2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)最新文献_第2页

Performance Evaluation Of Hsrp, Glbp And Vrrp With Interior Gateway Routing Protocol And Exterior Gateway Routing Protocol 内部网关路由协议和外部网关路由协议下Hsrp、Glbp和Vrrp的性能评价

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734227

Aayush Agarwal, S. Sharma

引用次数: 2

Software Defect Data Collection Framework for Github Github软件缺陷数据收集框架

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734131

Vikas Suhag, S. Dubey, Bhupendra Kumar Sharma

{"title":"Software Defect Data Collection Framework for Github","authors":"Vikas Suhag, S. Dubey, Bhupendra Kumar Sharma","doi":"10.1109/confluence52989.2022.9734131","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734131","url":null,"abstract":"Software has become part of every sphere of life. This increasing dependence on software has put tremendous pressure on software development teams to deliver software applications as early as possible at the cost of compromised software quality and reliability. Software quality requires extensive testing and validation of software, which is not possible with limited human resources, time and budget, so researchers moved to a new paradigm of software quality assurance i.e., Software Defect Prediction (SDP). SDP aims to build automated Machine Learning (ML) models to aid development teams in prioritizing the key aspects of software testing while maintaining the short software development life cycle. SDP requires huge amount of data to train and test ML models, traditionally PROMISE and NASA defect datasets are most prominently used by researchers, but with changes in programming languages, programming styles and limited size of datasets has made them infeasible for SDP in current scenarios. In this paper, we have developed a software defect dataset collection framework, which mines commit level defect data from GitHub. The efficiency of data mining, accuracy of data and validity of data is verified by SDP models. Results shows that proposed method is feasible as well as efficient to execute even on regular computer systems.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128831190","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

Design and Implementation of Fuzzy logic-2DOF controller for Emulation of wind turbine System 风电系统仿真模糊逻辑二自由度控制器的设计与实现

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734164

P. Manjeera, G. N. Nagesh Kumar, V. Rafi

{"title":"Design and Implementation of Fuzzy logic-2DOF controller for Emulation of wind turbine System","authors":"P. Manjeera, G. N. Nagesh Kumar, V. Rafi","doi":"10.1109/confluence52989.2022.9734164","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734164","url":null,"abstract":"Relating to global warming and the depletion of fossil resources have prompted a radical shift toward renewable energy. In order to satisfy our energy needs, we must use all available renewable resources. Wind energy is becoming increasingly popular, prompting study and development of high-power wind turbines. A controlled test that is not dependent on wind availability is required. Therefore, it is necessary to promote R&D and teaching in wind energy conversation system (WECS). The majority of existing methods employed different emulation topologies for torque compensating and system controlling. However, by using these existing topologies, system complexity increases and disturbance occur in output side. To overcome existing problems for wind turbine emulation, this paper proposes a new topology of a Fuzzy logic controller along with two degrees of freedom control (FLC-2DOF). The proposed fuzzy logic control for emulation of a wind turbine-induction motor drive system along with doubly fed induction generator (DFIG) which is simulated by using MATLAB/SIMULINK software. In this study, the results of a comparison of the suggested FLC- 2DOF control topologies with IDOF and 2DOF control topologies are presented.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115189148","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

A Novel Deep Learning Model for Accurate Prediction of Image Captions in Fashion Industry 一种新的深度学习模型用于时尚行业图像说明的准确预测

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734171

Pulkit Dwivedi, Anushka Upadhyaya

{"title":"A Novel Deep Learning Model for Accurate Prediction of Image Captions in Fashion Industry","authors":"Pulkit Dwivedi, Anushka Upadhyaya","doi":"10.1109/Confluence52989.2022.9734171","DOIUrl":"https://doi.org/10.1109/Confluence52989.2022.9734171","url":null,"abstract":"As the need for automation in the IT sector is growing, several fashion companies are employing models that can create appropriate descriptions for product images. This will assist buyers to better understand the goods, resulting in increased sales for the apparel company. For creating the image descriptions, the researchers used a variety of feature extraction approaches, including convolution neural networks with several layers like VGG-16 and VGG-19. Once the image features are extracted using these convolution neural network (CNN) models, processing of text data is done using a recurrent neural network (RNN) that represents the input sequence of text as a fixed length output vector. Finally, both the vector outputs obtained from the digital image and its description are combined to train the image caption generator model. In this work, we put forward a smaller 5 layer convolution neural network (CNN-5) and compared it with transfer learning models like VGG-16 and VGG-19. The experiments were carried out on the Fashion MNIST dataset, which consists 70,000 gray scale images of size of 28x28 pixels. Each image is linked to one of ten labels (0-9) that represent ten different fashion items. We compared the performance of the proposed methodology as well as the state-of-the-art models using Bilingual Evaluation Understudy: BLEU-I, BLEU-2, BLEU-3 and BLEU-4 scores. The research demonstrates that a smaller layered convolution neural network can reach a similar degree of accuracy for the Fashion MNIST dataset as compared to state-of-the-art methods.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128599067","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: 8

Nirjas: An open source framework for extracting metadata from the source code Nirjas:一个从源代码中提取元数据的开源框架

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734222

Ayushi Bhardwaj, Sahil, Kaushlendra Pratap, Gaurav Mishra

{"title":"Nirjas: An open source framework for extracting metadata from the source code","authors":"Ayushi Bhardwaj, Sahil, Kaushlendra Pratap, Gaurav Mishra","doi":"10.1109/confluence52989.2022.9734222","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734222","url":null,"abstract":"Metadata/Comments are the critical element of any software development process. In this paper, we went on to explain how the metadata/comments in the source code can play an essential role in comprehending the software. We introduced a python based open-source framework “Nirjas” that helps us in extracting the metadata in a structured manner. There are various syntax, types and widely accepted conventions for adding a comment in the source file of different programming languages. Various edge cases can create noise in our extraction, for which we used Regex to accurately retrieve these metadata. The non-Regex method can give us the result but misses out on accuracy and noise separation. Nirjas can also separate different types of comments, source code and provide us with the details about those comments in terms of the line number, file name, language used, total SLOC, etc. Nirjas is a standalone python framework/library and can be easily and quickly installed using the source installation or using pip (package installer for Python). Nirjas was first created and started out as one of the projects during the Google Summer of Code program and is currently developed and maintained under the FOSSology organization","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128782324","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

Stroke Prediction using Machine Learning Methods 使用机器学习方法进行中风预测

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734197

Saumya Gupta, Supriya Raheja

{"title":"Stroke Prediction using Machine Learning Methods","authors":"Saumya Gupta, Supriya Raheja","doi":"10.1109/confluence52989.2022.9734197","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734197","url":null,"abstract":"Stroke, a medical emergency that occurs due to the interruption of flow of blood to a part of brain because of bleeding or blood clots. Worldwide, it is the second major reason for deaths with an annual mortality rate of 5.5 million. Every year, more than 15 million people worldwide have a stroke, and in every 4 minutes, someone dies due to stroke. A stroke is generally a consequence of a poor style of living and hence, preventable in up to 80% of the cases. Therefore, the prediction of stroke becomes necessary and should be used to prevent permanent damage by stroke. The current work predicted the stroke using the different machine learning models namely, Gaussian Naive Bayes, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbours, AdaBoost Classifier, XGBoost Classifier, and Random Forest Classifier. The paper presents the comparison among all machine learning algorithms. Analysis of results revealed that the AdaBoost, XGBoost and Random Forest Classifier made the least value of incorrect predictions and had the greatest accuracy scores 95%, 96% and 97% respectively. Hence, they were the best suited model for stroke prediction and can feasibly be used by physicians to predict stroke in real world.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114066287","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

Mining Influence of people on Viral Marketing 挖掘人对病毒式营销的影响

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734146

Arushi Gulati, Natansha Sahgal, Neetu Narayan, Avneesh Atrey, Jitendra Kumar Singh Jadon

{"title":"Mining Influence of people on Viral Marketing","authors":"Arushi Gulati, Natansha Sahgal, Neetu Narayan, Avneesh Atrey, Jitendra Kumar Singh Jadon","doi":"10.1109/confluence52989.2022.9734146","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734146","url":null,"abstract":"Organizations rely on marketing strategies and social reach for the success of their products and ideas. As soon as a product catches the eye of influential audience, goes viral in the society. Influence plays key role in affecting our sensibilities and choices. Every person on a social networking platform possesses a certain influencing power and by recognizing and valuing the most persuasive user having influential capabilities, creates a scope for social media marketing. This study proposes a methodology to recognize the users with the maximum influential power in a social network while demonstrating the evolving links in sync with their varied interests using the underlying concepts of Signed Social Networking. The study was caried forward in two folds. The first fold aims id identifying the communities in a raw social network. It considers the factors responsible for forming communities using graph evolutionary parameters. The second fold aims id determining the most influential users in a community by giving a marketing perspective to the existing Evolutionary model. The result of this study highlights the product users in the social network who are capable of maximising the positive word of mouth and knowingly or unknowingly market the product they are using in the society. Organizations can take this concept to their by harnessing the marketing opportunities of product users that such social foci’s offer thereby increasing profit in less marketing expense.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"89 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122852674","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 Prediction Of Weather Using Hybrid_stacked Bi-Long Short Term Memory 基于深度学习的混合堆叠双长短时记忆天气预测

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734133

Uma Sharma, Chilka Sharma

{"title":"Deep Learning Based Prediction Of Weather Using Hybrid_stacked Bi-Long Short Term Memory","authors":"Uma Sharma, Chilka Sharma","doi":"10.1109/confluence52989.2022.9734133","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734133","url":null,"abstract":"Climate and environment forecast is under control of very high dimensionality as well as interactions on various scale of temporal and spatial factors and disorganized dynamics resulting numerous problems and complications in the field. Furthermore cutting edge mathematical models, in spite of their immense computational expenses are not adequate for some applications. In this way, it is interesting to utilize arising new innovations like Artificial Intelligence or computerized reasoning to handle these issues. This work illustrates the utilization of deep learning models to imitate the full dynamics of improved general circulation model, provide improvised results in the weather prediction as well as accurate and much stable long-term climate time series. Combinations of different techniques of deep learning are used in this work for prediction of weather. Hybridunderscore Stacked Bi-LSTM model is proposed which comprises of both LSTM and Bi-LSTM to train our model and before training our model the data used for this has been pre-processed using standard scaling technique to make it accurate and in desired format. An improvised weather prediction technique is presented here, using historical data from various climate stations to prepare Deep Learning models, which helps to predict futuristic weather conditions within a very short time span. The performance of proposed model is computed using various regression metrics and the results shows that the model is performing better than the present state-of-the-art techniques.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131793254","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

Performance Skew Prediction in HPCC Systems HPCC系统的性能偏差预测

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734182

A. Karthik, Harsh Mishra, S. Jayanth, G. Shobha, Jyoti Shetty

引用次数: 1

Prediction of abnormal pregnancy in pregnant women with Advanced maternal age and Pregestational Diabetes using Machine learning models 使用机器学习模型预测高龄孕妇和妊娠期糖尿病的异常妊娠

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734210

Aastha Singh, Madhulika Bhatia, A. Garg

{"title":"Prediction of abnormal pregnancy in pregnant women with Advanced maternal age and Pregestational Diabetes using Machine learning models","authors":"Aastha Singh, Madhulika Bhatia, A. Garg","doi":"10.1109/Confluence52989.2022.9734210","DOIUrl":"https://doi.org/10.1109/Confluence52989.2022.9734210","url":null,"abstract":"Number of women with advanced maternal age is increasing along with some preexisting medical condition like diabetes. The objective of this paper is to use machine learning models to predict number of women above 35 with pregestational diabetes having high risk of abnormal pregnancy. Using data from Open access CTG database and Mother’s Significant Health dataset we compare women older than 35 with preexisting medical conditions to determine the risk prediction. The outcomes were compared between women 18 to 70 years and women above 35 years to see contrast in risk prediction and also the affected women already medical condition. Machine learning models of Random Forest, Logistic Regression and Decision tree were used to predict the outcomes. The target variable was set as pregestational diabetes. Random Forest and Logistic Regression performed equally better in both provided cases for women 18 to 70 years and women above 35 years of 87% and 83% respectively. Pregnant women above 35 having pregestational diabetes are at high risks of experiencing abnormal pregnancies. Future research and machine learning models may be able to detect the factors of the high risks involved for older pregnant women.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116067979","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