Search Results - computer programs testing data processing.

Table of Contents: “...Data imprecision /...”
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Table of Contents: “...Cover -- TOC36;Table of Contents -- Part I58; Homogeneous Partitioning of the Surveillance Volume -- CH36;Chapter 1 Introduction -- CH36;Chapter 2 A New Approach to Radar Detection Based on the Partitioning and Statistical Characterization of the Surveillance Volume -- CH36;Chapter 3 Statistical Analysis of the Nonhomogeneity Detector 40;for Excluding Nonhomogeneous Samples from a Subdivision41; -- CH36;Chapter 4 A New Technique for Univariate Distribution Approximation of Random Data -- CH36;Chapter 5 Probability Density Distribution Approximation and Goodness45;of45;Fit Tests of Random Data -- CH36;Chapter 6 Applications -- Part II58; Adaptive Antennas -- CH36;Chapter 7 Introduction -- CH36;Chapter 8 Adaptive Implementation of Optimum Space8211;Time Processing -- CH36;Chapter 9 A Printed45;Circuit Smart Antenna with Hemispherical Coverage for High Data45;Rate Wireless Systems -- CH36;Chapter 10 Applications -- Part III58; Adaptive Receivers -- CH36;Chapter 11 Introduction -- CH36;Chapter 12 Spherically Invariant Random Processes for Radar Clutter Modeling44; Simulation44; and Distribution Identification -- CH36;Chapter 13 Weak Signal Detection -- CH36;Chapter 14 A Generalization of Spherically Invariant Random Vectors 40;SIRVs41; with an Application to Reverberation Reduction in a Correlation Sonar -- CH36;Chapter 15 Applications -- Appendices -- Acronyms -- References -- Computer Programs available at CRC Press Website -- IDX36;Index -- Last Page....”
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Table of Contents: “...Before Implementing a Proper Planning and Scheduling Program --...”
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Table of Contents: “...Data storage --...”
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Table of Contents: “...Balancing keyed end-drive adapters -- Half-key method -- Balancing arbors -- Definition -- Basic design criteria -- Error analysis -- Statistical evaluation of errors -- Balancing the arbor -- Special design features -- Biasing an arbor -- The double compensator -- Unbalance correction methods -- Addition of mass -- Removal of mass -- Mass centering -- Testing balancing machines -- Tests for production machines -- Basic test concepts -- Inboard proving rotors for horizontal machines -- Test masses -- Test procedures -- Umar (or traverse) test -- Unbalance reduction test -- Balance tolerances -- Balance quality grades -- Special conditions to achieve quality grades G1 and G0.4 -- Applying tolerances to single-plane rotors -- Applying tolerances to two-plane rotors -- Experimental determination of tolerances -- Applying tolerances to rotor assembly components -- Testing a rotor for tolerance compliance -- Balance errors due to drive elements -- Balance errors due to rotor support elements -- Index-balancing procedure -- Recommended margins between balance and inspection tolerances -- Computer-aided balancing -- Features -- Prompting guides, storage, and retrieval -- Multiple machine control and programs -- Field balancing overview -- Field balancing equipment -- Field balancing examples -- First problem: Unbalance vibration in blowers -- Solution: Field balancing in one plane -- Second problem: Unbalance vibration in centrifuges -- Solution: Field balancing in two planes -- Explanation of schedule and of calculator program -- Third problem: Unbalance vibration in twisting and stranding machine -- Solution: Field balancing in several planes -- The vector diagram -- Appendix 6-A. ...”
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Table of Contents: “...Simulating the Self-Propulsion Test by a Coupled Viscous/Potential Flow Computation /...”
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Table of Contents: “...8.2 Methodology -- 8.3 AI-Based Predictive Modeling -- 8.3.1 Linear Regression -- 8.3.2 Random Forests -- 8.3.3 XGBoost -- 8.3.4 SVM -- 8.4 Performance Indices -- 8.4.1 Root Mean Squared Error (RMSE) -- 8.4.2 Mean Squared Error (MSE) -- 8.4.3 R2 (R-Squared) -- 8.5 Results and Discussion -- 8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase -- 8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase -- 8.5.3 K Cross Fold Validation -- 8.6 Conclusions -- References -- Chapter 9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery -- 9.1 Introduction -- 9.2 Literature Survey -- 9.3 Research Methodology -- 9.3.1 Dataset and Metrics -- 9.4 Result and Discussion -- 9.5 Conclusion -- References -- Chapter 10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA -- 10.1 Introduction -- 10.2 System Model -- 10.3 User Clustering -- 10.4 Optimal Power Allocation for EE-SE Tradeoff -- 10.4.1 Multiobjective Optimization Problem -- 10.4.2 Multiobjective PSO -- 10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA -- 10.5 Numerical Results -- 10.6 Conclusion -- References -- Chapter 11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews -- 11.1 Introduction -- 11.1.1 Related Work -- 11.2 Materials and Methods -- 11.2.1 Data Cleaning and Pre-Processing -- 11.2.2 Feature Extraction -- 11.2.3 Classifiers -- 11.3 Results and Experiments -- 11.4 Conclusion -- References -- Chapter 12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm -- 12.1 Introduction -- 12.2 Genetic Algorithm GA: An Evolutionary Computational Technique -- 12.3 Design of Multiobjective Optimization Problem....”
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Table of Contents: “...Optimizing Product Distribution Processes in Online Business Using Data-Driven Models --...”
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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: “...-- A Sociotechnical Approach to Clinical Informatics -- Hardware and Software Computing Infrastructure -- Clinical Content -- Human-Computer Interface -- People -- Workflow and Communication -- Internal Organizational Policies, Procedures, and Culture -- External Rules, Regulations, and Pressures -- System Measurement and Monitoring -- How to Use This Book... -- CATEGORY DEFINITIONS -- Academic Degree -- Anatomy -- Application Development -- Artificial Intelligence -- Body System -- Bone -- Chemistry -- Clinical Decision Making -- Clinical Decision Support -- Clinical Disorder -- Clinical Procedure -- Clinical Role -- Clinical Specialty -- Clinical Syndrome -- Communication -- Computational Algorithm -- Computer Application -- Computer Architecture -- Computer Hardware -- Computer Networking -- Computer Programming -- Computer Security -- Computer-Based Education -- Corporation -- Data Analysis -- Data Structure -- Data Type -- Data Visualization -- Data Warehousing -- Disease -- Electronic Health Record Function -- Evaluation -- Field of Study -- Genetics -- Government Funding -- Government Organization -- Health Insurance -- Healthcare Finance -- Hospital Department -- Human-Computer Interaction -- Identity Management -- Imaging -- Information Resource -- Information Retrieval -- Journal -- Law -- Logic -- Malware -- Management -- Mathematics -- Measurement -- Measurement Unit -- Medical Billing -- Medical Device -- Medical Facility -- Medication -- Natural Language Processing -- Network Security -- Organization -- Patient Safety -- People -- Probability Distribution -- Professional Organization -- Programming Language -- Quality Management -- Screening Test -- Standard -- Standards Organization -- Statistical Test -- Statistics -- Study Design -- Surgical Procedure -- System Implementation -- Terminology -- Theory -- Unified Medical Language System Vocabulary -- Workflow....”
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Table of Contents: “...Complexification of the past global changes data /...”