2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)最新文献_第9页

Deep Neural Architecture for Face mask Detection on Simulated Masked Face Dataset against Covid-19 Pandemic 基于新型冠状病毒大流行模拟面罩数据集的面罩检测深度神经结构

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397196

Alok Negi, Krishan Kumar, Prachi Chauhan, R. S. Rajput

{"title":"Deep Neural Architecture for Face mask Detection on Simulated Masked Face Dataset against Covid-19 Pandemic","authors":"Alok Negi, Krishan Kumar, Prachi Chauhan, R. S. Rajput","doi":"10.1109/ICCCIS51004.2021.9397196","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397196","url":null,"abstract":"The dangerous COVID-19 (SARS-CoV-2) is rising steadily and globally, with more than 72,851,747 confirmed cases observed to WHO including 1,643,339 deaths till 17 December 2020. The country’s economy is now almost fully halted, people are stuck up and investment becomes deteriorating. So, this is turning to worry of the government for a development and health. Health organizations are often desperate for evolving decision-making innovations to overcome this viral virus and encourage people to receive rapid and effective responses in real-time. Thus, it is important to create auto-mechanisms as a preventive shield to ensure healthy humanity against SARS-CoV-2. Advanced analytics methods and other strategies could also empower researchers, learners and the pharmaceutical industry to acknowledge the hazardous COVID-19 and speed it up care procedures by efficiently testing vast volumes of research data. The prevention method consequence is being used to effectively manage, calculate, forecast and monitor current infected people and future potential cases. Therefore, we proposed CNN and VGG16 based deep learning models to incorporate and enforce AI-based precautionary measures to detect the face mask on Simulated Masked Face Dataset (SMFD). This technique is capable of recognizing masked and unmasked faces to help monitor safety breaches, facilitate the use of face masks, and maintain a secure working atmosphere.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132317005","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: 29

Vertical Localization by Multipath Analysis in Artificial Human Ears for Robots 基于多路径分析的机器人人工耳垂直定位

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397239

Piyanat Sirisawat, Saiyan Saiyod, Woottichai Nonsakhoo

引用次数: 0

Efficient Classification Approach Based on COVID-19 CT Images Analysis with Deep Features 基于深度特征分析的新型冠状病毒CT图像高效分类方法

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397189

Mostafa A. Kamel, M. Abdelshafy, Mustafa AbdulRazek, Osama Abouelkhir, A. Fawzy, A. Sahlol

{"title":"Efficient Classification Approach Based on COVID-19 CT Images Analysis with Deep Features","authors":"Mostafa A. Kamel, M. Abdelshafy, Mustafa AbdulRazek, Osama Abouelkhir, A. Fawzy, A. Sahlol","doi":"10.1109/ICCCIS51004.2021.9397189","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397189","url":null,"abstract":"Currently, a new coronavirus(COVID-19) has affected millions of people worldwide. For this reason, it’s not sufficient that radiologists can slow down the virus spreading manually. Convolutional Neural Networks (CNNs) can be utilized as a tool to aid radiologists in diagnosing COVID-19 images, which consequently can save efforts and time. In this work, a dataset of CT images of confirmed and negative COVID-19 was used for the screening of COVID-19. Some preprocessing operations were applied to enhance the COVID-19 CT images which aim at including only the Area of Interest (AOI). This was accomplished in three stages. First, a conversion of the CT images to the binary scale was performed by applying a global threshold algorithm. Then, the median filter algorithm was applied to remove random noise. Then, we include only the ROI (the lung) and exclude other parts of the images. Finally, we applied VGGNet 19 to extract features from the preprocessed CT images, which is a popular CNN architecture, trained previously on ImageNet. The proposed pipeline showed high performance by achieving 98.31%, 100%, 98.19% and 98.64% of accuracy, recall, precision and f1-score, respectively. To the best of our knowledge, these results are the best published on this dataset when compared to a set of recently published works. Also, the proposed model overcomes several popular CNNs architectures.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116072028","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: 7

Online Human Action Recognition Using Deep Learning for Indoor Smart Mobile Robots 基于深度学习的室内智能移动机器人在线人体动作识别

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397242

Jih-Tang Hsieh, Meng-Lin Chiang, C. Fang, Sei-Wang Chen

{"title":"Online Human Action Recognition Using Deep Learning for Indoor Smart Mobile Robots","authors":"Jih-Tang Hsieh, Meng-Lin Chiang, C. Fang, Sei-Wang Chen","doi":"10.1109/ICCCIS51004.2021.9397242","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397242","url":null,"abstract":"This research proposes a vision-based online human action recognition system. This system uses deep learning methods to recognise human action under moving camera circumstances. The proposed system consists of five stages: human detection, human tracking, feature extraction, action classification and fusion. The system uses three kinds of input information: colour intensity, short-term dynamic information and skeletal joints. In the human detection stage, a two-dimensional (2D) pose estimator method is used to detect a human. In the human tracking stage, a deep SORT tracking method is used to track the human. In the feature extraction stage, three kinds of features, spatial, temporal and structural, are extracted to analyse human actions. In the action classification stage, three kinds of features of human actions are respectively classified by three kinds of long short-term memory (LSTM) classifiers. In the fusion stage, a fusion method is used to leverage the three output results from the LSTM classifiers. This study constructs a computer vision and image understanding (CVIU) Moving Camera Human Action dataset (CVIU dataset), containing 3,646 human action sequences, including 11 types of single human actions and 5 types of interactive human actions. This dataset was used to train and evaluate the proposed system. Experimental results showed that the recognition rates of spatial features, temporal features and structural features were 96.64%, 81.87% and 68.10%, respectively. Finally, the fusion result of human action recognition for indoor smart mobile robots in this study was 96.84%.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123893531","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: 3

Development of Spectral Signature of Land Cover and Feature Extraction using Artificial Neural Network Model 基于人工神经网络模型的土地覆盖光谱特征及其特征提取研究

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397172

Saurabh Kumar, S. Shwetank, K. Jain

{"title":"Development of Spectral Signature of Land Cover and Feature Extraction using Artificial Neural Network Model","authors":"Saurabh Kumar, S. Shwetank, K. Jain","doi":"10.1109/ICCCIS51004.2021.9397172","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397172","url":null,"abstract":"The remote sensing (RS) imagery is important to the development of the spectral signature of mango orchards, vegetation, and other land-use features, and to geospatial feature extraction using artificial neural networks (ANNs). The geospatial information is useful for monitoring vegetation growth, urban development, and land-use / land-cover (LU/LC) change detection. The objective of this study to develop a spectral signature and feature extraction of land-use classes using the multi-temporal and multi-spectral (MTMS) Landsat imagery dataset. The imagery dataset has obtained three images from various sensors of the Landsat satellite system from the years 2003 to 2017. The pre-processing of the imagery is crucial for geospatial feature extraction and analysis of land-use features. The vegetation index (VI) is used in this study to monitor the health and growth of orchards, vegetation, and crop. The resulting accuracy of classification using ANNs method for different years (2017, 2010, and 2003) are 90.10%, 75.75%, and 78.37%. The results of the presented study indicated that significant changes have occurred in the study region, which has affected the environment and human activities. The information of LU/LC's situation in the region will help the urban planners and decision-makers to plan for effectively managing future LU/LC change.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115069043","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: 3

Design and Performance Analysis of an IoT Based Health Monitoring System for Hospital Management 基于物联网的医院管理健康监测系统设计与性能分析

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397186

Prabin Kumar Rath, Neelam Mahapatro, Subham S. Sahoo, S. Chinara

引用次数: 2

State Feedback Control of a Platoon in the Presence of Parametric Uncertainty and Communication Delay 存在参数不确定性和通信延迟的队列状态反馈控制

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397162

Jaswandi Sawant, U. Chaskar

引用次数: 1

Operating Strategies Optimization of STATCOM for Improvement of Voltage Profile 为改善电压剖面而优化STATCOM运行策略

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397120

S. Jaiswal, S. Lata, Shashank Singh, S. Singh, Jay Singh

引用次数: 0

Intrinsic Plagiarism Detection System Using Stylometric Features and DBSCAN 基于文体特征和DBSCAN的内在抄袭检测系统

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397187

Anu Saini, Manepalli Ratna Sri, Mansi Thakur

{"title":"Intrinsic Plagiarism Detection System Using Stylometric Features and DBSCAN","authors":"Anu Saini, Manepalli Ratna Sri, Mansi Thakur","doi":"10.1109/ICCCIS51004.2021.9397187","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397187","url":null,"abstract":"Plagiarism is the act of using someone else’s words or ideas without giving them due credit and representing it as one’s own work. In today's world, it is very easy to plagiarize others' work due to advancement in technology, especially by the use of the Internet or other offline sources such as books or magazines. Plagiarism can be classified into two broad categories on the basis of detection namely extrinsic and intrinsic plagiarism. Extrinsic plagiarism detection refers to detecting plagiarism in a document by comparing it against a given reference dataset, whereas, Intrinsic plagiarism detection refers to detecting plagiarism with the help of variation in writing styles without using any reference corpus. Although there are many approaches which can be adopted to detect extrinsic plagiarism, few are available for intrinsic plagiarism detection. In this paper, a simplified approach is proposed for developing an intrinsic plagiarism detector which is helpful in detecting plagiarism even when no reference corpus is available. The approach deals with development of an intrinsic plagiarism detection system by identifying the writing style of authors in the document using stylometric features and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. The proposed system has an easy to use interactive interface where user has to upload a text document to be checked for plagiarism and the result is displayed on the web page itself. In addition, the user can also see the analysis of the document in the form of graphs.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129171045","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: 3

Cone Based Tree to Improve Ray Tracing 基于锥的树改进光线追踪

2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397073

Kowshik Ahmed, Sofiul Azam Sony, Tanha Tanjimat, Mir Moynuddin Ahmed Shibly, Maimuna Rahman, T. Jabid

引用次数: 0