CISCom 2025 invites full-length original research contributions from science and engineering professionals from industries, R&D organizations, academic institutions, government departments, and research scholars worldwide. The manuscript should contribute original research ideas, developmental ideas, analysis, findings, results, etc. Authors are invited to submit full-length original research contributions and review articles between 7 to 15 pages in length, written in English, with a maximum length of single-spaced, single-column pages using 10pt size, including all figures, tables, and references. Please note that the minimum page length for the manuscript is 7 and maximum page length is 15. The manuscript should not have been published in any journals/magazines or conference proceedings and should not be under review in any of them.

CISCom 2025 is organized into two distinct themes, each targeting a specific aspect of intelligent systems.

Theme 1: Computational Intelligence for Automated Information Processing

This theme concentrates on leveraging advanced computational intelligence techniques to automate and optimize data processing and information retrieval. It emphasizes the development of adaptive, self-learning systems that mitigate human bias and enhance the efficiency of information systems. The subtopics include:

• Explainable AI (XAI) for Information Retrieval: Enhancing transparency and interpretability in automated information retrieval systems.
• Reinforcement Learning for Adaptive Information Systems: Utilizing reinforcement learning to create systems that dynamically adjust to changing data environments.
• Automated Image Analysis for Pattern Recognition: Applying intelligent image processing methods to detect and interpret complex visual patterns.
• Large Language Models (LLMs) for Automated Information Processing: Leveraging state-of-the-art language models to streamline and automate data handling.
• Quantum Computing for High-Dimensional Data Analysis: Exploring quantum-based methods to efficiently process and analyze large-scale, high-dimensional datasets.
• Semantic Search using NLP and Knowledge Graphs: Integrating natural language processing with semantic technologies to enhance search accuracy.
• Generative AI Content Generation & Curation: Harnessing generative models for automated content creation and intelligent curation.
• Deep Learning for Automated Feature Engineering: Implementing deep learning techniques to automate the extraction of meaningful features from data.

Theme 2: Soft Computing based Intelligent Solutions for a Smarter Society

This theme is designed to explore how soft computing methodologies can be applied to develop intelligent, real-world solutions that improve societal well-being. It highlights the adaptability and robustness of soft computing in handling uncertainty and complex challenges across various domains. The subtopics include:

• AI in Healthcare Information Systems and Decision Support: Developing intelligent systems to enhance healthcare delivery and decision-making processes.
• Intelligent Cybersecurity and Fraud Detection: Advancing soft computing techniques to bolster  cybersecurity measures and detect fraudulent activities.
• Fuzzy Logic-based Disease Diagnosis and Prognosis Systems: Utilizing fuzzy logic to improve  diagnostic accuracy and patient prognosis.
• Soft Computing for Precision Farming and Crop Yield Prediction: Applying adaptive algorithms to optimize agricultural practices and forecast crop outcomes.
• Bio-Inspired Algorithms for Privacy-Preserving: Innovating algorithms inspired by natural processes  to enhance data privacy.
• Hyperparameter Optimization for Robust Network Architectures: Refining network performance through advanced optimization strategies.
• Blockchain and Soft Computing Integration for Secure Transactions: Combining blockchain technology with soft computing to create secure and efficient transaction systems.

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