Indonesian Journal of Electrical Engineering and Computer Science最新文献_第4页

Ontological model of the process of intensification of teachers’ competencies 教师能力强化过程的本体论模型

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp446-458

M. Bazarova, Karlygash Alibekkyzy, Saltanat Adikanova, Alina Bugubayeva, G. Zhomartkyzy, Akmaral Jaxalykova, A. Baidildina, Talshyn Keribayeva

{"title":"Ontological model of the process of intensification of teachers’ competencies","authors":"M. Bazarova, Karlygash Alibekkyzy, Saltanat Adikanova, Alina Bugubayeva, G. Zhomartkyzy, Akmaral Jaxalykova, A. Baidildina, Talshyn Keribayeva","doi":"10.11591/ijeecs.v35.i1.pp446-458","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp446-458","url":null,"abstract":"Currently, there is a need to improve the education system and develop interdisciplinary research at all levels of education, from school to postgraduate education. The introduction of interdisciplinary connections contributes to the formation of a holistic understanding of natural phenomena and the connections between them. Thus, this knowledge becomes more meaningful and applicable in practice. This article proposes a conceptual model of the content of education in the form of a thesaurus and ontology. The use of these models will allow you to adaptively select and systematize educational information. The article also discusses the possibilities and experience of using ontological modeling and engineering for the conceptual description of school and higher education. In addition, the article discusses the development of an ontological model of the process of expanding teachers’ competencies with the integration of science, technology, engineering and mathematics (STEM) education. The use of ontological engineering methods will improve the quality of teacher education through the semantic description of knowledge in the subject area and the use of interdisciplinary and STEM approaches in the educational process. ","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141705074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy management enhancement of a smart home supplied by renewable energy system 加强可再生能源系统智能家居的能源管理

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp20-31

Hasanain H. Shakir, F. B. Salem

引用次数: 0

Integration of statistical methods and neural networks for temperature regulation parameter optimization 整合统计方法和神经网络，优化温度调节参数

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp124-132

Leila Benaissa Kaddar, Said Khelifa, Mohamed El Mehdi Zareb

{"title":"Integration of statistical methods and neural networks for temperature regulation parameter optimization","authors":"Leila Benaissa Kaddar, Said Khelifa, Mohamed El Mehdi Zareb","doi":"10.11591/ijeecs.v35.i1.pp124-132","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp124-132","url":null,"abstract":"Temperature control plays a crucial role in various industrial processes, ensuring optimal performance and product quality. The conventional approach to optimizing temperature controller parameters involves manual tuning, which can be time-consuming, labor-intensive, and often lacks precision. This paper introduces an innovative methodology for optimizing the parameters of a temperature controller by integrating statistical methods in the preparation of the experimental plan utilized by neural networks. The integration of statistical techniques in designing the experimental framework enhances the efficiency of data collection, providing a robust foundation for subsequent analysis. The neural network leverages this well-structured dataset to model and optimize the temperature controller parameters, resulting in improved precision and performance. The synergistic integration of statistical methods and neural networks not only streamlines the optimization process but also enhances the reliability of the temperature control system. The effectiveness of the proposed approach is demonstrated through case studies on the Procon level/flow and temperature 38-003 process. The results show significant improvements in temperature control performance, with reduced process variability and faster response times.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction 利用 RAE-LSTM 融合技术提升下一代交通预测的智能城市流动性

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp503-510

N. Rafalia, Idriss Moumen, Fatima Zahra Raji, J. Abouchabaka

{"title":"Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction","authors":"N. Rafalia, Idriss Moumen, Fatima Zahra Raji, J. Abouchabaka","doi":"10.11591/ijeecs.v35.i1.pp503-510","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp503-510","url":null,"abstract":"The burgeoning demand for efficient urban traffic management necessitates accurate prediction of traffic congestion, spotlighting the essence of time series data analysis. This paper delves into the utilization of sophisticated deep learning methodologies, particularly long short-term memory (LSTM) networks, convolutional neural networks (CNN), and their amalgamations like Conv-LSTM and bidirectional-LSTM (Bi-LSTM), to elevate the precision of traffic pattern forecasting. These techniques showcase promise in encapsulating the intricate dynamics of traffic flow, yet their efficacy hinges upon the quality of input data, emphasizing the pivotal role of data preprocessing. This study meticulously investigates diverse preprocessing techniques encompassing normalization, transformation, outlier detection, and feature engineering. Its discerning implementation significantly heightens the performance of deep learning models. By synthesizing advanced deep learning architectures with varied preprocessing methodologies, this research presents invaluable insights fostering enhanced accuracy and reliability in traffic prediction. The innovative RD-LSTM approach introduced herein harnesses the hybridization of a reverse AutoEncoder and LSTM models, marking a novel contribution to the field. The implementation of these progressive strategies within urban traffic management portends substantial enhancements in efficiency and congestion mitigation. Ultimately, these advancements pave the way for a superior urban experience, enriching the quality of life within cities through optimized traffic management systems.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141695741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance analysis of the proxy-based and collusion-resistant revocable CPABE framework 基于代理和抗串通可撤销 CPABE 框架的性能分析

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp378-387

Shobha Chawla, Neha Gupta

{"title":"Performance analysis of the proxy-based and collusion-resistant revocable CPABE framework","authors":"Shobha Chawla, Neha Gupta","doi":"10.11591/ijeecs.v35.i1.pp378-387","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp378-387","url":null,"abstract":"An efficient revocation of access rights in ciphertext policy attribute-based encryption (CPABE) schemes has multiple challenges, particularly for lightweight devices. Thus, extensive research on the existing studies enforcing and governing access control has been conducted. The methodologies used in the existing CPABE (bilinear pairing cryptography based) schemes to revoke users at the system and attribute levels have been focused on in the current study. The existing studies have been examined on the basis of the following parameters for revocation: type of revocation addressed, level of collusion resistance, dynamicity achieved, scalability of revocation, and computational cost incurred. It has been observed in the study that no single scheme achieves all the revocation properties and addresses both types of revocation. The module proposed in proxy-based and collusion-resistant multi-authority revocable CPABE (PCMR-CPABE) efficiently addresses both types of revocation and is fully collusion-resistant, dynamic, and scalable. The present paper extends the study on PCMRCPABE and presents a performance analysis of the module in terms of functional specifications and computational cost. The presented analysis has compared the performance of the existing cutting-edge schemes with the PCMR-CPABE module and has proved that the proposed module is better in terms of functionality and is computationally inexpensive.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of mathematical methods for diagnosing kidney diseases using fuzzy set tools 利用模糊集工具开发诊断肾脏疾病的数学方法

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp405-417

Alua Myrzakerimova, Kateryna Kolesnikova

{"title":"Development of mathematical methods for diagnosing kidney diseases using fuzzy set tools","authors":"Alua Myrzakerimova, Kateryna Kolesnikova","doi":"10.11591/ijeecs.v35.i1.pp405-417","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp405-417","url":null,"abstract":"An approach based on fuzzy set theory is presented in the scientific article to enhance the efficiency of diagnosing kidney diseases by decreasing the time required for making medical decisions. The suggested approach employs fuzzy models and algorithms that consider the uncertainty and variability of clinical data to optimize the assessment of the functional state of the kidneys, taking into account various risk factors and individual characteristics of patients. The paper suggests a technique to develop a system of fuzzy decision rules. This technique combines E. Shortliff’s iterative rules with functions from the studied classes of kidney diseases. Mathematical modeling and experimental studies have indicated relatively high effectiveness in classifying different forms of kidney diseases. The results can be used to formulate intelligent decision support systems in clinical practice and improve diagnostic and monitoring processes. Moreover, the findings may aid in shaping more targeted and effective health policies at the national and regional levels, enhancing access to healthcare, and promoting the population’s overall health.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Method level static source code analysis on behavioral change impact analysis in software regression testing 软件回归测试中行为变化影响分析的方法级静态源代码分析

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp665-672

F. M. Muthengi, D. Mugo, Stephen Makau Mutua, F. Musyoka

{"title":"Method level static source code analysis on behavioral change impact analysis in software regression testing","authors":"F. M. Muthengi, D. Mugo, Stephen Makau Mutua, F. Musyoka","doi":"10.11591/ijeecs.v35.i1.pp665-672","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp665-672","url":null,"abstract":"Though a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons of software modifications. In assessing the impact of the changes made in software, static source code analysis plays a key role. However, static source code analysis can be a little complex depending on the reason for the expedition. Despite the work done so far, little focus has been made on the potential of changed methods analysis during static source code analysis in assessing the impact of the changes made in a software system. We propose and investigate a static source code analysis technique that would generate information on the modified methods in the source code. This study analyzes four aThough a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons for software modifications. In assessing the impact of the changes made in the software, static source code analysis can be a little complex depending on the reason for the expedition. Despite the works done so far, little focus has been directed on the potential of changed methods during static source code analysis, in assessing the impact of the changes made in software. This study investigates a method-level static source code analysis technique that would generate information on the methods affected by changes made in the software. The work analyzed three Java projects. The results indicate an improvement in leveraging on the knowledge of edited methods in change impact assessment during regression testing. The approach enhances code review efforts in light of assessing operational behavior impacted by the changes made.Java projects and shows that an analysis of the changed methods reveals the level of regression testing that ought to be conducted for the changes made.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing learner performance prediction on online platforms using machine learning algorithms 利用机器学习算法加强在线平台上学习者的成绩预测

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp343-353

Mohammed Jebbari, B. Cherradi, S. Hamida, Mohamed-Amine Ouassil, Taoufiq El Harrouti, A. Raihani

{"title":"Enhancing learner performance prediction on online platforms using machine learning algorithms","authors":"Mohammed Jebbari, B. Cherradi, S. Hamida, Mohamed-Amine Ouassil, Taoufiq El Harrouti, A. Raihani","doi":"10.11591/ijeecs.v35.i1.pp343-353","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp343-353","url":null,"abstract":"E-learning has emerged as a prominent educational method, providing accessible and flexible learning opportunities to students worldwide. This study aims to comprehensively understand and categorize learner performance on e-learning platforms, facilitating timely support and interventions for improved academic outcomes. The proposed model utilizes various classifiers (random forest (RF), neural network (NN), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNN)) to predict learner performance and classify students into three groups: fail, pass, and withdrawn. Commencing with an analysis of two distinct learning periods based on days elapsed (≤120 days and another exceeding 220 days), the study evaluates the classifiers’ efficacy in predicting learner performance. NN (82% to 96%) and DT (81%-99.5%) consistently demonstrate robust performance across all metrics. The classifiers exhibit significant performance improvement with increased data size, suggesting the benefits of sustained engagement in the learning platform. The results highlight the importance of selecting suitable algorithms, such as DT, to accurately assess learner performance. This enables educational platforms to proactively identify at-risk students and offer personalized support. Additionally, the study highlights the significance of prolonged platform usage in enhancing learner outcomes. These insights contribute to advancing our understanding of e-learning effectiveness and inform strategies for personalized educational interventions.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141716493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance evaluation of multiclass classification models for ToN-IoT network device datasets 针对 ToN-IoT 网络设备数据集的多分类模型性能评估

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp485-493

Soni Soni, Muhammad Akmal bin Remli, K. M. Daud, Januar Al Amien

{"title":"Performance evaluation of multiclass classification models for ToN-IoT network device datasets","authors":"Soni Soni, Muhammad Akmal bin Remli, K. M. Daud, Januar Al Amien","doi":"10.11591/ijeecs.v35.i1.pp485-493","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp485-493","url":null,"abstract":"Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141694282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of a payload for monitoring biological samples in microgravity and hypergravity conditions 开发用于在微重力和超重力条件下监测生物样本的有效载荷

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp78-89

Elber E. Canto-Vivanco, Sebastian Ramos-Cosi, Victor N. Romero-Alva, N. Vargas-Cuentas, A. Roman-Gonzalez

引用次数: 0