On Integrating Computation into the Built Environment (Hardcover, 1)

1.0. Introduction? 1.1. The Predisposed Model & Responsive Environments? 1.2. Machinic Life? 2.0. Cybernetics & the Predisposed Model? 2.1. Heuristics? 2.2. Ultrastability? 2.3. The Performative Idiom? 3.0. ALife & the Predisposed Model? 3.1. Distributed Cognition? 3.2. Database Amplification? 3.3. Curiosity? 4.0. Behavior Based Robotics & the Predisposed Model? 4.1. Fast, Cheap and Out of Control? 4.2. Reflex? 4.3. Emotion? 5.0. Dreaming? 6.0. Conclusion

On Integrating Computation into the Built Environment (Paperback, 1)

1.0. Introduction? 1.1. The Predisposed Model & Responsive Environments? 1.2. Machinic Life? 2.0. Cybernetics & the Predisposed Model? 2.1. Heuristics? 2.2. Ultrastability? 2.3. The Performative Idiom? 3.0. ALife & the Predisposed Model? 3.1. Distributed Cognition? 3.2. Database Amplification? 3.3. Curiosity? 4.0. Behavior Based Robotics & the Predisposed Model? 4.1. Fast, Cheap and Out of Control? 4.2. Reflex? 4.3. Emotion? 5.0. Dreaming? 6.0. Conclusion

Blockchain Frameworks for Scalable, Secure, and Regulated Ecosystems (Hardcover, 1)

Chapter 1: Consensus Mechanisms and Performance Challenges. Chapter 2: Greening the Chain: Sustainable Protocols and Energy-Efficient Architectures for Blockchain. Chapter 3: Green Blockchain Frameworks for Resource-Constrained IoT Systems. Chapter 4: Blockchain-Enabled Cyber-Physical Systems: Architectures, Applications, and Security in Industry 4.0. Chapter 5: When TinyML Meets Blockchain: Trustworthy Intelligence at the Edge. Chapter 6: Blockchain-Enabled Privacy and Trust in Federated Learning Architectures. Chapter 7: Towards Trustworthy Misinformation Detection: Integrating Blockchain, Federated Learning, and AI Watermark Verification. Chapter 8: AI-Driven Cognitive Cyber Defence for Secure Blockchain?Cloud?IoT Healthcare Ecosystems. Chapter 9: Blockchain for Trustworthy Driver Behavior Monitoring Systems: Current Advances, Challenges, and Future Directions. Chapter 10: Blockchain-Enabled Supply Chains: Enhancing Transparency, Efficiency, and Traceability in Logistics. Chapter 11: Blockchain and the Future of Sustainable Healthcare: A Road Ahead Toward Efficiency, Trust, and Equity. Chapter 12: Financial Disruption through Blockchain: Analyzing Cryptocurrencies and Decentralized Finance. Chapter 13: Cryptocurrency and Blockchain as Enablers of White-Collar Crimes in India.

Advances in Distributed Computing and Machine Learning: Proceedings of Icadcml 2026, Volume 2 (Paperback)

Cloud Computing : Concepts and Technologies (Hardcover, 2 ed)

Chapter 1 - Introduction: 1.1 Cloud Computing, 1.2 Service Delivery Models, 1.3 Characteristics and Benefits of Cloud Computing, 1.4 Cloud Computing Platforms and Technologies, Bibliography; Chapter 2 - Distributed and Parallel Computing: 2.1 Distributed Computing, 2.2 Properties of Distributed Systems, 2.3 Performance Consideration in Distributed Computing, 2.3.1 High-Level Architecture of Remoting Protocols, 2.3.2 Synchronous and Asynchronous SOAP Architecture, 2.4 Parallel Computing, 2.5 Performance Consideration in Parallel Computing, 2.6 Amdahl's Law, 2.7 Types of Parallelism, 2.8 Flynn's Classical Taxonomy, 2.9 Classes of Parallel Computers, Bibliography; Chapter 3 - Multicore Architectures: 3.1 Multicores in Cloud Computing, 3.1.1 Parallel Hardware, 3.1.2 Parallel Software, 3.2 Classes of Parallelism, 3.3 Limitations of Multicore Architectures, Bibliography; Chapter 4 - Virtualization: 4.1 Virtualization Technology, 4.2 Virtualization Platforms, 4.2.1 Xen Virtualization, 4.3 Virtualization Techniques, 4.4 Pros and Cons of Virtualization, Bibliography; Chapter 5 - Infrastructure-as-a-Service (IaaS) Delivery Model in Cloud: 5.1 IaaS in Cloud, 5.2 IaaS Benefits, 5.3 Management of VMs in IaaS, 5.4 IaaS Providers, 5.5 Keys to Successfully Moving to IaaS, 5.6 Challenges of IaaS, Bibliography; Chapter 6 - SaaS and PaaS in Cloud: 6.1 SaaS in Cloud, 6.2 SaaS Characteristics, 6.3 SaaS Implementation, 6.4 SaaS Examples, 6.5 Advantages and Disadvantages of SaaS, 6.6 PaaS in Cloud, 6.7 PaaS Characteristics, 6.8 PaaS Implementation, 6.9 PaaS Examples, 6.10 Advantages and Disadvantages of PaaS, Bibliography; Chapter 7 - Capacity Planning in Cloud: 7.1 Cloud Capacity Model, 7.2 Probabilistic Auto-scaling for Capacity Planning, 7.3 AI/ML for Predictive Capacity Planning in Cloud, 7.4 Implementation Architecture: The MAPE-K Loop, 7.5 Advantages of Capacity Planning, Bibliography; Chapter 8 - SLA Management in Cloud Computing: 8.1 Components of SLA, 8.2 Life Cycle of SLA,...


외국도서 > 컴퓨터 > 로직 설계
외국도서 > 컴퓨터 > 인공지능(AI)
외국도서 > 컴퓨터 > 정보통신기술(IT)
외국도서 > 컴퓨터 > 컴퓨터 일반
외국도서 > 기술공학 > 기술공학 > 모바일/무선통신
외국도서 > 컴퓨터 > 네트워킹 > 일반
외국도서 > 컴퓨터 > 시스템 구조 > 분산 시스템/컴퓨팅
외국도서 > 컴퓨터 > 인터넷 > 일반
외국도서 > 기술공학 > 기술공학 > 전력자원 > 전기 에너지

Cloud Computing : Concepts and Technologies (Paperback, 2 ed)

Chapter 1 - Introduction: 1.1 Cloud Computing, 1.2 Service Delivery Models, 1.3 Characteristics and Benefits of Cloud Computing, 1.4 Cloud Computing Platforms and Technologies, Bibliography; Chapter 2 - Distributed and Parallel Computing: 2.1 Distributed Computing, 2.2 Properties of Distributed Systems, 2.3 Performance Consideration in Distributed Computing, 2.3.1 High-Level Architecture of Remoting Protocols, 2.3.2 Synchronous and Asynchronous SOAP Architecture, 2.4 Parallel Computing, 2.5 Performance Consideration in Parallel Computing, 2.6 Amdahl's Law, 2.7 Types of Parallelism, 2.8 Flynn's Classical Taxonomy, 2.9 Classes of Parallel Computers, Bibliography; Chapter 3 - Multicore Architectures: 3.1 Multicores in Cloud Computing, 3.1.1 Parallel Hardware, 3.1.2 Parallel Software, 3.2 Classes of Parallelism, 3.3 Limitations of Multicore Architectures, Bibliography; Chapter 4 - Virtualization: 4.1 Virtualization Technology, 4.2 Virtualization Platforms, 4.2.1 Xen Virtualization, 4.3 Virtualization Techniques, 4.4 Pros and Cons of Virtualization, Bibliography; Chapter 5 - Infrastructure-as-a-Service (IaaS) Delivery Model in Cloud: 5.1 IaaS in Cloud, 5.2 IaaS Benefits, 5.3 Management of VMs in IaaS, 5.4 IaaS Providers, 5.5 Keys to Successfully Moving to IaaS, 5.6 Challenges of IaaS, Bibliography; Chapter 6 - SaaS and PaaS in Cloud: 6.1 SaaS in Cloud, 6.2 SaaS Characteristics, 6.3 SaaS Implementation, 6.4 SaaS Examples, 6.5 Advantages and Disadvantages of SaaS, 6.6 PaaS in Cloud, 6.7 PaaS Characteristics, 6.8 PaaS Implementation, 6.9 PaaS Examples, 6.10 Advantages and Disadvantages of PaaS, Bibliography; Chapter 7 - Capacity Planning in Cloud: 7.1 Cloud Capacity Model, 7.2 Probabilistic Auto-scaling for Capacity Planning, 7.3 AI/ML for Predictive Capacity Planning in Cloud, 7.4 Implementation Architecture: The MAPE-K Loop, 7.5 Advantages of Capacity Planning, Bibliography; Chapter 8 - SLA Management in Cloud Computing: 8.1 Components of SLA, 8.2 Life Cycle of SLA,...


외국도서 > 컴퓨터 > 로직 설계
외국도서 > 컴퓨터 > 인공지능(AI)
외국도서 > 컴퓨터 > 정보통신기술(IT)
외국도서 > 컴퓨터 > 컴퓨터 일반
외국도서 > 기술공학 > 기술공학 > 모바일/무선통신
외국도서 > 컴퓨터 > 네트워킹 > 일반
외국도서 > 컴퓨터 > 시스템 구조 > 분산 시스템/컴퓨팅
외국도서 > 컴퓨터 > 인터넷 > 일반
외국도서 > 기술공학 > 기술공학 > 전력자원 > 전기 에너지

Smart and Distributed Computing (Paperback, 1)

1.???? Impact of Environmental and Host Factors on Mustard Plant Diseases: A DenseNet-Based Deep Learning Framework for Early Detection of Alternaria Blight

2.???? Vision-Based Techniques for Structural Damage Assessment: Opportunities and Challenges

3.???? Edge AI and Computer Vision for Real Time Surgical Guidance

4.???? Agent AI Framework for Intelligent Talent Acquisition and Employee Retention in Smart Organizations

5.???? AI Powered Customer Behavior Prediction in E Commerce Using Hybrid Deep Learning and IoT Analytics

6.???? Agent-Based AI for Corporate Social Responsibility (CSR): Towards Data-Driven Sustainability

7.???? A Hybrid IoT?ML Framework for Forecasting, Anomaly Detection, and Economic Control in Urban Supply Chains

8.???? AI-Augmented Workforce Analytics: A Deep Learning Framework for Talent Optimization

9.???? Explainable AI Framework for Predicting Socio-Economic Indicators in Emerging Markets

10.? AI-Driven Adaptive Learning Systems Using Reinforcement Learning for Personalized Student Modeling and Instructional Adaptation

11.? Attention Enhanced Deep Learning Framework for Multi-Crop Disease Classification Using Real-Time UAV Imagery

12.? Intelligent Agent?Driven AI Framework for Talent Acquisition and Workforce Retention in Smart Organizations

13.? A 5G-Integrated IoT Framework for Real-Time Crop Disease Detection and Farmer Advisory Services

14.? Augmented Human Resource Analytics: A Deep Learning Approach for Talent Optimization

15.? Blockchain IoT Integration for Transparent and Secure Financial Transactions

16.? Hybrid Deep Learning and Bioelectrical Signal Analysis for Accurate Prediction of Kidney Function Anomalies

17.? IoT and AI Driven Smart Agriculture: Predictive Models for Crop Yield and Resource Efficiency

18.? Explainable AI-Driven Edge IoT System for Continuous Cardiovascular Health Monitoring

19.?...


외국도서 > 컴퓨터 > 영상처리
외국도서 > 컴퓨터 > 인공지능(AI)
외국도서 > 컴퓨터 > 자연 언어 처리(NPL)
외국도서 > 컴퓨터 > 전문가 시스템
외국도서 > 컴퓨터 > 컴퓨터 일반
외국도서 > 컴퓨터 > CAD-CAM
외국도서 > 기술공학 > 기술공학 > 공학일반
외국도서 > 컴퓨터 > 보안 > 일반
외국도서 > 컴퓨터 > 시스템 구조 > 분산 시스템/컴퓨팅
외국도서 > 컴퓨터 > 하드웨어 > 일반
외국도서 > 기술공학 > 기술공학 > 전력자원 > 전기 에너지

Smart and Distributed Computing (Hardcover, 1)

1.???? Impact of Environmental and Host Factors on Mustard Plant Diseases: A DenseNet-Based Deep Learning Framework for Early Detection of Alternaria Blight

2.???? Vision-Based Techniques for Structural Damage Assessment: Opportunities and Challenges

3.???? Edge AI and Computer Vision for Real Time Surgical Guidance

4.???? Agent AI Framework for Intelligent Talent Acquisition and Employee Retention in Smart Organizations

5.???? AI Powered Customer Behavior Prediction in E Commerce Using Hybrid Deep Learning and IoT Analytics

6.???? Agent-Based AI for Corporate Social Responsibility (CSR): Towards Data-Driven Sustainability

7.???? A Hybrid IoT?ML Framework for Forecasting, Anomaly Detection, and Economic Control in Urban Supply Chains

8.???? AI-Augmented Workforce Analytics: A Deep Learning Framework for Talent Optimization

9.???? Explainable AI Framework for Predicting Socio-Economic Indicators in Emerging Markets

10.? AI-Driven Adaptive Learning Systems Using Reinforcement Learning for Personalized Student Modeling and Instructional Adaptation

11.? Attention Enhanced Deep Learning Framework for Multi-Crop Disease Classification Using Real-Time UAV Imagery

12.? Intelligent Agent?Driven AI Framework for Talent Acquisition and Workforce Retention in Smart Organizations

13.? A 5G-Integrated IoT Framework for Real-Time Crop Disease Detection and Farmer Advisory Services

14.? Augmented Human Resource Analytics: A Deep Learning Approach for Talent Optimization

15.? Blockchain IoT Integration for Transparent and Secure Financial Transactions

16.? Hybrid Deep Learning and Bioelectrical Signal Analysis for Accurate Prediction of Kidney Function Anomalies

17.? IoT and AI Driven Smart Agriculture: Predictive Models for Crop Yield and Resource Efficiency

18.? Explainable AI-Driven Edge IoT System for Continuous Cardiovascular Health Monitoring

19.?...


외국도서 > 컴퓨터 > 영상처리
외국도서 > 컴퓨터 > 인공지능(AI)
외국도서 > 컴퓨터 > 자연 언어 처리(NPL)
외국도서 > 컴퓨터 > 전문가 시스템
외국도서 > 컴퓨터 > 컴퓨터 일반
외국도서 > 컴퓨터 > CAD-CAM
외국도서 > 기술공학 > 기술공학 > 공학일반
외국도서 > 컴퓨터 > 보안 > 일반
외국도서 > 컴퓨터 > 시스템 구조 > 분산 시스템/컴퓨팅
외국도서 > 컴퓨터 > 하드웨어 > 일반
외국도서 > 기술공학 > 기술공학 > 전력자원 > 전기 에너지

Sustainability and Reliability of Networks : Technology and Applications (Hardcover)

Chapter 1. Review of Software Reliability Growth Models. Chapter 2. Risk-Informed Planning for Maritime Network Reliability Under Operational Uncertainty. Chapter 3. Ply-Board Manufacturing Plant with Perfect Switch Over Device: Reliability and Availability Analysis. Chapter 4. Bayesian Availability Analysis of Repairable k-out-of-n Systems Using Weighted Exponential Lindley Model. Chapter 5. Reliability Assessment of Cloud Security System Using Markov Model. Chapter 6. Multistate Network Reliability: An Overview. Chapter 7. Maximum Reliability Travelling Salesman Tour in a Stochastic Network. Chapter 8. Improvement the Agro-Food Supply Chain by Asset Management. Chapter 9. Multi-Objective Risk-Based Optimization of Single-Story Steel Frames: Balancing Lifecycle Costs and Reliability for Sustainable Industrial Buildings. Chapter 10. Evaluating k-out-of-(p+w+c) system under Sequential standby policy via. Lz-Transform Technique. Chapter 11. Reliability and Availability analysis of 4×4 Shuffle Exchange Network using Lz transform. Chapter 12. Mathematical Modelling and Reliability Attributes of Feeding System in Sugarcane Industry. Chapter 13. AI-Driven UAV Solutions for Sustainable and Reliable Infrastructure Monitoring in Industry 5.0. Chapter 14. Sustainable Availability and MTTF Estimation of Machine Repair Systems under Fuzzy Conditions. Chapter 15. Reliability Analysis of an Off-Grid Fire Safety System. Chapter 16 Availability and Reliability Analysis of An Interchangeable 3-Unit Series-Parallel System

Distributed Computer and Communication Networks: 28th International Conference, Dccn 2025, Moscow, Russia, September 22-26, 2025, Revised Selected Pap (Paperback)

Scheduling Tasks in Distributed Cloud and Edge Computing Systems with Evolutionary Optimizers (Hardcover)

Nonlinear Analysis for Human Movement Variability (Hardcover, 2 ed)

1. Introduction. 2.? Time Series. 3.? State Space Reconstruction. 4.? Lyapunov Exponent. 5.? An Introduction to Recurrence Quantification Analysis for Movement Scientists. 6.? Surrogation. 7.? Autocorrelation Function, Mutual Information, and Correlation Dimension. 8.? Movement is Rough and Irregular: An Introduction to Fractal Analysis. 9.? Multifractal analysis: A window on the nonlinear release/constraint of degrees of freedom. 10.? Multiscale probability density function analysis. 11.? Fractal Methods of Association. 12.? Measuring Complexity: A Guided Tour of Information Focused Univariate Time Series Methods. 13.? Time-Frequency Analysis of Human Movement. 14.? Symbolic Regression: A Tool for Discovering Equations of Human Movement Dynamics.?

Programming Massively Parallel Processors: A Hands-On Approach (Paperback, 5)

1. Introduction

Part I. Fundamental Concepts
2. Heterogeneous data parallel computing
3. Multidimensional grids and data
4. Compute architecture and scheduling
5. Memory architecture and data locality
6. Performance considerations

Part II. Parallel Patterns
7. Convolution
8. Stencil
9. Parallel histogram
10. Reduction
11. Prefix sum (scan)
12. Merge

Part III. Advanced Patterns and Applications
13. Sorting
14. Filtering (new)
15. Sparse matrix computation
16. Wavefront Algorithms (new)
17. Graph traversal
18. Deep learning
19. Multi-GPU API (new)
20. Electrostatic potential map
21. Parallel programming and computational thinking

Part IV. Advanced Practices
22. Programming a heterogeneous computing cluster
23. Advanced Optimizations for Matrix Multiplication (new)
24. Advanced practices and future evolution
25. Conclusion and outlook