cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Imam Much Ibnu Subroto
Contact Email
imam@unissula.ac.id
Phone
-
Journal Mail Official
ijai@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 1,974 Documents
 191   192   193   194   195 
Spark-powered bioactivity prediction: a comparison of machine learning approaches Nazif Tchagafo; Abderrahmane Ez-Zahout; Ahiod Belaid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2423-2430

Abstract

The arduous and expensive nature of drug discovery has long been a bottleneck in scientific progress. However, recent breakthroughs in computational power, notably machine learning (ML) and artificial intelligence (AI), are profoundly transforming the field. Automated machine learning (AutoML) presents itself as a significant advancement, streamlining model selection, and hyperparameter tuning. This study delves into the potential of AutoML to accelerate drug discovery by comparing it to classical ML techniques. The focus lies on predicting the bioactivity of epidermal growth factor receptor (EGFR), a critical protein implicated in many cancers. By utilizing the scalability of Apache Spark, vast and diverse datasets encompassing biological, chemical, and genomic data tied to EGFR are processed. This comparative analysis aims to evaluate the comparative performance of both approaches, thereby contributing actionable insights to drug discovery research.
DriveShield: attention-based hybrid neural network for intrusion detection in automotive controller area networks Vismaya Kootayi Kunnacheri; Arul Leena Rose Peter Joseph
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2618-2632

Abstract

Vehicle network security is important as increasing amounts of connected technology are being added to vehicles nowadays, putting them at risk of cyberattacks. This paper presents DriveShield, a novel real-time intrusion detection system (IDS) that is the first to combine gated recurrent units (GRU), convolutional neural networks (CNN), and long short-term memory (LSTM) with an attention mechanism. The systematic pre-processing pipeline, which includes feature engineering, the synthetic minority oversampling technique (SMOTE) for class balancing, and normalization. The model was validated on the open training intrusion detection system (OTIDS) dataset and the Hacking and Countermeasure Research Lab (HCRL) car hacking dataset. In the HCRL dataset, the model had an accuracy of 96.30% with F1-scores as high as 96% for all kinds of attacks. On the OTIDS dataset, it performed very well in terms of generalization, with a highest accuracy of 99.78% and a weighted F1-score of 99.78%. The addition of an attention mechanism enabled the model to concentrate on the most significant features, providing better adaptability to changing threats. These findings demonstrate the efficacy, scalability, and reliability of the system for in-vehicle network security. The future research will focus on performance on lower-frequency attacks through the study of unsupervised learning methods and real-world deployment trials.
Enhancing phishing website detection: a comparative study of SMOTETomek-XGB and SMOTEENN-XGB Kamal Omari; Ayoub Oukhatar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2935-2945

Abstract

In the evolving landscape of cybersecurity, phishing websites continue to be a persistent threat, challenging detection methods due to the significant class imbalance between phishing and legitimate websites. This study evaluates the effectiveness of two advanced hybrid-resampling techniques SMOTETomek and SMOTEENN integrated with the extreme gradient boosting (XGBoost) classifier to enhance phishing website detection. SMOTETomek combines the synthetic minority over-sampling technique (SMOTE) with Tomek links, creating synthetic examples and eliminating overlapping instances to address dataset imbalance. SMOTEENN, on the other hand, merges SMOTE with edited nearest neighbors (ENN) to improve class balance through synthetic sample generation and noise reduction. The comparative analysis reveals that both methods significantly enhance classification performance, SMOTETomek-XGB consistently outperforms SMOTEENN-XGB across key evaluation metrics, including accuracy, F1 score, recall, and receiver operating characteristic - area under the curve (ROC-AUC), underscoring its superior effectiveness in distinguishing phishing sites from legitimate ones. This study offers practical insights into the application of advanced resampling methods for improving machine learning model performance in cybersecurity.
Machine learning approaches for anomaly detection of Jakarta air quality index Muhammad Rizky Nurhambali; Yenni Angraini; Anwar Fitrianto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2543-2553

Abstract

Anomalies in time series data are observations that deviate markedly from surrounding values or overall patterns. Air quality index (AQI) data, which vary over time, provide a suitable context for anomaly detection. Time series anomaly detection can be done with machine learning approaches like long short-term memory (LSTM) and extreme gradient boosting (XGBoost). These methods have advantages over conventional methods in handling nonlinearity and large data dimensions. This study compares LSTM and XGBoost methods for detecting anomalies in Jakarta's hourly AQI data. The dataset was obtained from the AirNow website and covers the period from January 1, 2018, to December 31, 2023. Anomalies in the observed data were labeled using moving range (MR) (2) and (3) approaches with three and four-sigma thresholds, and feature engineering (FE) was applied to improve model performance. The results indicate that LSTM is more suitable than XGBoost for forecasting and classification tasks in AQI data. LSTM achieved an average mean absolute percentage error (MAPE) of 10.3840%, a root mean square error (RMSE) of 10.5913, and a balanced accuracy (BACC) of 0.9424 under MR (2) labeling with the four-sigma rule. The anomalies detected mostly occurred between 21:00 and 09:00 and during the rainy season.
Combination of binary particle swarm optimization and random forest for stroke disease prediction Sutikno Sutikno; Rismiyati Rismiyati; Khadijah Khadijah; Abdul Karim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2290-2299

Abstract

Stroke is a leading cause of death and disability worldwide, making early risk prediction critical for prevention. Machine learning methods such as random forest (RF) have shown strong predictive performance, but accuracy can be further improved through effective feature selection. This research proposes an integrated model that combines binary particle swarm optimization (BPSO) for feature selection with RF for stroke risk classification. Experiments were conducted on two public datasets: the stroke prediction dataset (SPD) and the brain stroke dataset (BSD). Data preprocessing included handling missing values, normalization, and the synthetic minority oversampling technique (SMOTE) to mitigate the minority and majority classes. BPSO was employed to select the most informative features, followed by RF for classification. The BPSO-RF model delivered superior accuracies of 96.13% on the SPD and 96.07% on the BSD, outperforming competing classifiers and feature selection techniques. Important features such as gender, age, work type, residence type, average glucose level, body mass index (BMI), and smoking status were consistently identified as key predictors. These results indicate that integrating swarm intelligence with ensemble learning can effectively improve stroke risk prediction and support clinical decision-making.
Optimizing papaya yield: the evaluation of deep learning models for automated disease detection Tejas Rana; Chintan Thacker
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2664-2673

Abstract

The current research will create a robust and successful deep learning (DL) system to recognize and classify papaya leaf diseases. The traditional disease detection techniques are both time-consuming and unreliable, and extensively rely on expert knowledge, therefore limiting them in terms of scalability in agricultural practice. To tackle this issue, the convolutional neural network (CNN)-based method is suggested and tested on the BDPapayaLeaf that includes 2,159 images of papaya leaf with four disease categories and healthy papaya leaves, i.e., anthracnose, bacterial spot, leaf curl (reversal), and ring spot. The data was split into training 80%, validation 10%, and testing 10% data. Pictures were downscaled to 224×224 and normalized before training. Six trained CNN structures VGG16, VGG19, InceptionV3, DenseNet121, MobileNetV2, and ResNet50 were examined. The top model in terms of classification accuracy, according to them, was InceptionV3 with 89% in terms of classification accuracy, showing a high level of performance on true positive and false negative. The findings indicate that DL is an effective and precise method of automated detection of papaya leaf disease and is useful in improving precision and reliability in agricultural diagnostics.
K-fold ensemble 3D convolutional neural network for predicting MGMT promoter methylation in glioblastoma Manzoor Mohammad; Burra Vijaya Babu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2786-2796

Abstract

Medical intervention is necessary for brain tumors, which represent a critical health threat. Chemotherapy response and patient survival outcomes depend on the methylation status of the O-methylguanine-DNA methyltransferase (MGMT) promoter. Biopsy and laboratory testing currently provide the only method to obtain this specific information. This study investigates a non invasive technique for measuring MGMT promoter methylation through magnetic resonance imaging (MRI) scanning. The BraTS 2021 dataset provided fluid attenuated inversion recovery (FLAIR) and contrast-enhanced T1 (T1ce) MRI data to develop a 3D convolutional neural network (CNN) system. The model used five-fold stratified cross-validation for training and testing to create a reliable assessment method. Prediction accuracy improved through the use of an ensemble that combined the best models from each cross-validation fold. The model achieved an average accuracy of 0.718 and an area under the curve (AUC) of 0.727 on the validation data. The results demonstrate that MRI features can provide essential molecular details despite using restricted imaging techniques. The proposed framework shows that deep learning enables early non-invasive detection of MGMT promoter methylation status in glioblastoma (GBM). The methods help doctors with treatment planning while also identifying patients who will benefit from temozolomide-based therapies.
Hybrid convolutional neural network-bidirectional long short-term memory model for Arabic sentence readability assessment Mohamed Amine Ouassil; Mohammed Jebbari; Rabia Rachidi; Mouaad Errami; Soufiane Hamida; Bouchaib Cherradi; Abdelhadi Raihani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2849-2862

Abstract

In the current educational landscape, a large number of educators prefer using generative artificial intelligence techniques to produce textual content to be presented for learning. However, these generated texts may not meet the specific needs of learners or align with their abilities. Many traditional methods and techniques can be employed to assess the complexity of a text, such as traditional readability formulas, but these techniques are time consuming and labor-intensive. In this paper, we introduce a deep learning approach for automatically evaluating the readability of Arabic texts by analyzing and classifying sentences into different difficulty levels within educational content. The initial stage of the proposed approach is preprocessing textual content and leveraging natural language processing (NLP) methodologies for feature extraction, such as Word2Vec. The approach then concentrates on refining and evaluating a deep learning model to classify text into different readability levels. This paper introduces a hybrid classification model that combines convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) layers, attaining an accuracy of 96.68%. This model demonstrates the significance of applying hybrid deep learning models in analyzing educational materials and establishes a foundation for subsequent progress in the field of automated Arabic readability assessment.
Applications of machine intelligence and decision analytics in hospitality: a systematic review Omar Jawabreh; Ehab Abdul Raheem Alshatnawi; Rami Mahmoud
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2024-2040

Abstract

This systematic review synthesizes how machine intelligence (MI) and decision analytics (DA) are deployed across hospitality functions (customer relationship management (CRM)/personalization, revenue management (RM) and pricing, and operations) and clarifies barriers and research frontiers. Following a PRISMA-oriented protocol for the 2008–2025 period, the study searched Scopus, Web of Science, IEEE/Elsevier, and SpringerLink using combined terms for MI/artificial intelligence (AI)/machine learning (ML) and hospitality/tourism with CRM, revenue/pricing, forecasting, and operations. The inclusion criteria focused on peer-reviewed English studies reporting concrete models/applications or empirical evidence. Studies were thematically coded into four streams: CRM and personalization; RM and dynamic pricing; operations and scheduling; and governance (privacy/ethics/skills). The findings consistently show that MI/DA improves customer segmentation, demand forecasting, dynamic pricing (revenue per available room (RevPAR)), and workforce/maintenance efficiency. Yet, adoption is slowed by data governance (privacy/general data protection regulation (GDPR)) and siloed systems. Unlike prior broad IT/e Tourism reviews, this study integrates recent MI advances (spatiotemporal deep learning, real-time decisioning, and human–AI teaming) into a hospitality-specific decision pipeline and outlines a testable research agenda around scalability/drift, privacy-by-design, and controlled field experiments.
AI tools in teaching: EFL teachers’ perceptions of usefulness, challenges, and the role of designation and AI-training Md. Mahadhi Hasan; Kaniz Fatema; Rashed Mahmud
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2092-2102

Abstract

This study aims to investigate the integration of artificial intelligence (AI) into the teaching of English as a foreign language (EFL) at Bangladeshi Universities, focusing on teachers' perceptions of its benefits and challenges. A mixed-methods research design was employed, with a 19-item survey administered to 123 EFL teachers across 20 universities. Qualitative data were collected through semi-structured interviews with 5 teachers. Reliability and validity were assessed through pre-testing and Cronbach's alpha for internal consistency. Member checking and triangulation also contributed to the trustworthiness of qualitative data collected. Quantitative analysis used mean, standard deviation (SD), factor analysis, and chi-square tests, complemented by qualitative thematic analysis. Chi-square test results indicate no significant difference in teachers' designations regarding challenges (p >0.05). However, teachers with AI training do significantly better than those without in recognizing AI-generated EFL content (p =0.03). The qualitative findings show that teachers perceive AI as a tool that enhances teaching strategies, offers better time management, and facilitates innovative instructional practices. However, concerns about maintaining academic integrity prevail. Despite these challenges, the study emphasizes an optimistic outlook towards the role of AI in teaching English. It suggests integrating AI tools into EFL classrooms and proposes structured training programs to fully leverage their potential to enhance teaching effectiveness and improve learning outcomes.

Page 193 of 198 | Total Record : 1974

 191   192   193   194   195 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 15, No 3: June 2026 Vol 15, No 2: April 2026 Vol 15, No 1: February 2026 Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue