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31,377 Article Results

An optimized deep learning framework for brain tumor classification using magnetic resonance imaging

10.11591/ijai.v15.i4.pp3722-3731
Komal Kumar Napa , Rajkumar Govindarajan , Senthil Murugan Janakiraman , Jayanthi Arumugam
Accurate and interpretable classification of brain tumors in magnetic resonance imaging (MRI) scans plays a crucial role in early diagnosis and effective treatment planning. This study introduces a deep learning (DL) framework based on a customized YOLOv5m architecture integrated with a bidirectional feature pyramid network (BiFPN) for multi-class brain tumor classification. The integration of BiFPN enhances multi-scale feature fusion, improving detection across varied tumor types, while YOLOv5m ensures real-time inference capabilities. To mitigate class imbalance, a class-weighted cross-entropy loss is adopted. The model is evaluated on multiple performance metrics, achieving a test accuracy of 88.86%, precision of 88.70%, recall of 88.20%, and F1-score of 88.25%. It also reports a mean average precision (mAP@0.5) of 94.36%, with high class-wise average precision (AP) for glioma, meningioma, pituitary, and no-tumor categories. Computational time for training (12484.81 seconds) and testing (146.82 seconds) confirms the model’s feasibility for real-time clinical deployment. To support interpretability, gradient-weighted class activation mapping (Grad-CAM) is integrated for visualizing class-discriminative regions, helping clinicians understand the model’s predictions. A gradio-based user interface is also developed, enabling intuitive interaction with the system.
Volume: 15
Issue: 4
Page: 3722-3731
Publish at: 2026-08-01

Analyzing academic acceptance of artificial intelligence using extended technology acceptance model

10.11591/ijai.v15.i4.pp3090-3102
Nanang Suryadi , Abdurrahman Hakim , Adelia Shabrina Prameka , Wildan Syafitri , Muhammad Irfan Islami
This study aims to analyze the acceptance of artificial intelligence (AI) technology among Indonesian academics using extended technology acceptance model (TAM). The analysis involved general extended technology acceptance model for e-learning (GETAMEL) independent variables, which include subjective norm (SN), experience (EXP), enjoyment (ENJOY), computer anxiety (CA), and self-efficacy (SE), as well as mediator variables: perceived usefulness (PU) and perceived ease of use (PEOU). This study uses technology innovations (TI) as moderator variable and behavioral intention (BI) as a dependent variable. The analysis reveals that SN has a significant influence over PU but not PEOU. ENJOY and SE have a significant positive influence over both PU and PEOU, while EXP and CA don’t have a significant influence over both variables. PU and PEOU have a significant positive influence over BI. TI strengthens the relation between PU and BI, but weakens the relation between PEOU and BI. This finding provides an important outlook for developing a strategy to increase the acceptance of AI technology in the academic environment through an approach that takes into account the factors of pleasure, self-confidence, and TI.
Volume: 15
Issue: 4
Page: 3090-3102
Publish at: 2026-08-01

Hybridization of hybrid-ARIMA-EM and XGBoost for enhanced price predictive modeling

10.11591/ijai.v15.i4.pp3131-3143
Isam Ahmed M. Yaqoob , Khairul Azhar Kasmiran , Teh Noranis Mohd Aris , Nor Azura Husin , Mohd Yunus Sharum
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.
Volume: 15
Issue: 4
Page: 3131-3143
Publish at: 2026-08-01

Smart parking management system: a seamless parking solution using YOLO and QR code payment technology

10.11591/ijai.v15.i4.pp3614-3624
Sumit Kumar , Ruchi Rani , Sanjeev Kumar Pippal
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.
Volume: 15
Issue: 4
Page: 3614-3624
Publish at: 2026-08-01

Real-time object detection for autonomous driving: a comparative study of YOLO and Faster R-CNN

10.11591/ijai.v15.i4.pp3581-3590
Madhura M. Bhosale , Yogesh S. Angal
Over the past few years, object detection has experienced remarkable progress and development, primarily driven by the development of one-stage and two-stage detection algorithms. Among these, Faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO) have achieved notable success due to their strong performance and computational efficiency. Object detection plays a crucial role in various applications, particularly in autonomous driving systems, where accurate detection of pedestrians, vehicles, and road signs is essential for ensuring safety and reliability. This paper conducts a comparative evaluation of YOLO and Faster R-CNN to analyze their performance in autonomous driving environments. The experiments were conducted using the KITTI open-source dataset, which is widely used for benchmarking object detection models. All experiments were performed on an NVIDIA RTX A5000 GPU to ensure efficient computation, with implementations developed using Python version 3.9.13. The experimental findings indicate that YOLO surpasses Faster R-CNN in performance, attaining an accuracy rate of 90%. These findings highlight the effectiveness of YOLO for real-time object detection tasks, making it a suitable and preferred choice for time-sensitive applications such as autonomous driving systems.
Volume: 15
Issue: 4
Page: 3581-3590
Publish at: 2026-08-01

Long short-term memory based activity detection using skeleton joints data: a systematic review

10.11591/ijai.v15.i4.pp3026-3035
Bakkala Santha Kumar , R. Shankar
In today's security and surveillance applications, recognizing abnormal activity is critical component of identifying possible hazardous or unusual human behaviors. There is need for new technologies that can detect abnormal human behaviors precisely. The present review investigates various aspects of detection process, focusing on skeleton joints input data. It then explores adoption of deep learning (DL) architectures, such as long short-term memory (LSTMs) and transformers, to improve accuracy and robustness of recognition models. The review explores synergistic integration of LSTMs and transformers to improve recognition of unusual activity. By integrating LSTMs' processing capabilities and attention mechanisms of transformers, enhanced models can accurately identify intricate patterns of activity. Despite the advancements that have been made in the field, the challenges that remain are still related to recognition of unusual activities. These include lack of scalability for large datasets, need for models that can recognize complex behaviors across diverse applications, and need to ensure that detection is performed in a low-latency manner. The paper explores future directions of developing LSTM-based models that can recognize unusual activity using skeleton joint data in a cloud-based environment. The review emphasizes the potential of such solutions that can take advantage of the processing power of graphics processing units (GPUs) and tensor processing units (TPUs) and enable real-time recognition of activity in large datasets.
Volume: 15
Issue: 4
Page: 3026-3035
Publish at: 2026-08-01

A review of brain signal analysis in food perception studies

10.11591/ijai.v15.i4.pp3967-3980
Harish S. Sinai Velingkar , Roopa R. Kulkarni , Prashant Patavardhan , Abdul Haq Nalband
Food perception is more than just taste. It is a multisensory experience shaped by smell, texture, and even how the food looks. The human brain works to make sense of all these signals, influencing everything from cravings to food choices. This review looks at more than 40 studies that used electroencephalography (EEG) and other brainwave analysis tools to understand how to process food related stimuli. In order to identify patterns associated with attention, mood, and appetite, these studies looked at brain responses using techniques including event related potentials (ERPs) and spectral analysis. Current research shows that human desire for food, level of focus on it, and the feelings it evokes are all correlated with certain brain signals. However, majority of the studies investigated individual senses in isolation and focused on basic taste attributes such as sweet, salty, or bitter. The complexity of eating in real life, where several senses interact, is missed by this. Furthermore, a lot of research is lab based, short term, and does not represent real world situations. Future research must adopt practical, multimodal methods in order to fully understand how humans perceive food. Such studies could help improve food design, predict consumer behavior, and even support interventions for eating disorders.
Volume: 15
Issue: 4
Page: 3967-3980
Publish at: 2026-08-01

Prioritizing ransomware indicators of compromise using algorithmic scoring for enhanced threat detection

10.11591/ijai.v15.i4.pp3865-3877
Krishna Prasad D. Subramanya , Prasanna Kumar H. Ramakrishna
Ransomware is a very serious cybersecurity threat. Its attack methods are continually evolving, which means that it is not very easy to detect it quickly. A big challenge faced in the ransomware defense is how to prioritize indicators of compromise (IoC) efficiently. This paper introduces a hybrid IoC ranking scheme based on static and dynamic analysis of the behavior of commonly circulating ransomware variants. Each IoC is given a weighted score according to its significance for investigations based on actual patterns of occurrence and contextual behavior. Experimental results clearly separate high-confidence indicators from low-confidence ones. Deleting shadow copy and connecting to Tor2web receive the highest rank scores of about 99.99, while older behaviors like locking screen show lower relevance, around 73.11. The proposed algorithm has a linear time complexity of O(n·m) for score calculation and a bounded space complexity of O(n·m), allowing it to scale for large IoC sets. The findings show that this ranked IoC framework enhances early ransomware detection and helps prioritize responses based on evidence in current security systems.
Volume: 15
Issue: 4
Page: 3865-3877
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

Systematic review of fraud detection using AI and ML with an emphasis on telecommunication industry

10.11591/ijai.v15.i4.pp3269-3285
Soly Mathew , Sindi Rryta
The telecommunications industry is one of the top industries affected by fraudulent activities. Given the financial impact, on top of confidentiality breaches, security concerns, and reduced service quality as well as consumer dissatisfaction, there is an immediate need to implement effective fraud detection approaches. While there have been different fraud detection systems implemented, technological advancements as well as the improved techniques of fraudsters have made the traditional approaches no longer efficient. This paper aims to further investigate the use of artificial intelligence (AI) and machine learning (ML) to create efficient and advanced fraud detection models based on the strategy used, models applied, accuracy of the system, as well as future research work suggested. A systematic review of 50 papers was conducted. The most prevalent strategy was supervised one, majority of papers used software instead of hardware, and the most common ML models were artificial neural network (ANN), support vector machines (SVM), and decision tree.
Volume: 15
Issue: 4
Page: 3269-3285
Publish at: 2026-08-01

Metaheuristic optimization for atrial fibrillation detection: feature extraction, selection, and hyperparameter tuning

10.11591/ijai.v15.i4.pp3878-3887
Zaid Nouna , Hamid Bouyghf , Mohammed Nahid , Issa Sabiri
Atrial fibrillation (AF) detection from electrocardiogram (ECG) signals is crucial for early diagnosis and intervention. This study presents a multi-objective optimization approach for AF detection, focusing on feature extraction, selection, and neural network hyperparameter tuning. The methodology uses cross-validation during the training of the two concatenated ECG dataset features and simultaneously minimizes the error rate on the separate validation folds of each dataset and reduces the number of selected features, enhancing model generalization and efficiency. Particle swarm optimization (PSO), grey wolf optimization (GWO), and differential evolution (DE) algorithms were implemented to navigate this multi-objective space. While all three algorithms were explored, the final solution, demonstrating a superior trade-off between accuracy and feature reduction, was obtained using DE. This approach effectively identifies optimal feature subsets and neural network configurations, yielding a robust and compact AF detection model. The proposed approach has shown promising results, with the model achieving accuracies of 96.38% and 90.69%, and corresponding area under the curve (AUC) values of 0.99 and 0.96, for the first and second datasets, respectively, using 10 optimally selected features.
Volume: 15
Issue: 4
Page: 3878-3887
Publish at: 2026-08-01

A machine learning framework for predicting and optimizing return on investment across marketing channels

10.11591/ijai.v15.i4.pp3528-3536
Chandra Chathura , Keerthan Saya , Sathishkumar Mani
In the current fast-paced competitive marketing environment, firms require data-centric methods to maximize their investments in several avenues. This research work applies machine learning techniques to estimate the return on investment (ROI) for marketing costs, which helps organizations in budgeting more effectively. Four models including random forest, extreme gradient boosting (XGBoost), gradient boosting, and linear regression were utilized for their accuracy in making predictions. Results showed that the highest accuracy was achieved by linear regression at 99.39%, random forest at 99.07%, gradient boosting at 99.01%, and XGBoost at 98.81%. It was further noted that digital marketing avenues such as social media and online stores gave the highest ROI, indicating that companies should prioritize digital marketing more than traditional marketing. On a practical level, this approach helps marketing team for choosing high performing channels since it estimates expected returns from each marketing channels and make smarter budget allocation. Yet, the study is done by using Kaggle dataset. In order to improve its generality, future research may use larger real-world datasets and extensive visualization techniques.
Volume: 15
Issue: 4
Page: 3528-3536
Publish at: 2026-08-01

Symmetry index-based gait improvement prediction using a CNN-LSTM-attention framework

10.11591/ijai.v15.i4.pp3625-3636
Pushpalatha Obanna , Premkumar Ramesh
The shortcomings of existing clinical assessments are addressed by using a deep learning (DL) framework. This study will assess the gait improvement of lower limb fractured patients using the publicly available GaitRec dataset. The dataset contains the vertical ground reaction force (GRF) data used to compute biomechanical kinetic features. This framework considers hip, knee, ankle, and calcaneus fractures as lower limb fracture class, and the symmetry indices computation is done between affected and unaffected limbs, and their temporal changes were examined to analyze and track the rehabilitation progress of individual subject. The framework successfully identifies the reduction in the asymmetry between left and right leg features. The overall gait improvement in patients is computed using initial and final session composite asymmetry index (ASI) values and if it is at least 30% then overall gait is improved, demonstrating its robustness and clinical relevance. The suggested convolutional neural network (CNN)-long short-term memory (LSTM)-attention hybrid model outperformed with regression metrics of root mean square error (RMSE) of 1.09, mean absolute error (MAE) by 0.64, and R2 score of 0.98 and classification metrics of accuracy 97.1%, precision of 97.6%, recall of 96%, and F1-score of 96.8% by capturing both the local patterns and temporal dynamics of the gait data.
Volume: 15
Issue: 4
Page: 3625-3636
Publish at: 2026-08-01

Strategic optimization of artificial intelligence digital learning in informatics engineering

10.11591/ijai.v15.i4.pp2999-3008
Aan Ansori , Birru Muqdamien , Ahmad Tabrani , Reza Syafrizal , Sutanto Sutanto , Eko Wahyu Wibowo , Syifa Amara Dhestiyani
This study examines the strategic optimization of artificial intelligence (AI)-based digital learning in the Department of Informatics Engineering by analyzing the interaction between internal and external factors that influence successful implementation. Employing a qualitative approach based on strengths, weaknesses, opportunities, and threats (SWOT) analysis, this research utilizes the internal factor analysis summary (IFAS) and external factor analysis summary (EFAS) to assess institutional readiness, constraints, and strategic opportunities for AI integration in higher education. The results indicate that external factors exert a stronger strategic influence than internal factors, as reflected by a higher EFAS score (1.50) compared to the IFAS score (1.25). Key external opportunities include the increasing demand for AI competencies, supportive national policies, and opportunities for industry collaboration, while the main internal limitations are limited faculty expertise in AI and insufficient AI-specific learning resources. Based on these findings, this study formulates a SWOT-based strategic framework that converts analytical outcomes into practical recommendations for curriculum development, faculty capacity building, and the adoption of adaptive AI learning technologies. This research contributes a context-specific and empirically grounded strategic model that advances AI integration beyond descriptive analysis, supporting more effective, personalized, and sustainable digital learning, particularly within Informatics Engineering programs in Indonesian Islamic higher education institutions.
Volume: 15
Issue: 4
Page: 2999-3008
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
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