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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 2,057 Documents
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Detection of illegal drug activities in Indonesia via social media X Adi Hanif Sedar; Lukman Yudokusumo; Indra Budi; Amanah Ramadiah; Aris Budi Santoso; Prabu Kresna Putra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3376-3388

Abstract

In today's digital world, social media platforms such as X (formerly Twitter) are widely used for communication but also for illegal activities, such as selling illicit drugs. This study examines how posts from X related to illegal drug sales in Indonesia can be analyzed using machine learning techniques, with a focus on incorporating features related to personally identifiable information (PII) and web URLs. The main questions are how to use classification methods to detect these posts and which drugs are most often mentioned. A total of 11,777 posts from X were collected using web scraping. After cleaning and processing the data, five machine learning models were used. The support vector machine (SVM) model showed the best results with a weighted F1-score of 97.08%. The findings show that abortion pills are the most frequently mentioned drugs, followed by sexual enhancement and anesthetic drugs. This study also used visual tools to show the most common hashtags, posts distribution over time, and types of drugs discussed. This study underlines the importance of monitoring social media to protect public health and highlights how incorporating PII features can improve the detection of illegal activities. Future research should look at posts in other languages and from other platforms, considering PII features for enhanced analysis.
Consciousness in artificial intelligence systems: an ontological approach Andreas Yumarma; Wan Sen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3009-3025

Abstract

The question of whether artificial intelligence (AI) systems can attain consciousness remains central ontological challenge within interdisciplinary discourse spanning AI, philosophy of mind, and cognitive neuroscience. This study investigates the metaphysical status of AI systems by interrogating the explanatory clarity of their existence and the potential emergence of subjectivity from non-biological matter. Employing an integrative methodology that combines a comprehensive literature review of scholarly texts and recent empirical findings with philosophical critical analysis, the research explores the conditions under which AI systems might be considered conscious entities. Findings suggest that the expanding societal integration of AI necessitates a re-evaluation of consciousness and cognition beyond anthropocentric parameters. The study posits a novel interpretive framework for addressing the ontological problem of AI, one that implicates future legal, moral, and interactive structures surrounding conscious non-biological agents. This conceptual repositioning invites a deeper inquiry into the criteria for recognizing, engaging with, and regulating advanced AI systems. By grounding design principles in ontological clarity, the framework offers guidance for constructing AI systems capable of reflexive processing, minimal phenomenality, and ethically aligned behavior that bridges conceptual analysis with implementation pathways.
Deep learning intrusion detection for software-defined networking using synthetic minority oversampling Prajwalasimha Sindugatta Nagaraja; Navya Rajashekara; Pushpa Bangalore Ramesh; Druva Kumar Siddaraju; Santhosh Kumar Ramachandragowda; Trupti Shripad Tagare
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3703-3711

Abstract

This article proposes an advanced method for network intrusion detection using a combination of recurrent neural networks (RNNs), specifically long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BiLSTM) models, enhanced by synthetic minority oversampling technique (SMOTE) to address class imbalance in datasets like network security laboratory–knowledge discovery in databases (NSL-KDD). The method aims to accurately classify network traffic by learning temporal patterns of both normal and malicious activities. SMOTE is employed to balance the dataset, ensuring that underrepresented attack types receive adequate model attention, thereby improving model robustness. The proposed models (LSTM, GRU, and BiLSTM) are trained and evaluated on the NSL-KDD dataset, with hyperparameter tuning performed through RandomizedSearchCV for optimal performance. The results show a significant improvement in accuracy, precision, recall, and F1-score, with BiLSTM demonstrating the highest performance, achieving near-perfect classification results (99.5% accuracy). This method not only mitigates the issue of class imbalance but also leverages the power of RNNs for sequence modeling, offering a promising solution for effective intrusion detection in modern networks.
Hybridization of hybrid-ARIMA-EM and XGBoost for enhanced price predictive modeling Isam Ahmed M. Yaqoob; Khairul Azhar Kasmiran; Teh Noranis Mohd Aris; Nor Azura Husin; Mohd Yunus Sharum
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3131-3143

Abstract

Managing finance entails the art and science of distributing available and potential funds among various competing needs. Government expenditures fund programs that provide a wide range of services to different population segments. As a result, the demand for enhanced and additional services often surpasses the government's financial capacity. Firstly, the price forecasting procedures for the extreme gradient boosting (XGBoost), gated recurrent unit (GRU), and hybrid-ARIMA-EM models will be summarized. Secondly, the accuracy of the models will be assessed on two real datasets collected from Kaggle (Crude_Oil_Price and KL_apartment). This study then proposes combining the hybrid-ARIMA-EM model with XGBoost to enhance the price forecasting performance in terms of time series analysis. Experimental results show that the suggested combination outperforms other selected models in price forecasting accuracy.
A transfer learning-based approach for automatic monument detection Santosh Giri; Jebish Purbey; Sunil Adhikari; Paarit Pokharel; Babu R. Dawadi; Bipun Man Pati; Atsushi Ito; Sushant Chalise; Sanjivan Satyal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3326-3341

Abstract

Architectural heritage connects us to the cultural achievements of past civilizations. Patan Durbar Square in Nepal is home to many such structures, yet identifying them remains a challenge for tourists. This paper presents an automated monument recognition system built on the backbone of convolutional neural networks (CNNs). A dataset of 1832 images of 9 important monuments from Patan was created, and build a detection system with MobileNetV2, a light-weight CNN, to detect monuments in Patan Durbar Square with a near-perfect F1 score of 98.94%. The approach utilizes transfer learning to adapt the model to local architectural styles. A detailed ablation study is performed to determine the optimal network design and augmentation strategies. Class-wise performance is further analyzed to verify robustness against visual occlusion and similarity. Finally, the model is deployed as a mobile application using Flutter and the FastAPI framework. This work demonstrates the viability of lightweight CNNs for real-time cultural heritage preservation.
The role of big data in precision medicine and healthcare monitoring using the MapReduce framework Meenakshi Sankarasubramanian; Meena Chavan; Govindan Manoharan Karthik; Jhansi Pandiri; Arumalla Nagaraju; Idimadakala Madhavilatha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3852-3864

Abstract

Data analytics has become a cornerstone of precision medicine by enabling doctors and scientists to extract meaningful insights from vast, complex data sets. Most healthcare data are high-dimensional data that not only require longer computational time but also affect the accuracy of analysis. In order to overcome these issues, the MapReduce based big data healthcare monitoring framework is proposed. The proposed work comprises preprocessing, the MapReduce framework, and data classification. The preprocessing can be done using improved min-max normalization, and the big data can be handled using the improved support vector machine (SVM)-recursive feature elimination (RFE) method. Finally, the classification can be done using a deep Q-network (DQN). The performance of the proposed method is analyzed in terms of accuracy, precision, F-measure, and Matthew's correlation coefficient (MCC).
Performance of majority voting transfer learning deep learning monkeypox disease detection Nur Nafiiyah; Muhammad Nurul Huda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3782-3791

Abstract

Global health concerns have been raised by the advent of monkeypox following the COVID-19 epidemic, highlighting the need for reliable automated systems to support early skin disease screening. This study proposes a monkeypox skin disease classification framework using a majority voting ensemble based on transfer learning. The ensemble combines predictions from multiple pretrained convolutional neural networks (CNNs) to improve classification robustness. A publicly accessible dataset comprising four classes: monkeypox, chickenpox, measles, and normal skin was used for the experiments. The results indicate that while a single ResNet50 model achieved the highest overall accuracy (99.15%), the majority voting approach yielded higher precision than several individual models, demonstrating improved reliability in distinguishing monkeypox cases. These findings suggest that ensemble-based majority voting can enhance the robustness of monkeypox skin disease classification and may support computer-aided screening systems.
An empirical comparison of clustering approaches for recency, frequency, and monetary customer segmentation Upekkha Lau; Meditya Wasesa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3402-3410

Abstract

This study evaluates effectiveness of three clustering techniques—k-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN)—applied to the recency-frequency-monetary (RFM) model for customer segmentation in the retail sector. Using sales transaction data from a distributor of computer accessories and printing products. The results show that k-means achieved the best clustering validation scores and effectively identified high-value customers, hierarchical clustering generated less meaningful groupings than k-means, and DBSCAN misclassified key customers as noise. These findings highlight k-means as the most suitable technique for RFM-based segmentation in this retail business context. The study offers practical insights for retail and distribution businesses aiming to adopt data-driven customer strategies and suggests future research to enhance segmentation robustness and refine the RFM framework.
Android-based tomato leaf disease classification using a lightweight MobileNetV2 convolutional neural network Andi Riansyah; Irfan Eka Mahdy; Mochamad Abdul Basir; Noorminshah A. Iahad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3318-3325

Abstract

Early screening of tomato leaf diseases is important because foliar symptoms can reduce plant vigor and delay appropriate crop management. This study develops an Android-based tomato leaf disease classification system using MobileNetV2 as a lightweight convolutional neural network (CNN) architecture. The contribution of this work is the integration of model training, independent testing, and on-device Android deployment that supports camera and gallery inputs without relying on server-side computation. The dataset consisted of 1,200 balanced tomato leaf images from five classes: bacterial spot, late blight, target spot, tomato yellow leaf curl virus, and healthy leaf. Images were resized, normalized, augmented for training, and divided into training, validation, and independent testing subsets. The model obtained 94.12% training accuracy, 93.00% validation accuracy, and 89.00% independent test accuracy. The confusion matrix showed that tomato yellow leaf curl virus was classified without error, whereas bacterial spot, late blight, target spot, and healthy leaves produced several misclassifications because of similar lesion and discoloration patterns. The results show that MobileNetV2 is suitable for lightweight mobile disease screening, although larger field datasets, cross-validation, model comparison, and explainability analysis are still needed for broader deployment.
Intravenous immunoglobulin resistance prediction in Kawasaki disease using oversampled transformer embeddings Namitha Thattarassery Nanappan; Raghavendra Srinivasaiah; Vinith Rejathalal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3944-3954

Abstract

Kawasaki disease (KD) is a leading cause of acquired heart disease in children under five. Although intravenous immunoglobulin (IVIG) treatment is usually effective, 10–20% of cases are resistant and at higher risk for coronary complications. Early prediction of IVIG resistance is critical but difficult due to the rarity of KD and imbalanced clinical data. To address this, we propose a novel technique called sentence transformer embeddings with synthetic minority over-sampling technique (SMOTE) oversampling (STESO), which leverages the complementary strengths of transformer-based representation learning and synthetic oversampling. Pretrained models such as paraphrase-MiniLM-L3-v2 are used to convert tabular clinical data into dense text-based embeddings, capturing deeper semantic relationships across features. By coupling these rich embeddings with SMOTE, we balance class distributions directly in the semantic space, enabling traditional machine learning (ML) models to more effectively detect minority (resistant) cases. This synergy yielded substantial improvements in sensitivity and F1-score, with random forest (RF) combined with STESO (RF-STESO) achieving the highest overall performance. Among the models evaluated, our proposed model attained best result as accuracy of 0.85, a sensitivity of 0.81, a specificity of 0.89, and an F1-score of 0.85. Our results underscore that the joint use of transformer embeddings and oversampling is more effective than either approach in isolation, offering a promising pathway for rare disease prediction tasks such as IVIG resistance prediction in KD.

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