2021 2nd International Conference on Computing and Data Science (CDS)最新文献_第3页

A Comparative Study of Credit Card Fraud Detection Using the Combination of Machine Learning Techniques with Data Imbalance Solution 结合机器学习技术与数据不平衡解决方案的信用卡欺诈检测比较研究

2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00026

Faroque Ahmed, R. Shamsuddin

{"title":"A Comparative Study of Credit Card Fraud Detection Using the Combination of Machine Learning Techniques with Data Imbalance Solution","authors":"Faroque Ahmed, R. Shamsuddin","doi":"10.1109/CDS52072.2021.00026","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00026","url":null,"abstract":"Due to the rapid spread of fraud and cybersecurity risks in digital economy, fraud detection stands as a prime issue of modern technology. However, the analysis of fraud cases is computationally difficult because, fraud cases conjure less than 0.2% of the transactions. Thus to figure out the best classification technique to use for fraud detection, this paper has conducted a thorough experimentation of Machine Learning (ML) techniques. It has implemented six ML techniques i.e. Logistic Regression (LR), Support Vector Machine (SVM), Naíve Bayes (NB), Random forest (RF), Decision Tree (DT), and K-nearest neighbour (KNN) classifiers to detect credit card fraud. The investigation used five type of datasets i.e. imbalanced data, Under Sampled (US) data, Over Sampled (OS) data, sampled data using Synthetic Minority Over Sampling Technique (SMOTE) and Adaptive Synthetic Sampling Method for Imbalanced Data (ADASYN). The best combination of these classification approaches is selected based on five performance evaluation criteria i.e. Accuracy, Area Under the Curve (AUC), Precision, Recall score and fl-score. After evaluation of the classifiers it has showed that among 30 different classification approaches, RF classifier with over sampling (OS) technique was found to be the best approach in terms of all the performance criteria. It showed 99.99 % accurate and precise results with 99.99 % AUC, fl-score and 100 % Recall rate. Our choosen approach has obtained the highest accuracy over other studies on the same dataset. The banking sector as well as other financial institutions might use this suggested machine learning based combination approach to minimize (debit/credit card) frauds.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122140303","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: 5

Digital Character CAPTCHA Recognition Using Convolution Network 基于卷积网络的数字字符CAPTCHA识别

2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00029

Yu Cao

引用次数: 1

Research on software defect prediction technology based on deep learning 基于深度学习的软件缺陷预测技术研究

2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00024

Pengcheng Jiang

引用次数: 5

Application of Blockchain in Civil Aviation 区块链在民航领域的应用

2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00042

Yucheng Peng

引用次数: 2

Development of Obstacle Avoidance for Autonomous Vehicles and an Optimization Scheme for the Artificial Potential Field Method 自动驾驶汽车避障研究进展及人工势场法优化方案

2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00010

Xuefei Yang

引用次数: 2

Research on Traffic Congestion Prevention Model Based on Internet of Things Virtualization Technology 基于物联网虚拟化技术的交通拥堵预防模型研究

2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00072

Jianbing Chen, Bo Xu, Wei Tang

引用次数: 0

Business Intelligence based novel Marketing Strategy Approach using Automatic Speech Recognition and Text Summarization 基于商业智能的基于自动语音识别和文本摘要的新型营销策略方法

2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00108

Pratidnya Hegdepatil, K. Davuluri

引用次数: 3

An Investigation on Key Technologies for On-chip Optical Interconnection 片上光互连关键技术研究

2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00044

Lehao Wang

引用次数: 0

Rate My Professors: A Study Of Bias and Inaccuracies In Anonymous Self-Reporting 评价我的教授:匿名自我报告中的偏见和不准确性研究

2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00098

Alexander Katrompas, V. Metsis

{"title":"Rate My Professors: A Study Of Bias and Inaccuracies In Anonymous Self-Reporting","authors":"Alexander Katrompas, V. Metsis","doi":"10.1109/CDS52072.2021.00098","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00098","url":null,"abstract":"Commentary-driven and opinion-driven “ratings” websites such as Yelp, Amazon Reviews, and Rate My Professors are ubiquitous on the Web. These review sites engage in voluntary self-reporting which is well known to be fraught with bias and inaccurate representations. This study is an investigation into the website known as Rate My Professors which ostensibly seeks to collect and report on college and university professor quality. Rate My Professors (RMP), like many review sites, is anonymous. While it is commonly believed that anonymity increases accuracy, studies have shown otherwise. Studies using anonymous self-reporting are well known to be unreliable, so much so that researchers are required to take compensatory measures to validate their results. Rate My Professors takes no such measures, and in fact there is no guarantee that a “student reviewer” is even a student. This study investigates Rate My Professors for bias, inaccuracy, and invalid data. The study will show compelling evidence which supports the idea that anonymous self-reporting, without compensatory validation measures, is flawed and unsuitable for use in a decision making process.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126199083","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 Deep Learning Based Hybrid Model for Sales Prediction of E-commerce with Sentiment Analysis 基于深度学习的电子商务销售预测与情感分析混合模型

2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00091

Haotian Zhu

{"title":"A Deep Learning Based Hybrid Model for Sales Prediction of E-commerce with Sentiment Analysis","authors":"Haotian Zhu","doi":"10.1109/CDS52072.2021.00091","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00091","url":null,"abstract":"The market of E-commerce has developed rapidly since the emergence of Internet. Many companies or shops of E-commerce want to seek a way to predict the sales of their products. The prediction of sales can help the merchants to formulate a sales strategy, in order to obtain a bigger profit and attract more investment. However, many studies simply use the daily sales or some very basic daily information of the product to predict the sales, without considering the effects of reviews. In this paper, a hybrid network including Bi-directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) is proposed to solve this prediction task. Through careful selection, several attributes and comments of the products are utilized. Feature engineering is used to normalize the different kinds of data. BiLSTM is conducted to analyze all the comments. CNN is utilized to make predictions by using the data provided by feature engineering. Analysis is described to show the advantages and effectiveness of this network. By using the attributes of the product, along with the polarization of comments, CNN can predict the sales of the product.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"1988 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125490298","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: 6