Bulletin of Electrical Engineering and Informatics最新文献_第4页

Structured query language query join optimization by using rademacher averages and mapreduce algorithms 利用雷达马赫平均法和 mapreduce 算法优化结构化查询语言查询连接

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-06-01 DOI: 10.11591/eei.v13i3.6837

Yathish Aradhya Bandur Chandrashekariah, D. H. A.

{"title":"Structured query language query join optimization by using rademacher averages and mapreduce algorithms","authors":"Yathish Aradhya Bandur Chandrashekariah, D. H. A.","doi":"10.11591/eei.v13i3.6837","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6837","url":null,"abstract":"Query optimization involves identifying and implementing the most effective and efficient methods and strategies to enhance the performance of queries. This is achieved by intelligently utilizing system resources and considering various performance metrics. Table joining optimization involves optimizing the process of combining two or more tables within a database. Structured query language (SQL) optimization is the progress of utilizing SQL queries in the possible way to achieve fast and accurate database results. SQL optimization is critical to decreasing the no of queries in research description framework (RDF) and the time for processing a huge number of relatable data. In this paper, four new algorithms are proposed such as hash-join, sort-merge, rademacher averages and mapreduce for the progress of SQL query join optimization. The proposed model is evaluated and tested using waterloo sparql diversity test suite (WatDiv) and lehigh university benchmark (LUBM) benchmark datasets in terms of time execution. The results represented that the proposed method achieved an enhanced performance of less execution time for various queries such as Q3 of 5362, Q8 of 5921, Q9 of 5854 and Q10 of 5691 milliseconds. The proposed gives better performance than other existing methods like hybrid database-map reduction system (AQUA+) and join query processing (JQPro).","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"11 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235604","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

Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon 用于量化和测量麝香瓜感染和传播严重程度的杂交测报仪

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-06-01 DOI: 10.11591/eei.v13i3.5432

D. Kannan, Amutha Balakrishnan, K. M. Devi, Nagendra Singh, P. A. Kiruba, R. Ramkumar, D. Karthikeyan

{"title":"Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon","authors":"D. Kannan, Amutha Balakrishnan, K. M. Devi, Nagendra Singh, P. A. Kiruba, R. Ramkumar, D. Karthikeyan","doi":"10.11591/eei.v13i3.5432","DOIUrl":"https://doi.org/10.11591/eei.v13i3.5432","url":null,"abstract":"Disease severity index (DIS) is a way of calculating the percentage of infection spread across the field. The percentage of infection in each leaf has been considered at a time stamp is being calculated and based on that disease, severity of disease spread is analyzed. With the advancement in machine learning and deep learning algorithms in the field of computer vision, identification and classification of diseases is effortless. Percentage of infection in a particular leaf, disease index (DI) is calculated using image processing techniques like Otsu threshold method. With this DI and scales, grading the severity of the infection across the field can be achieved. In this paper various scales used for grading severity of infection namely Horsfall-Barratt (H-B scale) quantitative ordinal scale, Amended 20% ordinal scale, and nearest percent estimates (NPEs) in muskmelon is explored, and based on the empirical results Amended 20% ordinal scale is most efficient method of estimating the DIS is to use the midpoint of the severity scope for each class with twenty percent adjusted to ordinal scale. The results show that the density of leaves is directly proportional to spread of diseases in muskmelon plant.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"58 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231622","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

Dissecting of the two-stages object detection models architecture and performance 剖析两阶段物体检测模型的结构和性能

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-06-01 DOI: 10.11591/eei.v13i3.6424

Sara Bouraya, A. Belangour

{"title":"Dissecting of the two-stages object detection models architecture and performance","authors":"Sara Bouraya, A. Belangour","doi":"10.11591/eei.v13i3.6424","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6424","url":null,"abstract":"Artificial intelligence (AI) is the discipline focused on enabling computers to operate autonomously without explicit programming. Within AI, computer vision is an emerging field tasked with endowing machines with the ability to interpret visual data from images and videos. Over recent decades, computer vision has found applications in diverse fields such as autonomous vehicles, information retrieval, surveillance, and understanding human behavior. Object detection, a key aspect of computer vision, employs deep neural networks to continually advance detection accuracy and speed. Its goal is to precisely identify objects within images or videos and assign them to specific classes. Object detection models typically consist of three components: a backbone network for feature extraction, a neck model for feature aggregation, and a head for prediction. The focus of this study lies on two stage detectors. This study aims to provide a comprehensive review of two stage detectors in object detection, followed by benchmarking to offer insights for researchers and scientists. By analyzing and understanding the efficacy of these models, this research seeks to guide future developments in the field of object detection within computer vision.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"4 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230125","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

Automated 3D convolutional neural network architecture design using genetic algorithm for pulmonary nodule classification 利用遗传算法自动设计用于肺结节分类的三维卷积神经网络架构

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-06-01 DOI: 10.11591/eei.v13i3.6828

K. Rahouma, Shahenda Mahmoud Mabrouk, Mohamed Aouf

{"title":"Automated 3D convolutional neural network architecture design using genetic algorithm for pulmonary nodule classification","authors":"K. Rahouma, Shahenda Mahmoud Mabrouk, Mohamed Aouf","doi":"10.11591/eei.v13i3.6828","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6828","url":null,"abstract":"Cancer of the lungs is considered one of the primary causes of death among patients globally. Early detection contributes significantly to the success of pulmonary cancer treatment. To aid the pulmonary nodule classification, many models for the analysis of medical image utilizing deep learning have been developed. Convolutional neural network (CNN) recently, has attained remarkable results in various image classification tasks. Nevertheless, the CNNs performance is heavily dependent on their architectures which still heavily reliant on human domain knowledge. This study introduces a cutting-edge approach that leverages genetic algorithms (GAs) to automatically design 3D CNN architectures for differentiation between benign and malignant pulmonary nodules. The suggested algorithm utilizes the dataset of lung nodule analysis 2016 (LUNA16) for evaluation. Notably, our approach achieved exceptional model accuracy, with evaluations on the testing dataset yielding up to 95.977%. Furthermore, the algorithm exhibited high sensitivity, showcasing its robust performance in distinguishing between benign and malignant nodules. Our findings demonstrate the outstanding capabilities of the proposed algorithm and show an outstanding performance and attain a state of art solution in lung nodule classification.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"48 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232235","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 cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions 农村地区经济高效的心电图监测：利用人工神经网络实现高效医疗解决方案

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-06-01 DOI: 10.11591/eei.v13i3.6866

Md. Obaidur Rahaman, M. A. Kashem

{"title":"A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions","authors":"Md. Obaidur Rahaman, M. A. Kashem","doi":"10.11591/eei.v13i3.6866","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6866","url":null,"abstract":"Cardiovascular diseases engender serious public health concerns in developing nations since access to specialized medical equipment is often limited and standard treatment expenses can be prohibitive. This study proposes an efficient and relatively affordable electrocardiogram (ECG) monitoring system that reads and analyzes a person's electrocardiogram data to provide affordable and quality healthcare solutions. The device initially extracts features from electrocardiogram records by reading electrical signals in the heart. Extracted data are then analyzed by a trained deep learning model to determine precisely if the heart is in a healthy state or undergoing complexities. Experimental results showed that the fine-tuned ANN architecture outperformed the state-of-the-art architectures in this field with an accuracy of 98.95%. The data can also be sent to specialists through an MQTT server if necessary, allowing for remote diagnosis and treatment. The system is intended to be deployed in countries where rural regions lack access to specialized healthcare equipment and professionals. Additionally, the device is inexpensive and, hence can be made accessible to people with limited affordability.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"38 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232595","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

Smart indoor gardening: elevating growth, health, and automation 智能室内园艺：提高生长、健康和自动化水平

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-06-01 DOI: 10.11591/eei.v13i3.7101

Tawfiq Alrawashdeh, Ibrahim Alkore Alshalabi, Moha'med Al-Jaafreh, M. Alksasbeh

{"title":"Smart indoor gardening: elevating growth, health, and automation","authors":"Tawfiq Alrawashdeh, Ibrahim Alkore Alshalabi, Moha'med Al-Jaafreh, M. Alksasbeh","doi":"10.11591/eei.v13i3.7101","DOIUrl":"https://doi.org/10.11591/eei.v13i3.7101","url":null,"abstract":"Recently, indoor systems for growing plants have emerged as a promising approach to address the problems related to extreme weather conditions outdoors. However, such systems must manage the plants surrounding environments to satisfy the environmental and the economical requirements. In this line, any proposed solution must address challenging factors such as plants diseases and unordinary climate situations. In this paper, propose an internet of things (IoT) indoor system that can be used to facilitate the plant growing process. The proposed system is designed to provide alternatives for outdoor climate dependency such as the vitamins provided through sunlight. Moreover, renewable energy sources (sunlight) are employed to reduce the impact on the environment. With the help of several types of sensors, the system continuously monitors the plants through their growing journey. Whereas actuator devices are employed to control the plant-feeding process based on the sensors’ reported values. All the collected data will be uploaded to the cloud for analysis, utilizing a website. Additionally, the architecture of the provided system eliminates the need for human involvement, which has a degrading effect on the plant growing process.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"39 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232697","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

Load frequency control of interconnected power system using cuckoo search algorithm 使用布谷鸟搜索算法控制互联电力系统的负载频率

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-06-01 DOI: 10.11591/eei.v13i3.6714

Soumya Mishra, Pujari Harish Kumar, Rajarajan Ramasamy, Renjini Edayillam Nambiar, Praveena Puvvada

引用次数: 0

Speed synchronization of two DC motors with independent loads based on the higher load torque 基于较高负载扭矩实现两个独立负载直流电机的速度同步

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-06-01 DOI: 10.11591/eei.v13i3.6188

Ali Saqer Akayleh, Addasi Emad Said

引用次数: 0

User authentication using gait and enhanced attribute-based encryption: a case of smart home 利用步态和增强型属性加密进行用户身份验证：智能家居案例

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-06-01 DOI: 10.11591/eei.v13i3.5347

Lim Wei Pin, Manmeet Mahinderjit Singh

引用次数: 0

Early prediction of COVID-19 infection using data mining and multi machine learning algorithms 利用数据挖掘和多机器学习算法对 COVID-19 感染进行早期预测

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-06-01 DOI: 10.11591/eei.v13i3.6912

Ahmed Jaddoa Enad, Mustafa Aksu

{"title":"Early prediction of COVID-19 infection using data mining and multi machine learning algorithms","authors":"Ahmed Jaddoa Enad, Mustafa Aksu","doi":"10.11591/eei.v13i3.6912","DOIUrl":"https://doi.org/10.11591/eei.v13i3.6912","url":null,"abstract":"The fields of artificial intelligence (AI) and machine learning (ML) have attracted significant interest and investment from a diverse range of industries, especially during the last several years. Despite the fact that AI methods have been used extensively and put through extensive testing in the healthcare industry, the recently discovered coronavirus disease (COVID-19) necessitates the use of these methods in order to prevent the emergence of the disease. The proposed system is based on six ML algorithms to predict COVID-19 infection as random forest (RF) algorithm, naive bayes (NB) algorithm, support vector machine (SVM) algorithm, decision tree (DT) algorithm, multi-layer perceptron (MLP), and k-nearest neighbor (KNN). It is based on two steps: first, we uploaded the dataset to train the model. Then, we test our model on those cases to work directly after making a trained classifier so it can directly discover with automatic COVID-19 prediction state of a patient suspected or not. The proposed system results showed the high accuracy of NB, DT, and SVM as 98.646%. Besides the better time to build the model and early predict the state of patients is 31 ms of the NB algorithm.","PeriodicalId":502860,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"8 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229470","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