2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)最新文献_第9页

Demographic Customer Segmentation of banking users Based on k-prototype methodology 基于k-原型方法的银行用户人口客户细分

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734169

Rishi Gupta, Horesh Kumar, Tarun Jain, Anita Shrotriya, Aditya Sinha

{"title":"Demographic Customer Segmentation of banking users Based on k-prototype methodology","authors":"Rishi Gupta, Horesh Kumar, Tarun Jain, Anita Shrotriya, Aditya Sinha","doi":"10.1109/confluence52989.2022.9734169","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734169","url":null,"abstract":"With the ever-increasing population size, comes an ever-increasing diversity in tastes and preferences. Catering to each of these nearly 7 billion preferences individually is an unimaginable task. Whereas providing the same service to whole population would nullify the meaning of ‘preferences. This is where customer segmentation acts as a middle ground. Customer segmentation is a way to cater to tastes and preferences of groups of individuals rather than individuals itself. Although, the individuals in these groups might not have the exact same preferences, but they lie in the same ballpark, making them more similar to each other than the individuals of other groups. Segmentation is the first step in ‘targeted marketing’, which is followed my targeting and eventually by positioning. One way of performing said segmentation is by manually segregating customers one by one, be it by using MS Excel or any query language. But this way is very cumbersome and error prone, it is also very time inefficient. Therefore, machine learning algorithms are used for big data sets. This not only eliminates the above problems, but it also increases the scope of analysis through data manipulation and visualization. The most common machine learning algorithms used for customer segmentation are the unsupervised clustering algorithms out of which k-means is the most popular one. We are going to study a variation of this k-prototype and look at how it performs when it comes to customer segmentation.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"203 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":"115188661","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

Protein-protein interaction prediction from primary sequences using supervised machine learning algorithm 基于监督机器学习算法的初级序列蛋白质相互作用预测

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734190

Monika Khandelwal, R. Rout, Saiyed Umer

引用次数: 2

Detection of Malicious Cyber Fraud using Machine Learning Techniques 利用机器学习技术检测恶意网络欺诈行为

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734181

Parv Rastogi, Eksha Singh, Vanshika Malik, Abhishek Gupta, Surbhi Vijh

{"title":"Detection of Malicious Cyber Fraud using Machine Learning Techniques","authors":"Parv Rastogi, Eksha Singh, Vanshika Malik, Abhishek Gupta, Surbhi Vijh","doi":"10.1109/Confluence52989.2022.9734181","DOIUrl":"https://doi.org/10.1109/Confluence52989.2022.9734181","url":null,"abstract":"As the technology and internet have come to their dawn, the rate of cyber-crimes has also increased. This increases the risk of information insecurity and the spread of crimes such as spam, farming and phishing, financial fraud, etc. Particularly, the attackers/hackers spread malicious uniform resource locators (URLs) to exploit vulnerabilities of the system and gain the personal information of the users. Thus, a study on malicious URL detection is necessary to prevent such attacks. Several studies exist which show numerous ways to determine malicious URLs based on machine learning (ML) and deep learning (DL), but there are some problems, for example, malicious features cannot be extracted efficiently. In this research, a model is proposed to ascertain malicious URLs, which is formulated on random forest, support vector machine (SVM), deep neural network (DNN), convolutional neural network (CNN). The several datasets are considered containing malicious and benign URLs to train the model to detect URL behaviour and attributes. The empirical results show that the suggested method can detect malicious URLs efficiently, based on URL behaviour and attributes. Thus, the solution may be advised as an efficient and reliable solution for the problem of malicious URL detection.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"72 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":"129680085","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

Elliptic Curve Cryptography in Cloud Security: A Survey 椭圆曲线密码在云安全中的应用综述

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734138

Mohammad Anas, Raza Imam, Faisal Anwer

引用次数: 1

Dedicated Farm-Haystack Question Answering System for Pregnant Women and Neonates Using Corona Virus Literature 利用冠状病毒文献的孕妇和新生儿专用农场-干草堆问答系统

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734125

Revathi S Nambiar, Deepa Gupta

{"title":"Dedicated Farm-Haystack Question Answering System for Pregnant Women and Neonates Using Corona Virus Literature","authors":"Revathi S Nambiar, Deepa Gupta","doi":"10.1109/confluence52989.2022.9734125","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734125","url":null,"abstract":"The global pandemic, COVID-19 has made it more important to quickly and precisely retrieve critical information for effective use by specialists in a wide range of fields. Domain question answering system will work or produce good results to certain extent but still favour more to the pretrained dataset. In this work we target developing a customized question answering framework that can assist the medical network with retrieval of answers to important logical questions like risk factors, effective modes of communication, various treatment options for target high-risk populaces like pregnant women and neonates.The proposed framework uses a customized Farm-Haystack question answering system and introduces a novel pipeline architecture using latent dirichlet allocation and bidirectional encoder representation from transformers for embedding the information. The system is modeled to produce the best and reliable answers for the delicate population, which requires more efficient answers rather than generic population, which can be answered using pretrained systems. In this context, the system has showed the accurate and compact answers for different inquiries related to the sensitive population.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"00 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":"123976603","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

Face Mask detection using Convolutional Neural Network 基于卷积神经网络的面罩检测

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734156

Vaibhavi Srivastava, Surbhi Vijh

{"title":"Face Mask detection using Convolutional Neural Network","authors":"Vaibhavi Srivastava, Surbhi Vijh","doi":"10.1109/confluence52989.2022.9734156","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734156","url":null,"abstract":"The ever expanding research with advancement in the field of computer vision provided an innovative solution to face mask detection. An outbreak of infectious disease coronavirus causes severe acute respiratory syndrome. The pandemic diseases at initial stages included the symptoms of cough, fever, dizziness, shortness of breath and fatigue. Although being highly contagious (spread or transmission) this disease has a low rate of mortality with around 80% experiencing a mild effect and 15-20% as high/severe effects, there are no vaccines or specific antiviral medicine available yet but few are in initial stages. Therefore, face mask detectors have become a very important problem in image processing and computer vision. Several recent algorithms have been designed using convolutional architectures to make the algorithm as precise as possible. In this approach, convolutional neural network architecture is applied to design a face mask detector that can detect face in the frame and then label it as “with mask” or “without a mask” The experiments were performed and reached a validation precision of 93.55 after model training.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"60 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":"133812963","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 Learning Based Influence Maximization across Multiple Social Networks 跨多个社会网络的基于学习的影响力最大化

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/Confluence52989.2022.9734145

Nida Shakeel, Rajendra Kumar Dwivedi

引用次数: 1

Sentiment Analysis for Arabic Tweets on Covid-19 Using Computational Techniques 基于计算技术的新冠肺炎阿拉伯语推文情感分析

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734188

Surbhi Bhatia, Malak Alhaider, Maitha Alarjani

{"title":"Sentiment Analysis for Arabic Tweets on Covid-19 Using Computational Techniques","authors":"Surbhi Bhatia, Malak Alhaider, Maitha Alarjani","doi":"10.1109/confluence52989.2022.9734188","DOIUrl":"https://doi.org/10.1109/confluence52989.2022.9734188","url":null,"abstract":"The Coronavirus pandemic has affected the regular course of life. Usage of social media like Twitter is rapidly increasing in Arab’s world regarding this phenomenon that has taken over the world by storm. This platform allows Arabian to easily write comments and share their feelings, thoughts and suggestions that can be positive or negative comments. This paper examines the Arabic sentiment analysis of Coronavirus-related tweets, as well as how Arab sentiment has changed over time in various countries. The goal of this study is to extract Arabic tweets from different periods of the pandemic and apply various preprocessing operations to them. Furthermore, the different state-of-art deep learning and machine learning classifiers are applied on the dataset and the accuracy of the classifiers are evaluated using several visualization tools. This paper focused on predictive modelling of tweets to find how the people’s opinion keeps on dwindling with the change of time during the course of time before, during and post pandemic. It also analyzes the different facts reveled before and after lockdown post pandemic, performing sentiment analysis to justify the claims that social media is not the reliable source to take preventive measures by the government agencies, imposing any decision. Deep learning algorithm has achieved more accuracy than machine learning in both periods, in the peck pandemic period, the RFC accuracy was around 83% where the DNN had 84%.","PeriodicalId":261941,"journal":{"name":"2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":"136 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":"123257595","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

An Approach of Categorization and Summarization of News using Topic Modeling 基于主题建模的新闻分类与摘要方法

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734216

Uttara Behera, Sumita Gupta

引用次数: 3

Research on the Appraisal of the Attraction of Technological Talents in the Shuangcheng Economic Circle of Chengdu-Chongqing Region 成渝地区双城经济圈科技人才吸引力评价研究

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2022-01-27 DOI: 10.1109/confluence52989.2022.9734224

Lida Hu, Asif Khan, Amit Yadav, Hong Liu

引用次数: 0