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31,377 Article Results

Feature-guided transformer approach for detecting distributed denial of service attacks

10.11591/ijai.v15.i4.pp3712-3721
Lokeshwaran Kanagaraj , Raguraman Purushothaman , Sathya Subramanian , Durga Devi Saravanan , Komal Kumar Napa , Cornelius Karunakaran , Billa Manindhar
Distributed denial of service (DDoS) attacks continues to pose serious risks to modern networks, with their growing intensity making early detection both critical and challenging. Conventional machine learning (ML) models often struggle with the nonlinear and highly dynamic nature of attack traffic, which motivates the use of advanced architectures. In this study investigate a transformer-based classifier for DDoS detection on the CIC-DDoS2019 dataset. The workflow included preprocessing, feature scaling, and domain-guided feature selection. Logistic regression (LR) was employed as a baseline, achieving 92.1% accuracy and F1-score of 0.90, thereby revealing the limitations of linear models. The transformer, after hyperparameter tuning and 5-fold cross-validation, reached an average accuracy of 99.95% with precision, recall, and F1-scores all above 99.9%. The model demonstrated stable convergence and generalization across folds. These results highlight the strength of attention mechanisms in capturing feature dependencies, while also pointing to future directions such as real-time deployment, explainability, and resilience to zero-day attacks.
Volume: 15
Issue: 4
Page: 3712-3721
Publish at: 2026-08-01

Machine learning techniques for rainfall prediction: a systematic literature review

10.11591/ijai.v15.i4.pp3441-3451
Deepa Sharma , Anand Kumar Shukla , Punam Rattan
There are numerous aspects of human life in which knowing how much rain to expect might be beneficial. Heavy rainfall events such as flash floods and landslides, as well as droughts, can be predicted with effective rainfall forecasting. Because of reliable weather forecasts, the infrastructure required to capture rainwater and cultivate crops may be planned ahead of time. A variety of machine learning (ML) and deep learning (DL) algorithms enable accurate weather forecasting. This work seeks to provide a full overview of the numerous ML algorithms used for rainfall prediction by focusing on the technique, input parameters, and several performance measures. The review consists of 51 works divided into three sections. It is found that long short-term memory (LSTM), one of the DL algorithms, is mostly used by researchers for developing the model, but in recent years, ensemble learning and hybrid learning have also gained popularity among researchers as they give more accurate results. These methods need to be explored further.
Volume: 15
Issue: 4
Page: 3441-3451
Publish at: 2026-08-01

AI-driven hybrid neural network for electrocardiogram-based authentication and predictive health monitoring

10.11591/ijai.v15.i4.pp3309-3317
Swati Lakshmi Boppana , Velicheti Anantha Lakshmi , Padala SriKavitha , Venkata Ashok Kalaga , Venkateswara Rao Naramala , Suneetha Thalluru , Gunturi S. Raghavendra
The increasing adoption of digital healthcare systems demands secure and reliable patient authentication mechanisms. Traditional methods such as passwords and PINs are vulnerable to security breaches, motivating the use of biometric-based solutions. Among various biometrics, the electrocardiogram (ECG) signal is distinctive, stable, and non-invasive, making it suitable for secure authentication. This paper proposes CardioGuard, an artificial intelligence (AI)–based authentication framework that employs a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to extract discriminative features from ECG signals and classify users as genuine or impostors. In addition to access control, the system analyzes ECG patterns to support early detection of potential cardiovascular abnormalities. Experimental results demonstrate that CardioGuard achieves improved authentication accuracy and enhanced predictive health insights compared to conventional approaches, highlighting its effectiveness for secure and intelligent healthcare monitoring.
Volume: 15
Issue: 4
Page: 3309-3317
Publish at: 2026-08-01

Genetic algorithm-optimized BERTopic with SHAP explainability for institutional research trend analysis

10.11591/ijai.v15.i4.pp3818-3826
Muhammad Dedi Irawan , Yustria Handika Siregar , Hewa Majeed Zangana , Ali Ikhwan
Institutional research grant titles constitute short-text grey literature characterized by heterogeneous semantic structures, making topic identification and research trend analysis challenging. This study proposes an integrated bidirectional encoder representation from transformers-based topic modeling (BERTopic) framework combining genetic algorithm (GA)-based hyperparameter optimization and Shapley additive explanations (SHAP)-based interpretability to improve semantic topic quality and model transparency. GA was applied to optimize dimensionality-reduction and density-based clustering parameters, while SHAP was used to estimate the contribution of bigram features to the surrogate classifier’s predictions of BERTopic-generated topic labels. Experimental results demonstrated that the proposed framework improved topic coherence from 0.367 to 0.543 while reducing the outlier ratio from 21.12% to 13.55%. In addition, the number of topics decreased from 46 to 10, resulting in a more compact and less fragmented topic structure. The resulting topic structure revealed dominant themes related to higher education, religious moderation, Islamic counseling, halal tourism, and sharia banking. Overall, the proposed framework contributes to the development of more coherent, interpretable, and semantically robust topic modeling for institutional short-text grey literature analysis.
Volume: 15
Issue: 4
Page: 3818-3826
Publish at: 2026-08-01

Hybrid quantum-classical neural networks for brain computed tomography scan diagnosis

10.11591/ijai.v15.i4.pp3342-3351
Bilal R. Altamer , Muhamad Azhar Abdilatef Alobaidy , Aws Hazim Saber Anaz , Zahraa Tarik AlAli
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.
Volume: 15
Issue: 4
Page: 3342-3351
Publish at: 2026-08-01

Deep learning for lung cancer diagnosis: a comparative artificial intelligence study

10.11591/ijai.v15.i4.pp3120-3130
Phaneendra Varma Chintalapati , Prasanth Aruchamy , Alur Praneetha
Early and accurate lung cancer diagnosis (LCD) is critical, yet traditional imaging methods such as X-rays and computed tomography (CT) scans are costly, invasive, and heavily reliant on expert interpretation. This study investigates artificial intelligence (AI)-driven diagnostics by comparing deep learning models (convolutional neural network (CNN), residual network (ResNet), and visual geometry group 16 (VGG16)) with traditional machine learning algorithms (logistic regression (LR) and support vector machine (SVM)), using lung image database consortium and image database resource initiative (LIDC-IDRI) and Kaggle lung CT scan datasets. Performance was evaluated across multiple metrics: accuracy, sensitivity, specificity, and area under the curve-receiver operating characteristic (AUC-ROC). Among the models, ResNet achieved the highest performance, with an accuracy of 94%, sensitivity of 95%, specificity of 93%, and AUC-ROC close to 1. CNN and VGG16 also showed superior metrics compared to LR and SVM, highlighting the robustness of deep learning techniques. These results demonstrate that deep learning models not only achieve higher diagnostic accuracy but also significantly reduce false detections compared to traditional approaches. The findings support the potential of AI to automate and enhance LCD, thereby improving accessibility, consistency, and speed in clinical settings. Future work will emphasize clinical validation, address ethical challenges, and focus on integrating AI models into real-world healthcare workflows to improve patient outcomes.
Volume: 15
Issue: 4
Page: 3120-3130
Publish at: 2026-08-01

Comparison of machine learning algorithms to identify muscular atrophy and muscular dystrophy

10.11591/ijai.v15.i4.pp3505-3517
Ganga Bhavani Billa , Venkateswara Rao Peddada
Muscular dystrophy is a genetic disorder characterized by progressive muscle weakness and degeneration, while muscular atrophy involves the loss of muscle tissue due to pathological conditions. Accurate differentiation between these neuromuscular disorders is critical for effective clinical diagnosis and treatment planning. This study presents a machine learning– based framework for classifying muscular dystrophy and muscular atrophy using surface electromyography (sEMG) signals. sEMG data were acquired and processed to extract informative features with low computational complexity. Several machine learning classifiers several techniques were used and assessed, such as random forest (RF), support vector machine (SVM), gradient boosting (GB), and extreme gradient boosting (XGBoost). To further enhance classification performance, a novel ensemble learning approach combining multiple machine learning models was proposed. Experimental results exhibit that the suggested ensemble model completes significantly higher grouping accurateness and robustness compared to individual classifiers and existing methods reported in the literature. The results of this investigation indicate that ensemble-based machine learning techniques integrated with sEMG analysis provide a reliable and non-invasive diagnostic aid, with strong potential for early detection and improved clinical management of neuromuscular disorders.
Volume: 15
Issue: 4
Page: 3505-3517
Publish at: 2026-08-01

Utilizing Poisson modeling and advanced machine learning techniques for enhanced detection of slowloris attacks

10.11591/ijai.v15.i4.pp3154-3163
Prathima Mabel John , Mamatha Ram , Vani Kurugod Ashwathanarayana Reddy , Santhosh Babu , Rama Mohan Babu Kasturi Nagappasetty
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.
Volume: 15
Issue: 4
Page: 3154-3163
Publish at: 2026-08-01

Design of intelligent embedded system for personal protective equipment detection and face recognition access control

10.11591/ijai.v15.i4.pp3176-3188
Mariam Mesfer , Aoosh Matar , Reem Saif , Alyazy Saleh , Irfan Ahmed , Moath Awawdeh , Anees Bashir
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.
Volume: 15
Issue: 4
Page: 3176-3188
Publish at: 2026-08-01

Bridging gaps in health artificial intelligence: challenges in MDPI research articles

10.11591/ijai.v15.i4.pp3053-3067
Irwan Bastian , Aqilla Rahman Musyaffa , Lukman Nulhakim , Novia Putri Bahirah , Dewi Agushinta R.
Technological advancements in artificial intelligence (AI) have transformed healthcare by improving early disease detection, personalized treatment, predictive analytics, and clinical decision support systems. However, AI adoption in healthcare faces critical challenges, including data privacy concerns, algorithmic bias, regulatory barriers, usability issues, and system interoperability. Addressing these issues requires standardized regulations, ethical frameworks, and interdisciplinary collaboration to ensure responsible AI integration. This study systematically analyzes research trends in health AI over the past six years through a systematic literature review (SLR) and a bibliometric analysis using VOSviewer. The review focuses on Multidisciplinary Digital Publishing Institute (MDPI) journal articles to identify key contributors, emerging trends, and research gaps in AI-driven healthcare. Findings highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics, while exposing persistent challenges such as a lack of standardized AI models, ethical concerns, and accessibility disparities. By mapping the research landscape, this study provides evidence-based insights and recommendations to address AI adoption barriers, improve transparency, and guide future research in healthcare AI. The results contribute to developing a more equitable, efficient, and trustworthy AI-driven healthcare system.
Volume: 15
Issue: 4
Page: 3053-3067
Publish at: 2026-08-01

Determining student scaffolding levels in geometry problem-solving: a fuzzy inference system using Mamdani method

10.11591/ijai.v15.i4.pp3286-3298
Yuniar Ika Putri Pranyata , Susiswo Susiswo , Tjang Daniel Chandra
This study aims to adopt a fuzzy logic inference system using Mamdani method to determine the appropriate scaffolding level for students based on errors in solving geometry problems. The fuzzy inference system (FIS) assessed the students' understanding and provided scaffolding to address specific learning needs by analyzing common mistakes in geometry problem-solving. This method optimized the educational process by offering personalized support, enhancing students' problem-solving skills, and reducing error rates. The data processing criteria using the FIS involved analyzing the scores of mathematics education students at a private university in Malang when solving geometry problems, based on Pólya's stages. Mamdani was used to provide recommendations based on students' cognitive data while solving geometry problems, in line with scaffolding components. The results showed that the system personalized scaffolding levels for students working on geometry problems. This contributed to the field of educational technology by presenting a new approach to adaptive learning and emphasized the importance of personalized pedagogical support. The approach was particularly beneficial for geometry teachers who provided individualized scaffolding based on students' errors, contributing to improved learning outcomes.
Volume: 15
Issue: 4
Page: 3286-3298
Publish at: 2026-08-01

Energy loss prediction using least absolute shrinkage and selection operator regression and SHAP explainability

10.11591/ijai.v15.i4.pp3103-3119
Nur Diana Izzani Masdzarif , Siti Azirah Asmai , Yogan Jaya Kumar , Muhammad Hafidz Fazli Md Fauadi
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.
Volume: 15
Issue: 4
Page: 3103-3119
Publish at: 2026-08-01

Enhancing early detection of autism spectrum disorder through ensemble-based machine learning classifiers

10.11591/ijai.v15.i4.pp3732-3744
Shabeena Lylath , Laxmi B. Rananavare
Autism spectrum disorder (ASD) is a developmental disability characterized by significant social, communication, and behavioral challenges. Machine learning is a practical approach for autism detection. The proposed ensemble-based machine learning classifier methodology presented in this study seeks to revolutionize the diagnosis of ASD by harnessing the collective power of various machine learning algorithms. This ensemble approach is designed to enhance diagnostic precision, mitigate the subjectivity associated with traditional diagnostic methods, and accelerate the detection process. This methodology addresses the urgent need for early and accurate ASD identification, enabling timely interventions. Leveraging complex data analysis, it offers deeper diagnostic insights, facilitating informed clinical decisions and advancing ASD research. The methodology's accessibility across healthcare settings marks a significant step forward in making early ASD detection more universally available, showcasing the transformative potential of machine learning in healthcare. In deploying the “ensemble-based machine learning classifier” for ASD diagnosis, this study utilizes an extensive dataset comprising behavioral and medical profiles from diverse demographics, including toddlers, children, adolescents, and adults with ASD. Upon the preliminary analysis, the dataset enables the methodology to learn from a wide array of ASD manifestations, ensuring its robustness and applicability across different age groups and severity levels.
Volume: 15
Issue: 4
Page: 3732-3744
Publish at: 2026-08-01

Computational framework for smart tourism management: hybrid time series decomposition and predictive modeling

10.11591/ijai.v15.i4.pp3421-3430
Iwan Ady Prabowo , Hendro Wijayanto , Teguh Susyanto
Smart tourism management in rural multi-destination settings requires forecasting methods that are accurate enough to support visitor allocation, infrastructure readiness, and ecological protection. This study presents a decomposition-based forecasting framework for Sidowayah Village, Central Java, Indonesia, which integrates three attractions with different demand profiles: Umbul Manten, Siblarak, and Kampung Dolanan. Using monthly visitation data from May 2023 to April 2024, the study compares additive and multiplicative decomposition models within a common workflow of data collection, preprocessing, trend-seasonal decomposition, model evaluation, and sustainability-oriented interpretation. The contribution of the study lies in clarifying destination-specific criteria for selecting additive versus multiplicative models, improving methodological transparency in preprocessing and temporal validation, and translating forecast outputs into practical smart tourism actions aligned with sustainable development goals (SDGs) 11 and 12. The results show that the multiplicative-average all model yields the lowest mean absolute percentage error (MAPE) for Umbul Manten (14.1%) and Siblarak (56.8%), while the additive-centered moving average model is more suitable for Kampung Dolanan based on mean absolute deviation (MAD) (162.6). Although the 12-month dataset limits long-term generalization, the framework provides a reproducible basis for data-informed tourism management in rural destinations.
Volume: 15
Issue: 4
Page: 3421-3430
Publish at: 2026-08-01

Production scheduling using a hybrid approach based on genetic algorithm and convergent random search

10.11591/ijeecs.v43.i2.pp534-546
Belbachir Djelloul , Kadri Boufeldja
The flexible job shop scheduling problem (FJSSP) is a highly complex combinatorial optimization problem widely encountered in modern manufacturing systems. Its complexity arises from the simultaneous determination of operation sequencing and machine assignment, making it significantly more challenging than the classical job shop scheduling problem (JSSP). Recent advances in hybrid metaheuristics and intelligent optimization methods have improved solution quality; however, achieving an effective balance between global exploration and local exploitation remains a critical challenge. In this paper, a novel hybrid metaheuristic approach combining a genetic algorithm (GA) and convergent random search (CRS) is proposed to address the FJSSP. The proposed method exploits the global search capability of GA to explore the solution space, while CRS is employed as an adaptive local refinement mechanism applied to elite individuals. This hybridization strategy enhances convergence speed and avoids premature stagnation. Extensive computational experiments are conducted on well-known benchmark instances, including Brandimarte and Kacem datasets. The results indicate that the proposed GA–CRS approach significantly improves the makespan compared to classical GA and PSO based methods. In addition, the algorithm exhibits faster convergence behavior, reaching high-quality solutions in fewer iterations. Statistical analysis using non-parametric tests confirms the superiority of the proposed method. These findings demonstrate that the proposed hybrid GA–CRS algorithm provides a robust and efficient optimization framework for solving large-scale and complex FJSSP instances, outperforming several state-of the-art approaches.
Volume: 43
Issue: 2
Page: 534-546
Publish at: 2026-08-01
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