Journal of Artificial Intelligence and Technology

Sentiment analysis has become an important field of study in recent years because it enables the evaluation of public opinions collected from multiple data sources. This study highlights the importance of understanding public perceptions regarding specific areas and communities, which is essential for urban planning, tourism, real estate, and community engagement. By using diverse sources such as…

Accurate identification of wild mushrooms remains a persistent challenge due to the high morphological similarity between edible and toxic species. Traditional manual methods are labor-intensive, subjective, and error-prone, while existing computational approaches often prioritize algorithmic performance over practical, user-friendly solutions. This study presents an integrated intelligent recogn…

Accurate electroencephalogram (EEG)-based seizure detection is important for early epilepsy diagnosis and timely intervention, yet existing methods often trade predictive performance for interpretability. Deep learning models can achieve high accuracy but function as black boxes, limiting clinical trust. Conventional machine learning models are more transparent, but they often ignore neurophysiol…

The internet of things (IoT) is becoming increasingly significant in computer networks and applications. The existing deep learning (DL)-based intrusion detection system (IDS) suffers from high computational complexity and poor generalization, where irrelevant network flows are due to the inability of the filter. To address this challenge, this research proposes a novel entropy-guided gradient pr…

Background: Fetal ventriculomegaly (VM), which is defined by abnormal ventricular size, is one of the most common brain malformations detected during fetal screening. Early and precise diagnostic tools are important in predicting neurodevelopment, but ultrasound examination is highly operator-dependent, thereby creating subjectivity in its application. Objective: To design a precise, reliable, an…

The competitive nature of admission to the top Indian institutes on the basis of the Graduate Aptitude Test in Engineering (GATE) requires intelligent systems in the student–college matching. Conventional recommendation procedures usually do not take into consideration important aspects like cutoff differences and institutional ratings, resulting in minimal impartiality and transparency to the st…

It is now important to forecast customer purchase patterns and classify stores appropriately considering the effects of the global pandemic and changing consumer preferences in the retail sector. Machine learning (ML) algorithms can be used to predict customer buying patterns and classify stores. However, conventional ML algorithms are very sensitive to their parameters. Hyperparameter optimizati…

Injuries in the knee or any wear and tear of the joint causes knee abnormalities. Here, we propose a light weight novel model "Knee Osteoarthritis Multi-Modal Classification Framework (KOMMCF)" aimed at early detection of knee abnormalities by leveraging the KL grading system. We have trained our model by using X-ray images ranging from [grade 0 - 4 normal to severe] and 14 clinical biomarkers an…

Epileptic seizure detection from electroencephalogram (EEG) signals is an essential task for real-time neurological monitoring. Traditional models face challenges with interpretability, energy efficacy, and capturing temporal causality in neural data. To address these drawbacks, this manuscript proposes a Causality-Aware Attention with Spiking Transformer Network (CAAST-Net). The frequency-domain…

Background: Artificial intelligence intelligence (AI) and cloud computing are reshaping customer relationship management (CRM) systems, with Salesforce leading this transformation through its Einstein AI and cloud-based architecture. However, the actual impact of these innovations on user experience (UX) remains underexplored. Objective: This study investigates how AI-powered features within Sale…

Diabetic foot ulcers (DFUs) are among the most serious complications of diabetes mellitus and frequently lead to infection, hospitalization, and lower-limb amputation. Early and accurate assessment of wound severity is therefore essential for preventing complications and supporting clinical decision-making, particularly in telemedicine settings. This study proposes a hybrid deep learning framewor…

This study investigates the use of large language models (LLMs)—ChatGPT-5, Claude Opus 4.1, Gemini Advanced 2.5 Pro, DeepSeek Pro, Qwen-3 Max, and Mistral Le Chat Pro—and a locally fine-tuned LLaMA 3.3 70B Instruct model for automating assessment tasks in language education. Specifically, the study looks to examine LLM capabilities in automating assessments with authentic midterm exam sheets from…

How much can we trust highly complex predictive models like ChatGPT? This study tests if subtle changes in prompt structuring do not produce significant variations in the classification results of sentiment polarity analysis generated by the LLM GPT-4o mini. The model classified 100.000 comments in Spanish on four Latin American presidents as positive, negative, or neutral on 10 occasions, varyin…

This study presents the development of a domain-specific ontology to structure knowledge for aging research in Thailand, in response to the increasing fragmentation across disciplines. The ontology formally represents 10 core domains, including research, policy, health, quality of life, and age-friendly cities, reflecting national research priorities while remaining compatible with established ge…

A modern industrial system with its critical machinery is very sensitive to unexpected equipment failure and may experience extensive operation interruption, danger to safety, and cost. The traditional maintenance approaches, reactive and preventative, lack intelligence and flexibility to make predictions of the failure based on real-time information, leading to failures that are expensive to fix…

Accurate detection of vehicle damages such as dents, scratches, and cracks is essential for improving the efficiency, consistency, and scalability of insurance claim assessment. Conventional inspection procedures rely heavily on manual evaluation, making them time-consuming, subjective, and costly. To address these limitations, this paper presents a three-stage progression of mixture-of-experts (…

This work presents a dialect-aware and noise-robust Kannada automatic speech recognition (ASR) system that bridges the gap between low-resource linguistic contexts and state-of-the-art deep learning models. We design a two-stage approach: (i) a scratch-built convolutional neural network (CNN)–Transformer hybrid trained on curated Kannada speech data with fast Fourier transform-based noise reducti…

Traumatic brain injury (TBI) remains a major global health concern, where rapid and accurate identification of intracranial hemorrhage on computed tomography (CT) scans is essential for improving patient outcomes. Manual radiological assessment, although clinically effective, is time-consuming and subject to inter-observer variability, creating a need for reliable automated diagnostic systems. Mo…

This study presents SmartGRI, a systematic innovation in the form of a Group Decision Support System (GDSS) specifically designed to improve the accuracy, efficiency, and objectivity of sustainability audits based on the Global Reporting Initiative (GRI) framework. The main novelty of this study lies in integrating the first of its three multi-attribute decision-making(MADM) methods (Eckenrode, T…

Accurate classification of Arecanut plant disease is essential for crop damage prevention, ensuring healthy yields and sustainable farming. Timely identification of diseases enables farmers to take prompt action to minimize yield losses and improve overall plantation management. However, plant diseases vary in scales from fine details like spots or small lesions to larger areas of discoloration w…