Indonesian Journal of Electrical Engineering and Computer Science最新文献_第2页

Inventory model having preservation technology with fix lifetime under two level trade credit policy 两级贸易信贷政策下具有固定寿命保存技术的库存模型

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp610-619

Deep Kamal Sharma, Ompal Singh

{"title":"Inventory model having preservation technology with fix lifetime under two level trade credit policy","authors":"Deep Kamal Sharma, Ompal Singh","doi":"10.11591/ijeecs.v35.i1.pp610-619","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp610-619","url":null,"abstract":"Supply-chain management involves moving storage supplies from origin to consumption, with manufacturers running production based on quadratic demand, distributors and retailers monitoring inventory. When a new product is released, demand often rises linearly and then declines dramatically when an alternative becomes available. Shortages are not allowed. Players' inventory will decrease at a rate of (1/(1+m-t)), where m is fixed lifetime, greater than the replenishment time. Deteriorating goods experience constant mass loss or usefulness, but preservation technology can help the damaged item to be consumed. Retailers with direct customer relationships can reduce stock spoilage through good warehouses. Manufacturers' storage systems have a higher deterioration rate. Two-tier trade credit financing is examined in this model. Distributors offer specific credit terms to stores, while manufacturers provide a grace period for invoicing. Distributors and retailers must pay interest on unsold inventories if invoices aren't settled on time. An integrated storage system reduces costs by minimizing costs through multiple shipments from manufacturers to distributors and retailers, and by adjusting replenishment times for each player. The resolution process is designed so that the supply chain operator gets the best possible decision. Therefore, results are authorized using mathematical examples for different scenarios. Management decisions are suggested.","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":"141692832","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

Artificial intelligence powered internet of vehicles: securing connected vehicles in 6G 人工智能驱动的车联网：确保 6G 联网汽车的安全

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp213-221

D. R. Kumar Raja, Z. A. Abas, Chandra Sekhar Akula, Yellapalli Dileep Kumar, Goshtu Hemanth Kumar, Venappagari Eswari

{"title":"Artificial intelligence powered internet of vehicles: securing connected vehicles in 6G","authors":"D. R. Kumar Raja, Z. A. Abas, Chandra Sekhar Akula, Yellapalli Dileep Kumar, Goshtu Hemanth Kumar, Venappagari Eswari","doi":"10.11591/ijeecs.v35.i1.pp213-221","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp213-221","url":null,"abstract":"The rapid advancements in automotive technology and the emergence of next-generation networks such as 5G and 6G are laying the foundation for the internet of vehicles (IoV), a revolutionary concept to transform transportation systems. The convergence of artificial intelligence (AI) and connected vehicles IoV is driving a paradigm shift in the transportation sector, especially in the dynamic framework of 5G and future 6G networks. This survey paper provides a thorough survey of the evolving AI-based IoV security landscape. We explore key areas of 5G/6G networks, focusing on the complex interplay of machine learning (ML) and deep learning (DL) in enhancing vehicle-to-everything (V2X) security and connected vehicles. Addressing the unique challenges of 6G, this paper outlines future directions for improving security and highlights open research issues. This comprehensive survey, which aims to provide information and guidance to both researchers and practitioners, contributes to a detailed understanding of the security issues associated with connected vehicles in the emerging 6G era.","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":"141690810","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

A review of intrusion detection system and security threat in internet of things enabled environment 入侵检测系统与物联网环境中的安全威胁综述

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp428-435

N. Nisha, N. S. Gill, P. Gulia

{"title":"A review of intrusion detection system and security threat in internet of things enabled environment","authors":"N. Nisha, N. S. Gill, P. Gulia","doi":"10.11591/ijeecs.v35.i1.pp428-435","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp428-435","url":null,"abstract":"Thousands of devices communicate globally to share data and information without any human intervention. A network of physical objects with numerous sensors and other network hardware to exchange data with servers and additional devices that are linked is referred to as the \"internet of things (IoT)”. The actions hurting the communication system are known as intrusions. Security features such as (integrity, and confidentiality) within IoT networks are compromised when any kind of intrusion occurs. To identify multiple infiltration types in an environment where IoT is enabled, an intrusion detection system (IDS) is required. In environments where IoT is enabled, security vulnerabilities are now more prevalent than ever. In this study, the IoT architecture is reviewed, and potential security risks at each tier are investigated. It is also hoped that this research will stimulate thought about the expanding risks posed by unprotected IoT devices. The paper also intends to provide an in-depth analysis of intrusion detection systems for identifying and classifying security threats in an IoT-enabled environment. Furthermore, this study investigates a variety of efficient machine learning-based methods for detecting cyberattacks on IoT devices.","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":"141707558","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

Design of an enhanced dual-band microstrip patch antenna with defected ground structures for WLAN and WiMax 为 WLAN 和 WiMax 设计带缺陷接地结构的增强型双频微带贴片天线

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp165-174

Mohamed Lemine El Issawi, Dominic Bernard Onyango Konditi, A. D. Usman

{"title":"Design of an enhanced dual-band microstrip patch antenna with defected ground structures for WLAN and WiMax","authors":"Mohamed Lemine El Issawi, Dominic Bernard Onyango Konditi, A. D. Usman","doi":"10.11591/ijeecs.v35.i1.pp165-174","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp165-174","url":null,"abstract":"This research presents an innovative dual-band microstrip patch antenna design enhanced with defected ground structures (DGS) and barium strontium titanate (BST) thin film, tailored for wireless local area network (WLAN) and WiMax applications. The first design phase involved the development of an microstrip patch antenna (MPA) using an flame retardant (FR4) substrate with a permittivity (εr1) of 4.3 and a thickness of 1.524 mm, enhanced with DGS. This configuration achieved a single-band resonance at 4.1 GHz, with a bandwidth of 0.82 GHz and a return loss (S11) of -32 dB. The second phase involved the integration of a BST thin film, with a high permittivity(εr2) of 250 and a thickoness of 0.1 mm, into the DGS-enhanced microstrip patch antenna (MPA). This mdification led to a transformation in the antenna's performance, enabling dual-band operation at resonance frequencies of 2.8 GHz and 5.8 GHz. Further, there was a corresponding substantial increase in bandwidth to 1.34 GHz and 1.25 GHz, respectively, an improvement in S11 values to -16.3 dB and -21.4 dB. Moreover, and antenna’s size of 14×10×1.524 mm3 . The study underscores the critical role of innovative material use and design optimization in advancing antenna technology, offering significant enhancements in bandwidth, and miniaturization, for wireless communication 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":"141710525","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

Breast cancer relapse disease prediction improvements with ensemble learning approaches 利用集合学习方法改进乳腺癌复发疾病预测

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp335-342

Ghanashyam Sahoo, Ajit Kumar Nayak, Pradyumna Kumar Tripathy, Abhilash Pati, Amrutanshu Panigrahi, Adyasha Rath, Bhimasen Moharana

{"title":"Breast cancer relapse disease prediction improvements with ensemble learning approaches","authors":"Ghanashyam Sahoo, Ajit Kumar Nayak, Pradyumna Kumar Tripathy, Abhilash Pati, Amrutanshu Panigrahi, Adyasha Rath, Bhimasen Moharana","doi":"10.11591/ijeecs.v35.i1.pp335-342","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp335-342","url":null,"abstract":"Diagnosis and prognosis are especially difficult areas of medical research related to cancer due to the high incidence of breast cancer, which has surpassed all other cancers in terms of female mortality. Another factor that has a substantial influence on the quality of life of cancer patients is the fear that they may experience a relapse of their disease. The objective of the study is to give medical practitioners a more effective strategy for using ensemble learning techniques to forecast when breast cancer may recur. This research aimed to investigate the usage of deep neural networks (DNNs) and artificial neural networks (ANNs) in addition to machine learning (ML) based approaches, including bagging, averaging, and voting, to enhance the efficacy of breast cancer relapse diagnosis on two breast cancer relapse datasets. Results from the empirical study demonstrate that the proposed ensemble learning-enabled approach improves accuracies by 96.31% and 95.81%, precisions by 96.70% and 96.15%, sensitivities by 98.88% and 98.68%, specificities by 84.62% in both, F1-scores by 97.78% and 97.40%, and area under the curve (AUCs) of 0.987 and 0.978, with University Medical Centre, Institute of Oncology (UMCIO) and Wisconsin prognostic breast cancer (WPBC) datasets respectively. Consequently, these improved disease outcomes may encourage physicians to use this model to make better treatment choices.","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":"141713868","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

Aspect term extraction from multi-source domain using enhanced latent Dirichlet allocation 利用增强型潜在 Dirichlet 分配从多源域提取特征词

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp475-484

R. Dhanal, V. R. Ghorpade

引用次数: 0

Unveiling deep learning powers: LSTM, BiLSTM, GRU, BiGRU, RNN comparison 揭开深度学习的神秘面纱：LSTM、BiLSTM、GRU、BiGRU、RNN 比较

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp263-273

Z. M. Shaikh, S. Ramadass

{"title":"Unveiling deep learning powers: LSTM, BiLSTM, GRU, BiGRU, RNN comparison","authors":"Z. M. Shaikh, S. Ramadass","doi":"10.11591/ijeecs.v35.i1.pp263-273","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp263-273","url":null,"abstract":"Deep learning algorithms have revolutionized various fields by achieving remarkable results in time series analysis. Among the different architectures, recurrent neural networks (RNNs) have played a significant role in sequential data processing. This study presents a comprehensive comparison of prominent RNN variants: long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), bidirectional GRU (BiGRU), and RNN, to analyze their respective strengths and weaknesses of national stock exchange India (NSEI). The Python application developed for this research aims to evaluate and determine the most effective algorithm among the variants. To conduct the evaluation, data from the public domain covering the period from 1/1/2004 to 30/06/2023 is collected. The dataset considers significant events such as demonetization, market crashes, the COVID-19 pandemic, downturns in the automobile sector, and rises in unemployment. Stocks from various sectors including banking, automobile, oil and gas, metal, and Pharma are selected for analysis. Finally, the results reveal that algorithm performance varies across different stocks. Specifically, in certain cases, BiLSTM outperforms, while in others, both BiGRU and LSTM are surpassed. Notably, the overall performance of simple RNN is consistently the lowest across all stocks.","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":"141713594","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 machine learning algorithm performance through feature selection for driver behavior classification 通过为驾驶员行为分类选择特征来提高机器学习算法性能

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp354-365

S. Bouhsissin, N. Sael, F. Benabbou, Abdelfettah Soultana

{"title":"Enhancing machine learning algorithm performance through feature selection for driver behavior classification","authors":"S. Bouhsissin, N. Sael, F. Benabbou, Abdelfettah Soultana","doi":"10.11591/ijeecs.v35.i1.pp354-365","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp354-365","url":null,"abstract":"Machine learning (ML) techniques empower computers to learn from data and make predictions or decisions in various domains, while preprocessing methods assist in cleaning and transforming data before it can be effectively utilized by ML. Feature selection in ML is a critical process that significantly influences the performance and effectiveness of models. By carefully choosing the most relevant and informative attributes from the dataset, feature selection enhances model accuracy, reduces overfitting, and minimizes computational complexity. In this study, we leverage the UAH-DriveSet dataset to classify driver behavior, employing Filter, embedded, and wrapper methods encompassing 10 distinct feature selection techniques. Through the utilization of diverse ML algorithms, we effectively categorize driver behavior into normal, drowsy, and aggressive classes. The second objective is to employ feature selection techniques to pinpoint the most influential features impacting driver behavior. As a results, random forest emerges as the top-performing classifier, achieving an impressive accuracy of 96.4% and an F1-score of 96.36% using backward feature selection in 7.43 s, while K-nearest neighbour (K-NN) attains an accuracy of 96.29% with forward feature selection in 0.05 s. Following our comprehensive results, we deduce that the primary influential features for studying driver behavior include speed (km/h), course, yaw, impact time, road width, distance to the ahead vehicle, vehicle position, and number of detected vehicles.","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":"141695710","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

Study of BaTiO3-doped Bi2O3/ZnO varistor microstructure and its electrical characteristics 掺杂 BaTiO3 的 Bi2O3/ZnO 变阻器微观结构及其电气特性研究

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp42-51

Faiçal Kharchouche, Yousra Malaoui, O. Bouketir

{"title":"Study of BaTiO3-doped Bi2O3/ZnO varistor microstructure and its electrical characteristics","authors":"Faiçal Kharchouche, Yousra Malaoui, O. Bouketir","doi":"10.11591/ijeecs.v35.i1.pp42-51","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp42-51","url":null,"abstract":"This study presents the characterization and optimization of BaTiO3-doped ZnO-based varistors for electrical and electronic applications. The varistors were prepared using a conventional ceramic procedure and were sintered at a temperature of 1,000 °C with different concentrations of BaTiO3 (0 and 3 mol%) added to the Bi2O3/ZnO-based varistor composition (99.5 mol% ZnO and 0.5 mol% Bi2O3). The results showed that the addition of BaTiO3 led to the formation of various oxides and solid solutions, such as Bi12TiO20, BaTiO3, and (Bi2O3)0.80 (BaO)0.20. The dielectric constant and grain size decreased with increasing BaTiO3 content, while the non-linearity coefficient, electric fields (Eb) increased, and dielectric loss (Tanδ) decreased. The optimized varistor contains 2 mol% BaTiO3 and an electric field of 148.08 V/mm, which are superior to those of the BaTiO3/Bi2O3/ZnO-based varistor. During this study, we were able to observe that a slight addition of BaTiO3 will increase the breakdown voltage and the coefficient of nonlinearity and this will allow us to develop low-dimensional varistors and install them in the high-voltage domain.","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":"141710054","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

Automating cloud virtual machines allocation via machine learning 通过机器学习自动分配云虚拟机

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp191-202

F. Kamoun-Abid, Hounaida Frikha, Amel Meddeb-Makhoulf, F. Zarai

{"title":"Automating cloud virtual machines allocation via machine learning","authors":"F. Kamoun-Abid, Hounaida Frikha, Amel Meddeb-Makhoulf, F. Zarai","doi":"10.11591/ijeecs.v35.i1.pp191-202","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp191-202","url":null,"abstract":"In the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering traditional firewalls’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83%.","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":"141705750","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