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28,188 Article Results

Leveraging machine learning for column generation in the dial-a-ride problem with driver preferences

10.11591/ijai.v14.i4.pp2826-2838
Sana Ouasaid , Mohammed Saddoune
The dial-a-ride problem (DARP) is a significant challenge in door-to-door transportation, requiring the development of feasible schedules for transportation requests while respecting various constraints. This paper addresses a variant of DARP with time windows and drivers’ preferences (DARPDP). We introduce a solution methodology integrating machine learning (ML) into a column generation (CG) algorithm framework. The problem is reformulated into a master problem and a pricing subproblem. Initially, a clustering-based approach generates the initial columns, followed by a customized ML-based heuristic to solve each pricing subproblem. Experimental results demonstrate the efficiency of our approach: it reduces the number of the new generated columns by up to 25%, accelerating the convergence of the CG algorithm. Furthermore, it achieves a solution cost gap of only 1.08% compared to the best-known solution for large instances, while significantly reducing computation time.
Volume: 14
Issue: 4
Page: 2826-2838
Publish at: 2025-08-01

Non-small cell lung cancer active compounds discovery holding on protein expression using machine learning models

10.11591/ijai.v14.i4.pp2815-2825
Hamza Hanafi , M’hamed Aït Kbir , Badr Dine Rossi Hassani
Computational methods have transformed the field of drug discovery, which significantly helped in the development of new treatments. Nowadays, researchers are exploring a wide ranger of opportunities to identify new compounds using machine learning. We conducted a comparative study between multiple models capable of predicting compounds to target non-small cell lung cancer, we focused on integrating protein expressions to identify potential compounds that exhibit a high efficacy in targeting lung cancer cells. A dataset was constructed based on the trials available in the ChEMBL database. Then, molecular descriptors were calculated to extract structure-activity relationships from the selected compounds and feed into several machine learning models to learn from. We compared the performance of various algorithms. The multilayer perceptron model exhibited the highest F1 score, achieving an outstanding value of 0,861. Moreover, we present a list of 10 drugs predicted as active in lung cancer, all of which are supported by relevant scientific evidence in the medical literature. Our study showcases the potential of combining protein expression analysis and machine learning techniques to identify novel drugs. Our analytical approach contributes to the drug discovery pipeline, and opens new opportunities to explore and identify new targeted therapies.
Volume: 14
Issue: 4
Page: 2815-2825
Publish at: 2025-08-01

Using the ResNet-50 pre-trained model to improve the classification output of a non-image kidney stone dataset

10.11591/ijai.v14.i4.pp3182-3191
Kazeem Oyebode , Anne Ngozi Odoh
Kidney stone detection based on urine samples seems to be a cost-effective way of detecting the formation of stones. Urine features are usually collected from patients to determine if there is a likelihood of kidney stone formation. There are existing machine learning models that can be used to classify if a stone exists in the kidney, such as the support vector machine (SVM) and deep learning (DL) models. We propose a DL network that works with a pre-trained (ResNet-50) model, making non-image urine features work with an image-based pre-trained model (ResNet-50). Six urine features collected from patients are projected onto 172,800 neurons. This output is then reshaped into a 240 by 240 by 3 tensors. The reshaped output serves as the input to the ResNet-50. The output of this is then sent into a binary classifier to determine if a kidney stone exists or not. The proposed model is benchmarked against the SVM, XGBoost, and two variants of DL networks, and it shows improved performance using the AUC-ROC, Accuracy and F1-score metrics. We demonstrate that combining non-image urine features with an image-based pre-trained model improves classification outcomes, highlighting the potential of integrating heterogeneous data sources for enhanced predictive accuracy.
Volume: 14
Issue: 4
Page: 3182-3191
Publish at: 2025-08-01

Survey on 3D biometric traits for human identification

10.11591/ijai.v14.i4.pp3143-3152
Divya Gangachannaiah , Mamatha Aruvanalli Shivaraj , Honganur Chandrasekharaiah Nagaraj , Prasanna Gururaj Paga
Individuals are verified and identified using Biometric technology based on their biological or behavioral traits. Biometric-based personal authentication systems are more reliable and user friendly, overruns the traditional personal authentication systems. The physiological biometric traits get abraded due to aging and massive work, while the behavioral biometric traits are having high variations due to external factors such as fatigue, and mood. Among the physiological biometric traits, Finger geometry patterns are widely deployed authentication system reason being its stability, user acceptability and uniqueness. Recent trends in Biometrics attempt to incorporate 3D domain traits, 3D reconstruction is done using 2D multiple images. 3D images are usually more robust and illumination invariant as compared to their 2D counterparts. 3D reconstruction algorithms are compared by finding mean square error (MSE).
Volume: 14
Issue: 4
Page: 3143-3152
Publish at: 2025-08-01

Contract-based federated learning framework for intrusion detection system in internet of things networks

10.11591/ijai.v14.i4.pp3324-3333
Yuris Mulya Saputra , Divi Galih Prasetyo Putri , Jimmy Trio Putra , Budi Bayu Murti , Wahyono Wahyono
A plethora of national vital infrastructures connected to internet of things (IoT) networks may trigger serious data security vulnerabilities. To address the issue, intrusion detection systems (IDS) were investigated where the behavior and traffic of IoT networks are monitored to determine whether malicious attacks or not occur through centralized learning on a cloud. Nonetheless, such a method requires IoT devices to transmit their local network traffic data to the cloud, thereby leading to data breaches. This paper proposes a federated learning (FL)-based IDS on IoT networks aiming at improving the intrusion detection accuracy without privacy leakage from the IoT devices. Specifically, an IoT service provider can first motivate IoT devices to participate in the FL process via a contract-based incentive mechanism according to their local data. Then, the FL process is executed to predict IoT network traffic types without sending IoT devices’ local data to the cloud. Here, each IoT device performs the learning process locally and only sends the trained model to the cloud for the model update. The proposed FL-based system achieves a higher utility (up to 44%) than that of a non-contract-based incentive mechanism and a higher prediction accuracy (up to 3%) than that of the local learning method using a real-world IoT network traffic dataset.
Volume: 14
Issue: 4
Page: 3324-3333
Publish at: 2025-08-01

Federated deep learning intrusion detection system on software defined-network based internet of things

10.11591/ijai.v14.i4.pp3109-3120
Heba Dhirar , Ali H. Hamad
The internet of things (IoT) and software-defined networks (SDN) play a significant role in enhancing efficiency and productivity. However, they encounter possible risks. Artificial intelligence (AI) has recently been employed in intrusion detection systems (IDSs), serving as an important instrument for improving security. Nevertheless, the necessity to store data on a centralized server poses a potential threat. Federated learning (FL) addresses this problem by training models locally. In this work, a network intrusion detection system (NIDS) is implemented on multi-controller SDN-based IoT networks. The interplanetary file system (IPFS) FL has been employed to share and train deep learning (DL) models. Several clients participated in the training process using custom generated dataset IoT-SDN by training the model locally and sharing the parameters in an encrypted format, improving the overall effectiveness, safety, and security of the network. The model has successfully identified several types of attacks, including distributed denial of service (DDoS), denial of service (DoS), botnet, brute force, exploitation, malware, probe, web-based, spoofing, recon, and achieving an accuracy of 99.89% and a loss of 0.005.
Volume: 14
Issue: 4
Page: 3109-3120
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

Technology in halal certification: a ten-year bibliometric study

10.11591/ijeecs.v39.i2.pp1280-1298
Yan Putra Timur , Sri Abidah Suryaningsih , Clarashinta Canggih , Fira Nurafini , Maryam Bte Badrul Munir , Asiah Binti Ali
This study explores the role of technology in halal certification using bibliometric analysis. Based on 88 articles from the Scopus database (2014–2024), the research employs tools like Publish or Perish (PoP), Microsoft Excel, and VOSviewer to reveal the intellectual framework of relevant literature. The finding indicates a steady increase in manuscript productivity from 2014-2024 despite a declining citation trend. Journal of Islamic Marketing, Mohd Zabiedy Mohd Sulaiman, Malaysia, and the National Defence of Malaysia emerged as most prolific journal, author, country, and institution that produce the most, respectively, in publishing on the topic. The paper that has influenced other research the most is Rejeb et al.’s integrating the IoT in the halal food supply chain: a systematic literature review and research agenda. Five significant keyword clusters that frequently show up in the 88 articles examined in this study are halal supply chain, consumer behavior towards halal foods, the role of blockchain in the halal industry, the role of information technology in halal cosmetics, and halal logo in food products. This study highlights the increasing integration of technology in halal certification, emphasizing the need for continuous innovation, interdisciplinary collaboration, and alignment with industry demands to maintain relevance. Additionally, it underscores Malaysia’s leadership in this field while noting the global expansion of halal research, the impact of emerging technologies like blockchain and IoT, and the need for stronger institutional collaboration to enhance transparency, traceability, and market growth.
Volume: 39
Issue: 2
Page: 1280-1298
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

Ethics in human-robot interaction research

10.11591/ijeecs.v39.i2.pp1005-1012
Robinson Jimenez Moreno , Anny Astrid Espitia Cubillos , Javier Eduardo Martinez Baquero
This paper explores the basic ethical and bioethical considerations necessary to mediate interaction with various everyday robots, analyzing several stateof-the-art reports and own research, considering advances in human-robot interaction (HRI) and artificial intelligence (AI). It is important to indicate that the adoption of robotic assistance systems is limited by users' nervousness about the enforcement of ethics, security and privacy of their information, in addition to the regular threats of Internet use, considering that HRI must reason its social and ethical impacts by including specific issues associated with HRI such as autonomy, transparency, deception and policies. In this way, it is relevant both to evaluate how robotic architectures influence people's daily lives and to study how to avoid possible negative impacts. Finally, it is significant to establish the ethical considerations required to enable the development of AI algorithms that help HRI in a natural way.
Volume: 39
Issue: 2
Page: 1005-1012
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

The validity of the mobile gamification in economic subject

10.11591/ijere.v14i4.30341
Mohd Zaim Zainal Adnan , Mohamad Zuber Abd Majid , Nofouz Mafarja , Nur Fadzlunnisaa’ Wakimin , Maslawati Mohamad
Mobile gamification has shown growing adoption in education, demonstrating potential to enhance engagement and learning outcomes. This study addresses challenges in economics education, including moderate student achievement, reliance on teachers, and lack of student motivation. To tackle these issues, a mobile gamification tool was developed for secondary school economics. The study’s objective was to validate the content and educational relevance of this tool. Using a sequential exploratory mixed-method design, the research comprised two phases. First, focus group discussions (FGD) were conducted with seven economics and technology experts to assess the tool’s interface and educational content. In the second phase, a content validity index (CVI) assessment quantified expert agreement on five key content areas. Results indicated a high level of expert consensus, with CVI values ranging from 0.87 to 1.00. These findings demonstrate that the gamified mobile tool is a valid educational resource that aligns with curriculum standards and can enhance student engagement in economics. The study concludes that mobile gamification is an effective strategy to support Economics education, encouraging self-directed learning and classroom interaction.
Volume: 14
Issue: 4
Page: 2979-2989
Publish at: 2025-08-01

Collaborative work in the performance of the panpipes in university students in times of semi-presenciality

10.11591/ijere.v14i4.32248
Olga Mendoza-León , Iris Liliana Vasquez-Albuquerque , Yoya Betzabé Flores Pérez , Joselito Moisés Luján Miguel , Wilson Hugo Chunga Amaya , José Wualter Peláez Amado
In the context of blended learning and music education, difficulties were identified in the learning of traditional instruments, such as the panpipes, due to the absence of collaborative methodologies that encourage teamwork. This study was conducted with the objective of improving the performance of the panpipes in university students through collaborative learning, promoting a positive attitude towards cooperation and a constant feedback process. The research included the participation of 46 students and was developed under a qualitative approach, using the action research method. For data collection, techniques such as participant observation, discourse analysis, taking photographs and grounded theory were used. The findings showed significant discursive patterns and revealed students’ perceptions and interactions during the process. The implementation of collaborative strategies fostered active participation, negotiation, and joint construction of musical knowledge, while continuous feedback improved both students’ attitudes toward learning the panpipes and their musical performance. In conclusion, collaborative learning was shown to be an essential methodology for more effective music teaching.
Volume: 14
Issue: 4
Page: 3135-3147
Publish at: 2025-08-01

Influence of playing online video games on Filipino college students’ confidence in speaking English

10.11591/ijere.v14i4.32842
Allan Jay Esteban , Kiwan Sung
Online video games that require players to communicate in English provide opportunities for students to practice their language skills and overcome their fear of speaking in English. Unfortunately, the literature reveals an existing gap in investigating how such games can influence students’ confidence in speaking English, especially in the Philippine context. Therefore, this study surveyed 148 Filipino college English-as-a-second language (ESL) students to examine differences in their perceived confidence in speaking English depending on learner variables such as gender, time spent online gaming (TSOG), number of games played (NOGP), self-rated speaking proficiency (SRSP), and game interactivity.Using independent t-tests and one-way analysis of variance (ANOVA) analyses, results revealed statistically significant differences in the development of communication skills in English (DCSE) depending on the TSOG, willingness to communicate (WTC) in English depending on the NOGP, and enhancement of communication skills in English, active participation in class, and reduced anxiety in using English (RAUE) depending on the SRSP. This exploratory study indicates that online video games can be valuable tools in increasing English speaking confidence among Filipino college students. Further research is posited to understand the extent to which online games influence ESL learners’ speaking confidence in different educational and cultural contexts.
Volume: 14
Issue: 4
Page: 2555-2564
Publish at: 2025-08-01
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