Editorial to the Inaugural Issue of Journal of Visual Artificial Intelligence

The rapid advancement of Artificial Intelligence (AI) has transformed the field of visual computing, enabling intelligent systems to perceive, interpret, and generate visual information with unprecedented accuracy. [1] Recent developments in computer vision, deep learning, multimodal AI, and generative models have accelerated innovations across diverse application domains, including healthcare, autonomous systems, robotics, smart manufacturing, digital twins, and human-computer interaction. [2–4] By integrating visual intelligence with edge computing, large language models, and real-time data analytics, next-generation AI systems can deliver adaptive, efficient, and context-aware solutions for complex real-world challenges. [5]

Journal of Visual Artificial Intelligence (https://gr-journals.com/journals/vai) (accessed 30 June 2026) is an open-access, quarterly, peer-reviewed journal that publishes high-quality research articles focused on the latest advances and innovations at the interplay of computer vision, visual computing and artificial intelligence. The journal serves as a platform for high-quality research on how intelligent visual systems perceive, process, interpret, and interact with the world, spanning both fundamental theory and mission-critical applications. The journal focuses on the core methodologies of AI, as well as on next-generation visual technologies. It has a well-rounded but integrated range of topics, such as image and video understanding, pattern recognition, multimodal learning, generative visual models, autonomous system perception, and human-AI visual interaction. It also features new areas of explainable visual AI, embodied visual intelligence and ethics-motivated system design.

This journal welcomes a variety of article types, including original research papers, comprehensive reviews, and impactful case studies and surveys offering a dynamic space for scholarly exchange and professional insights. This first issue (June 2026) brings together a diverse collection of high-quality research articles. Satheeshkumar R et al. presents a comprehensive analysis of recent advancements in intelligent and cyber-resilient Load Frequency Control (LFC) for interconnected power systems. By synthesizing 124 studies published between 2015 and 2026, it highlights emerging trends in AI-driven control, cyber-attack resilience, distributed architectures, and secure communication for next-generation smart grids. [6] Baenova et al. present an intelligent SQL learning support system that integrates an educational ER schema, validator-based error typing, L1-L6 markup, explainable CART diagnostics, and a pilot multidimensional item-response modeling layer. By combining automated error classification, adaptive feedback, and interpretable machine learning, the proposed framework provides personalized guidance that significantly improves students' learning outcomes, achieving a 17.8 percentage-point gain compared with 3.8 percentage points under conventional instruction. The study highlights the potential of explainable AI-driven intelligent tutoring systems for personalized SQL learning. [7] Kunjumon et al. introduce a Hidden Markov Model (HMM)-based Drift Tracking (HDT) framework that formulates concept drift detection as a probabilistic latent-state inference problem instead of conventional threshold-based binary decisions. [8] Ansari et al. present an offline edge AI framework for real-time exercise posture correction and repetition tracking using computer vision. The proposed system combines MediaPipe BlazePose, a Finite State Machine (FSM), and a locally deployed Meta Llama 3 large language model to perform bio-mechanically validated posture analysis, accurate repetition counting, and personalized workout feedback while preserving user privacy. By processing all data locally, the framework eliminates cloud dependency, achieves processing latency below 45 ms, and attains 85% repetition-counting accuracy, demonstrating an efficient, privacy-preserving solution for intelligent fitness monitoring. [9]

As a newly launched journal in this transformative era of Artificial Intelligence (AI) and Machine Learning (ML), we are dedicated to providing a dynamic platform for sharing groundbreaking ideas and developments. We are committed to upholding the rigorous and efficient peer-review process ensuring that every published work meets the highest standards.

On behalf of the Editorial Office, we extend a heartfelt welcome to all our readers, authors, and reviewers. Your participation and engagement are critical to the success of this journal. We encourage you to contribute your work, share your insights, and help us shape this journal into a leading forum for innovation and discovery.

There is no conflict of interest.

The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement, grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical content, experimental implementation, results, and interpretations were independently developed and verified by the authors.

1. [01]   A. B. Rashid, M. A. K. Kausik, AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications, Hybrid Advances, 2024, 7, 100277, doi: 10.1016/j.hybadv.2024.100277.
2. [02]   N. Veeramani, P. Jayaraman, A promising AI based super resolution image reconstruction technique for early diagnosis of skin cancer, Scientific Reports, 2025, 15, 5084, doi: 10.1038/s41598-025-89693-8.
3. [03]   L. M. Ungureanu, I. S. Munteanu, Advanced Autonomous Systems and the Artificial Intelligence Stage, Technologies, 2026, 14, 9, doi: 10.3390/technologies14010009.
4. [04]   S. H. Hong, Y. Wang, Artificial Intelligence and Its Applications in Robotics, Applied Sciences, 2025, 15, 7907, doi: 10.3390/app15147907.
5. [05]   A. Rahman, G. C. Chung, Y. H. Ng, Applications, challenges, and future trends of artificial intelligence of things (AIoT)-enabled water quality and resource management, Water, 2026, 18, 919, doi: 10.3390/w18080919.
6. [06]   Satheeshkumar R, Jagatheesan K, Lenin V R, K. Naidu, Anand B, Cyber-resilient load frequency control in modern interconnected power systems: A systematic literature review of intelligent and secure control strategies, Journal of Visual Artificial Intelligence, 2026, 1, 26104, doi: 10.64189/vai.26104.
7. [07]   G. Baenova, B. Zharlykassov, K. Maulenov, A. Syzdykova, Building a Knowledge Verification System Using AI, Journal of Visual Artificial Intelligence, 2026, 1, 26101, doi: 10.64189/vai.26101.
8. [08]   K. Kunjumon, A. M. Paul, M. K Joby, Hemanth KS, B. Misra, A hidden markov framework for concept drift detection and classification, Journal of Visual Artificial Intelligence, 2026, 1, 26102, doi: 10.64189/vai.26102.
9. [09]   F. A. Ansari, H. Siddique, Z. Shaikh, Z. Siddiqui, Biomechanical posture analysis system using computer vision: an edge-computing architecture integrating finite state machines and large language models, Journal of Visual Artificial Intelligence, 2026, 1, 26103, doi: 10.64189/vai.26103.

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