Aims and Scope

AVE Trends in Intelligent Informatics Reports (ATIIR) is an open access, interdisciplinary journal committed to publishing high-quality research on intelligent systems and the global impact of digital technologies. The journal serves as a multidisciplinary platform that connects researchers, academics, and practitioners, fostering the exchange of innovative ideas across emerging areas of intelligent informatics. ATIIR welcomes theoretical, methodological, and applied contributions spanning artificial intelligence, robotics, computer science, cognitive science, intelligent control systems, Internet of Things (IoT), software engineering, computational intelligence, reinforcement learning, and information technology. A distinctive focus of the journal is on the societal dimensions of digital transformation and the role of intelligent technologies in shaping health, economic, social, and political systems. As a companion journal inspired by the scholarly direction of Telematics and Informatics Reports, ATIIR encourages research in domains such as health informatics, information security and privacy, blockchain technologies, mobile and wireless communication, digital media and culture, psychosocial impacts of social media, network public opinion, digital platforms, smart cities and society, e-governance, and knowledge management. Through rigorous peer review and global accessibility, ATIIR aims to disseminate impactful research that advances innovation, informs policy, and supports sustainable digital development.

Topics covered include:

• Artificial Intelligence, robotics, and IoT technologies
• Knowledge Discovery, Data Science, and Big Data Analytics
• Bioinformatics, Computational Biology, Healthcare Informatics
• Knowledge, cognitive computing, and human–machine interaction
• Computer Vision, Image, Signal, and Intelligent Sensing
• Computing, Fuzzy Systems, and Swarm Intelligence
• Adaptive Control, Intelligent Automation, and Control Systems
• Decision Support and Intelligent Expert Systems
• Architectures for Deep, Machine, and Neural Network Learning
• Cloud computing, distributed systems, and multi-agent technologies
• Sustainable computing, smart energy, and environmental monitoring
• Evolutionary Computing, Genetic Algorithms, and Optimisation
• Real-Time Intelligent Systems and Reinforcement Learning
• Intelligent Software Engineering, Smart Cities, Urban Informatics