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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 86 Documents
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Search results for , issue "Vol 15, No 1: February 2026" : 86 Documents clear
Adversarial examples in Arabic language Laatyaoui, Safae; Saber, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp941-952

Abstract

Adversarial attacks have a great popularity in the artificial intelligence (AI) domain. In the natural language processing (NLP) field, various techniques have been used to evaluate the vulnerability of deep learning (DL) models. It is observed that while most studies focused on generating adversarial examples in English language, Arabic adversarial attacks have received little attention. This paper presents a two-step method to create adversarial examples in Arabic language: first, the most important words are identified. Then, the proposed transformation algorithm is applied. Only small and imperceptible manipulations based on common mistakes in Arabic writing mislead the popular pre-trained language model (PLM) bidirectional encoder representations from transformers (BERT) retrained on the book reviews in Arabic dataset (BRAD) on the sentiment analysis (SA) task and decrease its performance: the classification accuracy was reduced by an average of 3.44%. This drop in accuracy shows that the model was successfully attacked.
Stroke prediction using data balancing method and extreme gradient boosting Rahim, Abd Mizwar A.; Baita, Anna; Asharudin, Firman; Ashari, Wahid Miftahul; Hakim, Walidy Rahman; Putra, Andriyan Dwi; Supriatin, Supriatin; Pramono, Eko
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp655-671

Abstract

Stroke is one of the leading causes of death worldwide, creating an urgent need for effective early detection systems, particularly because conventional methods often struggle with class imbalance and produce biased evaluations. Previous studies have primarily focused on accuracy while overlooking model consistency, data pre-processing quality, and probability-based evaluation. This study evaluates model performance under three conditions: original data using extreme gradient boosting (XGBoost) with scale_pos_weight, original data using the easy ensemble classifier, and class-balanced data generated using random oversampling (ROS), adaptive synthetic sampling (ADASYN), and synthetic minority over-sampling technique (SMOTE). Each model underwent missing value handling, normalization, feature preparation, and hyperparameter optimization using grid search. Performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), confidence intervals, calibration curves, Shapley additive explanations (SHAP), decision curve analysis (DCA), and external validation. The results demonstrate that data resampling significantly improves performance, with the XGBoost-SMOTE combination achieving the best results, including an accuracy of 0.99, AUROC of 0.998, and AUPRC of 0.986, outperforming the other approaches. This method provides more consistent and balanced predictions, supporting the application of artificial intelligence for early stroke risk identification.
Evolutionary trends in automatic speech recognition with artificial intelligence: a systematic literature review Oluwatobi Sobola, Gabriel; Adetiba, Emmanuel; Idowu-Bismark, Olabode; Abayomi, Abdultaofeek; Jules Kala, Raymond; Thakur, Surendra Colin; Moyo, Sibusiso
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp20-43

Abstract

Human beings depend greatly on communication and continually seek ways to overcome language barriers. Automatic speech recognition (ASR) has emerged as a vital tool for enhancing human interaction. Early ASR research relied on probabilistic models, particularly the hidden Markov model (HMM) and Gaussian mixture model (GMM), with mel-frequency cepstral coefficients (MFCCs) for feature extraction, leading to the creation of Audrey at Bell Laboratories. Subsequently, artificial intelligence (AI) approaches, especially deep learning, have transformed ASR and produced systems such as Jasper, Whisper, Google Assistant, Microsoft Cortana, Apple Siri, and Amazon Alexa. This paper presents a systematic literature review that examines ASR’s evolution, the AI architectures employed, their features, strengths and weaknesses, and the performance gains achieved since AI was integrated into probabilistic modelling. A snowballing approach was used to identify relevant studies from Google Scholar and Scopus to address five research questions, iterating through backward and forward searches until no new information was found. Findings reveal that ASR dates back to the 1920s with the Radio Rex toy and has since advanced through architectures including deep learning, recurrent neural networks (RNN), support vector machines (SVM), and transformers, all contributing to improved performance measured by reduced word error rates (WER).
Multi-scale features assisted knowledge distillation vision transformer for land cover segmentation and classification Gaikwad, Sujata Arjun; Musande, Vijaya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp361-373

Abstract

The most significant problem in remote sensing interpretation is semantic segmentation, which attempts to give each pixel in the image a particular class. This research work follows the various steps, such as pre-processing, segmentation, and classification. Initially, high spatial resolution remote sensing images (RSI) are collected from the open-source dataset. In the pre processing stage, an improved guided filter (Imp-GF) is used to remove various noises from images. Next, the segmentation is done by using a knowledge distillation-based vision transformer approach integrated with an atrous spatial multi-scale pyramidal module (KD-MuViTPy). Based on the segmented image, land cover classes such as vegetation, urban areas, forest, water bodies, and roads are classified. The proposed method outperformed the Bhuvan satellite dataset, achieving better accuracy, precision, recall, F1 score, Dice score, intersection over union (IoU), and Kappa score at values of 98.01%, 98.99%, 97.49%, 98.23%, 98.23%, 96.55%, and 95.91%, respectively.
Scalable resume screening using large language model Meta AI version 3 Deshmukh, Asmita; Raut, Anjali; Deshmukh, Vedant
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp953-961

Abstract

This research paper explores the use of large language model Meta AI 3 (LLaMA 3) for automating the resume screening process. Traditional resume screening methods that rely on keyword searching and human review can be inefficient, biased, and fail to identify qualified candidates. LLaMA 3, trained on large-scale text datasets, has the potential to accurately analyze resumes by understanding context and semantic details beyond simple keyword matching.The study presents a system that converts resume PDFs to text, inputs the text along with the job description into the LLaMA 3 model, and generates a ranked list of candidates with reasoning for their job fit. This discusses the data preparation, model setup, and performance evaluation of this system. Results show LLaMA 3 can rapidly process batches of resumes while reducing human bias in the screening process. The system aims to streamline hiring by automating the initial resume screening stage to surface top candidates for further in-depth evaluation. Key benefits include improved accuracy in identifying relevant skills, reduced bias compared to human screeners, and significant time savings for recruiters. The paper also examines ethical considerations around using AI for hiring decisions. Overall, this work demonstrates the promising application of large language models (LLMs) like LLaMA 3 to transform and enhance resume screening practices.
Optimizing Javanese script recognition using fine-tuned ResNet-18 and transfer learning Fateah, Nur; Subhan, Subhan; Ifriza, Yahya Nur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp443-453

Abstract

Javanese script, known as Aksara Jawa, is an ancient script used in historical and cultural texts. However, its complex character structure poses challenges for accurate recognition in modern digital applications. This study proposes an optimized classification approach for Aksara Jawa using a fine-tuned ResNet-18 model combined with the Adam optimization algorithm and transfer learning on the Hanacaraka image dataset. By leveraging the residual learning framework of ResNet-18, the model effectively captures deep spatial features of the script while reducing vanishing gradient issues. Fine-tuning is applied to enhance model adaptability, ensuring robust feature extraction specific to Javanese characters. Experimental results demonstrate that the fine-tuned ResNet-18 outperforms conventional deep learning architectures in recognizing Aksara Jawa characters, achieving 93% precision, 91% recall, 91% F1-score, and 91% accuracy. The study further explores the impact of hyperparameter tuning, data augmentation, and dropout regularization on model performance. The findings highlight the effectiveness of transfer learning in resource-limited scenarios, making it a feasible solution for optical character recognition (OCR) applications in Javanese script digitization. This research contributes to the preservation of cultural heritage through advancements in deep learning-based script recognition.
Adaptive feature fusion network for fetal head segmentation in ultrasound images Nagabotu, Vimala; Sana, Pavan Kumar Reddy; Bhavani, B. Lakshmi; Srikanth, Donapati
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp841-851

Abstract

The measurement of fetal biometrics from ultrasound images plays a vital role in assessing potential development during pregnancy. However, existing fetal segmentation methods failed to accurately segment and asses the head circumference that gives inaccurate segmentation results. To overcome this limitation, a feature feedback and global feature with adaptive feature fusion network (FGA–Net) model is proposed to enhance fetal head segmentation (FHS). It involves four key components for feature extraction, fusion, and correction, respectively. The adaptive feature fusion module (AFFM) and correction map integrate the local features and global features and refine the features to enhance accurate FHS from the ultrasound images efficiently. Initially, ultrasound images are obtained from the two publicly available datasets and preprocessed using normalization and data augmentation techniques. Finally, preprocessed images are fed to FHS by proposed FGA Net utilizing EfficientNet-B0 as the backbone network for efficient feature extraction. Experimental results of proposed FGA-Net are evaluated using the dice coefficient (DC) of 95.78% and 98.95% for FH-PS-AoP and HC-18 datasets, which shows better results than the existing segmentation approaches like inverted bottleneck patch expanding (IBPE) method.
Feature selection in supervised machine learning: a case study of poverty dataset in West Java, Indonesia Marshelle, Sean; Rahardiantoro, Septian; Kurnia, Anang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp524-535

Abstract

West Java, one of the largest provinces in Indonesia with a population exceeding 50 million, reported a poverty rate of 7.62% in 2023. Data from the national socio-economic survey or survei sosial ekonomi nasional (SUSENAS) show that poverty is multidimensional, encompassing aspects of employment, education, sanitation, housing, food security, technology, and government assistance. Addressing this complexity requires identifying the most influential factors that determine household welfare. This study applies and compares three feature selection approaches—filter, wrapper, and embedded—to the SUSENAS dataset to evaluate their effectiveness in identifying key poverty determinants. By prioritizing variables with the strongest predictive power, the study provides an evidence-based framework for more efficient and targeted poverty alleviation strategies. Results indicate that the information filter method combined with random forest (RF) and the least absolute shrinkage and selection operator (LASSO) embedded method combined with logistic regression (LR) deliver the best performance, improving model accuracy while reducing more than 65% of irrelevant features. The selected indicators highlight critical sectors such as food security, housing, and access to technology, which can serve as short-term policy priorities. In the long term, broader interventions in education, employment, sanitation, and government support are recommended. These findings demonstrate how data-driven feature selection can guide effective policy design for reducing poverty in West Java.
An artificial intelligence technology for promoting hom-thong banana agriculture system Tanveenukool, Ratsames; Somsuphaprungyos, Suwit; Nokkurth, Boonyarit; Chamuthai, Likit; Bonguleaum, Patumwadee; Natho, Parinya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp568-579

Abstract

The hom-thong banana, being a high-value Thai export variety, is facing significant risk from disease outbreaks affecting crop yield and quality. Traditional visual inspection methods in detection of diseases are labor consuming, error-prone. This research addresses these limitations by developing a new artificial intelligence (AI)-based automatic disease detection system for the hom-thong banana industry on top of cutting-edge computer vision technology. The study employed deep learning object detection models, contrasting Roboflow, you only look once (YOLO)v11, and YOLOv12 architectures, which were trained on a large dataset of 2,576 images of Thai banana plantations. With systematic data augmentation techniques, the dataset was augmented to 6,184 images of seven types of disease under varied environmental conditions. The method entailed extensive preprocessing and evaluation of performance through precision, recall, and mean average precision (mAP) metrics. Outcomes indicated that YOLOv12 outperformed with 93.3% accuracy, 83.3% sensitivity, and 86.3% mAP@50 compared to standard inspection schemes. This research is applicable to Thailand's smart agriculture initiative by providing farmers with low-cost, accurate, and effective disease monitoring equipment. The application of this AI system has the ability to enhance the yield of crops, reduce losses, and enhance the competitiveness of Thai banana exports in the global market, in support of sustainable agricultural development.
Instagram influencer classification using fine-tuned BERT model Sutramiani, Ni Putu; Dwikasari, Ni Made Dita; Trisna, I Nyoman Prayana; Darma, I Wayan Agus Surya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp1009-1018

Abstract

Influencer marketing has emerged as a powerful strategy in today’s digital world, where social media stars can influence how people think about products. However, the rapid growth of influencers and social media users presents novel challenges for brands in identifying suitable influencers for their marketing goals. Traditional approaches that rely on popularity and follower count are no longer the primary metrics for determining an influencer’s ability to affect consumer behavior. To address this gap, this study proposed an influencer classification to enhance audience targeting and marketing effectiveness. By utilizing deep learning, specifically fine tuned bidirectional encoder representations from transformers (BERT), influencer classification was carried out for Instagram users in Indonesia based on their post captions. The multilingual BERT model is optimized through hyperparameter tuning, including learning rate, batch size, and stop word removal variation. With an outstanding 80% accuracy, the model performs best in situations where stop words are not removed. This study on influencer classification using a fine-tuned BERT model has demonstrated the effectiveness of BERT in enhancing influencer selection. It contributes to the digital marketing domain by showcasing the potential of deep learning for social media analysis and content classification, paving the way for future data-driven marketing strategies.

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