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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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Stacking ensemble techniques for automated peripheral blood cell classification using Inception v3 features Marwa Mawfaq Mohamedsheet Al-Hatab; Maysaloon Abed Qasim; Nawar A. Sultan
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.pp2247-2259

Abstract

Robust distinction of blood cells is crucial in clinical evaluation. Manual examination is slow and exposed to errors. This work investigates using machine learning (ML) techniques for automated classification of eight categories of peripheral blood cell types from multi-color images. The Inception v3 network was used to extract features, a split of 66%/34% were used to evaluate the model along with 20-fold cross-validation. To reduce computational complexity, principal component analysis (PCA) was used to reduce the 2048-dimensional feature vectors to 100 components. Among all classifiers used, the highest performance without using PCA was achieved using the support vector machine (SVM) with an accuracy equal to 93.4% and an area under the curve (AUC) of 0.996. Using PCA, affected monocytes and immature granulocytes most due to the slight reduction in the accuracy and AUC which became 90.1% and to 0.991 respectively. Results were further enhanced when a stacked ensemble of neural network (NN), logistic regression (LR), and SVM were used, achieving an accuracy of 95.2% and an AUC of 0.998. The obtained findings confirmed the effectiveness of using stacked ensembles in providing a robust, high accuracy framework for automated blood cell classification, while PCA efficiently reduced dimensions with minimal performance loss.
Multilingual signs recognition using recurrent neural network Thouseef Ulla Khan; Dileep Marichi Ramachandra
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.pp2494-2510

Abstract

Recognition of sign language is a crucial step towards providing individuals with hearing and speech impairment meaningful communication, but the fact that there are a number of distinct sign languages and gestures remain complex makes it a challenge to the current automated systems. The present paper describes a real-time multilingual sign language recognition system that is based on a recurrent neural network with long short-term memory (RNN-LSTM) with hand landmark MediaPipe-based hand landmark detection to successfully receive spatial and temporal gesture features. The proposed system was trained and tested over a self-collected set of alphabet gestures of the Chinese, American, and Indian sign language, including one hand and two-hand gestures, and was run with Keras with extensive performance evaluation metrics. The strength and generalization abilities of the suggested approach as part of different gesture patterns and variations in users are confirmed by experimental outcomes that indicate high recognition rates of 99.58%, 99.62%, and 99.63% of the Chinese, American, and Indian sign languages, respectively. These results demonstrate the promise of the given framework as a dedicated assistive system of communication and give it a solid base to continue its development to the point of the system of the continuous sign language recognition (CSLR) and multimodal translators.
Deep learning-based integrated XAI for photovoltaic power forecasting considering actual power production period Promphak Boonraksa; Warunee Srisongkram; Kedsara Palachai; Boonruang Marungsri; Terapong Boonraksa
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.pp2970-2984

Abstract

This paper proposes a deep learning (DL)-based integrated explainable artificial intelligence (XAI) framework for photovoltaic (PV) power forecasting, explicitly considering the actual power production period to improve operational reliability. The framework uses solar irradiance, ambient temperature, and relative humidity as input features and evaluates nine DL architectures, including artificial neural networks (ANN), recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), CNN-LSTM, CNN-BiLSTM, RNN-LSTM, and RNN-BiLSTM. Model performance is evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results show that the residual-based RNN-LSTM model provides highest forecasting accuracy, achieving MAE of 1.21 kW, MAPE of 5.12%, and RMSE of 2.24 kW. In comparison, the LSTM and BiLSTM models exhibit substantially higher prediction errors, with MAPEs exceeding 21%, while hybrid convolutional models show moderate improvements but remain inferior. To enhance model transparency, XAI techniques are integrated to interpret feature contributions. The analysis confirms that solar irradiance is the dominant influencing factor, while temperature and humidity introduce secondary nonlinear effects captured effectively by recurrent architectures. The proposed framework provides a high-accuracy and interpretable solution for PV power forecasting, supporting reliable energy management and smart grid applications.
From audio to image: gunshot classification using Mel spectrogram convolutional neural networks Peerapol Khunarsa; Pafan Doungpaisan
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.pp2166-2180

Abstract

Accurate identification of firearm types from acoustic signals is essential for modern public safety and forensic applications. Traditional gunshot analysis methods often rely on physical evidence or handcrafted audio features, which can be unreliable under noisy and reverberant conditions. This study presents a systematic investigation of gunshot sound classification using Mel spectrogram representations and convolutional neural networks (CNNs). Raw audio signals are transformed into Mel spectrogram images, enabling firearm classification to be formulated as an image recognition problem. Thirteen CNN architectures, ranging from lightweight to deep models, are evaluated under a unified experimental protocol to analyze both classification performance and computational efficiency. Experiments are conducted on a publicly available multi-firearm dataset recorded in semi-controlled real-world environments. The results demonstrate that Mel spectrogram–based CNN models achieve classification accuracy exceeding 94%, while moderate-complexity architectures provide a favorable balance between accuracy and efficiency. The findings highlight the importance of representation–architecture alignment and offer practical design guidelines for selecting deployable CNN models in real-time gunshot detection systems.
Indonesia Islamic students' belief towards the performance of artificial intelligence: perceptions, impacts, and anticipations Yuni Sugiarti; Ence Oos Mukhamad Anwas; Fitroh Fitroh; Janu Arlinwibowo; Ihsan Maulana Anwas
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.pp3955-3966

Abstract

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.
Experimental validation of vision-based hybrid navigation for differential-drive robots Thanh Quang Duc Le; Trong Hieu Luu
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.pp3836-3851

Abstract

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.
Smart parking management system: a seamless parking solution using YOLO and QR code payment technology Sumit Kumar; Ruchi Rani; Sanjeev Kumar Pippal
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.pp3614-3624

Abstract

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.
Utilizing Poisson modeling and advanced machine learning techniques for enhanced detection of slowloris attacks Prathima Mabel John; Mamatha Ram; Vani Kurugod Ashwathanarayana Reddy; Santhosh Babu; Rama Mohan Babu Kasturi Nagappasetty
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.pp3154-3163

Abstract

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.
Deep transfer learning architecture for depression detection using PHQ-9 analysis in Bengaluru urban population U. Ananthanagu; Pooja Agarwal
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.pp3202-3217

Abstract

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.
Design of intelligent embedded system for personal protective equipment detection and face recognition access control Mariam Mesfer; Aoosh Matar; Reem Saif; Alyazy Saleh; Irfan Ahmed; Moath Awawdeh; Anees Bashir
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.pp3176-3188

Abstract

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.

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