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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 2,143 Documents
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Enhancing intrusion detection performance in industrial internet of things using modified hyperparameter optimization Arif Ullah; Javed Mansoori; Baber Akber; Abdullah Altaf; Abdul Rehman Altaf; Aseel Smerat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4188-4198

Abstract

Many sectors have seen significant improvements in operational efficiency and modern connection due to the increasing usage of industrial internet of things (IIoT) devices, a phenomenon known as IIoT. However, efficient intrusion detection systems (IDS) are essential for IIoT security because this quick growth also makes the network more susceptible to sophisticated cyberattacks. The high dimensionality and dynamic nature of IIoT data frequently cause traditional IDS algorithms to struggle, which results in insufficient threat detection and response. This paper uses a novel technique called Bayesian hyperparameter optimization with decision trees to enhance IDS performance in complex IIoT scenarios. Using the large IIoT-23 dataset, the method achieved an excellent 96.07% accuracy rate and a weighted average F1-score of 96.03%, in addition to demonstrating a significant increase in identifying a range of attack scenarios. In direction to growth the precision and robustness of our intrusion detection algorithms, this invention emphasizes how important it is to carefully modify complex hyperparameters and combine decision trees with ensemble methods like genetic algorithms and Bayesian optimization. The results show how successfully the approach adapts to budding threat landscape, expressively lessens false positives, and efficiently handles the enormous volumes of data that are a part of IIoT networks.
Synergistic deep learning ensemble of DenseNet, ResNet, and Xception for accurate brain tumor diagnosis Jayashree Shedbalkar Inchal; Sumit Gupta; Annapurna V. K.; Siddesha Kempaningaiah; Anoop Ganadalu Lingaraju; Ramya Shivamadegowda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4459-4469

Abstract

Brain tumor classification plays a pivotal role in medical diagnostics, treatment planning, and patient care. Currently, the biomedical domain has experienced a surge in demand of automated computer-aided diagnosis (CAD) systems for efficient diagnosis. Recently, deep learning (DL) architectures have experienced tremendous growth as prevailing tools for automated classification tasks. This study explores the synergistic potential of combining three state-of-the-art DL architectures i.e. densely connected convolutional network (DenseNet), residual network (ResNet), and extreme Inception (Xception) for brain tumor classification. Each architecture brings unique strengths, including feature reuse, skip connections, and depth-wise separable convolutions, respectively. This study proposes an ensemble methodology that capitalizes on the distinctive strengths of each architecture. DenseNet excels in feature reuse, ResNet employs skip connections to mitigate gradient issues, Inception captures multi-scale features, and Xception employs depth-wise separable convolutions for efficiency. By integrating predictions from these diverse models, our ensemble approach aims to achieve enhanced classification accuracy and robustness. After feature extraction, an improved feature selection method is introduced and finally, an ensemble classifier is introduced which uses majority voting method to achieve the classification task. The proposed approach has reported the average score as 0.99, 0.98, 0.99, 0.98, and 0.98 for precision, recall, specificity, sensitivity, and accuracy, respectively.
Robust trading in stochastic chaos: a hybrid fuzzy multi-expert approach Andrey Musayev; Dmitry Grigoriev
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4105-4112

Abstract

Classical trend-following strategies often exhibit unstable performance in the EUR/USD foreign exchange market due to filter lag, noise-induced false signals, and abrupt regime transitions associated with chaotic price dynamics. This study proposes a hybrid fuzzy multi-expert framework that integrates bidirectional exponential filtering for low-latency trend extraction with a Mamdani-type fuzzy inference system that jointly evaluates quantitative trend indicators and qualitative information derived from economic news and textual forecasts. The proposed approach is evaluated on minute-level EUR/USD data over 10-, 25-, and 100-day test windows. Empirical results show that the fuzzy consensus mechanism improves robustness under non-stationary conditions. In a representative 25-day evaluation period, net profit increases from 182 to 560 pips, the win rate improves from 55% to 67%, and the maximum drawdown decreases from 118 to 38 pips. These findings suggest that incorporating linguistic uncertainty and sentiment-like contextual cues into technical trading signals can mitigate spurious trade activation and stabilize decision-making in stochastic and chaotic market environments, offering a practical route toward more resilient algorithmic trading systems.
Optimized hybrid transformer-extreme gradient boosting model for black pepper disease classification Saritha Suvarna; Demian Antony Dmello
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4766-4779

Abstract

Black pepper (Piper nigrum) is a vital spice crop cultivated in several tropical countries and is highly susceptible to various leaf diseases that significantly affect crop yield and quality. Traditional manual disease identification methods are time-consuming, labor-intensive, and error-prone. While machine learning (ML) and deep learning (DL) techniques have shown promise in plant disease detection, limited work exists specifically on black pepper leaf diseases. Existing research often relies on small, imbalanced, and non-standardized datasets, leading to poor generalization and reduced accuracy. To address this challenge, the proposed study presents a novel approach utilizing deep attention-based transformer-optimized weighted hyper-tuned extreme gradient boosting network (DAT-OWH-XGBNet) for early black pepper leaf disease detection and classification. The model was trained using the black pepper leaf disease dataset, which includes anthracnose, yellowing, quick wilt, slow wilt, and healthy leaf images. The proposed model combines attention-based transformers with a class-weighted and hyperparameter-optimized extreme gradient boosting (XGBoost) classifier to extract high-level spatial features during feature extraction and optimized XGBoost for final classification. The findings show that the proposed model achieved 99.88% prediction accuracy and 99.82% classification accuracy, outperforming existing convolutional neural network (CNN) methods. Our model is designed to support farmers and agricultural specialists in detecting pepper leaf diseases at the early stage.
Micro dataset segmentation for vanilla disease detection Radityo H. J. Notonegoro; Dewi Agushinta R.; Diana Ikasari; Rifki Kosasih
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4424-4431

Abstract

Vanilla cultivation in Indonesia is often affected by various plant diseases, which pose significant challenges to productivity. However, images of vanilla disease data available on the internet are very limited for public access; the easily accessible disease images are mostly found in books related to vanilla or vanilla gardening. In this study, data collection involved 18 images of vanilla plants infected with basal stem rot caused by the fungus Sclerotium rolfsii and 20 images of healthy green vanilla plants sourced from websites. A pretrained U-Net model from “ImageNet” with a mobile network version 2 (MobileNetV2) backbone was employed, using parameters of 100 epochs, batch size 16, image size 256 × 256, and learning rate 0.0001. The model was trained using four different color preprocessing methods: RGB, CIE Lab, HSV, and YCbCr. Experimental results showed that the YCbCr color representations consistently achieved the best segmentation performance on both training and validation datasets. Color representations such as CIE Lab and YCbCr can better capture contrast and lighting variations compared to RGB, resulting in a more robust and generalizable model.
A deep learning framework for robust text extraction from heterogeneous image sources Dayananda Kodala Jayaram; Puttegowda Devegowda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4724-4732

Abstract

Text extraction from an image is characterized by various challenges that result in various evolving solutions at current times. Existing solutions using artificial intelligence (AI) do have some promising outcomes, but also have their own shortcomings associated with accuracy and efficiency. The manuscript introduces a simplified and yet innovative AI framework towards extracting text by unifying character region-aware text detection. The model uses a convolutional neural network (CNN) along with transformer-centric optical character recognition (OCR) towards better performance on scene text recognition. Analysis is carried out on the standard and benchmarked International Conference on Document Analysis and Recognition 2015 (ICDAR 2015) dataset to find that the proposed model accomplished 94.7% accuracy and 0.82 seconds per image of response time, which are much significant improvements in performance, while observed in contrast to existing AI models, proving the model to deliver high-performance and cost-effective outcomes.
Leveraging AI and pedagogical competence to enhance learning interest and outcomes: evidence from Islamic university Cecep Wahyu Hoerudin; Susanto Susanto; Apri Wardana Ritonga; Ayu Desrani; Darwis Hude; Dzaki Aflah Zamani; Ahmad Ari Masyhuri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4001-4014

Abstract

This study investigates the effects of artificial intelligence (AI)-supported intelligent educational systems and teacher pedagogical competence (TPC) on students’ learning interest (LI) and learning outcomes (LO) in an Islamic higher education context. AI is operationalized as AI-supported learning system features embedded in institutional learning platforms, particularly natural language processing (NLP)-based automated feedback and AI-driven recommendation functionalities that support adaptive and personalized learning. The study was conducted at the As-Syifa Institute, Subang, Indonesia, involving 366 undergraduate students from the first, third, and fifth semesters. Data were analyzed using partial least squares-structural equation modeling (PLS-SEM) with SmartPLS 4 to examine the structural relationships among AI system use, TPC, LI, and LO. The results demonstrate that AI-supported learning system features have significant positive effects on both LI and LO. TPC also shows a strong direct effect and mediates the relationship between AI-supported systems and student outcomes. These findings highlight the importance of human-AI integration, indicating that pedagogical competence plays a critical role in translating AI system functionalities into meaningful educational benefits. This study contributes to the AI-in-education literature by providing empirical evidence on pedagogically mediated AI-supported learning systems and offers insights for the design and evaluation of adaptive and intelligent learning environments in higher education.
Generative artificial intelligence personalization and consumer behavior analytics Endang Tjahjaningsih; Elen Puspitasari; Felix Andreas Sutanto; Dewi Handayani Untari Ningsih; Dwi Budi Santoso
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4919-4932

Abstract

Batik micro, small and medium enterprises (MSMEs) sustain Indonesia's textile heritage and local livelihoods but struggle with customer retention due to conventional marketing and limited artificial intelligence (AI) adoption. The shift to digital commerce requires affordable, culturally relevant AI strategies. This study proposes an integrated framework combining recency, frequency, monetary (RFM) analytics, K-means clustering (K = 5), random forest churn prediction, and transformer-based generative AI for personalized promotions delivered via WhatsApp business API. Evaluated over 12 months with 20 batik MSMEs in Central Java (300 customers, 873 transactions), the framework employed temporal data splitting (9 months training, 3 months testing) and A/B testing against generic campaigns. Human evaluation assessed message relevance and cultural appropriateness, while all data were anonymized with informed consent. Results show improvements of +17% in retention prediction accuracy, +23% in click-through rate, and +21% in purchase frequency versus baselines (p < 0.05, Cohen's d = 0.68, power > 0.8). The system costs below USD 20 per MSME per month, offering a scalable, human-centered pathway to strengthen loyalty in emerging markets.
Temporal convolutional network for Indonesian word by word sign language translator on MediaPipe framework Sarah Purnamawati; Albert Albert; Anggi Yohanes Pardede; Iqbal Fakhriza; Farhad Nadi; Mohammad Fadly Syahputra; Romi Fadillah Rahmat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4081-4095

Abstract

Sign language is a language created to help the deaf-mute communicate with others. In Indonesia, there are two sign languages: Indonesian sign language system or sistem isyarat bahasa Indonesia (SIBI) and Indonesian sign language or bahasa isyarat Indonesia (BISINDO), but the deaf community prefers to use BISINDO. Deaf-mute people use sign language to communicate, but normal people usually avoid learning it because they feel it's unnecessary for daily communication. This will certainly be a problem for the deaf-mute because people who do not learn sign language will certainly have difficulty understanding what they are trying to convey. Therefore, an application that can translate sign language is needed to help overcome communication problems between the deaf-mute and normal people. This research focuses on BISINDO translation using a temporal convolutional network (TCN) with MediaPipe methods. The amount of data used consists of 2,700 videos representing 27 word-level signs in BISINDO; this number increases to 32,400 after undergoing the data augmentation process. The model built was tested in translating BISINDO at the word level in simulation, and a thorough comparison between TCN and bidirectional long short-term memory (BiLSTM); both of them were running on the MediaPipe platform separately. From the experiment, it is shown that the performance of TCN exceeds BiLSTM with an accuracy rate as high as 88.64%.
Novel deep learning framework with hierarchical temporal feature refinement for enhanced epileptic seizure detection Namitha Arakere Raju; Siddartha Bommenahalli Krishne Gowda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4234-4248

Abstract

Epileptic seizure detection remains a significant challenge in the field of neurology due to the complexity and variability of electroencephalogram (EEG) signals. Traditional manual methods of analyzing EEG data are time-consuming, subjective, and prone to misdiagnosis, particularly in long-term recordings. To address these challenges, this study proposes a novel hierarchical temporal feature refinement network for epileptic seizure detection (HTFRNet-ESD) framework designed to enhance the accuracy and efficiency of epileptic seizure detection. The methodology comprises three key components: a spatial-temporal signal encoder (STSE) for capturing spatio-temporal attributes, an adaptive temporal dynamics encoder (ATDE) for detailed temporal feature analysis, and a hierarchical temporal refinement unit (HTRU) module for integrating frequency-driven power features. The proposed model leverages advanced deep learning techniques, including convolutional neural networks (CNNs) and transformers, to effectively learn both coarse and fine-grained temporal dynamics of EEG signals. Evaluation on publicly available EEG datasets demonstrates the model’s superior sensitivity, specificity, and overall accuracy compared to existing approaches. The HTFRNet-ESD sets a new benchmark for automated seizure detection, offering a reliable and scalable solution for real-world clinical applications. This work paves the way for improved patient outcomes through timely and precise epilepsy diagnosis.

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