International Journal of Electrical and Computer Engineering最新文献_第9页

Characterization of electricity demand based on energy consumption data from Colombia 基于哥伦比亚能源消耗数据的电力需求特征分析

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp4798-4809

Santiago Toledo-Cortés, Juan S. Lara, Alvaro Zambrano, F. G. González Osorio, J. Rosero García

{"title":"Characterization of electricity demand based on energy consumption data from Colombia","authors":"Santiago Toledo-Cortés, Juan S. Lara, Alvaro Zambrano, F. G. González Osorio, J. Rosero García","doi":"10.11591/ijece.v13i5.pp4798-4809","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp4798-4809","url":null,"abstract":"The development of dynamic energy distribution grids to optimize energy resources has become very important at the international level in recent years. A very important step in this development is to be able to characterize the population based on their consumption behaviour. However, traditional consumption meters that report information at a monthly rate provide little information for in-depth analysis. In Colombia, this has changed in recent years due to the implementation and integration of advanced metering infrastructure (AMI). This infrastructure allows to record consumption values in small time intervals, and the available data then allows for the execution of many analysis mechanisms. In this paper we present an analysis of the electricity demand profile from a new dataset of energy consumption in Colombia. A characterization of the users demand profiles is presented using a k-means clustering procedure. Whit this customer segmentation technique we show that is possible identify customer consumption patterns and to identify anomalies in the system. In addition, this type of analysis also allows to assess changes in the consumption pattern of users due to social measures such as those resulting from the coronavirus disease (COVID-19) pandemic.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46723864","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

Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region 摩洛哥-加勒布地区马铃薯作物的机器学习预测施肥模型

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5942-5950

Said Tkatek, Samar Amassmir, Amine Belmzoukia, J. Abouchabaka

{"title":"Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region","authors":"Said Tkatek, Samar Amassmir, Amine Belmzoukia, J. Abouchabaka","doi":"10.11591/ijece.v13i5.pp5942-5950","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5942-5950","url":null,"abstract":"Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R2Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R2, MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45923610","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

Comprehensive study: machine learning approaches for COVID-19 diagnosis 综合研究:新冠肺炎诊断的机器学习方法

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5681-5695

Amir Nasir Hussein, S. Makki, A. Al-Sabbagh

{"title":"Comprehensive study: machine learning approaches for COVID-19 diagnosis","authors":"Amir Nasir Hussein, S. Makki, A. Al-Sabbagh","doi":"10.11591/ijece.v13i5.pp5681-5695","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5681-5695","url":null,"abstract":"Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65382638","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

Image compression approach for improving deep learning applications 改进深度学习应用的图像压缩方法

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5607-5616

Raed Altabeiri, Moath Alsafasfeh, Mohanad Alhasanat

{"title":"Image compression approach for improving deep learning applications","authors":"Raed Altabeiri, Moath Alsafasfeh, Mohanad Alhasanat","doi":"10.11591/ijece.v13i5.pp5607-5616","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5607-5616","url":null,"abstract":"In deep learning, dataset plays a main role in training and getting accurate results of detection and recognition objects in an image. Any training model needs a large size of dataset to be more accurate, where improving the dataset size is one of the most research problems that needs enhancement. In this paper, an image compression approach was developed to reduce the dataset size and improve classification accuracy for the trained model using a convolutional neural network (CNN), and speeds up the machine learning process, while maintaining image quality. The results revealed that the best scenario for deep learning models that provided good and acceptable classification accuracy was one that had the following parameters: 80×80 image size, 10 epochs, 64 batch size, 40 images dataset quality (images compressed 60%), and gray image mode. For this scenario a Dog vs Cat dataset is used, and the training time was 48 minutes, classification accuracy was 86%, and images dataset size was 317 MB on storage device. This size makes up 58% of the size of the original image’s dataset, saves 42% of the storage space and reduces the processing resources consumption.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44579767","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

Optimal tuning proportional integral derivative controller on direct current motor using reptile search algorithm 基于爬行搜索算法的直流电机比例积分微分控制器优化整定

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp4901-4908

Widi Aribowo, B. Suprianto, Unit Three Kartini, A. Wardani

引用次数: 0

Improvement of voltage stability and loadability of power system employing the placement of unified power flow controller using artificial neural network 采用人工神经网络布置统一潮流控制器，提高电力系统电压稳定性和可负荷性

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp4868-4877

Muhammad Khalid Saifullah, Md. Monirul Kabir, K. Rafiqul Islam

{"title":"Improvement of voltage stability and loadability of power system employing the placement of unified power flow controller using artificial neural network","authors":"Muhammad Khalid Saifullah, Md. Monirul Kabir, K. Rafiqul Islam","doi":"10.11591/ijece.v13i5.pp4868-4877","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp4868-4877","url":null,"abstract":"This paper proposes a voltage stability and loadability improvement model of power systems by incorporating the optimal placement of flexible alternating current transmission systems (FACTS) using an artificial neural network (ANN) called OPFANN. The key aspect of this model is to identify the weakest lines which having the most probability of voltage collapse utilized for placing FACTS devices. As installing a new power system network with rapidly increasing power demand cannot be possible, the operator usually operates the power system close to the stability limit. In this regard, continuous monitoring and improvement of system voltage stability and loadability of the existing system are vital issues for energy management systems nowadays. However, the proposed OPFANN introduces a more straightforward and faster scheme for voltage stability monitoring systems using ANN. Intelligent and reliable data samples have been designed to train the ANN based on two-line voltage stability indices (LVSI) techniques. Compared with other works, OPFANN effectively improves voltage stability and loadability at the load point by installing the unified power flow controller (UPFC) FACTS devices to the weakest lines. OPFANN can provide information on voltage collapse points using ANN and reduce the further computational cost of LVSI. Finally, OPFANN ensures faster and more secure operation of the power system.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42430660","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

An ensemble model to detect packet length covert channels 一个用于检测包长度隐蔽通道的集成模型

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5296-5304

Muawia A. Elsadig, A. Gafar

{"title":"An ensemble model to detect packet length covert channels","authors":"Muawia A. Elsadig, A. Gafar","doi":"10.11591/ijece.v13i5.pp5296-5304","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5296-5304","url":null,"abstract":"Covert channel techniques have enriched the way to commit dangerous and unwatched attacks. They exploit ways that are not intended to convey information; therefore, traditional security measures cannot detect them. One class of covert channels that difficult to detect, mitigate, or eliminate is packet length covert channels. This class of covert channels takes advantage of packet length variations to convey covert information. Numerous research articles reflect the useful use of machine learning (ML) classification approaches to discover covert channels. Therefore, this study presented an efficient ensemble classification model to detect such types of attacks. The ensemble model consists of five machine learning algorithms representing the base classifiers. The base classifiers include naive Bayes (NB), decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). Whereas, the logistic regression (LR) classifier was employed to aggregate the outputs of the base classifiers and thus to generate the ensemble classifier output. The results showed a good performance of our proposed ensemble classifier. It beats all single classification algorithms, with a 99.3% accuracy rate and negligible classification errors.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48824109","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

Enhanced TextRank using weighted word embedding for text summarization 基于加权词嵌入的文本摘要增强TextRank

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5472-5482

E. Yulianti, Nicholas Pangestu, Meganingrum Arista Jiwanggi

{"title":"Enhanced TextRank using weighted word embedding for text summarization","authors":"E. Yulianti, Nicholas Pangestu, Meganingrum Arista Jiwanggi","doi":"10.11591/ijece.v13i5.pp5472-5482","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5472-5482","url":null,"abstract":"The length of a news article may influence people’s interest to read the article. In this case, text summarization can help to create a shorter representative version of an article to reduce people’s read time. This paper proposes to use weighted word embedding based on Word2Vec, FastText, and bidirectional encoder representations from transformers (BERT) models to enhance the TextRank summarization algorithm. The use of weighted word embedding is aimed to create better sentence representation, in order to produce more accurate summaries. The results show that using (unweighted) word embedding significantly improves the performance of the TextRank algorithm, with the best performance gained by the summarization system using BERT word embedding. When each word embedding is weighed using term frequency-inverse document frequency (TF-IDF), the performance for all systems using unweighted word embedding further significantly improve, with the biggest improvement achieved by the systems using Word2Vec (with 6.80% to 12.92% increase) and FastText (with 7.04% to 12.78% increase). Overall, our systems using weighted word embedding can outperform the TextRank method by up to 17.33% in ROUGE-1 and 30.01% in ROUGE-2. This demonstrates the effectiveness of weighted word embedding in the TextRank algorithm for text summarization.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47771489","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 multi-microcontroller-based hardware for deploying Tiny machine learning model 一种用于部署微小机器学习模型的基于多微控制器的硬件

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5727-5736

V. Nguyen, Vy Tran, H. Pham, Van-Muot Nguyen, Hoang-Dung Nguyen, Chi-Ngon Nguyen

{"title":"A multi-microcontroller-based hardware for deploying Tiny machine learning model","authors":"V. Nguyen, Vy Tran, H. Pham, Van-Muot Nguyen, Hoang-Dung Nguyen, Chi-Ngon Nguyen","doi":"10.11591/ijece.v13i5.pp5727-5736","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5727-5736","url":null,"abstract":"The tiny machine learning (TinyML) has been considered to applied on the edge devices where the resource-constrained micro-controller units (MCUs) were used. Finding a good platform to deploy the TinyML effectively is very crucial. The paper aims to propose a multiple micro-controller hardware platform for productively running the TinyML model. The proposed hardware consists of two dual-core MCUs. The first MCU is utilized for acquiring and processing input data, while the second is responsible for executing the trained TinyML network. Two MCUs communicate to each other using the universal asynchronous receiver-transmitter (UART) protocol. The multi-tasking programming technique is mainly applied on the first MCU to optimize the pre-processing new data. A three-phase motors faults classification TinyML model was deployed on the proposed system to evaluate the effectiveness. The experimental results prove that our proposed hardware platform was improved 34.8% the total inference time including pre-processing data of the proposed TinyML model in comparing with single micro-controller hardware platform.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41834887","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-efficient device-to-device communication in internet of things using hybrid optimization technique 基于混合优化技术的物联网设备间节能通信

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5418-5430

Yashoda M. Balakrishna, Vrinda Shivashetty

{"title":"Energy-efficient device-to-device communication in internet of things using hybrid optimization technique","authors":"Yashoda M. Balakrishna, Vrinda Shivashetty","doi":"10.11591/ijece.v13i5.pp5418-5430","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5418-5430","url":null,"abstract":"Device-to-device (D2D) communication has grown into notoriety as a critical component of the internet of things (IoT). One of the primary limitations of IoT devices is restricted battery source. D2D communication is the direct contact between the participating devices that improves the data rate and delivers the data quickly by consuming less battery. An energy-efficient communication method is required to enhance the communication lifetime of the network by reducing the node energy dissipation. The clustering-based D2D communication method is maximally acceptable to boom the durability of a network. The oscillating spider monkey optimization (OSMO) and oscillating particle swarm optimization (OPSO) algorithms are used in this study to improve the selection of cluster heads (CHs) and routing paths for D2D communication. The CHs and D2D communication paths are selected depending on the parameters such as energy consumption, distance, end-to-end delay, link quality and hop count. A simulation environment is designed to evaluate and test the performance of the OSMO-OPSO algorithm with existing D2D communication algorithms (such as the GAPSO-H algorithm, adaptive resource-aware split-learning (ARES), bio-inspired cluster-based routing scheme (Bi-CRS), and European society for medical oncology (ESMO) algorithm). The results proved that the proposed technique outperformed with respect to traditional routing strategies regarding latency, packet delivery, energy efficiency, and network lifetime.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65381990","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