2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)最新文献_第7页

Design and Implementing Brain Tumor Detection Using Machine Learning Approach 利用机器学习方法设计和实现脑肿瘤检测

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862553

G. Hemanth, M. Janardhan, L. Sujihelen

{"title":"Design and Implementing Brain Tumor Detection Using Machine Learning Approach","authors":"G. Hemanth, M. Janardhan, L. Sujihelen","doi":"10.1109/ICOEI.2019.8862553","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862553","url":null,"abstract":"Nowadays, brain tumor detection has turned upas a general causality in the realm of health care. Brain tumor can be denoted as a malformed mass of tissue wherein the cells multiply abruptly and ceaselessly, that is there is no control over the growth of the cells. The process of Image segmentation is adopted for extracting abnormal tumor region within the brain. In the MRI (magnetic resonance image), segmentation of brain tissue holds very significant in order to identify the presence of outlines concerning the brain tumor. There is abundance of hidden information in stored in the Health care sector. With appropriate use of accurate data mining classification techniques, early prediction of any disease can be effectively performed. In the medical field, the techniques of ML (machine learning) and Data mining holds a significant stand. Majority of which is adopted effectively. The research examines list of risk factors that are being traced out in brain tumor surveillance systems. Also the method proposed assures to be highly efficient and precise for brain tumor detection, classification and segmentation. To achieve this precise automatic or semi-automatic methods are needed. The research proposes an automatic segmentation method that relies upon CNN (Convolution Neural Networks), determining small 3 × 3 kernels. By incorporating this single technique, segmentation and classification is accomplished. CNN (a ML technique) from NN (Neural Networks)wherein it has layer based for results classification. Various levels involved in the proposed mechanisms are: 1. Data collection, 2. Pre-processing, 3. Average filtering, 4. segmentation, 5. feature extraction, 6. CNN via classification and identification. By utilizing the DM (data mining) techniques, significant relations and patterns from the data can be extracted. The techniques of ML (machine learning) and Data mining are being effectively employed for brain tumor detection and prevention at an early stage.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133085114","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: 60

Patch Antenna for ISM Band Application 适用于ISM波段的贴片天线

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862700

Shambhavi S. Salelkar, Palhavi Kerkar

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":null,"pages":null},"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

Web Based Environment Monitoring System Using IOT 基于网络的物联网环境监测系统

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862721

Pooja Ghule, Mansi Kambli

{"title":"Web Based Environment Monitoring System Using IOT","authors":"Pooja Ghule, Mansi Kambli","doi":"10.1109/ICOEI.2019.8862721","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862721","url":null,"abstract":"Nowadays people are very concerned about the environment because of the rapid changes in the environment which will harm to human health. Hence it is necessary to monitor environment where the people spend more time like at home, office, industry, any working area in real time and long term manner. Using internet of things we can control system as well as we can access system remotely using IoT. It first take information with help of different sensors and transfer sensors values on thingspeak directly, from which can be accessed at anytime and anywhere. Literature survey is done on use of wireless sensors, Cloud and Internet of things, and connection between devices with sensors and network connection will read sensor value which can be further monitored from the internet with the help of thingspeak. Monitoring environment is done through website & controlled manually and automatically by detecting sensor values. We can controlled it manually through website and it can automatically controlled by sensing values. The main Objective design of cloud storage environment is used to store data and to process the data. Internet of things allows physical devices or things which are not computer system, that only act very smartly and makes collaborations decision which are beneficial for different applications. That application allow things to capture value of devices. They transfer “things from being passively computing” and makes an individually decisions in active manner and communicate and collaborate to form single difficult decision. IoT technologies of computing, embedded sensors, communication protocol and internet protocol for communication allow internet of things to provide significant which impose number of challenges and introduces standards which require to specialize and communication","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115100233","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

Review on Feature Extraction Methods in Neuromuscular Disease Diagnosis 神经肌肉疾病诊断中的特征提取方法综述

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862601

C. J. Mariya, K. A. Nyni

引用次数: 2

Health Record Management through Blockchain Technology 通过区块链技术进行健康记录管理

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862594

V. Harshini, Shreevani Danai, H R Usha, Manjunath R. Kounte

{"title":"Health Record Management through Blockchain Technology","authors":"V. Harshini, Shreevani Danai, H R Usha, Manjunath R. Kounte","doi":"10.1109/ICOEI.2019.8862594","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862594","url":null,"abstract":"The world is moving towards progress, to achieve the desired progress, the world should have a healthy population and health records are the projections of an individual's health over time. The centralised approach of maintaining the health records lead to data breaches. According to 2017 Ponemon Cost of Data Breach Study, the cost of the data breach for healthcare organizations estimated to be $380 per record. According to 2016 Breach Barometer Report, 27,314,647 patient records were affected. So we moved towards institution-driven approach of record maintenance, which didn't make much difference with the previously existing one. Since the patient have no control over the data, the chances of data being misused is high. So we need a patient-centered approach which is completely decentralised, which can identify data thefts, prevent data manipulation, and patient has the right in access control. Blockchain Technology serves as a best solution to address all the problems and fulfill the needs. Blockchain being a decentralised and distributed ledger it can also impact on billing, record sharing, medical research, identify thefts and financial data crimes in days to come. Implementation of smart contracts in health care can simplify things even better. Where invoking, record creation and validation will be done on Blockchain. This paper highlights on the patient-driven model of record maintenance using Blockchain technology where smart contracts can be incorporated in future days making it more potential in data exchange. Finding its huge scope, hoping that more researches will be carried out and practically implemented.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116237961","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: 23

Analysis of Image Segmentation Algorithms for the Effective Detection of Leukemic Cells 有效检测白血病细胞的图像分割算法分析

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862696

T. Bhagya, K. Anand, D. S. Kanchana, Ajai A S Remya

引用次数: 6

Robust Method to Detect and Track the Runway during Aircraft Landing Using Colour segmentation and Runway features 基于颜色分割和跑道特征的飞机着陆跑道检测与跟踪方法

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862529

B. Ajith, S. Adlinge, Sudin Dinesh, U. Rajeev, E. S. Padmakumar

引用次数: 4

Variants of phishing attacks and their detection techniques 网络钓鱼攻击的变体及其检测技术

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862697

G. Jaspher Willsie Kathrine, P. M. Praise, A. Amrutha Rose, Eligious C Kalaivani

引用次数: 18

Smart Gardening Automation using IoT With BLYNK App 智能园艺自动化使用物联网与BLYNK应用程序

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2019-04-01 DOI: 10.1109/ICOEI.2019.8862591

Mitul Sheth, Pinal Rupani

引用次数: 32