2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)最新文献_第2页

Comparison of Heuristic techniques:A case of TSP 启发式技术的比较:以TSP为例

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9058211

Shruti Gupta, A. Rana, Vineet Kansal

引用次数: 28

A new ensemble method for outlier identification 一种新的离群值识别集成方法

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9058219

Stamatios-Aggelos N. Alexandropoulos, S. Kotsiantis, Violetta E. Piperigou, M. Vrahatis

引用次数: 5

Analytical Approach towards Prediction of Diseases Using Machine Learning Algorithms 使用机器学习算法预测疾病的分析方法

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9058120

Ayushi Grover, Anukriti Kalani, S. Dubey

引用次数: 4

Identification and Implementation of Network Science Models for Centrality Measure using Social Network Analysis 社会网络分析中心性测度的网络科学模型识别与实现

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9058308

Megha Kasera, R. Johari

{"title":"Identification and Implementation of Network Science Models for Centrality Measure using Social Network Analysis","authors":"Megha Kasera, R. Johari","doi":"10.1109/Confluence47617.2020.9058308","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058308","url":null,"abstract":"A social structure constituting a set of nodes, representing social actors and edges or lines representing relation between these nodes or actors is a social network. Social network plays a vital role in circulation of information and innovation leading to analysis of the network and attracted attention in research field. The analysis of social network as a whole means, representation of all its actors and structure present in that social network forming part of a community. Community detection aims to divide the network into dense areas of graph. The dense regions usually correlates to entities which are familiar to each other, and form part of a community. Similar tastes and desires of the members in a community, enables exchange of information amongst various communities. Network science is the study of complex networks. There are various models defined in network science. Erdős–Rényi random graph model, Configuration model, Watts–Strogatz small world model, Barabási–Albert (BA) preferential attachment model, Mediation-driven attachment model, Fitness model are the various network science models. The current research work consists of application of these models on real world data and comparison between the final output with the use of R Studio.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127378102","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

A UWB MIMO Antenna With Circular Ring Slotted Dual Notched Band High Isolation Between Two Input Ports For Wireless Network Applications 用于无线网络应用的两个输入端口之间具有环形开槽双陷波带高隔离的超宽带MIMO天线

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9058201

D. P. Singh, Manish Sharma

引用次数: 2

Technology based Streamlined Agro-Farming Techniques 基于技术的流线型农业耕作技术

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9057983

A. Gupta, Debangsi Satapathy, Sanjeev Thakur

引用次数: 0

Mission Swachhta : Mobile application based on Mobile Cloud Computing 使命:基于移动云计算的移动应用

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9057926

Ankul Sharma

{"title":"Mission Swachhta : Mobile application based on Mobile Cloud Computing","authors":"Ankul Sharma","doi":"10.1109/Confluence47617.2020.9057926","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057926","url":null,"abstract":"Smartphones are playing a major role in delivering effective solutions to the day-to-day problems of our society. Smartphones have smart applications that encompasses a whole range of domains ranging from the online payments systems such as (Paytm, Phonepe, BHIM etc) to the food delivery systems such as (Zomato, Swiggy, Uber eats etc). In this paper, we have proposed and designed a cloud-based smart-phone application “Mission Swachhta” that focuses on delivering a clean society. The main aim of this application is to bridge the gap between the end-users (people) and the authorities to attain a clean environment. This paper covers both the system and hardware requirements of this application. Also, it briefs about the functionalities of each components and inter-components interactions. The supposed target audience for this report includes capacity traders and technical designers. Additionally, even though the meant target market of this record isn't the ultimate client, its miles intended to be readable through a typical Mission Swachhta app person. This report will not only give the software and hardware requirements for the functioning of the software however will even give a detailed view of the interrelation and interaction of the consumer with the software and the way to efficaciously enforce the cloud for record garage and infrastructure. The consumer can speak with the desired authority in addition to can register his grievances and dutiful movement is taken at the registered grievance.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128779919","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

An Analysis of DDoS Attacks in a Smart Home Networks 智能家庭网络中的DDoS攻击分析

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9058087

Utkarsh Saxena, J. Sodhi, Yaduveer Singh

引用次数: 12

Affects in Tweets with Real Time Emotions using Deep Learning Techniques: A Novel Approach 使用深度学习技术的实时情绪推文中的影响:一种新颖的方法

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9057913

Rohit Kumar Kaliyar, K. Ram, Akansha Sharma, Smita Tiwari, N. Ahuja, Mohit Agrawal

{"title":"Affects in Tweets with Real Time Emotions using Deep Learning Techniques: A Novel Approach","authors":"Rohit Kumar Kaliyar, K. Ram, Akansha Sharma, Smita Tiwari, N. Ahuja, Mohit Agrawal","doi":"10.1109/Confluence47617.2020.9057913","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057913","url":null,"abstract":"Twitter is an online microblogging tool that has 400 million messages per day. SemEval-2018 Tasks have already been presented and explored in the previous years by the name of “Affect in Tweets” but the scope for improvement never ends. So, in this research paper, we come up with deep learning architecture which is extremely coherent for the given task of extracting emotion intensity and classes from tweets (description of the task is given on www.codalab.com for details). Deep learning models are productive due to their automatic learning capability and automatic feature extraction. This research paper highlights the implementation of deep learning-based models such as convolutional neural networks and LSTM for classifications. The implemented tasks are-:1. emotion intensity regression 2. Emotion intensity ordinal classification,z 3. Multilabel emotion classification. We have expressed that the fine-grained intensity scores that we have obtained are reliable. Our dataset is beneficial for testing supervised machine learning algorithms for multi-label classification, intensity regression, sleuthing ordinal category of intensity of feeling (low, moderate, etc.). We have implemented various machine learning and deep learning-based models and achieved an accuracy of 77.64% in E-oc (Emotion ordinal classification) task, which is the highest among all competitors.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126385633","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 Characterization of Equalization Techniques in MIMO System under Co-channel Interference and Spatial Correlation 同信道干扰和空间相关条件下MIMO系统均衡技术的性能表征

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI: 10.1109/Confluence47617.2020.9058021

Sarthak Seth, Dhruv C. Mathur

引用次数: 1