The International Conference on Artificial Intelligence Centric Systems, Services, and Applications

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Call for Papers 
 
Artificial Intelligence (AI) systems are computational frameworks designed to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. These systems integrate algorithms, data, and computing power to enable machines to adapt and improve their performance over time. AI systems encompass a wide range of technologies, including machine learning, natural language processing, computer vision, and autonomous robotics, and they are increasingly applied in domains such as healthcare, finance, cybersecurity, transportation, and smart cities. As they continue to evolve, AI systems are not only transforming industries and enhancing human capabilities but also raising important considerations around ethics, transparency, and trustworthiness. 
 
The International Conference on Artificial Intelligence Centric Systems, Services, and Applications (AISSA 2026) aims to bring together leading researchers, academics, and practitioners from around the world to share their latest findings, innovations, and applications in Artificial Intelligence and intelligent systems. AISSA 2026 provides a premier interdisciplinary platform for presenting advances and exchanging insights on the theoretical foundations, cutting-edge technologies, and real-world implementations of AI. AISSA 2026 will feature keynote talks, technical paper sessions, workshops, and tutorials covering a wide range of AI topics. We invite submissions of high-quality research papers describing original and unpublished results in all areas of Artificial Intelligence Systems. addresses the use of advanced intelligent systems in providing cybersecurity solutions in many fields, and the challenges, approaches, and future directions. We invite the submission of original papers on all topics related to Intelligent Systems with special interest in but not limited to: 
 
 Machine Learning and Data-Driven Systems 
 Deep Learning, Representation Learning, and Transfer Learning 
 Reinforcement Learning and Adaptive Systems 
 Federated, Distributed, and Edge AI 
 Reinforcement Learning and Autonomous Agents 
 Federated Learning and Distributed AI Systems 
 AI Applications and Intelligent Systems 
 Scalable and High-Performance AI Architectures 
 Edge AI, Cloud AI, and Embedded Intelligence 
 Optimization and Resource-Aware AI Systems 
 
 Natural Language and Multimodal AI 
 Natural Language Processing and Understanding 
 Speech Recognition and Conversational Agents 
 Multimodal Learning and Vision-Language Systems 
 Knowledge Representation and Reasoning 
 Data Mining, Knowledge Discovery, and Information Retrieval 
 Automated Planning and Scheduling 
 
 Generative AI, Foundation Models, and Large Language Models 
 Generative AI and Large Language Models (LLMs) 
 Explainable and Responsible AI 
 Federated, Distributed, and Edge AI 
 Reinforcement Learning and Autonomous Agents 
 AI for Decision Support Systems 
 Natural Language Processing and Computational Linguistics 
 Multilingual and Low-Resource Language Models 
 Human-Computer Interaction (HCI) and Assistive Technologies 
 Conversational AI and Dialogue Systems 
 Computational Language and Human-Centered Systems 
 Prompt Engineering and Fine-tuning 
 Multimodal LLMs 
 Agentic AI 
 
 Computer Vision, Image and Video Processing 
 Image and Video Understanding 
 Object Detection, Recognition, and Tracking 
 Multimodal Learning and Vision-Language Systems 
 3D Vision and Scene Understanding 
 Affective Computing and Emotion Recognition 
 
 Artificial Intelligence Applications 
 Robotics and Autonomous Systems 
 AI for Healthcare, Smart Cities, and Transportation 
 AI in Finance, Education, and Industry 4.0 
 AI for Cybersecurity, Privacy, and Digital Trust 
 AI Infrastructure and Engineering 
 Recommender Systems and Personalization 
 AI for Social Good and Humanitarian Applications 
 Emerging Trends and Future Directions in AI Systems 
 
 Artificial Intelligence Security 
 Responsible and Trustworthy AI 
 Explainable AI and Transparency 
 Ethical and Societal Implications of AI 
 AI Safety, Robustness, and Security 
 AI Policy, Regulation, and Governance