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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
ISBN978-1-804338-79-7

Invited Topics


  1. Foundations of Intelligent Systems and Modern AI Paradigms
  2. Data-Centric AI: Improving Intelligence Through Better Data
  3. Deep Neural Network Architectures for Real-World Applications
  4. Transformer Models and Their Impact on Intelligent Computing
  5. Natural Language Understanding for Domain-Specific Intelligence
  6. Large Language Models: Capabilities, Limitations, and Use Cases
  7. Retrieval-Augmented Generation for Reliable AI Systems
  8. Multimodal Learning: Combining Text, Vision, and Audio Intelligence
  9. Explainable AI Methods for Transparent Decision Making
  10. Interpretable Machine Learning in High-Stakes Domains
  11. Fairness and Bias Mitigation in Intelligent Systems
  12. AI Ethics, Accountability, and Responsible Deployment
  13. Privacy-Preserving Machine Learning with Federated Learning
  14. Differential Privacy Techniques for Data-Driven Intelligence
  15. Secure AI: Adversarial Attacks and Defensive Strategies
  16. Robustness Testing for Machine Learning Models in Production
  17. MLOps Pipelines: Automation from Training to Deployment
  18. Monitoring, Drift Detection, and Continual Model Improvement
  19. Edge AI Architectures for Low-Latency Intelligent Applications
  20. TinyML and Efficient Intelligence for Resource-Constrained Devices


For more topics: Click here
ISBN

ISBN: 978-1-804338-79-7

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


Author Guidelines

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Send your chapter on rubiconpublications@gmail.com



Deadline

31 Jan 2026