Bulletin of Electrical Engineering and Informatics最新文献_第6页

A survey to build framework for optimize and secure migration and transmission of cloud data 为建立优化和安全迁移及传输云数据的框架而进行的调查

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-04-01 DOI: 10.11591/eei.v13i2.5181

Ravinder Bathini, Naresh Vurukonda

引用次数: 0

Exploratory analysis on the natural language processing models for task specific purposes 针对特定任务的自然语言处理模型探索性分析

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-04-01 DOI: 10.11591/eei.v13i2.6360

G. Shidaganti, Rithvik Shetty, Tharun Edara, Prashanth Srinivas, Sai Chandu Tammineni

{"title":"Exploratory analysis on the natural language processing models for task specific purposes","authors":"G. Shidaganti, Rithvik Shetty, Tharun Edara, Prashanth Srinivas, Sai Chandu Tammineni","doi":"10.11591/eei.v13i2.6360","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6360","url":null,"abstract":"Natural language processing (NLP) is a technology that has become widespread in the area of human language understanding and analysis. A range of text processing tasks such as summarisation, semantic analysis, classification, question-answering, and natural language inference are commonly performed using it. The dilemma of picking a model to help us in our task is still there. It’s becoming an impediment. This is where we are trying to determine which modern NLP models are better suited for the tasks set out above in order to compare them with datasets like SQuAD and GLUE. For comparison, BERT, RoBERTa, distilBERT, BART, ALBERT, and text-to-text transfer transformer (T5) models have been used in this study. The aim is to understand the underlying architecture, its effects on the use case and also to understand where it falls short. Thus, we were able to observe that RoBERTa was more effective against the models ALBERT, distilBERT, and BERT in terms of tasks related to semantic analysis, natural language inference, and question-answering. The reason is due to the dynamic masking present in RoBERTa. For summarisation, even though BART and T5 models have very similar architecture the BART model has performed slightly better than the T5 model.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"47 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140357849","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

Squirrel search method for deep learning-based anomaly identification in videos 基于深度学习的视频异常识别松鼠搜索法

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-04-01 DOI: 10.11591/eei.v13i2.5933

Laxmikant Malphedwar, Thevasigamani Rajesh Kumar

{"title":"Squirrel search method for deep learning-based anomaly identification in videos","authors":"Laxmikant Malphedwar, Thevasigamani Rajesh Kumar","doi":"10.11591/eei.v13i2.5933","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5933","url":null,"abstract":"The monitoring of human behavior and traffic surveillance in various locations has become increasingly important in recent years. However, identifying abnormal activity in real-world settings is a challenging task due to the many different types of worrisome and abnormal actions, including theft, violence, and accidents. To address this issue, this paper proposes a new framework for deep learning-based anomaly identification in videos using the squirrel search algorithm and bidirectional long short-term memory (BiLSTM). The proposed method combines the squirrel search algorithm, an optimization technique inspired by nature, with BiLSTM for anomaly recognition. The framework uses the knowledge gained from a sequence of frames to categorize the video as either typical or abnormal. The proposed method was exhaustively tested in several benchmark datasets for anomaly detection to confirm its functionality in challenging surveillance circumstances. The results show that the proposed framework outperforms existing methods in terms of area under curve (AUC) values, with a test set AUC score of 93.1%. The paper also discusses the importance of feature selection and the benefits of using BiLSTM over traditional unidirectional long short-term memory (LSTM) models for anomaly detection in videos. Overall, the proposed framework provides a highly precise computerization of the system, making it an effective tool for identifying abnormal human behavior in surveillance footage.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"1 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140352951","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 deep learning models (U-Net architectures) for segmenting brain tumors 用于分割脑肿瘤的深度学习模型（U-Net 架构）综述

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-04-01 DOI: 10.11591/eei.v13i2.6015

Mawj Abdul-Ameer Al-Murshidawy, O. Al-Shamma

{"title":"A review of deep learning models (U-Net architectures) for segmenting brain tumors","authors":"Mawj Abdul-Ameer Al-Murshidawy, O. Al-Shamma","doi":"10.11591/eei.v13i2.6015","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6015","url":null,"abstract":"Highly accurate tumor segmentation and classification are required to treat the brain tumor appropriately. Brain tumor segmentation (BTS) approaches can be categorized into manual, semi-automated, and full-automated. The deep learning (DL) approach has been broadly deployed to automate tumor segmentation in therapy, treatment planning, and diagnosing evaluation. It is mainly based on the U-Net model that has recently attained state-of-the-art performances for multimodal BTS. This paper demonstrates a literature review for BTS using U-Net models. Additionally, it represents a common way to design a novel U-Net model for segmenting brain tumors. The steps of this DL way are described to obtain the required model. They include gathering the dataset, pre-processing, augmenting the images (optional), designing/selecting the model architecture, and applying transfer learning (optional). The model architecture and the performance accuracy are the two most important metrics used to review the works of literature. This review concluded that the model accuracy is proportional to its architectural complexity, and the future challenge is to obtain higher accuracy with low-complexity architecture. Challenges, alternatives, and future trends are also presented.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"11 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353109","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 and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network 异构无线传感器网络中高能效混合数据聚合的设计与实现

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-04-01 DOI: 10.11591/eei.v13i2.5582

M. M. Al-Heeti, Jamal A. Hammad, Ahmed Shamil Mustafa

{"title":"Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network","authors":"M. M. Al-Heeti, Jamal A. Hammad, Ahmed Shamil Mustafa","doi":"10.11591/eei.v13i2.5582","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5582","url":null,"abstract":"Heterogeneous wireless sensor network (HWSN) is a trending technology in both the industrial and academic sectors, consisting of a large number of interconnected sensors. However, higher energy consumption and delay are significant drawbacks of this technology in applications such as military, healthcare, and industrial automation. The main objective of this research is to enhance the energy efficiency of HWSN using a clustering technique. In this article, a novel approach, namely power optimization and hybrid data aggregation (POHDA), is proposed to address these challenges in HWSN. POHDA-HWSN focuses on power optimization and congestion avoidance through effective CH selection using hybrid data aggregation based on parameters such as residual energy, distance, mobility, threshold value of the node, and latency. By weight-based effective cluster head (CH) selection, the energy consumption, end-to-end delay, and overhead during communication are reduced in this network. The POHDA-HWSN approach considers specific parameters to compare the results and outcomes with earlier research such as HCCS-WSN, FMCA-WSN, and APCC-WSN. The results prove that the proposed POHDA-HWSN approach achieves higher energy efficiency and delivery ratio.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"8 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353288","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

Reliability analysis in distribution system by deep belief neural network 利用深度信念神经网络进行配电系统可靠性分析

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-04-01 DOI: 10.11591/eei.v13i2.6324

Likhitha Ramalingappa, Prathibha Ekanthaiah, MD Irfan Ali, Aswathnarayan Manjunatha

{"title":"Reliability analysis in distribution system by deep belief neural network","authors":"Likhitha Ramalingappa, Prathibha Ekanthaiah, MD Irfan Ali, Aswathnarayan Manjunatha","doi":"10.11591/eei.v13i2.6324","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6324","url":null,"abstract":"Rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs posing growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. utilities are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metrics for gauging the effect of distributed renewable energy on distribution networks is reliability analysis. Reliability analysis in distribution systems involves evaluating the performance and robustness of electrical distribution networks. An artificial intelligence approach is implemented in this paper to improve reliability analysis with dispersed generations in distribution network. Deep belief neural networks (DBNNs) are a type of artificial neural network that can be used for various tasks, including analyzing complex data such as those found in power distribution systems. This paper integrated a DBNN using a particle swarm optimization (PSO) technique. The proposed model performance is assessed using mean square error, mean absolute error, root mean square error, and R squared error. The findings reveal that reliability analysis with this novel technique is more accurate.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"7 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140354292","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 novel energy-efficient dynamic programming routing protocol in wireless multimedia sensor networks 无线多媒体传感器网络中的新型节能动态编程路由协议

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-04-01 DOI: 10.11591/eei.v13i2.5855

E. H. Putra, Muhammad Haikal Satria, Hamid Azwar, Rendy Rianda, Muhammad Saputra, R. S. Darwis

{"title":"A novel energy-efficient dynamic programming routing protocol in wireless multimedia sensor networks","authors":"E. H. Putra, Muhammad Haikal Satria, Hamid Azwar, Rendy Rianda, Muhammad Saputra, R. S. Darwis","doi":"10.11591/eei.v13i2.5855","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5855","url":null,"abstract":"Wireless multimedia sensor networks (WMSNs) have characteristics that may influence the routing decisions, such as limited energy resources, storage and computing capacity. Therefore, a routing optimization needs to be done to match the characteristics of the WMSNs. Existing routing protocols only consider energy efficiency regardless of energy threshold, maximum energy, and link cost collectively as the primary basis of routing. In this work, the energy-efficient dynamic programming (EEDP) protocol is proposed to optimize routing decisions that take into account the energy threshold, the maximum energy, and the link cost. Then, the protocol is compared with the dynamic programming (DP), and the ant colony optimization (ACO) protocol. The simulation results show that the EEDP protocol can improve energy efficiency of nodes and network lifetime of the WMSNs. Then, the EEDP protocol is also implemented into a network topology of 10 NodeMCU ESP32 devices. As a result, the EEDP protocol can work very well by selecting routes based on nodes that have the remaining energy above 50 and has the shortest distance. The average delay in sending data for the entire route for the 10 iterations of sending data is 3.99 seconds.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"2 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140354529","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 the medical diagnosis of COVID-19 with learning based decision support systems 利用基于学习的决策支持系统加强对 COVID-19 的医疗诊断

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-04-01 DOI: 10.11591/eei.v13i2.6293

Mohammed Berrahal, Mohammed Boukabous, Mimoun Yandouzi, Mounir Grari, Idriss Idrissi

{"title":"Enhancing the medical diagnosis of COVID-19 with learning based decision support systems","authors":"Mohammed Berrahal, Mohammed Boukabous, Mimoun Yandouzi, Mounir Grari, Idriss Idrissi","doi":"10.11591/eei.v13i2.6293","DOIUrl":"https://doi.org/10.11591/eei.v13i2.6293","url":null,"abstract":"Since late December 2019, the COVID-19 pandemic has had substantial impact and long-lasting impact on numerous lives. The surge in patients has overwhelmed hospitals and exhausted essential resources such as masks and gloves. However, in response to this crisis, we have developed a robust solution that can ease the burden on emergency services and manage the influx of patients. Our proposed framework comprises deep learning and machine learning models that can predict and manage patient demand with high accuracy. The first model, is specifically designed to classify computed tomography (CT) scan images for COVID or non-COVID cases. We trained multiple convolutional neural network (CNN) models on a large dataset of CT scan images and evaluated their performance on a separate test set. Our evaluation showed that the ResNet50 model was the most effective, achieving an accuracy of 93.28%. The second model uses patient measurements dataset to predict the likelihood of intensive care unit (ICU) admission for COVID-19 patients. We experimented with the XGBoost machine learning algorithm and found that the accuracy score achieved 88.40%.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"57 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140357535","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

IoT-based health information system using MitApp for abnormal electrocardiogram signal monitoring 利用 MitApp 监测异常心电图信号的物联网健康信息系统

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-04-01 DOI: 10.11591/eei.v13i2.5205

B. Utomo, Triwiyanto Triwiyanto, Sari Luthfiyah, Wahyu Caesarendra, Vijay Anant Athavale

{"title":"IoT-based health information system using MitApp for abnormal electrocardiogram signal monitoring","authors":"B. Utomo, Triwiyanto Triwiyanto, Sari Luthfiyah, Wahyu Caesarendra, Vijay Anant Athavale","doi":"10.11591/eei.v13i2.5205","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5205","url":null,"abstract":"Information systems are currently developing very rapidly, and this is inseparable from the role of internet of things (IoT) technology, especially in the world of telemedicine. MitApp is an open-source application that can be used to monitor electrocardiogram (ECG) signals in real-time. The aim of this study is to develop an IoT-based ECG signal monitoring system that utilizes the MitApp application to detect abnormal ECG signals that are characterized by symptoms of cardiac arrhythmias. To process ECG signal data obtained from lead electrode results, the research method utilizes Arduino Uno as a microcontroller. The result is then displayed on the thin film transistor (TFT) layer using the Nextion module. The ESP32 module is used as a Wi-Fi module to send data to the MitApp app on a smartphone. The results showed that the results of the comparison test of ECG signal module data with ECG simulator tools with beats per minute values of 60, 80, 100, 120, and 140 obtained an error rate of 0.05. Based on these results, there is potential to develop this feature and integrate the system with the patient management system to improve the effectiveness of remote monitoring.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"11 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353122","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

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller 带下垂控制器的独立可再生微电网高级控制模块的比较分析和验证

Bulletin of Electrical Engineering and Informatics Pub Date : 2024-04-01 DOI: 10.11591/eei.v13i2.5849

Savitri Swathi, Bhaskaruni Suresh Kumar, Jalla Upendar

{"title":"Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller","authors":"Savitri Swathi, Bhaskaruni Suresh Kumar, Jalla Upendar","doi":"10.11591/eei.v13i2.5849","DOIUrl":"https://doi.org/10.11591/eei.v13i2.5849","url":null,"abstract":"A micro grid system with renewable source operation control is a complex part as each source operates at different parameters. This renewable micro grid with multiple sources like solar plants, wind farm, fuel cell, battery backup has to be operated in both grid connected and standalone condition. During grid connection the micro grid, inverter has to inject power to the grid and compensate load in synchronization to the grid voltages. And during standalone condition the inverter is controlled with droop control module which stabilizes the voltage and frequency of the system even during grid disconnection. The droop control module is further updated with new advanced controllers like fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) replacing the traditional proportional integral derivative (PID) and proportional integral (PI) controllers improving the response rate and for achieving better stabilization. This paper has comparative analysis of the micro grid system with different droop controllers under various operating conditions. Parameters like voltage magnitude (Vmag), frequency (F), load and inverter powers (Pload and Pinv) of the test system are compared with different controllers. A numeric comparison table is given to determine the optimal controller for the inverter operation. The analysis is carried out in MATLAB/Simulink software with graphical and parametric validations.","PeriodicalId":37619,"journal":{"name":"Bulletin of Electrical Engineering and Informatics","volume":"11 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353701","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