2021 8th International Conference on Smart Computing and Communications (ICSCC)最新文献_第7页

Inherently Safer Design Approaches and Improvement Strategies in Process Industries 过程工业中固有安全设计方法和改进策略

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528272

Baiju Karun, V. R. Renjith, Sudheep Elayidom

引用次数: 1

Comparative Study of Pretrained Network Feature Extraction and Classifiers for COVID-19 Detection 预训练网络特征提取与分类器在COVID-19检测中的比较研究

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528154

A. L., Vinod Chandra S.S

{"title":"Comparative Study of Pretrained Network Feature Extraction and Classifiers for COVID-19 Detection","authors":"A. L., Vinod Chandra S.S","doi":"10.1109/ICSCC51209.2021.9528154","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528154","url":null,"abstract":"Severe Acute Respiratory Syndrome (SARS-CoV-2) causes COVID-19, an infectious disease. It has since spread worldwide, leading to an ongoing pandemic. A coronavirus is a virus that infects the nose, sinuses, and upper throat. Fever, cough, trouble in breathing, loss of smell and taste are some of the symptoms. COVID-19 causes mild to moderate infection in most infected patients, who recover without the need for additional treatment. However, it is critical in the lives of older persons and persons with different diseases like diabetes, cancer, cardiovascular disease, and so on. In this study, we propose a method for detecting COVID-19 from CT images. Here the features are extracted using the pretrained network, ResNet-50, and categorized as COVID-19 infected or not using the KNN classifier. This study also focuses on the efficiency of pre-trained networks and other classification approaches for the automatic detection of COVID-19. The AlexNet, VGG-16, VGG-19, ResNet-50, ResNet-101, and DenseNet-201 pre-trained networks are used to extract features for analysis. We explored the Support Vector Machine(SVM), Ensemble based method, K Nearest Neighbour(KNN), Discriminant approach, Tree-based, and Naive Bayes classifiers to get the best classifier. The method was tested on the SARS-CoV-2 CT data set. The ResNet-50 with KNN classifier has a sensitivity, specificity accuracy, and F1-score of 95.99 %, 99.16%, 97.52%, and 97.56%, respectively, which is superior to the work reported.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127282954","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

Contextual Semantic analysis on Blogs/Websites and its Credibility 博客/网站的语境语义分析及其可信度

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528232

S. Gollapudi, S. Sasi

引用次数: 0

Wireless Programmable Healthy Neural Stimulator Prototype 无线可编程健康神经刺激器原型

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528004

G. L. K. Moganti, P. Y. Rishita

{"title":"Wireless Programmable Healthy Neural Stimulator Prototype","authors":"G. L. K. Moganti, P. Y. Rishita","doi":"10.1109/ICSCC51209.2021.9528004","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528004","url":null,"abstract":"Neural stimulator methods are used as treatment for the majority of neurological disorders. While the concept is used in a broad range of applications, some design issues need to be overcome. The first challenge is the operational range of the circuit, which must satisfy a wide range of patients. Secondly challenge is the amplitude of stimulation, which must be controlled in order to avoid pain and discomfort for patient. This paper introduces a programmable neural stimulator with a residual voltage monitor (RVM) circuit to solve both problems. This device is designed to adjust current stimulation amplitudes using mobile Bluetooth device. After every bi-phasic stimulation pulse, RVM circuit monitors the voltage across the Electrode-Tissue Impedance (ETI) and provides information about positive residual voltage (PRV), negative residual voltage (NRV) and safe residual voltage levels (SRVL) to the control logic unit (CLU). Based on this RVM information, the CLU determines if additional stimulation is needed to bring residual voltage back to a safe stimulation level. This system has been implemented with physical modules such as Arduino UNO, Bluetooth module (HC-05), 30n06 (NMOS) transistor, IRF9540(PMOS) transistor, 741 OP-AMP, diodes and logic gates for rapid test of the proposed circuit. As the proposed circuit monitors safety voltage and provides required stimulation current for patient,hence it is safe and reliable circuit without any discomfort or pain.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"48 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128527000","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

Simulated Annealing based approach for Virtual Machine Live Migration 基于模拟退火的虚拟机实时迁移方法

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528160

Avita Katal, Vinayak Bajoria, Vitesh Sethi

{"title":"Simulated Annealing based approach for Virtual Machine Live Migration","authors":"Avita Katal, Vinayak Bajoria, Vitesh Sethi","doi":"10.1109/ICSCC51209.2021.9528160","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528160","url":null,"abstract":"Virtual machine migration technique is used in cloud computing to increase the reliability and scalability of the cloud computing systems. It helps the service providers to achieve resource efficiency and quality of service. At the time of live migration, the underlying virtual machine continuously works until the entire or part of data is migrated from source to destination. Live migration of Virtual Machine (VM) acts as an important technique that allows the management of resources, maintenance of server and load balancing in cloud data centers, but there is a degradation of performance at the source and destination physical machines. Different live migrations techniques have been proposed, each showing different properties like completion time, amount of data transferred, down time of VM and degradation of performance of VM. In this paper, a live migration algorithm, Live Migration Annealing (LMA) Virtual Machine Migration that makes use of an evaluation function to perform analysis on the time series data collected over the iterations made during the live migration period is proposed. It embodies the concept of exploration and exploitation of knowledge and space. It also takes ideas from the Iterative depth first Search to perform the iterations and Simulated Annealing to find the pages eligible for the live Migration. All the eligible pages undergo a phase called Selection phase before being finally sent to the destination virtual machine which incorporates the idea of Second Chance. Live Migration is only performed if it isn’t happening at the expense of the downtime. An overall decrease in the number of iterations and downtime with least possible live migration time is achieved through the proposed algorithm.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131453872","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: 2

Low Complexity MIMO Detection Using Complement and LSB Set Approximation 基于补集和LSB集逼近的低复杂度MIMO检测

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528259

S. Chakraborty, N. B. Sinha, M. Mitra

引用次数: 1

Helmet Detection Using Faster Region-Based Convolutional Neural Networks and Single-Shot MultiBox Detector 基于更快区域卷积神经网络和单镜头多盒检测器的头盔检测

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528256

Prajval Mohan, Pranav Narayan, Lakshya Sharma, M. Anand

{"title":"Helmet Detection Using Faster Region-Based Convolutional Neural Networks and Single-Shot MultiBox Detector","authors":"Prajval Mohan, Pranav Narayan, Lakshya Sharma, M. Anand","doi":"10.1109/ICSCC51209.2021.9528256","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528256","url":null,"abstract":"In a country like India, with excessive population density in all big cities, motorcycles have become dominant modes of transport. It is observed that most motorcyclists avoid wearing helmets despite it being an indispensable safety equipment, whose use can significantly reduce the risk of severe head and brain injuries during accidents. Due to violations of most of the traffic and safety rules, motorcycle accidents have been skyrocketing in the recent years. Hence, it’s the need of the hour to build an effective and scalable system capable of automatic helmet detection by analyzing the surveillance camera’s traffic videos. Although several theoretical deep learning-based models have been proposed to detect helmets for the traffic surveillance aspect, an optimal solution for the industry application is less discussed. This paper demonstrates a novel implementation of the Faster R-CNN and SSD framework for accurate helmet detection in real-time low-quality surveillance videos. The experimental results claim that there is a trade-off between accuracy and execution speed. We also present a comprehensive comparative analysis of the two algorithms and determine the best real-time use case scenarios for each of them.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117294084","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: 2

Indian Sign Language Gesture Recognition Using Deep Convolutional Neural Network 基于深度卷积神经网络的印度手语手势识别

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528246

M. Varsha, C. Nair

引用次数: 6

Analysing and Identifying Crucial Evidences for the prediction of False Information proliferated during COVID-19 Outbreak: A Case Study 分析和识别新冠肺炎疫情期间虚假信息扩散预测的关键证据——以个案研究为例

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528205

Deepika Varshney, D. Vishwakarma

{"title":"Analysing and Identifying Crucial Evidences for the prediction of False Information proliferated during COVID-19 Outbreak: A Case Study","authors":"Deepika Varshney, D. Vishwakarma","doi":"10.1109/ICSCC51209.2021.9528205","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528205","url":null,"abstract":"In the current scenario social media platforms are one the efficient way to share opinions and thoughts of an individual. User can freely share their thoughts on an event/ situation. This can be a curse for the society if social media platform is utilized with some bad intention to spread false information and create chaos/ confusion among public which greatly degrades user experience. In the current pandemic many people have their eye on any news article related to corona cure. Malicious users take this as an opportunity to spread fake news in order to create confusion among public or some monetary benefits, the detection of which is of paramount importance. The proposed technique is leverages to learn crucial evidences based on Context Knowledge, Distance Metric and Word Resemblance with respect to news article headline and its content concerning top 10 google search results related to the claim, where considering COVID-19 as one of the special case studies from the application perspective. This paper proposed a novel scheme for the prediction of false information and generated a covidfakenews dataset that further be utilized for the analysis and evaluation of our model. The results reveals that the proposed intelligent strategy gives promising experimental results and quite effective in predicting False information.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129691747","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

Interleaved Edge Routing in Buffered 3D Mesh & CMesh NoC 缓冲3D网格和CMesh NoC中的交错边缘路由

2021 8th International Conference on Smart Computing and Communications (ICSCC) Pub Date : 2021-07-01 DOI: 10.1109/ICSCC51209.2021.9528183

Rose George Kunthara, N. K., Rekha K. James, Simi Zerine Sleeba

{"title":"Interleaved Edge Routing in Buffered 3D Mesh & CMesh NoC","authors":"Rose George Kunthara, N. K., Rekha K. James, Simi Zerine Sleeba","doi":"10.1109/ICSCC51209.2021.9528183","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528183","url":null,"abstract":"Many-core processors are widely used in areas such as cloud computing, big data processing, high performance computing and data centre applications. Network on Chip (NoC) is the preferred interconnect solution which can overcome scalability issues and communication bottleneck associated with them. Minimal latency, area and better throughput are the key performance parameters of on-chip network design. The performance can be greatly enhanced by replacing 2D NoC communication infrastructure with 3D NoC where multiple NoC layers are integrated using high-speed Through Silicon Via (TSV) based vertical links. 3D router designs incur extra power and area in addition to integration issues such as reliability and fabrication problems, related with TSV based interconnection. In this paper, we utilize an asymmetrical routing technique in Mesh & CMesh topologies where we make interleaved connections between edge routers in 3D buffered on-chip network to improve NoC performance. Simulation results indicate that our design approach, MML (Modified Multi Layer) network, has significant improvement in throughput and latency reduction on comparing with conventional buffered networks which employ same number of routers.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125607719","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