2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)最新文献_第4页

Vision-based Indoor Lighting Assessment Approach for Daylight Harvesting 基于视觉的日光采集室内照明评估方法

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) Pub Date : 2023-03-06 DOI: 10.1109/ICAECCS56710.2023.10104917

Seniguer Abderraouf, Aouache Mustapha, Abdelhamid IRATNI, Mekhermeche Haithem

{"title":"Vision-based Indoor Lighting Assessment Approach for Daylight Harvesting","authors":"Seniguer Abderraouf, Aouache Mustapha, Abdelhamid IRATNI, Mekhermeche Haithem","doi":"10.1109/ICAECCS56710.2023.10104917","DOIUrl":"https://doi.org/10.1109/ICAECCS56710.2023.10104917","url":null,"abstract":"Electricity-based lighting is a major source of energy consumption in buildings, which in turn has great potential to reduce its energy consumption by incorporating lighting control strategies, and it is highly recommended to evaluate energy savings before implementing such strategies. Thus, this study focuses on the development of a vision-based modeling approach to assess indoor lighting (VILM) from daylight, dispensing a network of expensive photosensors. The proposed VILM uses self-supervised feature extraction and machine learning algorithms including support vector machine (SVM), logistic regression (LR), and Random Forest (RF) to learn each time-variant scene feature and establish the required illuminance model. Experimental tests were conducted in a university building, laboratories, and classrooms equipped with cameras. The results obtained showed that the RF model yielded the best accuracy and predicted the speed of indoor lighting level compared to other models. In sum, lighting evaluation before integrating control is a very important module for managing and achieving energy-efficient lighting.","PeriodicalId":447668,"journal":{"name":"2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128843369","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

Iterative Method Based Optimization of Wireless Power Transfer for Biomedical Implants 基于迭代法的生物医学植入物无线传输优化

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) Pub Date : 2023-03-06 DOI: 10.1109/ICAECCS56710.2023.10104669

Bennia Fatima, B. Aimad, Nafaa Fares

引用次数: 0

Adoption and Application of Blockchain Technology in IoT: Survey and Open Issues 区块链技术在物联网中的采用和应用:调查和未决问题

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) Pub Date : 2023-03-06 DOI: 10.1109/ICAECCS56710.2023.10105010

Kheireddine Zaghouani, A. Yachir, Badis Djamaa, Abbes Boutouba

引用次数: 0

Sliding Mode Control of a Double Star Asynchronous Machine 双星异步电机的滑模控制

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) Pub Date : 2023-03-06 DOI: 10.1109/ICAECCS56710.2023.10104692

Benabdallah Sereir Boumediene, Bouhadi Manel, Belabbes Ikhlas

引用次数: 0

MFO Approach based Nonlinear MPC Scheme 基于非线性MPC方案的MFO方法

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) Pub Date : 2023-03-06 DOI: 10.1109/ICAECCS56710.2023.10104804

M. Abdelkrim, Kara Kamel, A. Oussama, Benrabah Mohamed

引用次数: 0

Deep Learning with EfficientNetB1 for detecting brain tumors in MRI images 利用EfficientNetB1深度学习检测MRI图像中的脑肿瘤

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) Pub Date : 2023-03-06 DOI: 10.1109/ICAECCS56710.2023.10104761

S. Benkrama, Nour El Houda Hemdani

{"title":"Deep Learning with EfficientNetB1 for detecting brain tumors in MRI images","authors":"S. Benkrama, Nour El Houda Hemdani","doi":"10.1109/ICAECCS56710.2023.10104761","DOIUrl":"https://doi.org/10.1109/ICAECCS56710.2023.10104761","url":null,"abstract":"Machine learning (ML) and computer vision system revolutionized the world, especially Deep learning (DL) for convolutional neural networks, which has proven breakthroughs in brain tumor (BT) diagnosis. This study investigates a Convolutional Neural Network CNN approach for image classification for BT detection using the EfficientNetBl architecture with Global Average Pooling (GAP) layers in a big data setting. A classification layer is done with a softMax layer. The system is created in the Apache Spark environment. Spark system is a unified and ultra-fast analysis engine for large-scale data processing. It is mainly dedicated to Big Data and DL. Experiments are carried out using the brain magnetic resonance imaging dataset containing 3264 MRI scans to predict the performance of the model. The dataset is decomposed into training and testing datasets. The model’s performance was assessed and compared to existing models, it yielded a high precision, precision, fl-score, and weighted average. In our work, we have obtained an accuracy of 97% and a performance of 98% on a dataset of 3064 brain MRI images.","PeriodicalId":447668,"journal":{"name":"2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125435133","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

Meteorological sensor network based on ESP8266 基于ESP8266的气象传感器网络

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) Pub Date : 2023-03-06 DOI: 10.1109/ICAECCS56710.2023.10105060

Sara Belkisse Merrah, Amar Adane

引用次数: 0

Upgraded-ABC Algorithm for Antenna Selection in Energy Efficient Massive MIMO System 节能大规模MIMO系统天线选择的改进abc算法

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) Pub Date : 2023-03-06 DOI: 10.1109/ICAECCS56710.2023.10105114

F. Bouchibane, H. Tayakout, N. Ziane, F. Siahmed, S. Hebib

引用次数: 1

Performance Analysis of Various Rectifier Topologies for 2.45 GHz RF Energy Harvesting Applications 2.45 GHz射频能量收集应用中各种整流器拓扑的性能分析

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) Pub Date : 2023-03-06 DOI: 10.1109/ICAECCS56710.2023.10105121

Djihen Bouchair, Fayçal Boukerroum

引用次数: 0

Features Extraction and Concatenation of Transfer Learning Models for Early Diagnosis of Diabetic Retinopathy 糖尿病视网膜病变早期诊断迁移学习模型的特征提取与拼接

2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) Pub Date : 2023-03-06 DOI: 10.1109/ICAECCS56710.2023.10104760

Omar Boukadoum, N. Benblidia

{"title":"Features Extraction and Concatenation of Transfer Learning Models for Early Diagnosis of Diabetic Retinopathy","authors":"Omar Boukadoum, N. Benblidia","doi":"10.1109/ICAECCS56710.2023.10104760","DOIUrl":"https://doi.org/10.1109/ICAECCS56710.2023.10104760","url":null,"abstract":"Diabetic retinopathy is of interest because it is the cause of blindness in the working population and because its incidence is also increasing sharply in developing countries. Early detection of this disease is essential for a good prognosis. However, the lack of ophthalmologists and the cost of diagnosis with an increasing number of patients with diabetic retinopathy pose many problems. Rapid detection of this disease can indeed be helpful for ophthalmologists. This study deals with the development of an automatic system to classify and predict the diagnosis of diabetic retinopathy through retinal images. The system adopted is based on the technique of convolutional neural networks using a concatenation for all the feature maps of extraction of three models of transfer learning techniques such as ResNet50, InceptionV3, and EfficientNetB5 to generate a detection and prediction model. Following that, a classifier was given these features in order to classify diabetic retinopathy. The dataset used in this research is the publicly available Kaggle dataset (APTOS2019) of retina images with different preprocessing steps. The results obtained are promising, where the best achieved was 97.24% accuracy and 0.07% loss. It can be noted that the developed system has good performance compared to those published in the literature.","PeriodicalId":447668,"journal":{"name":"2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131886543","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