7 Disaster response coordination 88 13.4 Challenges and Ethical Considerations 88 13.4.1 Data privacy and security 88 13.4.2 Bias in AI 89 13.4.3 Accessibility and equity 89 13.4.4 Accountability and decision-making 90 13.4.5 Overreliance on technology 90 13.4.6 Infrastructure and resource constraints 90 14 AI and Drones 93 14.1 Types of Drones 93 14.2 Key Components 94 14.3 The Convergence of AI and Drones 95 14.3.1 Benefits of combining AI and drones 95 14.3.2 Hardware and software integration 95 14.3.3 Real-time data processing 95 14.4 Applications of AI-Powered Drones 96 14.4.1 Agriculture and precision farming 96 14.4.2 Surveillance and security 96 14.4.3 Logistics and delivery 96 14.4.4 Disaster management and search and rescue 96 14.4.5 Environmental monitoring 96 14.5 Challenges and Ethical Considerations 97 14.5.1 Privacy concerns 97 14.5.2 Regulatory and legal challenges 97 14.5.3 Safety and security 97 14.5.4 Ethical use of AI in drones 97 14.6 Future Prospects 97 14.6.1 Advancements in AI and drone technology 97 14.6.2 Potential industry disruption 98 14.6.3 Ethical and regulatory frameworks 98 14.7 Last Word! ...”
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Table of Contents: “...Nethercote -- 20.1 Drivers for Change -- 20.2 Control of Change in the Pharmaceutical Industry -- 20.3 Implementing a Change -- References -- 21 Monitoring of Analytical Performance 487 Joachim Ermer -- 21.1 Sources of Performance Data and Information -- 21.1.1 System Suitability Test Parameters -- 21.1.2 Parameter from Calibration or Reference Standard Analysis -- 21.1.3 Parameter from Sample Analysis -- 21.1.4 Quality Control Samples (QCS) -- 21.1.5 Batch Results -- 21.1.6 Precisions from Stability -- 21.2 Systematic Monitoring Program -- 21.3 Analytical Performance Evaluation Tools -- 21.3.1 Visualization Charts -- 21.3.2 Average Performance Parameters -- 21.4 Assessment of Analytical Performance -- 21.4.1 Immediate Actions -- 21.4.2 Analytical Performance Review (APR) -- 21.5 Conclusion -- References -- Index....”
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