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

Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia 易感暴露的传染性恢复机器学习在沙特阿拉伯预测COVID-19

International Journal of Electrical and Computer Engineering Pub Date : 2023-08-01 DOI: 10.11591/ijece.v13i4.pp4761-4776

M. Alsmadi, Ghaith M. Jaradat, Sami A. Abahussain, M. Tayfour, U. Badawi, Hayat Alfagham, Muneerah Alshabanah, Daniah Alrajhi, H. ALkhaldi, Njoud Altuwaijri, H. ShoShan, H. M. Abouelnaga, Ahmed Baz Mohamed Metwally

{"title":"Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia","authors":"M. Alsmadi, Ghaith M. Jaradat, Sami A. Abahussain, M. Tayfour, U. Badawi, Hayat Alfagham, Muneerah Alshabanah, Daniah Alrajhi, H. ALkhaldi, Njoud Altuwaijri, H. ShoShan, H. M. Abouelnaga, Ahmed Baz Mohamed Metwally","doi":"10.11591/ijece.v13i4.pp4761-4776","DOIUrl":"https://doi.org/10.11591/ijece.v13i4.pp4761-4776","url":null,"abstract":"Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43270819","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

Reconfigurable negative bit line collapsed supply write-assist for 9T-ST static random access memory cell 用于9T-ST静态随机存取存储器单元的可重构负位线塌陷电源写入辅助

International Journal of Electrical and Computer Engineering Pub Date : 2023-08-01 DOI: 10.11591/ijece.v13i4.pp3747-3755

C. Ganesh, Fazal Noorbasha, K. Murthy

引用次数: 1

Drone’s node placement algorithm with routing protocols to enhance surveillance 无人机的节点放置算法和路由协议，以加强监视

International Journal of Electrical and Computer Engineering Pub Date : 2023-08-01 DOI: 10.11591/ijece.v13i4.pp4194-4203

E. K. Akut, A. D. Usman, K. A. Abubilal, H. Bello, Ahmed Tijani Salawudeen, A. Yaro

{"title":"Drone’s node placement algorithm with routing protocols to enhance surveillance","authors":"E. K. Akut, A. D. Usman, K. A. Abubilal, H. Bello, Ahmed Tijani Salawudeen, A. Yaro","doi":"10.11591/ijece.v13i4.pp4194-4203","DOIUrl":"https://doi.org/10.11591/ijece.v13i4.pp4194-4203","url":null,"abstract":"Flying ad-hoc network (FANET) is characterized by key component features such as communication scheme, energy awareness, and task distribution. In this research, a surveillance space considering standard petroleum pipe was created with three drones viz: drone 1 (D1), master drone (DM), and drone 2 (D2) to survey as FANET. DM aggregate packets from D1, D2 and communicate with the static ground control station (SGCS). The starting point of the three drones and their trajectories during deployment were calculated and simulated. Selection of DM, D1, and D2 was done using battery level before take-off. Simulation results show take-off time difference which depends on the distance of each drone to the SGCS during deployment. D1 take-off first, while DM and D2 followed after 0.0704 and 0.1314 ms respectively. The position-oriented routing protocols results indicated variation of information flow within time notch due to variation in the density of the transmitted packets. Packets delivery periods are 0.00136×103 sec, 0.00110×103 sec, and 0.00246×103 sec for time notch 1, 2, and aggregating time notch respectively. From the results obtained, two algorithms were used successfully in deploying the drones","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43600241","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: 1

Stability model integration for large scale solar photovoltaic system using Western electricity coordinating council model 基于西方电力协调委员会模型的大型太阳能光伏系统稳定性模型集成

International Journal of Electrical and Computer Engineering Pub Date : 2023-08-01 DOI: 10.11591/ijece.v13i4.pp3641-3650

Mohammad Nayeim Fazumy Mohd Tajudin, Mohd Najib Mohd Hussain, M. Hussain, I. R. Ibrahim

{"title":"Stability model integration for large scale solar photovoltaic system using Western electricity coordinating council model","authors":"Mohammad Nayeim Fazumy Mohd Tajudin, Mohd Najib Mohd Hussain, M. Hussain, I. R. Ibrahim","doi":"10.11591/ijece.v13i4.pp3641-3650","DOIUrl":"https://doi.org/10.11591/ijece.v13i4.pp3641-3650","url":null,"abstract":"Due to the increased demand for renewable energy, the interest in the large-scale solar photovoltaic (LSSPV) power plant has recently grown dramatically. However, when a large amount of electricity is produced from the LSSPV power plant to the grid interconnection, the system commonly experiences instability and thus disrupt the grid system in disturbance issues such as bus fault, line-to-line fault, three-phase fault, and tripping. This sudden disturbance occurrence is tended to interrupt the stability of the system from providing balanced electrical production within the electrical grid. A dynamics response from the simulation is used to study the stability and the behavior of the photovoltaic (PV) plant into the grid interconnection by developing 118 bus system. The observation of critical clearing time (CCT) duration shows that the result from the simulation where the duration takes less than t=15 s for the system to get back to its pre-fault condition in three-phase fault and tripping in a dynamic simulation to shows that the system reaches its stability been observed through the simulation result by using from user-specific models to generic models like those advocated by the Western electricity coordinating council (WECC) in power system simulator for engineering (PSSE) software.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65375512","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

Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm 用机器学习模型预测痴呆和用布谷鸟算法改进性能

International Journal of Electrical and Computer Engineering Pub Date : 2023-08-01 DOI: 10.11591/ijece.v13i4.pp4623-4632

Sivakani Rajayyan, Syed Masood Mohamed Mustafa

{"title":"Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm","authors":"Sivakani Rajayyan, Syed Masood Mohamed Mustafa","doi":"10.11591/ijece.v13i4.pp4623-4632","DOIUrl":"https://doi.org/10.11591/ijece.v13i4.pp4623-4632","url":null,"abstract":"Dementia is a brain disease that stays in the seventh position of death rate as per the report of the World Health Organization (WHO). Among the various types of dementia, Alzheimer’s disease has more than 70% of cases of dementia. The objective is to predict dementia disease from the open access series of imaging studies (OASIS) dataset using machine learning techniques. Also, the performance of the machine learning model is analyzed to improve the performance of the model using the cuckoo algorithm. In this paper, feature engineering has been focused and the prediction of dementia has been done using the OASIS dataset with the help of data mining techniques. Feature engineering is followed by prediction using the machine learning model Gaussian naïve Bayes (NB), support vector machine, and linear regression. Also, the best prediction model has been selected and done the validation. The evaluation metrics considered for validating the models are accuracy, precision, recall, and F1-Score and the highest values are 95%, 97%, 95%, and 95%. The Gaussian NB has been given these best results. The accuracy of the machine learning models has been increased by eliminating the factors which affect the performance of the models using the cuckoo algorithm.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65381157","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

Implementation of recurrent neural network for the forecasting of USD buy rate against IDR 预测美元对印尼卢比买入价的递归神经网络实现

International Journal of Electrical and Computer Engineering Pub Date : 2023-08-01 DOI: 10.11591/ijece.v13i4.pp4567-4581

Lady Silk Moonlight, B. Trilaksono, B. Harianto, Fiqqih Faizah

{"title":"Implementation of recurrent neural network for the forecasting of USD buy rate against IDR","authors":"Lady Silk Moonlight, B. Trilaksono, B. Harianto, Fiqqih Faizah","doi":"10.11591/ijece.v13i4.pp4567-4581","DOIUrl":"https://doi.org/10.11591/ijece.v13i4.pp4567-4581","url":null,"abstract":"This study implements a recurrent neural network (RNN) by comparing two RNN network structures, namely Elman and Jordan using the backpropagation through time (BPTT) programming algorithm in the training and forecasting process in foreign exchange forecasting cases. The activation functions used are the linear transfer function, the tan-sigmoid transfer function (Tansig), and the log-sigmoid transfer function (Logsig), which are applied to the hidden and output layers. The application of the activation function results in the log-sigmoid transfer function being the most appropriate activation function for the hidden layer, while the linear transfer function is the most appropriate activation function for the output layer. Based on the results of training and forecasting the USD against IDR currency, the Elman BPTT method is better than the Jordan BPTT method, with the best iteration being the 4000th iteration for both. The lowest root mean square error (RMSE) values for training and forecasting produced by Elman BPTT were 0.073477 and 122.15 the following day, while the Jordan backpropagation RNN method yielded 0.130317 and 222.96 also the following day.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65380714","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

Exploring machine learning techniques for fake profile detection in online social networks 探索在线社交网络中虚假个人资料检测的机器学习技术

International Journal of Electrical and Computer Engineering Pub Date : 2023-06-01 DOI: 10.11591/ijece.v13i3.pp2962-2971

Bharti Bharti, N. S. Gill, P. Gulia

{"title":"Exploring machine learning techniques for fake profile detection in online social networks","authors":"Bharti Bharti, N. S. Gill, P. Gulia","doi":"10.11591/ijece.v13i3.pp2962-2971","DOIUrl":"https://doi.org/10.11591/ijece.v13i3.pp2962-2971","url":null,"abstract":"The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data available on these sites. Therefore, protection against such behaviors has become an essential requirement. Though there are many types of security threats in online social networks but, one of the significant threats is the fake profile. Fake profiles are created intentionally with certain motives, and such profiles may be targeted to steal or acquire sensitive information and/or spread rumors on online social networks with specific motives. Fake profiles are primarily used to steal or extract information by means of friendly interaction online and/or misusing online data available on social sites. Thus, fake profile detection in social media networks is attracting the attention of researchers. This paper aims to discuss various machine learning (ML) methods used by researchers for fake profile detection to explore the further possibility of improvising the machine learning models for speedy results.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65371904","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: 1

Techniques of deep learning and image processing in plant leaf disease detection: a review 植物叶片病害检测中的深度学习和图像处理技术综述

International Journal of Electrical and Computer Engineering Pub Date : 2023-06-01 DOI: 10.11591/ijece.v13i3.pp3029-3040

Anita S. Kini, P. K. V. Reddy, Smitha N. Pai

{"title":"Techniques of deep learning and image processing in plant leaf disease detection: a review","authors":"Anita S. Kini, P. K. V. Reddy, Smitha N. Pai","doi":"10.11591/ijece.v13i3.pp3029-3040","DOIUrl":"https://doi.org/10.11591/ijece.v13i3.pp3029-3040","url":null,"abstract":"Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65372472","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: 2

Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit 基于图形处理单元的COVID-19药物-靶标相互作用预测深度学习优化

International Journal of Electrical and Computer Engineering Pub Date : 2023-06-01 DOI: 10.11591/ijece.v13i3.pp3111-3123

Refianto Damai Darmawan, W. Kusuma, H. Rahmawan

{"title":"Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit","authors":"Refianto Damai Darmawan, W. Kusuma, H. Rahmawan","doi":"10.11591/ijece.v13i3.pp3111-3123","DOIUrl":"https://doi.org/10.11591/ijece.v13i3.pp3111-3123","url":null,"abstract":"The exponentially increasing bioinformatics data raised a new problem: the computation time length. The amount of data that needs to be processed is not matched by an increase in hardware performance, so it burdens researchers on computation time, especially on drug-target interaction prediction, where the computational complexity is exponential. One of the focuses of high-performance computing research is the utilization of the graphics processing unit (GPU) to perform multiple computations in parallel. This study aims to see how well the GPU performs when used for deep learning problems to predict drug-target interactions. This study used the gold-standard data in drug-target interaction (DTI) and the coronavirus disease (COVID-19) dataset. The stages of this research are data acquisition, data preprocessing, model building, hyperparameter tuning, performance evaluation and COVID-19 dataset testing. The results of this study indicate that the use of GPU in deep learning models can speed up the training process by 100 times. In addition, the hyperparameter tuning process is also greatly helped by the presence of the GPU because it can make the process up to 55 times faster. When tested using the COVID-19 dataset, the model showed good performance with 76% accuracy, 74% F-measure and a speed-up value of 179.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47283789","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

Facial emotion recognition using deep learning detector and classifier 基于深度学习检测器和分类器的面部情感识别

International Journal of Electrical and Computer Engineering Pub Date : 2023-06-01 DOI: 10.11591/ijece.v13i3.pp3375-3383

Ng Chin Kit, C. Ooi, W. Tan, Yi-Fei Tan, S. Cheong

{"title":"Facial emotion recognition using deep learning detector and classifier","authors":"Ng Chin Kit, C. Ooi, W. Tan, Yi-Fei Tan, S. Cheong","doi":"10.11591/ijece.v13i3.pp3375-3383","DOIUrl":"https://doi.org/10.11591/ijece.v13i3.pp3375-3383","url":null,"abstract":"Numerous research works have been put forward over the years to advance the field of facial expression recognition which until today, is still considered a challenging task. The selection of image color space and the use of facial alignment as preprocessing steps may collectively pose a significant impact on the accuracy and computational cost of facial emotion recognition, which is crucial to optimize the speed-accuracy trade-off. This paper proposed a deep learning-based facial emotion recognition pipeline that can be used to predict the emotion of detected face regions in video sequences. Five well-known state-of-the-art convolutional neural network architectures are used for training the emotion classifier to identify the network architecture which gives the best speed-accuracy trade-off. Two distinct facial emotion training datasets are prepared to investigate the effect of image color space and facial alignment on the performance of facial emotion recognition. Experimental results show that training a facial expression recognition model with grayscale-aligned facial images is preferable as it offers better recognition rates with lower detection latency. The lightweight MobileNet_v1 is identified as the best-performing model with WM=0.75 and RM=160 as its hyper-parameters, achieving an overall accuracy of 86.42% on the testing video dataset.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65374096","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: 1