2018 Second International Conference on Computing Methodologies and Communication (ICCMC)最新文献

Deep Learning Framework for Diabetic Retinopathy Diagnosis 糖尿病视网膜病变诊断的深度学习框架

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2019-03-27 DOI: 10.1109/ICCMC.2019.8819663

G. Nagaraj, C. SumanthSimha, R. HarishChandraG, M. Indiramma

{"title":"Deep Learning Framework for Diabetic Retinopathy Diagnosis","authors":"G. Nagaraj, C. SumanthSimha, R. HarishChandraG, M. Indiramma","doi":"10.1109/ICCMC.2019.8819663","DOIUrl":"https://doi.org/10.1109/ICCMC.2019.8819663","url":null,"abstract":"Diabetic Retinopathy (DR) is one of the foremost causes for the presence of blindness in the recent times. Ophthalmologists usually diagnose the presence and severity of DR through visual assessment of the retinal fundus images by manual examination. This process of manual diagnosis of DR is a very laborious and time consuming task. With the increasing rate of diabetic retinopathy patients in the world, the number of color fundus images generated has increased exponentially. Due to this large number, there is a huge delay in recognizing the early symptoms of DR and providing timely treatment. Hence, to address this unmet and increasing need, there is a need for developing an automated framework of Diabetic Retinopathy diagnosis. Hence, in this study, we have proposed a Deep Learning framework for DR diagnosis. The study uses a modified version of one of the standard Convolutional Neural Network (CNN) for solving DR fundus image classification problems. The proposed framework efficiently and quickly report whether the person has DR or not and if present, reports the severity of the disease. The framework implemented helps in giving timely treatment to the patients irrespective of geographical and economic constraints.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"54 1","pages":"648-653"},"PeriodicalIF":0.0,"publicationDate":"2019-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79417030","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

Modelling of Audio Effects for Vocal and Music Synthesis in Real Time 建模的声音和音乐合成的音频效果在实时

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2019-03-27 DOI: 10.1109/ICCMC.2019.8819852

D. D’souza, V. D. Shastrimath

引用次数: 1

A Comprehensive Survey on Internet of Things Based Healthcare Services and its Applications 基于物联网的医疗服务及其应用综述

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2019-03-01 DOI: 10.1109/ICCMC.2019.8819642

N. HemaRajini.

引用次数: 7

Exploring Pain Insensitivity Inducing Gene ZFHX2 by using Deep Convolutional Neural Network 应用深度卷积神经网络研究疼痛不敏感诱导基因ZFHX2

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2019-03-01 DOI: 10.1109/ICCMC.2019.8819666

S. Akshayaa, R. Vidhya, M. HimavyshnaviA, K. KrishnanNambooriP

引用次数: 1

Atmospheric Weather Prediction Using various machine learning Techniques: A Survey 利用各种机器学习技术进行大气天气预报:综述

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2019-03-01 DOI: 10.1109/ICCMC.2019.8819643

L. Naveen, S. MohanH

引用次数: 3

Implementation of 32-Bit Arithmetic Logic Unit on Xilinx using VHDL 32位算术逻辑单元在Xilinx上的VHDL实现

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-10-09 DOI: 10.1109/ICCMC.2018.8487519

S. G. Nayak

引用次数: 2

Analyzing Load on Cloud: A Review 云负载分析综述

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487230

Shalu Mall, A. Sharma

引用次数: 2

INRUSH CURRENT DETECTION USING WAVELET TRANSFORM AND ARTIFICIAL NEURAL NETWORK 基于小波变换和人工神经网络的浪涌电流检测

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487832

Prachi R. Gondane, Rukhsar M. Sheikh, Kajol A. Chawre, Vivian V. Wasnik, A. Badar, M. Hasan

引用次数: 7

A novel approach on object detection and tracking using adaptive background subtraction method 一种基于自适应背景减法的目标检测与跟踪新方法

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8487514

K. Angelo

{"title":"A novel approach on object detection and tracking using adaptive background subtraction method","authors":"K. Angelo","doi":"10.1109/ICCMC.2018.8487514","DOIUrl":"https://doi.org/10.1109/ICCMC.2018.8487514","url":null,"abstract":"Image processing is an ever increasing research scope area where real time surveillance systems will increases the opportunity to the researchers for developing new modules for all the problems. Particularly in complex video processing operations security, intelligence processing is much needed in the society to satisfy the individuals. Basic object detection and tracking has different techniques and many automated systems are available now days to analyze the particular portion or object from the video. Estimation of moving object from the video sequence provides robustness for same colors for object and the background. In view of reducing the robustness and improving the performance of object detecting and tracking system the proposed model used Markov model based background subtraction. It uses neighborhood method to improve the background performance and Markov random field is used to estimate the energy function to optimize the real time experimental results. Generating saliency map combines the texture and cues to explore the linearly generated objects and tracked using component labeling.","PeriodicalId":6604,"journal":{"name":"2018 Second International Conference on Computing Methodologies and Communication (ICCMC)","volume":"109 1","pages":"1055-1059"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77854357","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: 10

Detection of Power System Harmonics Using NBPSO Based Optimally Placed Harmonic Measurement Analyser Units 基于NBPSO优化放置谐波测量单元的电力系统谐波检测

2018 Second International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2018-02-01 DOI: 10.1109/ICCMC.2018.8488114

P. Balamurali Krishna, P. Sinha

引用次数: 1