2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)最新文献_第10页

Speed Controlled Legal Driving System 法定速度控制驾驶系统

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI: 10.1109/IC3IOT53935.2022.9767919

M. K, R. R, RoshanRaj N, J. M, Sarathkumar S, S. G.

{"title":"Speed Controlled Legal Driving System","authors":"M. K, R. R, RoshanRaj N, J. M, Sarathkumar S, S. G.","doi":"10.1109/IC3IOT53935.2022.9767919","DOIUrl":"https://doi.org/10.1109/IC3IOT53935.2022.9767919","url":null,"abstract":"These days the number of accidents occurs frequently but how accidents happen. The main reasons for an accident is Rash-Driving and Violation of traffic rules. And also sometimes due to malfunctioning of our vehicle. Due to this more deaths and injuries are occurring. In recent days IOT, sensors, Artificial Intelligence, and Machine Learning are changing this world in many aspects. It provides solutions to many problems in this world in an easy and efficient manner. In order to control and prevent the accident we have proposed a Smart System using Raspberry Pi, Ultrasonic Sensor, Light Color Detection Sensor and Speed Governor system which has to be incorporated in vehicles. The main aim of our project is to develop an Automatic Speed Control System based on the type of road (i.e. whether the road is national highway or state highway or streets etc.‥) in order to prevent accidents and reduce the number of people who die as a result of them. In the present vehicle conditions, various vehicles are out and about, run by the driver who doesn't hold any legitimate permit(driving license), despite the fact that it is difficult to get the drivers license. Accordingly a framework can be created which will act to control these issues, prior to starting the vehicle, by making certain safety efforts. The vehicle should start provided that the genuine driving permit holder is driving that specific vehicle else the vehicle won't start, by this way we can getentrance over the non driving permit holders to drive the vehicle and stay away from frightful traffic circumstances. This can be done by using a Face Recognition Module installed inside the vehicle. This also indirectly acts as a security system and reduces theft of vehicles.","PeriodicalId":430809,"journal":{"name":"2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125208148","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

Improvising JSON Web Token Authentication in SDN 基于SDN的即席JSON Web令牌认证

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI: 10.1109/IC3IOT53935.2022.9767873

P. Varalakshmi, Guhan B, Vignesh Siva P, Dhanush T, Saktheeswaran K

引用次数: 1

CPW-Fed Quad-Band Dual Port Meandered Monopole Automotive Antenna cpw馈电四波段双端口弯曲单极汽车天线

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI: 10.1109/IC3IOT53935.2022.9767894

Lekha Kannappan, S. Palaniswamy, M. Kanagasabai, Sachin Kumar, T. Rao, Thennarasi Govindan

引用次数: 1

AN ERYTHEMATO SQUAMOUS DISEASE (ESD) DETECTION USING DBN TECHNIQUE 用DBN技术检测红斑鳞状疾病(esd)

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI: 10.1109/IC3IOT53935.2022.9768010

S. Gopalakrishnan, Abishek.B Ebenezer, A. Vijayalakshmi

{"title":"AN ERYTHEMATO SQUAMOUS DISEASE (ESD) DETECTION USING DBN TECHNIQUE","authors":"S. Gopalakrishnan, Abishek.B Ebenezer, A. Vijayalakshmi","doi":"10.1109/IC3IOT53935.2022.9768010","DOIUrl":"https://doi.org/10.1109/IC3IOT53935.2022.9768010","url":null,"abstract":"ESD is a serious type of skin disease that increases over the past decades in the world and as a sequel to curing strategy in the medical field, automatic detection of ESDs using dermoscopic images has been still challenging and complicated task. This kind of difficulty occurs in the diagnosis of ESD owing to the following factors such as indistinct ESD borders, poor color contrast, location-dependent, shape variations, and complex structures of the ESDs. The progressing public health burden issues have to be detected early and treated in proper ways to prevent further spreading to other organs of the body through which medical professionals and researchers can save several lives. When there is an abnormal change in the appearance of the skin, then there is a chance for the subject that may be affected by ESD. To obtain better solutions, the computer vision methods must be paired with dermatology knowledge for efficient ESD detection. Hence, it is important to develop Deep Belief Network (DBN) based detection techniques to assist clinicians to diagnose ESD at early stages.","PeriodicalId":430809,"journal":{"name":"2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134069449","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

Analysis of Customer Review and Predicting Future Release of the Product using machine learning concepts 使用机器学习概念分析客户评论并预测产品的未来发布

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI: 10.1109/IC3IOT53935.2022.9767956

P. R. Sabapathi, K. Kaliyamurthie

引用次数: 0

Myocardial Prediction and Identification using Convolution Neural Networks 使用卷积神经网络预测和识别心肌

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI: 10.1109/IC3IOT53935.2022.9767935

M. Sureshkumar, M.Sella Vallal Sibi, R.Pugazh Bharathi, P. Shanmugapriya

{"title":"Myocardial Prediction and Identification using Convolution Neural Networks","authors":"M. Sureshkumar, M.Sella Vallal Sibi, R.Pugazh Bharathi, P. Shanmugapriya","doi":"10.1109/IC3IOT53935.2022.9767935","DOIUrl":"https://doi.org/10.1109/IC3IOT53935.2022.9767935","url":null,"abstract":"Heart disease is considered as one of the major diseases which have been increasing due to modern lifestyle and it has become one of the factors of death as a deadly disease. There is a more sensitive disease to explore and we are on the edge and moving forward to gain the knowledge and explore it. There is humongous research and data about healthcare. Therefore, by using and examining new and appreciable techniques can make or predict the defect of a being who can be affected with the diseases related to heart diseases and can help in preventing and treating them in the early stages. In this research, we suggest a solution for them based on Machine Learning (ML) and Data Mining (DM) approaches, which has proven to be beneficial in the medical field. The goal of this study is to look at risk factors that lead to harmful consequences such as heart disease, as well as novel ways for detecting, predicting, and preventing heart disease, as well as overcoming the limitations of previous research. The article we submitted is a suggestion for method called Cardio plus, which incorporates a machine learning algorithm called (CNN) convolutional neural network to predict the likelihood of cardiovascular illness in patients. The suggested technique is concerned with temporal data modeling, and it makes use of CNN for HF prediction.","PeriodicalId":430809,"journal":{"name":"2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134549389","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: 0

Deep Learning based Forest Fire Detection and Alert System 基于深度学习的森林火灾探测与报警系统

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI: 10.1109/IC3IOT53935.2022.9767911

Mohnish S, A. P., G. S, Sarath Vignesh A, Pavithra P, E. S.

引用次数: 2

CNN Based Character Recognition and Classification in Tamil Palm Leaf Manuscripts 基于CNN的泰米尔棕榈叶手稿字符识别与分类

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI: 10.1109/IC3IOT53935.2022.9767866

Pravin Savaridass M, Haritha J, B. T, Vairavel K S, Ikram N, Janani M, Indrajith K

{"title":"CNN Based Character Recognition and Classification in Tamil Palm Leaf Manuscripts","authors":"Pravin Savaridass M, Haritha J, B. T, Vairavel K S, Ikram N, Janani M, Indrajith K","doi":"10.1109/IC3IOT53935.2022.9767866","DOIUrl":"https://doi.org/10.1109/IC3IOT53935.2022.9767866","url":null,"abstract":"Palm leaf manuscripts are extremely important as they have a rich source of information. As a result, simple access to ancient manuscripts must be provided to share this information with the rest of the world and to promote future study into ancient literature. This study deals with Convolutional Neural Network (CNN)-based Optical Character Recognition (OCR) system for accurately digitizing and identifying the characters for Tamil palm leaf manuscripts. The convolution layer, pooling layer, activation layer, fully connected layer, and classifier of the convolutional neural network is employed in this article. Palm-leaf manuscripts were scanned and the scanned images are used to generate the character set database. The database is divided into 67 separate classes, each of which contains roughly 100 individual samples. OCR recognition of the palm leaf manuscripts and problems associated with this are illustrated. A working example of the character recognition method for Tamil palm-leaf manuscript was implemented using the CNN model. The CNN model was found to have a better recognition rate. The prediction rate and accuracy are great because of the large number of features retrieved for each layer of CNN.","PeriodicalId":430809,"journal":{"name":"2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132768273","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

Modeling of Multiple Inputs Converter with Renewable Source 具有可再生电源的多输入变换器建模

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI: 10.1109/IC3IOT53935.2022.9767947

R. Sahoo, V. Jha, Priyanka Sen

引用次数: 1

Machine Learning and Deep Learning framework with Feature Selection for Intrusion Detection 基于特征选择的入侵检测机器学习和深度学习框架

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2022-03-10 DOI: 10.1109/IC3IOT53935.2022.9767727

A. Lakshmanarao, A. Srisaila, T. S. Ravi Kiran

{"title":"Machine Learning and Deep Learning framework with Feature Selection for Intrusion Detection","authors":"A. Lakshmanarao, A. Srisaila, T. S. Ravi Kiran","doi":"10.1109/IC3IOT53935.2022.9767727","DOIUrl":"https://doi.org/10.1109/IC3IOT53935.2022.9767727","url":null,"abstract":"Increases in the size of the network and associated data have been a direct effect of technological breakthroughs in the technology and communication areas. As a result, new types of assaults have emerged, making it more difficult for network security systems to identify potential threats. An intrusion Detection is a critical cyber security method that keeps track of the progress of the network's software or hardware. In order to keep up with the ever-increasing rate and diversity of cyber threats, researchers have turned to machine learning approaches to build intrusion detection systems (IDS). Using machine learning algorithms, it is possible to identify with high precision the major differences between normal and abnormal data. In this paper, we proposed three feature selection techniques followed by machine learning and deep learning for IDS. We collected two different datasets and used the ANOVA F-value based method, impurity-based feature selection, and mutual information-based techniques for identifying the best features. Later, we applied three ML algorithms K-NN, Decision Trees, Logistic Regression, and Deep Learning Feed Forward Neural Networks on two datasets and achieved an accuracy of 88%, 99.9% with feed forward neural networks. The results shown that our model performed well compared to conventional methods.","PeriodicalId":430809,"journal":{"name":"2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130508096","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