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28,593 Article Results

Monocular vision-based visual control for SCARA-type robotic arms: a depth mapping approach

10.11591/ijeecs.v40.i2.pp801-813
Diego Chambi Tula , Bryan Challco , Jonathan Catari , Walker Aguilar , Lizardo Pari
The accelerated growth of an increasingly automated industry requires the use of autonomous robotic systems. However, the use of these systems commonly requires an enormous amount of sensors. In this paper we evaluate the performance of a new system for visual control of a selective compliance assembly robot arm (SCARA) robotic arm using a monocular depth map that only requires one monocular camera. This system aims to be an efficient alternative to reduce the number of sensors in the robotic arm area while maintaining the effectiveness of traditional vision algorithms that use stereoscopic architectures of cameras. For this purpose, this system is compared with representative state-of the-art vision algorithms focused on the control of robotic arms. The results are statistically analyzed, indicating that the algorithm proposed in this research has competitive performance compared to state-of-the-art robotic arm visual control algorithms only using a single monocular camera.
Volume: 40
Issue: 2
Page: 801-813
Publish at: 2025-11-01

Interpretable federated deep learning models for predicting gait dynamics in biomechanics

10.11591/ijeecs.v40.i2.pp1087-1099
Shaik Sayeed Ahamed , Akram Pasha , Syed Ziaur Rahman , D. N. Puneeth Kumar
Accurate prediction of human joint angle dynamics and reliable gait classifica tion are essential for applications in rehabilitation, biomechanics, and clinical monitoring. Traditional machine learning (ML) models trained on centralized data raise concerns about privacy, scalability, and transparency. This study proposes a federated deep learning (DL) framework that integrates privacy preserving model training with interpretable predictions. Specifically, a gated recurrent unit- deep neural network (GRU-DNN) hybrid model is developed for regression of joint angles, while a Long short-term memory- convolutional neural network (LSTM-CNN) hybrid model is designed for binary and multi class gait classification. The framework is deployed using the federated av eraging (FedAvg) algorithm across simulated clients, with each client training locally on its data. To enhance interpretability, the local interpretable model agnostic explanations (LIME) algorithm is integrated at the client level to gener ate human-understandable explanations for model predictions. The experimen tal results demonstrate significant improvements, including a reduction in global mean squared error (GMSE) from 56.16 to 3.31 and an increase in R-squared score from 0.80 to 0.99 for regression, along with classification accuracies of 0.97 (binary) and 0.94 (multi-class). This scalable, privacy-preserving frame work bridges the gap between accuracy and transparency, offering impactful applications in biomechanics, healthcare, and personalized medicine.
Volume: 40
Issue: 2
Page: 1087-1099
Publish at: 2025-11-01

End-to-end system for translating bahasa isyarat Indonesia sign language gestures into Indonesian text

10.11591/ijeecs.v40.i2.pp719-734
Satria Putra , Erdefi Rakun
This study addresses critical challenges in developing an end-to-end bahasa isyarat Indonesia (BISINDO) SLT by integrating advanced deep learning techniques to overcome complex background interference, transitional gesture recognition, and limitations in dataset availability. While existing SLT systems struggle with isolated word recognition and manual preprocessing, our work introduces three key innovations: (1) implementation of YOLOv8 for optimized object detection, achieving 88% mAP and reducing WER to 11.40%, outperforming YOLOv5/v7 in handling complex backgrounds; (2) automated removal of transitional gestures using Threshold conditional random fields (TCRF), which attained 95.68% accuracy, significantly improving upon MobileNetV2’s performance (WER: 6.89% vs. 93.53%); and (3) end-to-end BISINDO SLT by expansion of the BISINDO dataset to 435 word labels, enabling comprehensive sentencelevel translation. Experimental results demonstrate the system’s robustness, with 8.31% of WER, 84.13% of SAcc, and 87.08% of SacreBLEU after dataset expansion and redundancy elimination through grouping methods. The proposed framework operates without manual intervention, marking a substantial advancement toward real-world applicability.
Volume: 40
Issue: 2
Page: 719-734
Publish at: 2025-11-01

Boosting carbon removal efficiency in wastewater treatment systems using a fuzzy model predictive control stategy

10.11591/ijeecs.v40.i2.pp629-639
Saïda Dhouibi , Raja Jarray , Soufiene Bouallègue
The efficient removal of carbon pollution has always presented a growing challenge facing wastewater treatment plants (WWTPs) operating with activated sludge process (ASP) technology. Enhancing pollution removal efficiency to meet standard wastewater quality limits remains a problematic in water pollution management. Recent progress in modeling and automatic control techniques can significantly improve the hydric pollution removal. In this paper, an effective carbon elimination strategy combining TakagiSugeno (TS) fuzzy modeling and model predictive control (MPC) is proposed to achieve high purification performance in terms of chemical oxygen demand (COD), biochemical oxygen demand (BOD5) and total suspended solids (TSS) indicators. A fuzzy TS model is established based on the concepts of quasi-linear parameter-varying (LPV) forms and convex polytopic transformations of the system nonlinearities. The concentrations of heterotrophic biomass, biodegradable substrate and dissolved oxygen as well as the effluent volume are controlled and maintained around their desired references with the aim of increasing pollution removal. Comparisons with the previously most used state-of-the-art parallel distributed compensation (PDC) are performed. High and competitive pollution removal percentages of 91% for COD and BOD5 indicators, and 92% for TSS metric, are achieved with the proposed MPC-based design, thus complying with the normative limits defined in WWTPs.
Volume: 40
Issue: 2
Page: 629-639
Publish at: 2025-11-01

Room energy management utilizing internet of things technology for decreasing electricity consumption

10.11591/ijres.v14.i3.pp734-744
Winasis Winasis , Suroso Suroso , Azis Wisnu Widhi Nugraha , Priswanto Priswanto
This paper proposes a novel internet of things (IoT)-based control system for energy management to reduce electricity consumption from the two most dominant loads in buildings: air conditioners (AC) and lighting. The proposed system provides a comprehensive operational control strategy that integrates scheduling, human detection, ambient temperature, and light intensity for optimal room-level energy management employed. The proposed system employs wireless fidelity (WiFi)-enabled temperature, presence, and light sensors for comprehensive room conditions monitoring. Additionally, a WiFi-connected infrared module serves as an actuator to regulate the AC unit. Testing results demonstrate compelling energy savings, achieving up to 36% for the AC and 72% for the lighting while maintaining a comfortable indoor environment. These results were obtained from an experimental test in a private room within a residence over an 8-hour daytime period with 50% occupancy time. The proposed IoT system offers a highly effective and easily deployable solution for sustainable energy reduction in residential settings.
Volume: 14
Issue: 3
Page: 734-744
Publish at: 2025-11-01

Enhancing cross-cutting concerns in the internet of things with applying aspect oriented programming

10.11591/ijres.v14.i3.pp745-753
Khalifa Fatiha , Guelta Bouchiba
Aspect oriented programming (AOP) is a new programming model that provides new concepts to handle cross-cutting concerns about code. The idea of introducing AOP in the internet of things (IoT) is inherited from the complexity of sensor operations involving data acquisition, processing, and communication, the need to support multiple simultaneous services for users particularly security services such as authentication, authorization, data traceability, and transaction management, and the challenges posed by the IoT deployments, the treatment of these data volumes lead to problematic code redundancy and cross-cutting concerns that compromise system maintainability. In this context, AOP enables the separation of core functionalities, data management, and cross-cutting concerns, allowing them to be developed and reused independently within the same codebase. To address these issues, this paper proposes an AOP model for IoT systems based on the Petri net representations. The model strategically integrates the core AOP advantages of modularity, reusability, and extensibility, microservices based architectural decomposition and specialized handling of sensor-specific requirements in IoT environments.
Volume: 14
Issue: 3
Page: 745-753
Publish at: 2025-11-01

Cloud computing needs to explore into sky computing

10.11591/csit.v6i3.p294-306
Arif Ullah , Hassnae Remmach , Hanane Aznaoui , Canan Batur Şahin , Amine Mrhari
This paper evaluates key issues in cloud computing and introduces a novel model, known as sky computing, to address these challenges. Cloud computing, a transformative technology, has played a critical role in reshaping modern operations—especially following the COVID-19 pandemic, when many human activities shifted to technology-driven platforms. It offers multiple service models, including Software as a Service, Hardware as a Service, Desktop as a Service, Backup as a Service, and Network as a Service, each tailored to user requirements. However, the rapid expansion of cloud-based technologies and interconnected systems has intensified infrastructure and scalability challenges. Sky computing, or the “cloud of clouds,” emerges as an advanced layer above traditional cloud models, enabling dynamically provisioned, distributed domains built over multiple serial clouds. Its core capability lies in offering variable computing capacity and storage resources with dynamic, real-time support, providing a robust and unified platform by integrating diverse cloud resources. This paper reviews related technologies, summarizes prior research on sky computing, and discusses its structural design. Furthermore, it examines the limitations of current cloud computing frameworks and highlights how sky computing could overcome these barriers, positioning it as a pivotal architecture for the future of distributed computing.
Volume: 6
Issue: 3
Page: 294-306
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

The impacts of optical display BaF2-Ce materials on solid-state lighting

10.11591/ijres.v14.i3.pp717-724
Luu Hong Quan , Nguyen Thi Phuong Loan
Transparent ceramic doped with barium fluorid cerium (BaF2-Ce) was created via a sintering method and its brightness and scintillation characteristics were examined. The luminescence is associated with the 5d-4f transitions in the Ce3+ ion and exhibits emitting maxima at 310 and 323 nm. For Na-22 radioisotopes, photo-maximum at 511 keV and 1274 keV were achieved using translucent ceramic BaF2-Ce. The translucent ceramic BaF2-Ce has been determined to have a power resolution of 13.5% at 662 keV. A luminescent production rate was measured for the BaF2-Ce (0.2%) ceramic, which is similar to sole crystal. Calculations of the scintillation degradation period beneath 662 keV gamma stimulation reveal a quick part of 58 ns and a somewhat sluggish part of 434 ns. The more gradual part in BaF2-Ce(0.2%) ceramic is linked to the dipole-dipole power transmission from the host structure to the Ce3+ luminous core and is quicker comparing to self-trapped excitons (STE) emitting in BaF2 host. BaF2-Ce offer various qualities, including significant illumination output, rapid degradation duration, and rapid scintillating reaction, which are desirable for many global fields such as medicine, radiation detection, industrial systems and nuclear safety.
Volume: 14
Issue: 3
Page: 717-724
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

Mediterranean and northern european archaeology: a computational comparison

10.11591/csit.v6i3.p326-334
Hamza Kchan , Saira Noor
Despite the proliferation of computational tools in archaeology, few studies systematically compare their regional adaptations or explore the epistemological assumptions guiding their application. This paper addresses four critical research gaps: (i) the lack of comparative regional analysis between the Mediterranean and Northern Europe in computational archaeology, (ii) the insufficient integration of philosophical and epistemological frameworks in predictive modeling, (iii) the underexplored application of artificial intelligence (AI) and network theory in spatial analysis, and (iv) the limited interdisciplinary synthesis of biological, geospatial, and digital data. By examining representative case studies from both regions, we highlight the methodological innovations, theoretical orientations, and institutional dynamics that shape regional practices. The study underscores the necessity of integrating computational methods with interpretive depth and interdisciplinary collaboration to foster a more reflective and inclusive digital archaeology. 
Volume: 6
Issue: 3
Page: 326-334
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

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

Design of a real-time prayer clock using geographic coordinates

10.11591/ijres.v14.i3.pp834-842
Massoum Noreddine‬‏ , Moulai Khatir Ahmed Nassim
Prayer times and calendar clock are a valuable system that relies on programs that we developed in Mikroc that allow to mathematically calculate these prayer times, which differ from one place (city) to another and from one day to another using geographical coordinates. The more precise these coordinates (latitude and longitude), the more precise the prayer times are. The research that we conducted was carried out using a 16F876A microcontroller that uses the 74HC595 circuit, an 8-bit serial input and parallel output shift register for storage. Outputs can be added to the microcontroller thanks to this. It is possible to manage this integrated circuit from three pins of our microcontroller.
Volume: 14
Issue: 3
Page: 834-842
Publish at: 2025-11-01
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