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

A meta-analytic review of student satisfaction studies in higher education 高等教育学生满意度研究的元分析综述

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262294

Vishnu H Lal, G. Varaprasad

引用次数: 0

A Novel HOSFS Algorithm for Online Streaming Feature Selection 一种新的在线流媒体特征选择的HOSFS算法

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262401

S. Sandhiya, U. Palani

{"title":"A Novel HOSFS Algorithm for Online Streaming Feature Selection","authors":"S. Sandhiya, U. Palani","doi":"10.1109/ICSCAN49426.2020.9262401","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262401","url":null,"abstract":"In recent days, Data stream mining is important for many of the real time and IOT based applications. Online feature selection is the one big topic of data stream mining which attracted researchers with intensive interest. This technique reduces the dimensionality of the streaming features by excluding inappropriate and redundant features. The researchers have proposed many online feature selection algorithm for streaming features like Grafting, Alpha-investing, OSFS, OGFS and SAOLA. Based on above studies the exiting algorithm has limitation over prediction accuracy and the large number of selected features. To overcome the limitations of above mentioned approaches, we propose an online feature selection algorithm for streaming features called Heuristic Online Streaming Feature Selection (HOSFS) which has advantages on choosing features from streaming features and omits the irrelevant and redundant features in real-time by using self-adaption sliding window protocol, and Heuristic function. The HOSFS algorithm assigns heuristic value to the features using the trained heuristic function and selects features with higher heuristic value where other features are considered as irrelevant features. This proposed technique results reduced number of strongly related features and obtains greater prediction accuracy with optimal features. HOSFS algorithm efficiency was tested with three different Health care datasets using MOA tools. Through the experimental outcomes, HOSFS has greater prediction accuracy and reduced number of selected features than alpha - investing, OSFS, and SAOLA.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"6 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":"74070358","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

A Smart Trolley for Smart Shopping 智能购物的智能手推车

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262350

T. K. Das, A. Tripathy, Kathiravan Srinivasan

引用次数: 3

FairIN: Throughput Fairness in Infrastructure-Based Wireless Access Networks 公平性:基于基础设施的无线接入网的吞吐量公平性

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) Pub Date : 2020-07-03 DOI: 10.1109/ICSCAN49426.2020.9262313

Soumita Biswas, Anuran Mitra, Sovan Mistry, T. Chowdhury, Soumodip Sinha, Ani Biswas, Raja Karmakar

{"title":"FairIN: Throughput Fairness in Infrastructure-Based Wireless Access Networks","authors":"Soumita Biswas, Anuran Mitra, Sovan Mistry, T. Chowdhury, Soumodip Sinha, Ani Biswas, Raja Karmakar","doi":"10.1109/ICSCAN49426.2020.9262313","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262313","url":null,"abstract":"Inter-Basic Service Sets (BSSes) interference and interference inside a BSS are unavoidable since the quantity of non-overlapping channels for wireless networks is limited; and multiple BSSes form an Extended Service Set (ESS). Nowadays, due to the rapid growth in the use of wireless devices like smart phones, laptop etc., access points (APs) in public areas are overheaded with a huge number of Internet accesses requested by users. When many wireless devices are assembled together to get and offer services, allocation and sharing of wireless channels become a critical issue since all wireless devices want to access the channel simultaneously and transmit data. Therefore, collisions occur in acquiring the channel and consequently, unfairness in channel access increases, which leads to the degradation of throughput fairness in wireless networks. This paper addresses aforesaid issue to propose a mechanism, called FairIN, to provide fairness in throughput such that all wireless stations can get an equal access of the channel. In FairIN, we design two mechanisms to handle the fairness inside a BSS and among APs separately. The performance analysis of FairIN through simulation in an IEEE 802.11ac network shows that FairIN can improve throughput fairness in infrastructure-based wireless networks.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"31 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":"75066733","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

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