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28,593 Article Results

Fuzzy multi-objective energy optimization of workflow scheduling

10.11591/ijeecs.v40.i2.pp871-882
Ayoub Chehlaf , Mohammed Gabli
Task scheduling is a key and challenging problem in cloud computing systems, requiring decisions regarding resource allocation to tasks to optimize a perfor mance criterion. This problem has required researchers and developers to over come significant challenges. Our goal in this study aims to minimize both the makespan and energy consumption in cloud computing systems by efficiently scheduling workflows. To achieve this, we first proposed a dynamic multi objective model, which wasthensimplified into a single-objective problem using dynamic weights. Then, we proposed a dynamic genetic algorithm (DGA) and a dynamic particle swarm optimization algorithm (DPSO) to address the prob lem. To deal with the situation where the makespan is uncertain and not exact, we present a fuzzy model, treating each value as a fuzzy number and we utilize both possibility and necessity metrics. The results are contrasted with the Het erogeneous earliest finish time (HEFT) algorithm and Considerably lowered the total energy consumption, especially for DGA.
Volume: 40
Issue: 2
Page: 871-882
Publish at: 2025-11-01

Maximizing QoS in railway radio networks: leaky cable and ray-tracing for optimal BER on bridges

10.11591/ijeecs.v40.i2.pp678-686
Maksim Sidorovich , Ponomarchuk Yulia
The future railway mobile communication system (FRMCS) standard is crucial for advancing railway communication and implementing intelligent train control systems. This research focuses on development of an efficient modeling method to evaluate and optimize FRMCS performance on railway bridges, particularly under high-density modulation and radio noise interference. The key aspect of this study involves computer modeling of the deployment of a leaky coaxial cable (LCX) and comparison of its performance to traditional methods of radio coverage modeling. Using the single-slot radiation pattern, we evaluate the quality of radio communication by comparison of the bit error rate (BER) metrics for the Ray Tracing propagation model with and without the use of LCX. The results show that the use of LCX significantly reduces BER values, providing a much clearer and more reliable signal. This improvement is crucial for the safety and reliability of railway operations, ensuring effective communication for train control and reducing the risk of accidents in complex and high-demanding transport networks. This research contributes to the optimization of railway information infrastructure, with the aim of ensuring safe, reliable, and efficient operations.
Volume: 40
Issue: 2
Page: 678-686
Publish at: 2025-11-01

Development and integration of a privacy computing gateway for enhanced interoperability

10.11591/ijeecs.v40.i2.pp1011-1022
Akhila Reddy Yadulla , Vinay Kumar Kasula , Bhargavi Konda , Mounica Yenugula , Supraja Ayyamgari
A new design of privacy computing gateway stands as the solution to secure efficient interoperability between heterogeneous platforms. The growing importance of data privacy, along with rising collaborative data analysis operations, creates an immediate need for standardized privacy-preserving frameworks that are adaptable to diverse situations. A three-layered architecture consisting of application protocol and communication layers receives support from an Adaptation mechanism designed for compatibility between separate privacy computing systems. Testing of the framework uses standard machine learning methods together with horizontal and vertical federated learning using diverse data quantities and feature distribution patterns. The gateway achieves satisfactory model performance and protects data privacy integrity in combination with platform interoperability. area under the curve (AUC) along with F1 score metrics, proves that the proposed system reaches performance equivalence with centralized models when operating within privacy-limited environments. The research introduces an effective solution for securing cross-platform data sharing that will enable secure inter-sector collaboration in finance, healthcare, and government applications.
Volume: 40
Issue: 2
Page: 1011-1022
Publish at: 2025-11-01

Enhancing the ternary neural networks with adaptive threshold quantization

10.11591/ijeecs.v40.i2.pp700-706
Son Ngoc Truong
Ternary neural networks (TNNs) with weights constrained to –1, 0, and +1 offer an efficient deep learning solution for low-cost computing platforms such as embedded systems and edge computing devices. These weights are typically obtained by quantizing the real weight during the training process. In this work, we propose an adaptive threshold quantization method that dynamically adjusts the threshold based on the mean of weight distribution. Unlike fixed-threshold approaches, our method recalculates the quantization threshold at each training epoch according to the distribution of real valued synaptic weights. This adaptation significantly enhances both training speed and model accuracy. Experimental results on the MNIST dataset demonstrates a 2.5× reduction in training time compared to conventional methods, with a 2% improvement in recognition accuracy. On Google Speech Command dataset, the proposed method achieves an 8% improvement in recognition accuracy and a 50% reduction in training time, compared to fixed-threshold quantization. These results highlight the effectiveness of adaptive quantization in improving the efficiency of TNNs, making them well-suited for deployment on resource constrained edge devices.
Volume: 40
Issue: 2
Page: 700-706
Publish at: 2025-11-01

Exploring stock price portfolio clusters in foreign exchange markets

10.11591/ijeecs.v40.i2.pp735-744
Challa Madhavi Latha , S. Bhuvaneswari , K. L. S. Soujanya , A. Poongodai
This study explores a novel portfolio management approach dividing the currency pairs into clusters of periodic returns. The primary purpose is to improve diversification and risk-return ratios with currencies. This research studied USD, Euro, and Chinese Yuan to collect historical data from April 2012 to March 2022. The present study makes use of K-means clustering to find clusters of assets with similar return patterns, which constitute diversified portfolios. Optimized portfolio vs. benchmark portfolio performance was also evaluated based on critical performance measures like cumulative return, Sharpe ratio, and volatility. The clustering approach was also tested through sensitivity analysis to check how market-specific it is. The results suggest that more clustered portfolios outperform traditional benchmarks and provide a better risk-adjusted return. The conclusion drawn here from the findings is that portfolio segmentation is a superior approach because of risk management in ever-changing volatile markets and identifying situations that link currency pairs. This is beneficial for those investors and portfolio managers looking to maximize their foreign exchange (FOREX) investments by allowing greater visibility into how the market is functioning, which can, in turn, improve decision-making processes. According to the study, portfolio clustering substantially enhances a portfolio's return for the foreign exchange market.
Volume: 40
Issue: 2
Page: 735-744
Publish at: 2025-11-01

Deep-learning-based hand gestures recognition applications for game controls

10.11591/ijeecs.v40.i2.pp883-897
Huu-Huy Ngo , Hung Linh Le , Man Ba Tuyen , Vu Dinh Dung , Tran Xuan Thanh
Hand gesture recognition is among the emerging technologies of human computer interaction, and an intuitive and natural interface is more preferable for such applications than a total solution. It is also widely used in multimedia applications. In this paper, a deep learning-based hand gesture recognition sys tem for controlling games is presented, showcasing its significant contributions toward advancing the frontier of natural and intuitive human-computer interac tion. It utilizes MediaPipe to get real-time skeletal information of hand land marks and translates the gestures of the user into smooth control signals through an optimized artificial neural network (ANN) that is tailored for reduced com putational expenses and quicker inference. The proposed model, which was trained on a carefully selected dataset of four gesture classes under different lighting and viewing conditions, shows very good generalization performance and robustness. It gives a recognition rate of 99.92% with much fewer param eters than deeper models such as ResNet50 and VGG16. By achieving high accuracy, computational speed, and low latency, this work addresses some of the most important challenges in gesture recognition and opens the way for new applications in gaming, virtual reality, and other interactive fields.
Volume: 40
Issue: 2
Page: 883-897
Publish at: 2025-11-01

Multi-visual modality for collaborative filtering-based personalized POI recommendations

10.11591/ijeecs.v40.i2.pp978-987
Sudarat Arthan , Kreangsak Tamee
Point-of-interest (POI) recommendation systems help users discover locations that match their interests. However, these systems often suffer from data sparsity due to limited user check-in history. To address this challenge, this study proposed a novel user profiling framework that incorporates multiple visual modalities derived from user-generated photos. Three types of visual-based user profiles were constructed: image label-based, image feature-based, and a fused profile, combining both modalities through score-level fusion. We conducted extensive experiments on two real-world datasets. The results demonstrate that visual-based profiles, particularly the image feature-based profile, consistently improve recommendation performance under sparse data conditions. Although the fused profile offered stable results, it did not consistently outperform the single modality. Furthermore, performance was sensitive to the number of nearest neighbors and the amount of training data. These findings highlight the importance of modality selection and fusion strategy in visual-based POI recommendation systems.
Volume: 40
Issue: 2
Page: 978-987
Publish at: 2025-11-01

Application of Naïve Bayes Algorithm in Expert System for Diagnosing Chilli Plant Diseases Based on Growth Phase on Peatland

10.11591/ijeecs.v40.i2.pp829-839
fatayat fatayat fatayat , Wahyu Lestari Wahyu Lestari Wahyu Lestari , Alfirman Alfirman Alfirman
Agricultural development on peatlands has its own challenges, especially in the cultivation of chili plants that are susceptible to various diseases. Therefore, an expert system is needed that can help farmers diagnose chili plant diseases quickly and accurately based on the plant growth phase. This research aims to apply the Naïve Bayes algorithm to the expert system for diagnosing Capsicum annum L (Chilli) plant diseases. The results of the expert system research offer an innovative and adaptive solution for the management of plant diseases in peatlands, with great potential to increase agricultural productivity and plant resistance to disease. The expert system is able to diagnose several types of diseases on chili plants in peatlands, such as anthracnose, fusarium wilt, and leaf curl disease. Each diagnosis is based on symptoms observed in each phase of plant growth, from the vegetative phase to the generative phase. Expert system testing results. This system is expected to increase the productivity and quality of chili crops on peatlands, as well as reduce losses due to disease attacks. In addition, this research also shows that the Naive Bayes algorithm has great potential to be applied in expert systems in other agricultural fields.
Volume: 40
Issue: 2
Page: 829-839
Publish at: 2025-11-01

Decision making with analytical hierarchy process algorithm and prototype model for exemplary teachers

10.11591/csit.v6i3.p225-234
Sumardiono Sumardiono , Norhafizah Ismail , Wiwit Priyadi , Agus Riyanto , Indra Martha Rusmana
The selection process for exemplary teachers in vocational schools in Bekasi City has so far been carried out subjectively without a structured system, relying on internal meetings and daily notes, thus causing problems of transparency, accuracy, and efficiency. To overcome this, this study developed an online decision support system (DSS) that makes use of the analytical hierarchy process (AHP) algorithm to create an objective and measurable selection method based on five criteria: discipline, travel costs, personality, teaching administration, and learning achievement. Quantitative methods were applied by collecting data through questionnaires and observations, while the system prototype was designed through the stages of problem analysis, design, implementation, and evaluation. The AHP algorithm was used to process the decision matrix, benefit-cost-based normalization, weighting, and pairwise comparisons, with a consistency test (CR =0.044) ensuring the reliability of the results. This system successfully identified Didi Saputra, S.Pdi., as the best exemplary teacher with the highest preference value (0.92), while providing a significant impact in the form of increased accuracy (reducing subjective bias), transparency (clear ranking reports), and efficiency (faster selection process). The research findings demonstrate the effectiveness of AHP as a structured solution for exemplary teacher selection, with potential for adoption by other educational institutions and sustainability through a web-based system.
Volume: 6
Issue: 3
Page: 225-234
Publish at: 2025-11-01

Characteristics ransomware stop/djvu remk and erqw variants with static-dinamic analysis

10.11591/csit.v6i3.p283-293
Dodon Turianto Nugrahadi , Friska Abadi , Rudy Herteno , Muliadi Muliadi , Muhammad Alkaff , Muhammad Alvin Alfando
Ransomware has developed into various new variants every year. One type of ransomware is STOP/DJVU, containing more than 240+ variants. This research to determine changes in differences characteristics and impact between ransomware variants STOP/DJVU remk, which is a variant from 2020, and the erqw variant from 2023, through a mixed-method research approach. Observation, simulation using mixing static and dynamic malware analysis methods. Both variants are from the Malware Bazaar site. The total characteristics based on dynamic analysis, the remk variant has 177, and the erqw variant has 190, which increased by 1.8%. The total characteristics based on static analysis, the remk variants have 586, and the erqw variants have 736, which increased by 5.7%. All characteristics from remk to erqw increasing in dynamic analysis, except the number of payloads that decreased about 20%. In static analysis, all characteristics from remk to erqw increase except the number of sections decreased about 1.5%. It can be the affected CPU performance, because the remk variant affects performance by increasing CPU work by 3.74%, while the erqw variant affects performance by reducing CPU work by 1.18%, both compared with normal CPU. which will affect the ransomware's destructive work and require changes in its handling.
Volume: 6
Issue: 3
Page: 283-293
Publish at: 2025-11-01

Predictive model for high-risk healthcare clients and claims frequency

10.11591/csit.v6i3.p346-354
Lenias Zhou , Mainford Mutandavari , Lucia Matondora
Global healthcare spending surged to approximately USD 9.8 trillion in the aftermath of the COVID-19 pandemic, intensifying the need for effective risk management strategies in healthcare insurance. This study proposes a predictive model designed to identify high-risk clients for timely targeted interventions and to forecast claims frequency for optimized resource allocation. A real-world claims dataset from a healthcare insurance provider was utilized. Bayesian optimization was employed to enhance data labelling. A deep learning (DL) model with sigmoid activation was used to classify high-risk clients, while a regression model forecasted claims frequency. The model was trained and validated, and gave an accuracy of 97%, a precision of 95.2%, a recall of 98.1% and an F1-score of 96.6%. The results confirmed the model’s accuracy in identifying high-risk clients and its ability to provide reliable forecasting of future claims frequency. Importantly, the model also provided the reason behind its classification decision, enhancing transparency and trust. This research provides valuable data-driven insights to both the healthcare insurers and clients, giving them the power to stay ahead in managing key risks, which ultimately reduces the cost of healthcare insurance. This work contributed a scalable and interpretable solution for risk prediction in healthcare insurance.
Volume: 6
Issue: 3
Page: 346-354
Publish at: 2025-11-01

A machine learning approach for early prediction of mental health crises

10.11591/csit.v6i3.p335-345
Hassan Chigagure , Lucy Charity Sakala
The global mental health crisis, intensified by the COVID-19 pandemic, placed unprecedented strain on healthcare systems and highlighted the urgent need for proactive crisis prevention strategies. This study investigated the effectiveness of various machine learning (ML) models in predicting mental health crises within 28 days post-hospitalization, leveraging an eight-year longitudinal dataset. Multiple data preprocessing techniques, including feature selection (EFSA, RFECV), imputation, and class imbalance handling (SMOTE, Tomek links), were systematically applied to enhance model performance. Six traditional classifiers—logistic regression, support vector machine, k-nearest neighbors, naive Bayes, XGBoost, and AdaBoost—were evaluated alongside ensemble learning (EL) methods (bagging, boosting, stacking). Performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC were used for comprehensive assessment. Results demonstrated that ensemble methods, particularly boosting and bagging, consistently achieved high predictive accuracy (up to 93%), with XGBoost and AdaBoost emerging as top performers. Feature selection and class imbalance techniques further improved model robustness and generalizability. The findings underscored the potential of ML-driven approaches for early identification of at-risk patients, enabling more effective resource allocation and timely interventions in mental health care. Recommendations for integrating these predictive tools into clinical workflows were discussed to support data-driven decision-making.
Volume: 6
Issue: 3
Page: 335-345
Publish at: 2025-11-01

Optimizing diplomatic indexing: full-parameter vs low-rank adaptation for multi-label classification of diplomatic cables

10.11591/csit.v6i3.p274-282
Dela Nurlaila , Abba Suganda Girsang
Accurate classification of diplomatic cables is crucial for Mission’s evaluation and policy formulation. However, these documents often cover multiple topics, hence a multi-label classification approach is necessary. This research explores the application of pre-trained language models (CahyaBERT, IndoBERT, and MBERT) for multi-label classification of diplomatic cable executive summaries, which align with the diplomatic representation index. The study compares full-parameter fine-tuning and low-rank adaptation (LoRA) techniques using cables from 2022-2023. Results demonstrate that Indonesian-specific models, particularly the IndoBERT, outperform multilingual models in classification accuracy. While LoRA showed slightly lower performance than full fine-tuning, it significantly reduced GPU memory usage by 48% and training time by 69.7%. These findings highlight LoRA’s potential for resource-constrained diplomatic institutions, advancing natural language processing in diplomacy and offering pathways for efficient, real-time multi-label classification to enhance diplomatic mission evaluation.
Volume: 6
Issue: 3
Page: 274-282
Publish at: 2025-11-01

Automated defect detection in submersible pump impellers using image classification

10.11591/ijeecs.v40.i2.pp1158-1166
Deepa Somasundaram , V. Pramila , G. Ezhilarasi , D. Lakshmi , P. Kavitha , R. Kalaivani
Casting is a crucial manufacturing process used to produce complex metal parts, but it is often plagued by defects such as cracks, porosity, shrinkage, and cold shuts, which can compromise quality and lead to financial losses. Traditional visual inspection methods for detecting these defects are slow and prone to human error, making them inefficient for large-scale production. This project proposes automating the defect detection process using advanced AI-powered non-destructive testing (NDT) techniques. Specifically, convolutional neural networks (CNNs), a deep learning model, are employed for real-time visual inspection of castings. CNNs, trained on high-resolution images, can accurately identify surface defects such as cracks, scratches, and dimensional irregularities, significantly improving inspection speed and accuracy. The performance metrics of the system include defect detection accuracy, false positive and false negative rates, processing time, and scalability for high-volume production environments. By minimizing human intervention, this automated system reduces error rates, enhances product quality, and lowers production costs. Furthermore, the real-time capabilities of CNNs allow for rapid feedback, preventing defective parts from advancing through the production line. Overall, the integration of AI-based vision systems boosts efficiency, sustainability, and profitability in manufacturing, ensuring castings meet customer specifications with minimal errors.
Volume: 40
Issue: 2
Page: 1158-1166
Publish at: 2025-11-01

Improved YOLOv8 for rail squat detection and identification

10.11591/ijeecs.v40.i2.pp1129-1140
Van-Dinh Do , Phuong-Ty Nguyen , Minh-Tuan Ha
Rail transport plays a vital part in the country's economy by ensuring the safe movement of both goods and passengers. Therefore, maintaining rail safety through consistent surface defect inspection is extremely importan. However, squat defect detection on rail surfaces faces considerable difficulties due to weather impacts, lighting changes, and variations in image contrast. These challenges hinder the accuracy and reliability of traditional inspection methods. To solve this problem, this study proposes an improved YOLOv8 model for the identification and classification of squat defects. The methodology involves capturing images of the rail track, preprocessing them to enhance image quality, labeling squat defects for training purposes, and training the proposed model using the labeled dataset. The improved YOLOv8 model incorporates enhancements such as multi-scale convolution modules and attention mechanisms to improve feature extraction and defect recognition. Experimental results demonstrate the effectiveness of the proposed method, achieving an impressive accuracy of 0.92 in detecting and categorizing squat defects. These findings highlight the potential of the proposed approach to enhance railway safety by providing a reliable and efficient solution for rail surface inspection.
Volume: 40
Issue: 2
Page: 1129-1140
Publish at: 2025-11-01
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