2021 International Conference on Computing, Communication and Green Engineering (CCGE)最新文献_第5页

Single Band 5G mmWave Two Port MIMO Antenna with Omnidirectional for High Speed Wireless Applications 全向高速无线应用的单频段5G毫米波双端口MIMO天线

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776428

Manish Sharma, Harsh Malhotra, S. Panda, S. Malhotra

引用次数: 0

Design and Development of Car RentalWebsite Using Mern Stack 基于Mern Stack的租车网站设计与开发

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776473

D. Vasanthi, T. Sivasakthi, V. Abarna, R. Arthi

{"title":"Design and Development of Car RentalWebsite Using Mern Stack","authors":"D. Vasanthi, T. Sivasakthi, V. Abarna, R. Arthi","doi":"10.1109/CCGE50943.2021.9776473","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776473","url":null,"abstract":"Lately, the necessity of car rental services around the world have been increased and growing quicky as cars have become the most convenient modes of transportation. If any urgent trip is ahead people can rely on the car rental service as they provide us immediate transportation. It is really time -consuming and difficult to find cars using traditional methods before a stipulated price and time. The refore, using online websites it is easy to reserve your car at the stipulated price and time, it also makes the listed cars accessible by just a simple reservation which comes handy. This website allows a user not only to book a car but also to host a car to earn from renting. Only verified users can book a car for rental and verification is done using a Driving Lice nse. The cars are tabulated by obtaining the location from the user. Filters can be applied to the listing like price the listing will be shown with the owner's name and details for contact purpose. Building a web application with React JS and Node JS the website a lot faster, increases productive ness, and also helpful for SEO. MongoDB database is a database with no schema and with good scalability, hence the management of data becomes handy, which helps the user to avoid delay and hardship in the process.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"15 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124465058","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

Detection of Epileptic Seizure using EEG- fMRI Integration 应用EEG- fMRI整合检测癫痫发作

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776356

S. V. Raut, D. M. Yadav

引用次数: 0

Lungs Diseases Prediction based on Convolutional Neural Network 基于卷积神经网络的肺部疾病预测

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776371

Sanika Shirsat, S. Kedar

引用次数: 0

Ensemble Learning for Detection of Types of Melanoma 用于黑色素瘤类型检测的集成学习

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776373

Rashmi Patil, Sreepathi Bellary

{"title":"Ensemble Learning for Detection of Types of Melanoma","authors":"Rashmi Patil, Sreepathi Bellary","doi":"10.1109/CCGE50943.2021.9776373","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776373","url":null,"abstract":"Melanoma is a potentially fatal type of skin cancer in these melanocytes develop uncontrollably. Malignant melanoma is another name for melanoma. Melanoma rates in Australia and New Zealand are the highest in the world. Melanoma is anticipated to strike one in every 15 white New Zealanders at some point in their lives. Invasive melanoma was the third most prevalent malignancy in both men and women in 2012. Melanoma can strike adults of any age, but it is extremely uncommon in youngsters. Melanoma is hypothesised to start as an uncontrolled proliferation of genetically transformed melanocytic stem cells. Early diagnosis of melanoma in Dermoscopy pictures boosts the survival percentage substantially. Melanoma detection, on the other hand, is extremely difficult. As a result, automatic identification of skin cancer is extremely beneficial to pathologists' accuracy. This paper offers an ensemble deep learning strategy for accurately classifying the kind of melanoma at an early stage. The proposed model distinguishes between lentigo maligna, superficial spreading and nodular melanoma, allowing for early detection of the virus and prompt isolation and treatment to prevent the disease from spreading further. The deep layer architectures of the convolutional neural network (CNN) and the shallow structure of the pixel-based multilayer perceptron (MLP) are neural network algorithms that represent deep learning (DL) technique and the classical non-parametric machine learning method. Two methods that have diverse behaviours, were combined in a simple and successful means for the classification of very fine melanoma type detection utilising a rule-based decision fusion methodology. On dataset retrieved from https://dermnetnz.org/, the efficiency of ensemble MLP-CNN classifier was examined. In compared to state-of-the-art approaches, experimental outcomes reveal that the proposed technique is worthier in terms of diagnostic accuracy","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126998602","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

Spatial Domain Texture Synthesis for Data Embedding 用于数据嵌入的空间域纹理合成

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776420

S. Patil, Rane Charushila Vijay

引用次数: 0

A Machine Learning based Model for Disease Prediction 基于机器学习的疾病预测模型

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776374

Monali Gulhane, T. Sajana

{"title":"A Machine Learning based Model for Disease Prediction","authors":"Monali Gulhane, T. Sajana","doi":"10.1109/CCGE50943.2021.9776374","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776374","url":null,"abstract":"People are now suffering from a variety of diseases as a result of the environment in which they live and their lifestyle choices. As a result, the goal of predicting disease at an earlier stage becomes increasingly critical. However, making an accurate prediction based on symptoms becomes too tough for doctors to do. The task of accurately predicting disease is one of the most difficult. Data mining is critical in overcoming this difficulty because it may be used to forecast the sickness. Every year, a great amount of data is generated in the field of medicine. Due to the extreme increase in the rate of information being collected in the health and medical industries, it has been possible to conduct precise analyses of medical data, which now has resulted in better patient outcomes. When disease data is used as a starting point, data mining can be used to identify hidden patterns in the huge number of medical data that currently exists. On the basis of the patient's symptoms, we suggested a generic disease prediction model. In ability to implement credible illness predictions, we apply machine learning methods such as convolutional neural networks (CNNs) for disease prediction. Disease symptom datasets are essential for disease forecasting purposes. In this general disease prediction model, the individual's lifestyle behaviour as well as examination data are taken into consideration for reliable disease prediction. It has been demonstrated that the accuracy of generalized predictive modeling that used the CNN algorithm is 98.7 percent, which really is better than those of the present technique. In addition, the time and memory requirements for existing mechanism are higher than those for CNN. When general disease is expected, this method is qualified to determine the threat related to institutional disease, which can be stronger or weaker than the previously mentioned of general disease.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131839561","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

Embankment Protection - React Native Application Cross-Platform Application for protection of embankments by crowd sourced data 堤防- React Native Application跨平台应用程序，用于保护堤防的众包数据

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776417

AnshulVarshav Borawake, Minal Shahakar

{"title":"Embankment Protection - React Native Application Cross-Platform Application for protection of embankments by crowd sourced data","authors":"AnshulVarshav Borawake, Minal Shahakar","doi":"10.1109/CCGE50943.2021.9776417","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776417","url":null,"abstract":"Developing mobile application compatible for both Android and iOS, hence a cross-platform development approach, as developers faced a challenge previously learning development specific language for Android and iOS. Compared to other hybrid mobile application frameworks, React Native has faster development time, wide market search and easy third party integration. It is also time and cost efficient for single codebase nature. Getting to the bottom of the solution for the underlying problem, this paper utilizes React Native framework to create an efficient hybrid mobile application “Embankment Protection App” capable of provisioning crowd sourced solutions pertaining to embankment surveys. The framework has been created for Android and iOS, the produced results reflects adequate experience for users on both the platforms. The framework develops truly native apps and does not compromise much with user experiences regardless of the platform. The programming language used for the solution of this research paper is a combination of Javascript.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133108920","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

Fast Implementation of Digital Signatures Using Parallel Techniques 使用并行技术快速实现数字签名

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776382

N. Kishore, Priya Raina, N. Nayar, Mukesh Thakur

{"title":"Fast Implementation of Digital Signatures Using Parallel Techniques","authors":"N. Kishore, Priya Raina, N. Nayar, Mukesh Thakur","doi":"10.1109/CCGE50943.2021.9776382","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776382","url":null,"abstract":"Digital signatures are widely used to check the authenticity of the identity of the signatory of the message/document and the integrity of the message sent. They are also used by the receiver for ensuring non-repudiation by the sender. They play an important role in making day-to-day processes electronic and paperless. Digital signatures are based on public key infrastructure (PKI). The message digest (hash) of the file is signed by the sender using a private key and appended to the file. The recipient extracts the signature, decrypting it with the sender's public key, and verifies if the received digest matches its own hash calculations. However, complex calculations for secure signatures imply that digital signatures are time consuming for large files. Hashing is the basic security mechanism used in digital signatures that is performed by all the parties and consumes most of the time. This paper presents a solution to this problem by using parallel hashing to achieve fast digital signatures, discussing two possible approaches. The first one uses only parallel hashing, keeping the rest of the algorithm the same as the reference algorithm based on RSA. The second approach parallelizes the entire reference algorithm. Both were implemented using the OpenMP framework, and the experimental results show a significant decline in the execution time in both the cases.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126654742","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

Optimization of Cogeneration in Sugar industry by Mixed integer linear programming Method 用混合整数线性规划方法优化制糖工业热电联产

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI: 10.1109/CCGE50943.2021.9776424

Manjusha A. Kanawade, Mrunmai M. Ranade

引用次数: 0