IAES International Journal of Artificial Intelligence (IJ-AI)
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.
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<![CDATA[Indonesia Islamic students' belief towards the performance of artificial intelligence: perceptions, impacts, and anticipations ]]>
<![CDATA[Yuni Sugiarti]]>;
<![CDATA[Ence Oos Mukhamad Anwas]]>;
<![CDATA[Fitroh Fitroh]]>;
<![CDATA[Janu Arlinwibowo]]>;
<![CDATA[Ihsan Maulana Anwas]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v15.i4.pp3955-3966
<![CDATA[This research aims to i) determine the belief level of Islamic university students towards AI for learning and ii) what factors related to student confidence in using AI for learning. This study employed a mixed-methods sequential explanatory design. The research subjects were students of the strata 1, Universitas Islam Negeri Syarif Hidayatullah, and students of Universitas Islam Negeri Sunan Kalijaga, totaling 411 students. The research sample was obtained using random techniques. The quantitative data collection method is questionnaire-based, and data in-depth analysis is carried out qualitatively using the focus group discussion (FGD) technique. The quantitative data analysis method was carried out using descriptive statistics, and the qualitative study was carried out thematically. This research found that the majority of students have a high level of confidence in the performance of artificial intelligence (AI) as a tool for them. This level of belief is significantly and positively related to understanding of the AI platform used, lecturer support and guidance, parental support and guidance, and support from fellow students. However, belief in AI cannot be absolute because it is feared that it will result in poor use. AI platform users, especially students, lecturers, researchers, and anyone who uses it, need to recheck and deepen its accuracy and correctness.]]>
<![CDATA[Experimental validation of vision-based hybrid navigation for differential-drive robots ]]>
<![CDATA[Thanh Quang Duc Le]]>;
<![CDATA[Trong Hieu Luu]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v15.i4.pp3836-3851
<![CDATA[Reliable mobile-robot deployment requires mapping, localization, global planning, and real-time obstacle avoidance to operate consistently under sensor noise and physical constraints. This study presents a vision-based hybrid navigation framework for a differential-drive mobile robot in a warehouse-like environment. The main contribution lies in the system-level integration and experimental validation of established techniques rather than the development of a new standalone navigation algorithm. A ceiling-mounted camera converts top-view images into an occupancy-grid map and world coordinates, while a convolutional neural network (CNN) recognizes goal markers. Odometry–augmented reality University of Cordoba (ArUco) fusion is used to correct accumulated localization drift. Particle swarm optimization (PSO) generates smooth global paths offline, whereas the dynamic window approach (DWA) performs real-time local motion control. Experiments in a 3.3 m × 2.4 m workspace achieved average obstacle-localization errors of 0.959 cm and 0.696 cm along the x- and y-axes, respectively, and a goal-recognition accuracy of 99%. The DWA controller required 12.61±2.42 ms per cycle, while rapidly-exploring random tree (RRT) and PSO required 4–7 s and 477–692 s, respectively, for global path generation. Simulation and physical experiments confirmed collision-free navigation and successful quick response (QR)-code-based goods inspection, demonstrating the feasibility of the proposed framework for small, structured indoor environments.]]>
<![CDATA[Smart parking management system: a seamless parking solution using YOLO and QR code payment technology ]]>
<![CDATA[Sumit Kumar]]>;
<![CDATA[Ruchi Rani]]>;
<![CDATA[Sanjeev Kumar Pippal]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v15.i4.pp3614-3624
<![CDATA[The inefficiencies of traditional parking systems, including manual entry and reliance on sensors, as well as slow payment lines, contribute to congestion and a lower level of user satisfaction. This paper suggests a smart parking management system (SPMS) based on the license plate recognition (LPR) technology using you only look once version 5 (YOLOv5)modelincombination with a QR code based payment system, to automate the parking system. It’s able to detect vehicle license plates in real-time, which means that data doesn’t have to be entered manually, and it also avoids the need for physical sensors, which cuts down on infrastructure costs. With a centralized database, the efficient tracking of vehicles is achieved, and QR-based payment will facilitate contactless payments at exit. Experimental evaluation shows that the proposed system out performs than the other systems, with an accuracy of 98.09% and recall of 98.25% in case of LPR. This system drastically decreases processing time, congestion and improves overall user experience. SPMS is more efficient, scalable, and reliable than traditional and existing smart parking systems. The results show that the proposed method is a cost-effective and practical solution for solving the modern parking management problem.]]>
<![CDATA[Utilizing Poisson modeling and advanced machine learning techniques for enhanced detection of slowloris attacks ]]>
<![CDATA[Prathima Mabel John]]>;
<![CDATA[Mamatha Ram]]>;
<![CDATA[Vani Kurugod Ashwathanarayana Reddy]]>;
<![CDATA[Santhosh Babu]]>;
<![CDATA[Rama Mohan Babu Kasturi Nagappasetty]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v15.i4.pp3154-3163
<![CDATA[This paper presents a secure framework for detecting and mitigating slowloris attacks in software defined networks (SDN) using Poisson arrival modeling to generate attack traffic. Slowloris attacks imitate legitimate traffic patterns, making them difficult to identify. The framework includes three modules: a data generation module, a feature selection module based on the blended statistical and information gain (BSIG) approach, and a traffic classification module using machine learning and deep learning algorithms. Poisson arrival modeling captures the stochastic nature of attack traffic by introducing variability in packet inter-arrival times, thereby simulating realistic network conditions. Monte Carlo simulations establish anomaly thresholds, while the BSIG method identifies key features, such as inter-arrival variability and CPU utilization. Support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) classifiers achieve high precision in distinguishing legitimate traffic from attack traffic. The integration of Poisson modeling with advanced analytical methods improves detection capability while maintaining scalability for similar stealth attacks. The proposed methodology provides high detection accuracy and adapts to dynamic network environments, enhancing web server defenses against slowloris attacks in real-world scenarios. The framework also reduces false positives, supports efficient resource utilization, and strengthens proactive security management in modern SDN infrastructures under varying conditions.]]>
<![CDATA[Deep transfer learning architecture for depression detection using PHQ-9 analysis in Bengaluru urban population ]]>
<![CDATA[U. Ananthanagu]]>;
<![CDATA[Pooja Agarwal]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v15.i4.pp3202-3217
<![CDATA[In the metropolitan cities in India, there is a scarcity of population-based statistics on depression prevalence. This study utilized the patient health questionnaire-9 (PHQ-9) instrument to look at the prevalence and risk factors for depression in major metropolitan cities. This study's population-based analysis identifies major demographic and socioeconomic characteristics associated with depression in an Indian urban context. This quantitative inquiry conducted a population-centric analysis of 987 people from a metropolitan city. Depression was measured and validated using the PHQ-9 questionnaire to calculate the prevalence rates and sociodemographic characteristics. A deep transfer learning framework integrating visual and textual features was proposed for automated classification of depression, to assess mental health patterns in urban populations. Depression symptoms were common, with many respondents reporting persistent symptoms like a persistent lack of interest, pervasive sorrow, sleep difficulties, and suicidal ideation. Bivariate studies found relationships between these symptoms and other demographic and socioeconomic characteristics, whereas multivariate analysis revealed significant predictors. In addition, the classification of people’s stress in metropolitan regions is analyzed through various pre-trained models to show better outcomes. The substantial link between sociodemographic characteristics and depression levels shows the importance of mental health interventions for this urban Indian population.]]>
<![CDATA[Design of intelligent embedded system for personal protective equipment detection and face recognition access control ]]>
<![CDATA[Mariam Mesfer]]>;
<![CDATA[Aoosh Matar]]>;
<![CDATA[Reem Saif]]>;
<![CDATA[Alyazy Saleh]]>;
<![CDATA[Irfan Ahmed]]>;
<![CDATA[Moath Awawdeh]]>;
<![CDATA[Anees Bashir]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v15.i4.pp3176-3188
<![CDATA[This paper presents an artificial intelligence (AI)-powered automated access control system that aims to reduce delays and improve safety. The primary problem addressed is effective monitoring of compliance with personal protective equipment (PPE) and secure access control for personnel entering sites. This study represents the design and development of an access control system that includes accurate detection of essential PPE items (e.g., safety helmets, gloves, goggles, and gas detectors), integration of facial recognition for identity verification, real-time monitoring of video feeds, and an intuitive user interface for security personnel to manage access and compliance efficiently. The software part uses you only look once (YOLO) version 8 for real-time object detection, classification, and drawing the bounding boxes around the detected object in a single forward pass. The hardware platform consists of NVIDIA Jetson AGX Orin 64 GB as an edge computing device. The developed AI-based embedded system is tested and validated with real-world scenarios and achieved a mean average precision (mAP) of 98.4% for PPE detection and 99.38% accuracy for face recognition.]]>
<![CDATA[Energy loss prediction using least absolute shrinkage and selection operator regression and SHAP explainability ]]>
<![CDATA[Nur Diana Izzani Masdzarif]]>;
<![CDATA[Siti Azirah Asmai]]>;
<![CDATA[Yogan Jaya Kumar]]>;
<![CDATA[Muhammad Hafidz Fazli Md Fauadi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v15.i4.pp3103-3119
<![CDATA[Technical energy loss estimation in power distribution systems is essential for improving operational efficiency and cost-effectiveness. However, distribution-level datasets are often failed to cope with the nonlinear behavior and sparse, low-resolution data typical in modern grid environments. This study proposes an interpretable artificial intelligence (AI) framework based on least absolute shrinkage and selection operator (LASSO) regression integrated with Shapley additive explanations (SHAP) to estimate and explain technical energy losses at the feeder level. An exploratory multicollinearity assessment using correlation analysis and variance inflation factor (VIF) revealed severe redundancy among operational variables, justifying the adoption of L1-regularized regression. Hyperparameter tuning via LassoCV identified an optimal regularization parameter, resulting in strong predictive performance. Comparative evaluation with nonlinear models, including random forest and gradient boosting, demonstrated that LASSO achieves competitive or superior generalization performance while preserving interpretability. Feature importance analysis and SHAP-based explanations confirmed that operational loading variables particularly infeed energy, load factor, and maximum demand are the dominant drivers of technical losses. SHAP dependence and interaction analyses further revealed context-dependent behavior among correlated predictors, enriching interpretability beyond coefficient-based rankings. The results demonstrate that regularized linear modeling, when combined with explainable AI techniques, provides a robust, transparent, and practically deployable solution for technical loss estimation in distribution networks.]]>
<![CDATA[Deep learning for categorizing microsatellite stability in colorectal cancer ]]>
<![CDATA[Sofyan El Idrissi]]>;
<![CDATA[Yassine Drider]]>;
<![CDATA[Ikram Ben Abdel Ouahab]]>;
<![CDATA[Mohammed Bouhorma]]>;
<![CDATA[Fatiha El Ouaai]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v15.i4.pp3761-3769
<![CDATA[Cancer remains a significant global health challenge, with its incidence rising steadily in recent decades. In colorectal cancer (CRC), microsatellite instability (MSI), and microsatellite stability (MSS) are important biomarkers that influence treatment decisions and patient outcomes. Accurate MSI classification is critical but traditional methods can be costly and time-consuming. This study explores the potential of deep learning to classify MSI and MSS in CRC. A large dataset of CRC patients with confirmed MSI and MSS status was utilized, obtained through standard testing images. Deep learning models were applied to histopathological images, analyzing tissue features from digital slides. Convolutional neural network (CNN) and residual network (ResNet)-18 models demonstrated high accuracy in distinguishing between MSI and MSS CRCs. The best-performing model, which integrated genomic and histopathological data, achieved an area under the curve (AUC) receiver operating characteristic (ROC) of 0.85, indicating strong discrimination capability. The findings suggest that deep learning could be a valuable tool for clinical decision-making and personalized medicine in CRC.]]>
<![CDATA[Hybrid quantum-classical neural networks for brain computed tomography scan diagnosis ]]>
<![CDATA[Bilal R. Altamer]]>;
<![CDATA[Muhamad Azhar Abdilatef Alobaidy]]>;
<![CDATA[Aws Hazim Saber Anaz]]>;
<![CDATA[Zahraa Tarik AlAli]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v15.i4.pp3342-3351
<![CDATA[Medical image classification is considered as very important field of diagnosis and treatment of neurological disorders, which include stroke, tumors, and hemorrhages, it can used to facilitate timely medical intervention. A hybrid quantum-classical convolutional neural network (QCNN) is presented by combining quantum information processing with classical deep learning techniques for improved feature extraction and classification accuracy. The model combines convolutional neural network (CNN), which handles initial feature extraction with a PennyLane and TensorFlow based layer that employs quantum entanglement and superposition principles to enhance classification performance. The model is trained and tested over a computed tomography (CT) scan image dataset which has four classes (normal, stroke, tumor, and hemorrhage). Various learning rates are tested and employed a hybrid backpropagation method to improve efficiency. Confusion matrices, region of conversion receiver operating characteristic (ROC) curves, and plots for the training convergence that all pointed to promising classification accuracy were derived with a detailed analysis accordingly. Overall, the results indicate that combining quantum computing with deep learning architectures could improve classification performance at a lowest computational cost. The results suggest the viability of hybrid quantum-classical models for medical imaging applications and indicate that quantum computing is a promising direction for enhancing diagnostic accuracy in the area of radiology.]]>
<![CDATA[Context-aware AgriBot using dual intent and entity transformer and hybrid deep learning model ]]>
<![CDATA[Binod Deka]]>;
<![CDATA[Ridip Dev Choudhury]]>;
<![CDATA[Utpal Barman]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v15.i4.pp3637-3645
<![CDATA[The agriculture sector has gone through vast technological improvement, leading to increased productivity and sustainability. This research introduces AgriBot, a context-aware virtual assistant (VA) that helps farmers in rice cultivation and identifies diseases while providing instant suggestions for subsequent task. Using the RASA framework, AgriBot has been designed to understand the farmer's queries. Dual intent and entity transformer (DIET) classifier has been used for entity classification, achieving training and testing accuracy of up to 98% and 97%, respectively. Additionally, the system incorporates machine learning (ML) models for rice disease detection, utilizing a dataset of 4,624 images covering three major rice diseases: bacterial blight, brown spot, and blast. Among the tested models—neural network (NN), random forest (RF), support vector machine (SVM) and naive Bayes (NB) achieved an accuracy of up to 99.9%, demonstrating excellent classification performance. Using text-based query handling with image-based disease identification and instant suggestion makes it a more robust support system for the farmers.]]>
