| Journal of Visual Artificial Intelligence

Published Online: 30 June 2026.

J. Vis. Artif. Intell., 2026, 1(1), 26105 | Volume 1 Issue 1 (June 2026) | DOI: https://doi.org/10.64189/vai.26105

This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)

Editorial to the Inaugural Issue of Journal of Visual

Artificial Intelligence

Nilanjan Dey

Department of Computer Science and Engineering, Techno International New Town, Kolkata, West Bengal, 700156, India

*Email: nilanjan.dey@tint.edu.in (Nilanjan Dey)

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.

Conflict of Interest

There is no conflict of interest.

Artificial Intelligence (AI) Use Disclosure

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.

References

[1]

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]

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]

L. M. Ungureanu, I. S. Munteanu, Advanced Autonomous Systems and the Artificial Intelligence Stage,

Technologies, 2026, 14, 9, doi: 10.3390/technologies14010009.

[4]

S. H. Hong, Y. Wang, Artificial Intelligence and Its Applications in Robotics, Applied Sciences, 2025, 15,

7907, doi: 10.3390/app15147907.

[5]

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]

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]

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]

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]

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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