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Publish a Book Chapter in "Advances in Computer Science and Intelligent Systems (Volume - 1)"

Call for Book Chapters: Submissions Now Open

978-1-804338-79-7

Computer Science Edited Book | Edited Book on Computer Science


This edited book on computer science  titled "Advances in Computer Science and Intelligent Systems" mainly focuses on various topics such as machine learning model development, deep learning architectures, natural language processing etc., and the rest are given below in the Scope of the book. This computer science edited book will be published with ISBN numbers after following a proper double blind peer reviewed process. All the chapters of this computer science edited book will be published in a proper style, so that reader can easily understand and learn.

Author can download this computer science edited book titled "Advances in Computer Science and Intelligent Systems" authorship responsibility and copyright form: Click Here

Indexed In


Indexed in Crossref Indexed in Dimensions Indexed in Bowker
Invited Topics

  1. Cloud-Native AI Systems and Scalable Training Infrastructure
  2. Distributed Training Techniques for Large-Scale Neural Models
  3. Intelligent Data Engineering: ETL, Feature Stores, and Governance
  4. Graph Databases and Knowledge Graphs for Enterprise Intelligence
  5. Semantic Search and Information Retrieval for Intelligent Systems
  6. Recommender Systems: Algorithms, Evaluation, and Personalization
  7. Conversational AI Design: Dialogue Management and Safety
  8. Speech Technologies: Recognition, Synthesis, and Speaker Modeling
  9. Computer Vision for Detection, Segmentation, and Tracking
  10. Vision Transformers and Next-Generation Visual Intelligence
  11. Reinforcement Learning for Control, Games, and Robotics
  12. Safe Reinforcement Learning for Real-World Decision Making
  13. Multi-Agent Reinforcement Learning and Cooperative Intelligence
  14. Swarm Intelligence Algorithms for Optimization and Routing
  15. Metaheuristics and Hybrid Optimization for Intelligent Computing
  16. Anomaly Detection in High-Volume Streaming Data
  17. Time-Series Forecasting with Deep Learning and Probabilistic Models
  18. Intelligent Systems for Predictive Maintenance and Reliability
  19. Digital Twins Powered by AI: Frameworks and Case Studies
  20. AI in Smart Cities: Mobility, Energy, and Public Services
  21. Intelligent Traffic Management Using Real-Time Data and AI
  22. Healthcare Intelligent Systems: Diagnosis, Risk Prediction, and Triage
  23. Medical Imaging Intelligence Using Deep Neural Networks
  24. Clinical Decision Support Systems with Explainable AI
  25. Financial Intelligence: Fraud Detection and Credit Risk Modeling
  26. Cybersecurity Intelligence: Threat Detection Using Machine Learning
  27. AI for Malware Analysis and Intrusion Detection Systems
  28. Blockchain-Enabled Intelligent Systems: Trust, Security, and Automation
  29. Intelligent Automation in Business Processes and RPA
  30. Human–AI Interaction: Usability, Trust, and Adoption
  31. Affective Computing and Emotion-Aware Intelligent Systems
  32. Intelligent Tutoring Systems and Adaptive Learning Platforms
  33. Educational Data Mining for Personalized Learning Insights
  34. AI for Agriculture: Crop Monitoring and Yield Prediction
  35. AI for Climate and Environmental Monitoring
  36. Green AI: Energy-Efficient Training and Sustainable Computing
  37. AutoML and Neural Architecture Search for Rapid Model Development
  38. Hyperparameter Optimization Methods for Better Model Performance
  39. Continual Learning and Lifelong Intelligent Systems
  40. Transfer Learning and Domain Adaptation in Real Applications
  41. Few-Shot and Zero-Shot Learning Techniques in Practice
  42. Self-Supervised Learning for Data-Scarce Environments
  43. Graph Neural Networks for Relational and Structured Data
  44. Causal Inference Methods for Robust AI Decision Making
  45. Probabilistic Graphical Models for Uncertainty-Aware Intelligence
  46. Bayesian Deep Learning for Reliable Predictions
  47. Program Synthesis and AI-Assisted Software Development
  48. AI for Software Testing, Debugging, and Quality Assurance
  49. Intelligent Code Completion and Developer Productivity Tools
  50. Intelligent Search and Ranking Systems for Digital Platforms
  51. Information Retrieval Evaluation Metrics and Benchmark Design
  52. Building Benchmarks for Realistic Intelligent System Evaluation
  53. Model Compression: Pruning, Quantization, and Distillation
  54. Efficient Inference: Accelerators, GPUs, and Specialized Hardware
  55. Neuromorphic Computing and Brain-Inspired Intelligent Systems
  56. Quantum Computing for Intelligent Systems: Opportunities and Limits
  57. Quantum Machine Learning Algorithms and Experimental Platforms
  58. Intelligent Robotics Software: Perception, Planning, and Control
  59. SLAM and Autonomous Navigation for Mobile Robots
  60. Vision-Based Robotics Grasping and Manipulation
  61. Safety and Verification of Autonomous Intelligent Systems
  62. Synthetic Data Generation for Training Intelligent Models
  63. Data Augmentation Strategies for Vision and Language Tasks
  64. Responsible Use of Generative AI in Content Creation
  65. Watermarking and Provenance for AI-Generated Media
  66. Detecting Deepfakes and Securing Digital Media Integrity
  67. Intelligent Systems in Supply Chain and Logistics Optimization
  68. Smart Manufacturing Analytics Using AI and IoT Data
  69. Knowledge Management Systems Powered by AI
  70. Enterprise AI Governance: Policies, Controls, and Audits
  71. Regulatory and Legal Considerations for AI Deployment
  72. Trustworthy AI: Reliability, Safety, and Human Oversight
  73. Designing AI Systems for Accessibility and Inclusion
  74. Multilingual NLP Systems for Global Applications
  75. Cross-Cultural Considerations in Conversational AI
  76. Real-Time Stream Processing for Intelligent Applications
  77. Event-Driven Architectures for AI-Enabled Microservices
  78. Building End-to-End Intelligent Systems: Case Study Patterns
  79. Future Trends in Intelligent Systems and Emerging Research Directions
  80. Roadmap for Building Scalable, Secure, and Responsible AI
Book Scope

  • Machine Learning Model Development
  • Deep Learning Architectures
  • Natural Language Processing
  • Computer Vision Applications
  • Reinforcement Learning
  • Generative AI Systems
  • Explainable AI Techniques
  • AI Ethics and Governance
  • Federated Learning
  • Edge AI Deployment
  • MLOps and Model Lifecycle
  • AI Model Monitoring
  • Data Engineering Pipelines
  • Big Data Analytics
  • Cloud Computing for AI
  • Distributed Systems Design
  • Cybersecurity Analytics
  • Secure AI Systems
  • Adversarial Machine Learning
  • Privacy-Preserving Computation
  • Blockchain and AI Integration
  • Knowledge Graphs
  • Semantic Web Technologies
  • Intelligent Recommender Systems
  • Human–AI Interaction
  • Multi-Agent Systems
  • Swarm Intelligence
  • Optimization Algorithms
  • Metaheuristics for AI
  • Time-Series Forecasting
  • Anomaly Detection
  • Predictive Maintenance Analytics
  • Intelligent Robotics Software
  • Autonomous Navigation Algorithms
  • IoT Data Intelligence
  • Smart City Intelligence
  • Healthcare AI Applications
  • FinTech AI Applications
  • Educational AI Systems
  • Intelligent Tutoring Systems
  • Speech Recognition Systems
  • Multimodal AI Models
  • Graph Neural Networks
  • AutoML Techniques
  • AI for Software Testing
  • Program Synthesis
  • Digital Twins in Computing
  • Quantum Machine Learning
  • Green AI and Efficiency
  • Benchmarking and Evaluation


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Deadline

31 Jan 2026