2020 International Conference on System, Computation, Automation and Networking (ICSCAN)最新文献_第3页

Monitoring Algorithm in Malicious Vehicular Adhoc Networks 恶意车载Adhoc网络中的监控算法

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262314

S. Padmapriya, R. Valli, M. Jayekumar

{"title":"Monitoring Algorithm in Malicious Vehicular Adhoc Networks","authors":"S. Padmapriya, R. Valli, M. Jayekumar","doi":"10.1109/ICSCAN49426.2020.9262314","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262314","url":null,"abstract":"Vehicular Adhoc Networks (VANETs) ensures road safety by communicating with a set of smart vehicles. VANET is a subset of Mobile Adhoc Networks (MANETs). VANET enabled vehicles helps in establishing communication services among one another or with the Road Side Unit (RSU). Information transmitted in VANET is distributed in an open access environment and hence security is one of the most critical issues related to VANET. Although each vehicle is not a source of all communications, most contact depends on the information that other vehicles receive from it. That vehicle must be able to assess, determine and respond locally on the information obtained from other vehicles to protect VANET from malicious act. Of this reason, message verification in VANET is more difficult due to the protection and privacy issues of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"26 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86638949","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

Cloud Extraction from INSAT-3D Satellite Image by K-Means and Fuzzy C-Means Clustering Algorithms 基于K-Means和模糊C-Means聚类算法的INSAT-3D卫星图像云提取

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262330

Pugazhenthi A, L. S. Kumar

引用次数: 2

Computer Aided Diagnosis of Aging Macular Deterioration Via Convolutional Neural Network 基于卷积神经网络的老年性黄斑退化计算机辅助诊断

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262441

G. Sharmila, S. Karthika, V. Rajesh, A. Yuvarani, E. Sangeetha

{"title":"Computer Aided Diagnosis of Aging Macular Deterioration Via Convolutional Neural Network","authors":"G. Sharmila, S. Karthika, V. Rajesh, A. Yuvarani, E. Sangeetha","doi":"10.1109/ICSCAN49426.2020.9262441","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262441","url":null,"abstract":"Aging Macular Deterioration (AMD) is a leading eye problem most commonly experienced by the old age people. If the problem is untreated over a prolonged time period, it results in permanent blindness. This eye problem is caused due to the damage of macula lutea which is a central region of retina needs for visualizing very fine details. However, only early detection can exhibit it from becoming severe and protect vision. This method proposes an automatic screening of all the three stages of AMD (i.e.) early (DMD), intermediate and late (WMD) using Convolutional Neural Network. A set of 400 color fundus images are taken for experimentation out of which 190 images are affected AMD images and 210 images are non-AMD images. Here, first the images are subjected to an image segmentation technique which adds-on the advantage of improving the accuracy of the system. Fuzzy c-means clustering is used as the image segmentation technique. Then the segmented images were trained and experimented using Convolutional Neural Network. This work thus obtained an overall accuracy of about 95.65%. The experimental results verify the effectiveness of this method.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88316638","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: 1

Big Data Analytics for Healthcare Recommendation Systems 医疗保健推荐系统的大数据分析

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262304

M. Lambay, S. Pakkir Mohideen

{"title":"Big Data Analytics for Healthcare Recommendation Systems","authors":"M. Lambay, S. Pakkir Mohideen","doi":"10.1109/ICSCAN49426.2020.9262304","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262304","url":null,"abstract":"Healthcare industry is an indispensable entity in the real world where large volumes of data is accumulated from time to time. Such data assumes characteristics of big data and it is desirable to analyze it and bring about latent relationships among variables in the healthcare data. Data in healthcare industry is rich in useful information. However, a comprehensive big data approach is essential to mine the data and acquire business intelligence. There are many use cases of big data analytics. However, in healthcare industry it is imperative to have knowledge-driven recommendations that help all stakeholders. With the emergence of cloud computing, big data analytics has become a reality. Distributed programming frameworks like Hadoop and Spark, to mention few, are available with associated Distributed File System (DFS) to manage big data. Many researchers contributed towards developing algorithms based on machine learning which is part of Artificial Intelligence (AI). Since healthcare industry is one of the sources of big data, it needs distributed environments for processing. Big data analytics is essential to analyze healthcare data in a comprehensive manner. The cloud computing and big data ecosystem is playing favorable role in realizing big data analytics for healthcare recommendations. A typical recommender system in healthcare industry is supposed to produce recommendations in various aspects of the domain. This paper throws light into different recommenders in healthcare domain that use big data analytics to generate recommendations. It not only provides useful insights but also discussed research gaps that can be used to investigate further to improve the state of the art.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"15 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77397989","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: 6

Synthesis Of Cerium Oxide Nanoparticles Using Marine Algae Sargassum Wightii Greville Extract: Implications For Antioxidant Applications 利用海洋藻类马尾藻提取物合成氧化铈纳米颗粒:抗氧化应用的意义

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262367

H. Rosi, Ramachandram Ethrajavalli, Mohammed Iqbal Janci

引用次数: 2

An Intelligent Computer Vision for Children Affected with Cerebral Palsy 脑瘫儿童的智能计算机视觉

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262275

G. Vengatesh, R. Rajesh, T. Naveenkumar

引用次数: 1

Brain Tumor Detection Model from MR Images using Convolutional Neural Network 基于卷积神经网络的MR图像脑肿瘤检测模型

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262373

C. Someswararao, Shiva Shankar Reddy, S. V. Appaji, Vmnssvkr Gupta

引用次数: 9

An Interaction System Using Speech and Gesture Based on CNN 基于CNN的语音和手势交互系统

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262343

S. Pariselvam, Dhanuja. N, D. S, S. B

{"title":"An Interaction System Using Speech and Gesture Based on CNN","authors":"S. Pariselvam, Dhanuja. N, D. S, S. B","doi":"10.1109/ICSCAN49426.2020.9262343","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262343","url":null,"abstract":"Nowadays, Hand gestures playing a important role for human interactions with the computer. Deep Learning is a part of machine learning methods which makes the recognition process easier by using Convolution Neural Networks (ConvNet/CNN). Convolution Neural Networks is a multilayer process network which includes Input layer, Convolution layer, Max pooling layer, Fully connected layer, Output layer. When compared to other algorithms, CNN can give more accurate results. CNN is mainly used to analyze visual images and for the image processing, segmentation and classification with higher accuracy. Here, this model consists of two main systems. One is voice input is converted into text and hand gestures and second approach is hand gestures conversion to text. These two systems are mainly used for abnormal people. These systems are implemented in Python and OpenCV is used to capture images. Each of these two systems has different modules. Human Computer Interaction are main source for the communication between humans and computer. So, these systems are helpful in communicating some information to humans. These systems are free from lighting conditions and background noise by using CNN algorithm.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"24 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87175696","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: 4

Optimized Convolutional Neural Network based Colour Image Fusion 基于优化卷积神经网络的彩色图像融合

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262439

B. Lakshmipriya, N. Pavithra, D. Saraswathi

引用次数: 2

Multi-Purpose Intelligent Drudgery Reducing Ecobot 多用途智能减少体力劳动的生态机器人

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262372

R. Poovendran, B. A. Kumar, V. Bhuvaneshwari, R. Aswini, K. Priya

引用次数: 0