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Creating inclusive UX: uncovering gender-bugs in higher education website through GenderMag’ing

10.11591/ijeecs.v39.i2.pp996-1004
Maria Isabel Milagroso Santos , Thelma Domingo Palaoag , Anazel Patricio Gamilla
Higher education websites serve as service-providing and information-disseminating platforms which may contain gender-related usability issues that affect how male and female users interact with digital platforms. This study applied the gender inclusiveness magnifier (GenderMag) method to identify and assess these gender-specific usability barriers. Researchers conducted cognitive walkthrough sessions using gendered personas, Abi (female) and Tim (male), uncovering key inclusivity bugs aligned to specific cognitive facets-motivation, information processing style, computer self-efficacy, risk aversion, and learning style. Insights from these walkthroughs guided the creation of a structured usability survey, administered to 200 respondents equally divided between males and females, comprising faculty and upper-year BS information technology students. Statistical analysis revealed significant gender differences specifically in information processing style (p=0.0003), emphasizing distinct preferences for content organization and navigation between genders. The integration of usability factors with GenderMag’s cognitive facets effectively pinpointed areas requiring inclusive design adjustments, guiding future efforts to enhance equitable digital interactions in educational environments.
Volume: 39
Issue: 2
Page: 996-1004
Publish at: 2025-08-01

IoT-enabled smart healthcare system with machine learning for real-time vital sign monitoring and anomaly detection

10.11591/ijeecs.v39.i2.pp1155-1163
Sanjay Deshmukh , Shrey Shah , Asim Wahedna , Nimish Sabnis
This paper presents an innovative IoT-enabled smart healthcare system that combines real-time vital sign monitoring with machine learning-based anomaly detection. The system utilizes a MAX30102 photoplethysmography sensor interfaced with an ESP-32 microcontroller to collect heart rate and blood oxygen saturation (SpO2) data. MQTT protocol ensures efficient data transmission to a cloud database. A long short-term memory (LSTM) neural network architecture is employed for time-series prediction of vital signs and anomaly detection. The system demonstrates high accuracy, with mean squared errors of 0.3% in offline testing and over 90% accuracy in real-time prediction. This affordable and scalable solution offers continuous monitoring capabilities, making it viable for widespread adoption in healthcare settings. The integration of IoT and machine learning techniques provides a robust framework for early detection of health anomalies, potentially improving patient care and outcomes in various medical scenarios.
Volume: 39
Issue: 2
Page: 1155-1163
Publish at: 2025-08-01

A framework for security risk assessment of blockchain-based applications

10.11591/ijeecs.v39.i2.pp952-962
Mohammad Qatawneh
Blockchain technology has revolutionized various industries by enabling decentralized, transparent, and tamper-resistant digital transactions. However, despite its benefits, blockchain-based applications are vulnerable to security threats such as smart contract exploits, 51% attacks, Sybil attacks, and private key compromises, posing significant risks to their integrity and reliability. Traditional security frameworks lack a comprehensive approach to systematically assess and mitigate these risks across different blockchain layers. To address this challenge, this paper proposes the blockchain cybersecurity risk assessment model (BCRAM), a structured framework designed to identify, analyze, evaluate, and mitigate security risks in blockchain systems. The methodology involves categorizing threats, assessing risks using quantitative and qualitative techniques, and validating the model through a case study on Ethereum. Results demonstrate that implementing BCRAM led to a 65% reduction in smart contract exploits, a 70% decrease in phishing incidents, and an 85% improvement in distributed denial of service (DDoS) resilience, proving its effectiveness. This research offers a standardized risk assessment approach, providing valuable insights for developers, security analysts to enhance blockchain security.
Volume: 39
Issue: 2
Page: 952-962
Publish at: 2025-08-01

Development of ResNet-18 architecture to lesion identification in breast ultrasound images

10.11591/ijeecs.v39.i2.pp1236-1248
Silfia Andini , Sumijan Sumijan , Iskandar Fitri
Breast ultrasound (USG) is widely used for early breast cancer detection, but challenges such as noise, low contrast, and resolution limitations hinder accurate lesion identification. This study proposes a modified residual network-18 (ResNet-18) architecture for breast lesion segmentation, aimed at improving detection accuracy. The methodology involves preprocessing steps including red green blue (RGB) to Grayscale conversion, contrast stretching, and median filtering to enhance image quality. The modified ResNet-18 model introduces additional convolutional layers to refine feature extraction. The proposed model was trained and validated on 30 breast ultrasound images, with evaluation metrics including accuracy, sensitivity, and specificity. Experimental results indicate that the modified architecture outperforms the baseline model, achieving an average accuracy of 0.97093, sensitivity of 0.90056, and specificity of 0.97705. Validation by a radiology specialist confirms the model’s clinical relevance. These findings suggest that the enhanced ResNet-18 model has the potential to assist radiologists in more accurately identifying breast lesions. Future research should focus on expanding the dataset, integrating multi-modal imaging, and optimizing model generalizability for real-time clinical applications. The study contributes to advancing artificial intelligence (AI)-driven breast cancer diagnostics, supporting early detection, and improving patient outcomes.
Volume: 39
Issue: 2
Page: 1236-1248
Publish at: 2025-08-01

Enhancing marketing efficiency through data-driven customer segmentation with machine learning approaches

10.11591/ijeecs.v39.i2.pp1399-1410
Fanindia Purnamasari , Umaya Ramadhani M. O. Putri Nasution , Marischa Elveny
The importance of understanding consumer behavior in transaction data has become a key to improving marketing efficiency. This study aims to explore the application of machine learning (ML) techniques for data-driven consumer segmentation, focusing on improving product marketing strategies. This work addresses the limitations in the existing literature, especially in terms of handling high-dimensional data that can reduce segmentation quality. Previously, various studies have used clustering algorithms such as K-means without considering dimensionality reduction, which often leads to decreased accuracy and long computation time. In this study, we propose a new approach that combines principal component analysis (PCA) for dimensionality reduction and K-means clustering for consumer segmentation based on purchasing behavior. Experimental results show that using PCA to reduce data dimensionality significantly improves segmentation quality with an inertia score of 1,455,650 and a silhouette score of 0.486366. By implementing this method, we can group consumers into three segments based on frequently purchased product categories and the most common payment methods. These findings provide a scalable, data-driven segmentation framework that can be applied to improve marketing effectiveness by providing special discounts on various products based on the payment method used.
Volume: 39
Issue: 2
Page: 1399-1410
Publish at: 2025-08-01

Binary white shark optimization algorithm with Z-shaped transfer function for feature selection problems

10.11591/ijeecs.v39.i2.pp1269-1279
Avinash Nagaraja Rao , Sitesh Kumar Sinha , Shivamurthaiah Mallaiah
Feature selection is critical for improving model performance and managing high-dimensional data, yet existing methods often face limitations such as inefficiency and suboptimal results. This study addresses these challenges by introducing a novel approach using the white shark optimization (WSO) algorithm and its binary variants to enhance feature selection. The proposed methods are evaluated on various datasets, including “Dorothea,” “Breast Cancer,” and “Arrhythmia,” focusing on classification accuracy, the number of features selected, and fitness values. Results demonstrate that the WSO algorithms significantly outperform traditional methods, offering notable improvements in accuracy and efficiency. Specifically, the WSO variants consistently achieve higher accuracy and better fitness values while effectively reducing the number of selected features. This research contributes to the field by providing a more effective optimization approach for feature selection, addressing existing inefficiencies, and suggesting future directions for further refinement and broader application. The findings highlight the potential of advanced optimization techniques in enhancing data analysis and model performance, offering valuable insights for practitioners and researchers.
Volume: 39
Issue: 2
Page: 1269-1279
Publish at: 2025-08-01

A simulation-based investigation into the bidirectional charge and discharge dynamics in lead-acid batteries

10.11591/ijeecs.v39.i2.pp783-796
Muhammad Aiman Noor Zelan , Muhammad Nabil Hidayat , Nik Hakimi Nik Ali , Muhammad Umair , Muhammad Izzul Mohd Mawardi , Ahmad Sukri Ahmad , Ezmin Abdullah
This paper presents a comprehensive simulation-based investigation into the bidirectional charge and discharge dynamics of lead-acid batteries within electric vehicles (EVs) and energy storage systems (ESS). Utilizing a bidirectional DC-DC converter (BDC) integrated with a lead-acid battery, the study explores the performance of these batteries through various charging and discharging scenarios. The simulation model, implemented using MATLAB, assesses the impact of charging strategies on battery behavior, focusing on key metrics such as state of charge (SOC), energy performance, and charging rates. The results reveal that lead-acid batteries, when paired with appropriate charging infrastructure and strategies, demonstrate enhanced performance and reliability in both EV and ESS applications. The study highlights the significant role of BDC topology in facilitating efficient energy transfer and optimizing battery usage. The findings underscore the potential for improved performance and widespread adoption of bidirectional converters in sustainable energy solution.
Volume: 39
Issue: 2
Page: 783-796
Publish at: 2025-08-01

Devising the m-learning framework for enhancing students' confidence through expert consensus

10.11591/ijeecs.v39.i2.pp1035-1052
Teik Heng Sun , Muhammad Modi Lakulu , Noor Anida Zaria Mohd Noor
Past research has shown the relationship between self-regulated learning (SRL) and academic success. Self-regulated learners will monitor their learning, reflect on what they have learnt, adjust their learning strategies accordingly, and repeat this entire process throughout their learning. The ability to perform SRL will require the individual to have the belief and confidence in his/her capacity to succeed and accomplish the tasks. Therefore, this study aims to devise a mobile learning (m-learning) framework for enhancing the students’ confidence. To achieve this, the Fuzzy Delphi method was used to validate the proposed framework where the survey questionnaire was distributed to 21 experts who are the experts in their respective fields for their consensus to be obtained. Consensus showed that “assessment data” can indicate the students’ confidence when they attempt the assessment. Experts opined that “goal expectation,” and “viewed lessons, chapters, or syllabus” exert the most influence on the students’ confidence when they attempt their assessment. There was strong consensus from experts that “data security” is the most important element in the system infrastructure, and the “text mining technique” element can be used to evaluate the students’ confidence.
Volume: 39
Issue: 2
Page: 1035-1052
Publish at: 2025-08-01

The design of an electronic load for mitigating transient overvoltage in the track circuits of railway signaling systems

10.11591/ijeecs.v39.i2.pp807-820
Ukrit Kornkanok , Sansak Deeon , Chuthong Summatta , Saktanong Wongcharoen
The research presented the design of safety electronic load suppression (SELS) for mitigating transient overvoltage in the track circuits of railway signaling systems while changing the track occupancy in the track circuits of the signaling system that caused damage to the BR966F2 relay. The analysis of the average failure of the electronic devices, the failure modes and effect analysis (FMEA), and the performance test of electronic devices were conducted. and the performance test of electronic devices were conducted. which can control the operation with 2oo3 processing mode (two out of three voting) under the series circuits pattern to resolve the damage caused by the application. Results illustrated that the mean operating time of the SELS between failures was 9,399 hours. In addition, regarding the performance of the electronic load for mitigating transient overvoltage of 1 kV at 31.4 V and overvoltage 50 VDC at 178.6 °C within 83 seconds at 35.4 V. Additionally, the SELS could function adequately without failure or causing any damage. Therefore, the SELS was more reliable.
Volume: 39
Issue: 2
Page: 807-820
Publish at: 2025-08-01

Efficient object detection for augmented reality based english learning with YOLOv8 optimization

10.11591/ijeecs.v39.i2.pp1189-1197
Arya Krisna Putra , Fiqri Ramadhan Tambunan , Samson Ndruru , Andry Chowanda
This study develops a mobile-based augmented reality (AR) application with machine learning for elementary school students to enhance basic English vocabulary learning. The application integrates an optimized YOLOv8 object detection model, designed to recognize 20 common classroom objects in real-time. The model optimization involves replacing standard Conv layers with GhostConv and the C2f block with the C2fCIB block that has significantly improved computational efficiency. Evaluation results show the optimized model reduces the parameters by 22.003% and decreases the file size from 6.2 MB to 4.9 MB. The model performance improved by achieving precision of 83.7%, recall of 73.5% and a mean Average Precision (mAP) of 81.4%. The model was integrated into the Unity platform via the Barracuda library, enabling real-time detection and interactive display of 3D objects. This aplication also complete with English text, translations, example sentences also audio pronunciation. 3D objects representing classroom vocabulary were specifically created to support AR-based learning. Performance testing on a Samsung A14 showed an improved frame rate of 6–12 FPS compared to the original model’s 5–10 FPS. These results demonstrate that the optimized YOLO model effectively integrates with AR technology, creating a more interactive and enjoyable vocabulary learning experience.
Volume: 39
Issue: 2
Page: 1189-1197
Publish at: 2025-08-01

Enhanced n-party Diffie Hellman key exchange algorithm using the divide and conquer algorithm

10.11591/ijict.v14i2.pp438-445
Nwanze Chukwudi Ashioba , Patrick Ogholorunwalomi Ejeh , Azaka Maduabuchuku
Cryptographic algorithms guarantee data and information security via a communication system against unauthorized users or intruders. Numerous encryption techniques have been employed to safeguard this data and information from hackers. By supplying a distinct shared secret key, the n-party Diffie Hellman key exchange approach has been used to protect data from hackers. Using a quadratic time complexity, the n-party Diffie-Hellman method is slow when multiple users use the cryptographic key interchange system. To solve this issue, the researchers created an effective shared hidden key for the n-party Diffie Hellman key exchange of a cryptographic system using the divide-and-conquer strategy. The current research recommends the use of the divide and conquer algorithm, which breaks down the main problem into smaller subproblems until it reaches the base solution, which is then merged to generate the solution of the main problem. The comparative analysis indicates that the developed system generates a shared secret key faster than the current n-party Diffie Hellman system.
Volume: 14
Issue: 2
Page: 438-445
Publish at: 2025-08-01

Secure lightweight CAN protocol handling for electric vehicles

10.11591/ijeecs.v39.i2.pp774-782
Vandana Vijaykumar Hanchate , Rupali Kamathe , Meghana Deshpande , Kalyani Joshi , Sheetal Borde , Abrar Inamdar , Vijayalakshmi Madduru
The integrity of controller area network (CAN) protocols in electric vehicles (EVs) is of paramount importance, due to their susceptibility to cyber intrusions and unauthorized access. Traditional encryption-based security solutions, such as advanced encryption standard (AES) and anomaly detection methods, often introduce high computational overhead and latency, making them unsuitable for real-time EV communication. This study proposes a secure lightweight CAN protocol (SLCP), implemented using ARDUINO Uno and MCP2515, which enhances message integrity, authentication, and fault recovery without compromising system efficiency. Experimental testing demonstrated that the proposed SLCP reduces message authentication latency by 25% and improves message integrity by 40% compared to conventional encryption techniques. Additionally, packet resynchronization time was reduced by 30%, ensuring minimal disruptions in case of message loss. These findings establish SLCP as a viable, real-time alternative for low-power EV communication networks. The study contributes to advancing lightweight security frameworks for EV networks, paving the way for scalable, real-time cybersecurity solutions in modern electric transportation.
Volume: 39
Issue: 2
Page: 774-782
Publish at: 2025-08-01

Detection of the Tajweed rules in the Qur’anic recitations

10.11591/ijeecs.v39.i2.pp914-926
Karim Aly Mohammad , Ahmed Hisham Kandil , Ahmed Mohamed El-Bialy , Sahar Ali Fawzi
Tajweed is the science of reciting the Holy Quran, focusing on the clarity and correctness of recitation. This paper aims to accurately detect the spoken Tajweed rules applied during Quranic recitation, providing a well-structured Tajweed rules database for further analysis, Tajweed learning, and the training of advanced classification models. The main contribution of this work is to identify a high-accuracy approach for Tajweed rules detection and analysis. An improved template matching approach is introduced to enhance detection accuracy by matching the Quranic verse audio file with multiple speech patterns of a specific rule and selecting the best match. The Quranic audio file is segmented into smaller patterns by finding the correlation between the adjacent audio frames. Then, the template matching is applied to these segmented patterns to identify the best-matching ones. The template matching technique relies on a Tajweed database of 487 patterns of the Madd, Noon Sakinah, Tanween, and Meem Sakinah rules. An overall detection accuracy of 97.1% is achieved, and the Tajweed-pattern database is expanded to include the newly detected rules, increasing their total count to 2,583. Furthermore, an application based on the detected rules in this study was developed to enhance the performance of new Tajweed learners.
Volume: 39
Issue: 2
Page: 914-926
Publish at: 2025-08-01

Detecting sensor faults in wireless sensor networks for precision agriculture using long short-term memory

10.11591/ijece.v15i4.pp3803-3812
Yassine Aitamar , Jamal El Abbadi
The reliable acquisition of soil data from wireless sensor networks (WSNs) deployed in farmlands is critical for optimizing precision agriculture (PA) practices. However, sensor faults can significantly degrade data quality, hindering PA techniques. Our work proposes a novel long short-term memory (LSTM) network-based method for fault detection in WSNs for PA applications. Unlike traditional methods, our approach utilizes a lightweight, transfer learning-based LSTM architecture specifically designed to address the challenge of limited labeled training data availability in agricultural settings. The model effectively captures temporal dependencies within sensor data sequences, enabling accurate predictions of normal sensor behavior and identification of anomalies indicative of faults. Experimental validation confirms the effectiveness of our method in diverse real-world WSN deployments, ensuring data integrity and enhancing network reliability. This study paves the way for improved decision-making and optimized PA practices.
Volume: 15
Issue: 4
Page: 3803-3812
Publish at: 2025-08-01

Cyber-physical resilience system for anomaly detection in industrial environments

10.11591/ijict.v14i2.pp497-505
Debani Prasad Mishra , Rakesh Kumar Lenka , Rampa Sri Sai Yagyna Duthsharma , Pavan Kumar , Lakshay Bhardwaj , Surender Reddy Salkuti
This work explores the topic of cybersecurity in the context of electric vehicles (EVs). It ensures the resilience of cyber-physical systems against anomalies, which is paramount for maintaining operational efficiency and safety. This paper presents a cyber-physical resilience system (CPRS) customized for anomaly detection. Maintaining operational efficiency and safety in today’s networked industrial contexts requires that cyber-physical systems be resilient to abnormalities. With an emphasis on EVs, this research introduces a unique CPRS designed for anomaly detection in industrial settings. By utilizing the combination of digital and physical elements, the CPRS uses sophisticated monitoring and reaction systems to identify and address irregularities instantly. The process includes creating algorithms for anomaly detection and putting in place a framework that is responsive enough to change with the dangers that it faces. The efficiency of the CPRS in detecting unusual behaviors in EVs is demonstrated by experimental findings, which also improve the overall resilience of the system. Moreover, the research’s ramifications go beyond EVs to include a variety of industrial settings, providing valuable information for the development and execution of resilient cyber-physical systems. This paper highlights the significance of proactive resilience measures in protecting critical infrastructure and advances anomaly detection approaches.
Volume: 14
Issue: 2
Page: 497-505
Publish at: 2025-08-01
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