Cmc-computers Materials & Continua最新文献_第8页

Automatic License Plate Recognition System for Vehicles Using a CNN 基于CNN的车辆车牌自动识别系统

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.017681

S. Ranjithkumar, S. Chenthur pandian

引用次数: 17

An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic 新型冠状病毒大流行时代基于快速rcnn迁移学习的自动实时口罩检测系统

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.017865

Maha Farouk S. Sabir, I. Mehmood, Wafaa Adnan Alsaggaf, Enas Fawai Khairullah, Samar Alhuraiji, Ahmed S. Alghamdi, Ahmed A. Abd El-Latif

{"title":"An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic","authors":"Maha Farouk S. Sabir, I. Mehmood, Wafaa Adnan Alsaggaf, Enas Fawai Khairullah, Samar Alhuraiji, Ahmed S. Alghamdi, Ahmed A. Abd El-Latif","doi":"10.32604/cmc.2022.017865","DOIUrl":"https://doi.org/10.32604/cmc.2022.017865","url":null,"abstract":"Today, due to the pandemic of COVID-19 the entire world is facing a serious health crisis. According to the World Health Organization (WHO), people in public places should wear a face mask to control the rapid transmission of COVID-19. The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places. Therefore, it is very difficult to manually monitor people in overcrowded areas. This research focuses on providing a solution to enforce one of the important preventativemeasures of COVID-19 in public places, by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19. This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked. The proposed framework is built by fine-tuning the state-of-the-art deep learning model, Faster-RCNN, and has been validated on a publicly available dataset named Face Mask Dataset (FMD) and achieving the highest average precision (AP) of 81% and highest average Recall (AR) of 84%. This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces. Moreover, this work applies to real-time and can be implemented in any public service area. © 2022 Tech Science Press. All rights reserved.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"7 3 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75567790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery 一种改进的数据挖掘和知识发现进化算法

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.021652

A. Siddiqa, Syed Abbas Zilqurnain Naqvi, Muhammad Ahsan, A. Ditta, Hani Alquhayz, M. A. Khan, Muhammad Adnan Khan

引用次数: 2

An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data 基于未知种群数据的时空疾病聚类特征空间检测方法

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.019029

Sami Ullah, Nurul Hidayah Mohd Nor, H. Daud, N. Zainuddin, Hadi Fanaee-T, Alamgir Khalil

{"title":"An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data","authors":"Sami Ullah, Nurul Hidayah Mohd Nor, H. Daud, N. Zainuddin, Hadi Fanaee-T, Alamgir Khalil","doi":"10.32604/cmc.2022.019029","DOIUrl":"https://doi.org/10.32604/cmc.2022.019029","url":null,"abstract":"Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies. The state-of-the-art method for this kind of problem is the Space-time Scan Statistics (SaTScan) which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson or Gaussian counts. Addressing this problem, an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution. However, it is based on the population counts data which are not always available in the least developed countries. In addition, the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales, where the catchment area for each hospital/pharmacy is undefined. We extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population counts. The proposed adaptation uses an estimator of expected disease count that does not depend on the population counts. The proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods: Multi-EigenSpot and SaTScan. The result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"89 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78985472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

SDN Based DDos Mitigating Approach Using Traffic Entropy for IoT Network 基于SDN的物联网网络流量熵DDos缓解方法

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.017772

Muhammad Ibrahim, Muhammad Hanif, Shabir Ahmad, Faisal Jamil, Tayyaba Sehar, Yunjung Lee, Dohyeun Kim

{"title":"SDN Based DDos Mitigating Approach Using Traffic Entropy for IoT Network","authors":"Muhammad Ibrahim, Muhammad Hanif, Shabir Ahmad, Faisal Jamil, Tayyaba Sehar, Yunjung Lee, Dohyeun Kim","doi":"10.32604/cmc.2022.017772","DOIUrl":"https://doi.org/10.32604/cmc.2022.017772","url":null,"abstract":": The Internet of Things (IoT) has been widely adopted in various domains including smart cities, healthcare, smart factories, etc. In the last few years, the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms. The IoT fitness devices collect trainee data that is being used for various decision-making. However, it will face numerous security and privacy issues towards its realization. This work focuses on IoT security, especially DoS/DDoS attacks. In this paper, we have proposed a novel blockchain-enabled protocol (BEP) that uses the notion of a self-exposing node (SEN) approach for securing fitness IoT applications. The blockchain and SDN architectures are employed to enhance IoT security by a highly preventive security monitoring, analysis and response system. The proposed approach helps in detecting the DoS/DDoS attacks on the IoT fitness system and then mitigating the attacks. The BEP is used for handling Blockchain-related activities and SEN could be a sensor or actu-ator node within the fitness IoT system. SEN provides information about the inbound and outbound traffic to the BEP which is used to analyze the DoS/DDoS attacks on the fitness IoT system. The SEN calculates the inbound and outbound traffic features’ entropies and transmits them to the Blockchain in the form of transaction blocks. The BEP picks the whole mined blocks’ transactions and transfers them to the SDN controller node. The controller node correlates the entropies data of SENs and decides about the DoS or DDoS attack. So, there are two decision points, one is SEN, and another is the controller. To evaluate the performance of our proposed system, several experiments are performed and results concerning the entropy values and attack detection rate are obtained. The proposed approach has outperformed the other two approaches concerning the attack detection rate by an increase of 11% and 18% against Approach 1 and Approach 2 respectively.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"62 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79025382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Towards Securing Machine Learning Models Against Membership Inference Attacks 保护机器学习模型免受成员推理攻击

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.019709

S. Ben Hamida, H. Mrabet, Sana Belguith, Adeeb M. Alhomoud, A. Jemai

引用次数: 3

Forecasting of Appliances House in a Low-Energy Depend on Grey Wolf Optimizer 基于灰狼优化器的低能耗家电住宅预测

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.021998

Hatim G. Zaini

{"title":"Forecasting of Appliances House in a Low-Energy Depend on Grey Wolf Optimizer","authors":"Hatim G. Zaini","doi":"10.32604/cmc.2022.021998","DOIUrl":"https://doi.org/10.32604/cmc.2022.021998","url":null,"abstract":": This paper gives and analyses data-driven prediction models for the energy usage of appliances. Data utilized include readings of temperature and humidity sensors from a wireless network. The building envelope is meant to minimize energy demand or the energy required to power the house independent of the appliance and mechanical system efficiency. Approx-imating a mapping function between the input variables and the continuous output variable is the work of regression. The paper discusses the forecasting framework FOPF (Feature Optimization Prediction Framework), which includes feature selection optimization: by removing non-predictive parameters to choose the best-selected feature hybrid optimization technique has been approached. k-nearest neighbors (KNN) Ensemble Prediction Models for the data of the energy use of appliances have been tested against some bases machine learning algorithms. The comparison study showed the powerful, best accuracy and lowest error of KNN with RMSE = 0.0078. Finally, the suggested ensemble model’s performance is assessed using a one-way analysis of variance (ANOVA) test and the Wilcoxon Signed Rank Test. (Two-tailed P-value: 0.0001).","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"27 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76460496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

COVID19 Outbreak: A Hierarchical Framework for User Sentiment Analysis covid - 19爆发:用户情绪分析的分层框架

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.018131

A. Ibrahim, M. Hassaballah, Abdelmgeid A. Ali, Yunyoung Nam

{"title":"COVID19 Outbreak: A Hierarchical Framework for User Sentiment Analysis","authors":"A. Ibrahim, M. Hassaballah, Abdelmgeid A. Ali, Yunyoung Nam","doi":"10.32604/cmc.2022.018131","DOIUrl":"https://doi.org/10.32604/cmc.2022.018131","url":null,"abstract":"Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and sparsity. In this paper, a hierarchical twitter sentiment model (HTSM) is proposed to show people’s opinions in short texts. The proposed HTSM has two main features as follows: constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width, as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner (VADER) sentiment analysis. The tweets for each extracted important aspect can be categorized as follows: strongly positive, positive, neutral, strongly negative, or negative. The quality of the proposed model is validated by applying it to a popular product and a widespread topic. The results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"875 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72662088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Coronavirus Detection Using Two Step-AS Clustering and Ensemble Neural Network Model 基于两步as聚类和集成神经网络模型的冠状病毒检测

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.024145

Ahmed Hamza Osman

{"title":"Coronavirus Detection Using Two Step-AS Clustering and Ensemble Neural Network Model","authors":"Ahmed Hamza Osman","doi":"10.32604/cmc.2022.024145","DOIUrl":"https://doi.org/10.32604/cmc.2022.024145","url":null,"abstract":"This study presents a model of computer-aided intelligence capable of automatically detecting positive COVID-19 instances for use in regular medical applications. The proposed model is based on an Ensemble boosting Neural Network architecture and can automatically detect discriminatory features on chest X-ray images through Two Step-As clustering algorithm with rich filter families, ion and weight-sharing properties. In contrast to the generally used transformational learning approach, the proposed model was trained before and after clustering. The compilation procedure divides the datasets samples and categories into numerous sub-samples and subcategories and then assigns new group labels to each new group, with each subject group displayed as a distinct category. The retrieved characteristics discriminant cases were used to feed the Multiple Neural Network method, which was then utilised to classify the instances. The Two Step-AS clustering method has been modified by pre-aggregating the dataset before applying Multiple Neural Network algorithm to detect COVID-19 cases from chest X-ray findings. Models for Multiple Neural Network and Two Step-As clustering algorithms were optimised by utilising Ensemble Bootstrap Aggregating algorithm to reduce the number of hyper parameters they include. The tests were carried out using the COVID-19 public radiology database, and a cross-validation method ensured accuracy. The proposed classifier with an accuracy of 98.02% percent was found to provide the most efficient outcomes possible. The result is a low-cost, quick and reliable intelligence tool for detecting COVID-19 infection. © 2022 Tech Science Press. All rights reserved.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"17 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72997354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework 基于传统和深度学习框架的皮肤病变分割和分类

IF 3.1 4区计算机科学

Cmc-computers Materials & Continua Pub Date : 2022-01-01 DOI: 10.32604/cmc.2022.018917

Amina Bibi, Muhamamd Attique Khan, M. Younus Javed, U. Tariq, Byeong-Gwon Kang, Yun-Seong Nam, Reham R. Mostafa, Rasha H. Sakr

引用次数: 21