2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)最新文献_第5页

An Efficient Object Detection Model Using Convolution Neural Networks 基于卷积神经网络的高效目标检测模型

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862698

Ulagamuthalvi., J.B. Janet Felicita, D. Abinaya

{"title":"An Efficient Object Detection Model Using Convolution Neural Networks","authors":"Ulagamuthalvi., J.B. Janet Felicita, D. Abinaya","doi":"10.1109/ICOEI.2019.8862698","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862698","url":null,"abstract":"Image processing and computer vision have gained an enormous advance in the field of machine learning techniques. Some of the major research areas within machine learning are Object detection and Scene Recognition. Though there are numerous existing works related to the specified fields object detection still encounters numerous challenges when it comes to implementing in the real-time scenario. The problem occurs in the detection due to various objects present in the background. Object detection mechanism detects a specified object when a particular scene is given. Classifiers like SVM and Neural Networks are used to train the classifier in such a way they are able to detect an object when a new image is given. In this paper, we have proposed a model which detects texts from an image. Bounding boxes are used to detect the texts and localize it. The neural network is used to train the model where numerous images having texts are given as the training set. The performance evaluation is done on the model and it is observed that it detects the texts when a new image is given. Object detection is a fundamental problem in computer vision, which aims to detect general objects in images.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"36 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134352060","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

Performance Evaluation of Deep Learning frameworks on Computer Vision problems 深度学习框架在计算机视觉问题上的性能评价

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862603

Madhumitha Nara, B. Mukesh, Preethi Padala, Bharath A. Kinnal

{"title":"Performance Evaluation of Deep Learning frameworks on Computer Vision problems","authors":"Madhumitha Nara, B. Mukesh, Preethi Padala, Bharath A. Kinnal","doi":"10.1109/ICOEI.2019.8862603","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862603","url":null,"abstract":"Deep Learning (DL) applications have skyrocketed in recent years and are being applied in various domains. There has been a tremendous surge in the development of DL frameworks to make implementation easier. In this paper, we aim to make a comparative study of GPU-accelerated deep learning software frameworks such as Torch and TenserFlow (with Keras API). We attempt to benchmark the performance of these frameworks by implementing three different neural networks, each designed for a popular Computer Vision problem (MNIST, CIFAR10, Fashion MNIST). We performed this experiment on both CPU and GPU(Nvidia GeForce GTX 960M) settings. The performance metrics used here include evaluation time, training time, and accuracy. This paper aims to act as a guide to selecting the most suitable framework for a particular problem. The special interest of the paper is to evaluate the performance lost due to the utility of an API like Keras and a comparative study of the performance over a user-defined neural network and a standard network. Our interest also lies in their performance when subjected to networks of different sizes.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132595406","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

Contention Based Forwarding Message in Vanet After an Emergency Event 紧急事件后Vanet中基于争用的转发消息

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862742

E. Brumancia, R. Gomathi, D.Ayyappa Naidu, D. Srikanth

引用次数: 1

Plant disease identification and classification using Back-Propagation Neural Network with Particle Swarm Optimization 基于粒子群算法的反向传播神经网络植物病害识别与分类

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862552

Moumita Chanda, M. Biswas

{"title":"Plant disease identification and classification using Back-Propagation Neural Network with Particle Swarm Optimization","authors":"Moumita Chanda, M. Biswas","doi":"10.1109/ICOEI.2019.8862552","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862552","url":null,"abstract":"Agriculture is the culture of land and rearing of plants to supply food to nourish and enhance life. Different types of plants are farmed every year based on environmental conditions and it is one of the main economic sources in India. These plants are prone to many diseases which hinders normal growth of the plants; these diseases are caused by seasonal changes, environmental variations, and cultivation procedures. To protect the plants from such damages, diseases need to be identified and properly diagnosed on time. Hence, innovation of feasible and powerful methods for identification and classification of plant diseases is an urgent need. There are lots of classifiers which are good in the classification of plant diseases: Back-propagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) but only using these methods do not make the best tradeoff between time and accuracy. So to remove this constraint, in this paper we have given an image processing solution to distinguish and classify plant diseases efficiently and accurately. In our proposed method, for classification first, we use back-propagation algorithm to get the weights of neural network (NN) connections and then we optimize these weights using Particle Swarm Optimization (PSO) to come out of the problems like local optima and overfitting which are very common in conventional NN training methods. We have used images of leaves affected by different bacterial and fungal diseases: Alternaria Alternata, Anthracnose, Bacterial Blight and Cercospora Leaf Spot in our experiment and our proposed method achieves 96.2% accuracy.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115880981","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

Improved Test Coverage by Observation Point Insertion for Fault Coverage Analysis 通过插入观测点来改进故障覆盖分析的测试覆盖率

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862789

V. Veena, E. Prabhu, N. Mohan

引用次数: 5

A Survey on Energy Harvesting Routing Protocol for WSN 无线传感器网络能量收集路由协议研究进展

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862780

T. Kavitha, S. Silas

引用次数: 5

Single Image Super-Resolution Based on Modified Interpolation Method Using MLP and DWT 基于MLP和DWT改进插值方法的单幅图像超分辨率

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862571

Sheetal Shivagunde, M. Biswas

{"title":"Single Image Super-Resolution Based on Modified Interpolation Method Using MLP and DWT","authors":"Sheetal Shivagunde, M. Biswas","doi":"10.1109/ICOEI.2019.8862571","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862571","url":null,"abstract":"Nowadays Super Resolution (SR) is a trending term in many fields of image processing area where resolution describes the degree of details an image holds like, pixel count, sharpness and clarity. In many applications captured images are of low quality known as low resolution (LR) image, these images hold very less details. Therefore there is a need to convert such LR images to High resolution (HR) images which can be obtained by various SR methods like, interpolation methods, frequency domain based methods, reconstruction based methods and learning based methods. HR images obtained from interpolation methods contain reconstruction artifacts like edge blurs, edge halos and ringing effects, whereas learning based methods produce good results but predict unknown HR pixel values based only on input LR/HR image pairs. Thus, to overcome above mentioned disadvantages we proposed modified interpolation method for obtaining HR image, which predicts unknown HR pixel values from interpolated LR patch and corresponding HR patch using multilayer perceptron (MLP) and discrete wavelet transform (DWT). Experimental results subjectively and objectively show that for considered test images corresponding HR images obtained by our proposed method are of better quality than classical bicubic, Chopade et al. and Man et al. method.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123531140","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

Precursory study on varieties of DDoS attacks and its implications in Cloud Systems 云系统中各种DDoS攻击及其影响的前期研究

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862722

Jones S.B Ribin, N. Kumar

{"title":"Precursory study on varieties of DDoS attacks and its implications in Cloud Systems","authors":"Jones S.B Ribin, N. Kumar","doi":"10.1109/ICOEI.2019.8862722","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862722","url":null,"abstract":"Cloud Computing has emerged into an inevitable platform for computing services by effectively implementing Service Oriented Architecture (SOA) and Virtualization. However it is still vulnerable to traditional security threats and offers scope for innovative security attacks such as EDoS [1]. While it offers platform to generate innumerable Virtual components from a single physical component, it inadvertently provides wide spectrum of possibilities for distributed attacks. Moreover such attacks have adapted to cloud platform and have exploited various inherent vulnerabilities. In an unprecedented manner, they became unpredictable, evasive and challenging to Cloud Security measures. Therefore various versions of DDoS attack that targets the cloud platform have been extensively researched and narrated. The Cloud Security faces unprecedented challenges such as the Single-point-of-Failure occurs when a Cloud Supervisory Component or hypervisor fails due to a security breach. Moreover Cloud requirements often require being liberal to meet the Clients needs. This does not help the CSP to adapt traditional stringent security measures in Cloud System the reasons have been discussed in details.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121865829","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

Brain Tumor Segmentation with Deep Learning Technique 基于深度学习技术的脑肿瘤分割

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862575

G. Madhupriya, Narayanan M Guru, S. Praveen, B. Nivetha

{"title":"Brain Tumor Segmentation with Deep Learning Technique","authors":"G. Madhupriya, Narayanan M Guru, S. Praveen, B. Nivetha","doi":"10.1109/ICOEI.2019.8862575","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862575","url":null,"abstract":"The proposed work is based on Deep learning technique which is a deep neural network and probabilistic neural network to detect unwanted masses in the brain. Our work is personalized for both high and low-level grades. Tumors can appear in anyplace of the brain and its natures like shape, contrast, and size have always been an uncertain one, which means that there is no standard fact about tumor structure. The rate at which people woe from brain tumor becomes increasing nowadays. These reasons stimulate us to provide an intelligent solution which uses deep learning technique to segment abnormal tissues in the brain. It can help to find out whether the tumor is in the brain or not. With the help of these MRI images, segmentation can be performed and the segmented images can be compared with the normal brain tissues also with the tumor cells. The results are provided (whether the brain contains a tumor or not) based on the comparison. In this paper, the segmentation is done using a convolution neural network and Probabilistic neural network. Here, the comparison sketch of various models is done. Based on that, we discovered an architecture which is based on Convolutional Neural Networks (CNN) with both $3^{ast} 3$ and $7^{ast} 7$ in an overlapped manner, and build a cascaded architecture, so that we can able to segment a tumor accurately in an effective manner, since we use Image dataset Brats13. Similarly, we use a probabilistic neural network for detecting tumors and compare the result of both of them. We proposed a unique CNN and PNN architectures which are different from those conventional models used in image processing and computer vision techniques. Our model deals with both local and global features.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125838637","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: 19

A Sustainable Vehicle Parking using IoT 利用物联网实现可持续停车

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862641

T. Anandhi, Kumaar V.S. Kishore, Ganesh S. Maha, R. Gomathi

{"title":"A Sustainable Vehicle Parking using IoT","authors":"T. Anandhi, Kumaar V.S. Kishore, Ganesh S. Maha, R. Gomathi","doi":"10.1109/ICOEI.2019.8862641","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862641","url":null,"abstract":"We present a highly efficient method for vehicle parking management in large scale parking lots based on IoT. The proposed system can be installed in a Raspberry pi module which will help us in solving the current problems in vehicle parking management. The system consists of an on-site deployment of an IoT module that is used to monitor and signalize the state of availability of each single parking slot. This can be done on a mobile phone using android application. This application can be managed by an administrator at the malls. This project primarily makes finding of parking systems more efficient and less time-consuming, and also eliminates the need of staff to be employed for such purpose of these places, thus increasing profit margins. The system helps a user know the availability of parking slots on a real time basis. Android mobile application is used to display the total parking slots in the parking lot, number of parking slots available, occupied parking slots and the reserved parking slots. To book the parking slot at that time your information are encoded to the QR code. This makes our parking system more secured. Once you park the vehicle in the slot, the timer will start to monitor the parking time. If time exceeds alert message is sent to the user's mobile.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124602746","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