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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 1,722 Documents
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Inexpensive human audiometric system using Raspberry Pi and artificial intelligence Maray, Abdulrafa Hussain; Hassan, Muataz Akram; Al-Hassan, Taha Hussein Marai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4502-4510

Abstract

The most common and widespread disease in Iraq is hearing impairment for children and newborns. Also, in cities, people are exposed to high levels of noise, loud sounds at work, like factories, and machinery noise. In this paper, a system was designed and implemented to measure the level of hearing in the human ear, in order to reduce the cost of these devices. This system uses Raspberry Pi 3 microcontrollers, which are considered cheap and have high capabilities in open-source programming. Their abundant availability will lead to the provision of these systems in homes, health centers, and hospitals. In this proposed algorithm, two sine waves are generated by the microcontroller with different frequencies. It is transmitted by the MP3 audio transmission cable through the analog-to-digital (ADC) port. These audio signals are generated at a frequency of (0.5 to 12 kHz), these frequencies are the ones that humans can hear, and they can be represented by pulse width modulator (PWM) technology (x=255 samples). Convolutional neural network (CNN) is trained on the dataset acquired through deep learning algorithms.
EmoVibe: AI-driven multimodal emotion analysis for mental health via social media dashboards Vora, Deepali; Sharma, Aryan; Garg, Mudit; Fransis, Steve
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4565-4578

Abstract

Monitoring mental health via social media often utilizes unimodal approaches, such as sentiment analysis on text or single-staged image categorization, or executes early feature fusion. However, in real-world contexts where emotions are conveyed via text, emojis, and images, unimodal approach leads to obscured decision-making pathways and overall diminished performance. To overcome these limitations, we propose EmoVibe, a hybrid multimodal AI framework for emotive analysis. EmoVibe uses attention-based late fusion strategy, where text embeddings are generated from bidirectional encoder representations from transformers (BERT) and visual features are extracted by vision transformer. Subsequently, emoticon vectors linked to avatars are processed independently. Later, these independent data features are integrated at higher levels, enhancing interpretability and performance. In contrast to early fusion methods and integrated multimodal large language models (LLMs) like CLIP, Flamingo, GPT-4V, MentaLLaMA, and domain-adapted models like EmoBERTa, EmoVibe preserves modality-specific contexts without premature fusion. This architecture saves processing cost, allowing for clearer, unambiguous rationalization and explanations. EmoVibe outperforms unimodal baselines and early fusion models, obtaining 89.7% accuracy on GoEmotions, FER, and AffectNet, compared to BERT's 87.4% and ResNet-50's 84.2%, respectively. Furthermore, a customizable, real time, privacy-aware dashboard is created, supporting physicians and end users. This technology enables scalable and proactive intervention options and fosters user self-awareness of mental health.
Deep learning approaches for Braille detection and classification: comparative analysis Janrao, Surekha; Fernandes, Tavion; Golatkar, Ojas; Dusane, Swaraj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4652-4660

Abstract

This study proposes a hybrid approach to Braille translation leveraging the strengths of both YOLO for object detection and multitude of classification models such as ResNet, and ResNet for accurate Braille character classification from images. Upon comparing numerous models on various performance metrics, ResNet and DenseNet outperformed other models, exhibiting high accuracy (0.9487 and 0.9647 respectively) and F1-scores (0.9481 and 0.9666) due to their deep, densely connected architectures adept at capturing intricate Braille patterns. CNNs with pooling showed balanced results, while MobileNetV2's lightweight design limited complex classification. ResNeXt's multi-path learning achieved respectable performance but lagged behind ResNet and DenseNet. In the future the results from our study could be further explored on contracted Braille recognition, be adapted to various Braille codes, and optimized for mobile devices, for real time Braille detection and translation on smartphones.
A bibliometric analysis of feature selection techniques: trends, innovations, and future directions Semmar, Oumaima; El Habti, Wissal; Wilson, Donalson; Azmani, Abdellah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4403-4414

Abstract

Feature selection techniques have become increasingly important in addressing the challenges of high dimensionality in machine learning and other artificial intelligence domains. In this study, we present a comprehensive bibliometric analysis of research on feature selection techniques over the past decade, focusing on mapping the intellectual structure, identifying emerging trends, and highlighting productive collaborations in the field. Using merged data from Scopus and Web of Science databases, we collected and analyzed 2,079 relevant documents published between 2014 and 2024, applying citation analysis, co-authorship networks, and keyword co-occurrence mapping. Our findings reveal that feature selection methodologies, including supervised, unsupervised, and hybrid approaches across filter, wrapper, and embedded techniques, have been widely applied across various domains. The authors who have most contributed to the development of these methods are primarily affiliated with institutions in China, India, and the USA. The insights provided by this analysis offer researchers and practitioners a valuable foundation for guiding future research directions in feature selection.
Enhancing software fault prediction through data balancing techniques and machine learning Raj, Akshat; Chavan, Durva Mahadeo; Agarwal, Priyal; Gigi, Jestin; Rao, Madhuri; Musale, Vinayak; Chanchlani, Akshita; Dholkawala, Murtaza Shabbirbhai; Kumar, Kulamala Vinod
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4787-4801

Abstract

Software fault prediction is essential for ensuring the reliability and quality of software systems by identifying potential defects early in the development lifecycle. However, the presence of imbalanced datasets poses a significant challenge to the effectiveness of fault prediction models. In this paper, we investigate the impact of different data balancing techniques, including generative adversarial networks (GANs), synthetic minority over-sampling technique (SMOTE), and NearMiss, on machine learning (ML) model performance for software fault prediction. Through a comparative analysis across multiple datasets commonly used in software engineering research, we evaluate the efficacy of these techniques in addressing class imbalance and improving predictive accuracy. Our findings provide insights into the most effective approaches for handling imbalanced data in software fault prediction tasks, thereby advancing the state-of-the-art in software engineering research and practice. An extensive experimentation is performed and analyzed in this study here that includes 8 datasets, 4 data balancing techniques, and 4 ML techniques in order to demonstrate the efficacy of various models in software fault prediction.
Optimizing brain tumor MRI classification using advanced preprocessing techniques and ensemble learning methods Pardede, Akim Manaor Hara; Zamsuri, Ahmad; Nuroini, Indi; Alkhairi, Putrama
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5106-5119

Abstract

Brain tumor classification is a critical task in medical imaging that directly impacts the accuracy of diagnosis and treatment planning. However, the complexity and variability of magnetic resonance imaging (MRI) images pose significant challenges, often resulting in reduced model reliability and generalization. This study addresses these limitations by proposing a novel ResNet+Bagging model, leveraging the strengths of residual networks and ensemble learning to enhance classification performance. Using publicly available brain tumor MRI datasets, including images labeled as benign, malignant, and normal, the study employs advanced preprocessing techniques such as normalization, data augmentation, and noise reduction to ensure high-quality inputs. The proposed model demonstrated significant improvements, achieving the highest testing accuracy of 72%, outperforming other tested models such as LeNet, standard ResNet, GoogleNet, and VGGNet. Precision (0.6010), recall (0.6000), and F1-score (0.5990) metrics further highlight its superior balance in detecting positive and negative classes. The novelty of this research lies in the application of Bagging to ResNet, which effectively mitigates overfitting and enhances predictive stability in complex medical datasets. These findings underscore the proposed model's potential as a robust solution for brain tumor classification, contributing to more accurate and reliable diagnostics.
Fine-tuning multilingual transformers for Hinglish sentiment analysis: a comparative evaluation with BiLSTM S. Verma, Jyoti; N. Undavia, Jaimin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4684-4693

Abstract

Growing trend of code-mixing in languages, in the form of Hinglish, greatly tests the skills of conventional sentiment analysis tools. The research contributes a fine-tuned multilingual transformer model built exclusively for classifying sentiment of Hinglish customer reviews. Drawing from pre trained BERT-base-multilingual-case architecture, the model gets transformed with the process of fine-tuning the same on synthetically prepared and balanced dataset simulating positive, negative, and neutral sentiments. Sophisticated methods like focal loss for addressing the class imbalance and mixed precision training for maximization of computational effectiveness are embedded within the training process. Experimental results suggest that the proposed method significantly captures the fine-grained linguistic patterns of code-mixed text, improving sentiment classification accuracy. The results show promising potential for enhancing customer feedback analysis in e-commerce, social media monitoring, and customer support use cases, where it is crucial to comprehend the sentiment behind code-mixed reviews.
Image segmentation using fuzzy clustering for industrial applications Jiménez-Moreno, Robinson; Vargas Duanca, Laura María; Espitia-Cubillos, Anny Astrid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4636-4642

Abstract

This paper presents a fuzzy logic clustering algorithm oriented to image segmentation and the procedure designed to evaluate its performance by varying two parameters: the number of clusters (c) and the diffusivity parameter (m), which leads to the conclusion that an adjusted number of clusters is sufficient to recognize main elements of the image, but a more detailed reconstruction requires a higher number of clusters. Also, the diffusivity parameter influences the smoothness of the boundaries between clusters, low values generate a segmentation with more abrupt transitions and sharper contours, high values smooth the segmentation, its excessive increase may cause the elements to merge, losing details. In general, the balance between these two parameters is key to obtaining an effective segmentation. Three validation scenarios were used, the first two allowed to establish the most appropriate parameters for segmentation, regulating the clusters to a maximum of 4 and keeping the diffusivity level at 2.0, the third scenario validated the algorithm with real images of industrial cleaning products, all with noise, establishing the computational cost and processing times for images of 350×350 and 2000×3000 pixels resolution. In conclusion, applications of the algorithm are foreseen in automatic quality control and inventory control of finished products and raw materials, thanks to its high efficiency and low response time, even in scenarios involving noisy and large images.
A comparative study of large language models with chain-of thought prompting for automated program repair Darwiyanto, Eko; Gusnaen, Rizky Akbar; Nurtantyana, Rio
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4579-4589

Abstract

Automatic code repair is an important task in software development to reduce bugs efficiently. This research focuses on developing and evaluating a chain-of-thought (CoT) prompting approach to improve the ability of large language models (LLMs) in automated program repair (APR) tasks. CoT prompting is a technique that guides LLM to generate step-by-step explanations before providing the final answer, so it is expected to improve the accuracy and quality of code repair. This research uses the QuixBugs dataset to evaluate the performance of several LLM models, including DeepSeek-V3 and GPT-4o, with two prompting methods, namely standard and CoT prompting. The evaluation is based on the average number of plausible patches generated as well as the estimated token usage cost. The results show that CoT prompting improves performance in most models compared with the standard. DeepSeek-V3 recorded the highest performance with an average of 36.6 plausible patches and the lowest cost of $0.006. GPT-4o also showed competitive results with an average of 35.8 plausible patches and a cost of $0.226. These results confirm that CoT prompting is an effective technique to improve LLM reasoning ability in APR tasks.
A web-based learning platform to assess student performance using online session activity engagement Hanumanthappa, Shashirekha; Prakash, Chetana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5240-5250

Abstract

Predicting students' performance and engagement is crucial for academic eLearning partners in colleges and universities as well as students themselves considering post-COVID-19 pandemic and university grant commission (UGC) dual degree regulation era. An educational system's data on students’ engagement in taking courses that are a significant component of an institution of higher learning with a cogent vertical syllabus can be used to make predictions. By examining how closely a student's course-taking actions correspond with the requirements of the syllabus, one can utilize the student's conduct in the classroom and online eLearning web tool as a predictor of future achievement. This paper presents a study that uses an eLearning web-based dataset to predict students' success throughout a series of online interactive sessions. The dataset records how students engage with each other during online lab work, including how many keystrokes they make, how long they spend on each task, and how well they perform on exams overall. The current methods lacks accuracy to assess student performance and engagement with high precision. In addressing this paper introduces novel multi-label ensemble learning (MLEL) using XGBoost (XGB) and K-fold cross validation. Experiment outcome shows the proposed (MLEL-XGB) achieves much improved outcome than other existing models.

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