IF 13.6 1区计算机科学

Big Data Mining and Analytics Pub Date : 2024-06-01 DOI: 10.26599/bdma.2023.9020023

Abed Mutemi, F. Bação

{"title":"E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review","authors":"Abed Mutemi, F. Bação","doi":"10.26599/bdma.2023.9020023","DOIUrl":"https://doi.org/10.26599/bdma.2023.9020023","url":null,"abstract":": The e-commerce industry’s rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":null,"pages":null},"PeriodicalIF":13.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unstructured Big Data Threat Intelligence Parallel Mining Algorithm 非结构化大数据威胁情报并行挖掘算法

IF 13.6 1区计算机科学

Big Data Mining and Analytics Pub Date : 2024-06-01 DOI: 10.26599/bdma.2023.9020032

Zhihua Li, Xinye Yu, Tao Wei, Junhao Qian

引用次数: 0

An Adaptive Scalable Data Pipeline for Multiclass Attack Classification in Large-Scale IoT Networks 用于大规模物联网网络多类攻击分类的自适应可扩展数据管道

IF 13.6 1区计算机科学

Big Data Mining and Analytics Pub Date : 2024-06-01 DOI: 10.26599/bdma.2023.9020027

Selvam Saravanan, Uma Maheswari Balasubramanian

引用次数: 0

KeyEE: Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt KeyEE：利用辅助关键词子提示增强低资源生成式事件提取能力

IF 13.6 1区计算机科学

Big Data Mining and Analytics Pub Date : 2024-06-01 DOI: 10.26599/bdma.2023.9020036

Junwen Duan, Xincheng Liao, Ying An, Jianxin Wang

引用次数: 0

ARGA-Unet: Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation ARGA-Unet：利用残差分组卷积和注意力机制进行脑肿瘤 MRI 图像分割的高级 U 网分割模型

Virtual Reality Intelligent Hardware Pub Date : 2024-06-01 DOI: 10.1016/j.vrih.2023.05.001

Siyi XUN , Yan ZHANG , Sixu DUAN , Mingwei WANG , Jiangang CHEN , Tong TONG , Qinquan GAO , Chantong LAM , Menghan HU , Tao TAN

{"title":"ARGA-Unet: Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation","authors":"Siyi XUN , Yan ZHANG , Sixu DUAN , Mingwei WANG , Jiangang CHEN , Tong TONG , Qinquan GAO , Chantong LAM , Menghan HU , Tao TAN","doi":"10.1016/j.vrih.2023.05.001","DOIUrl":"https://doi.org/10.1016/j.vrih.2023.05.001","url":null,"abstract":"<div><h3>Background</h3><p>Magnetic resonance imaging (MRI) has played an important role in the rapid growth of medical imaging diagnostic technology, especially in the diagnosis and treatment of brain tumors owing to its non-invasive characteristics and superior soft tissue contrast. However, brain tumors are characterized by high non-uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature. In addition, the labeling of tumor areas is time-consuming and laborious.</p></div><div><h3>Methods</h3><p>To address these issues, this study uses a residual grouped convolution module, convolutional block attention module, and bilinear interpolation upsampling method to improve the classical segmentation network U-net. The influence of network normalization, loss function, and network depth on segmentation performance is further considered.</p></div><div><h3>Results</h3><p>In the experiments, the Dice score of the proposed segmentation model reached 97.581%, which is 12.438% higher than that of traditional U-net, demonstrating the effective segmentation of MRI brain tumor images.</p></div><div><h3>Conclusions</h3><p>In conclusion, we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000232/pdfft?md5=5e16730452951aa1e3b2edacee01d06e&pid=1-s2.0-S2096579623000232-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141481556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and evaluation of Swift routing for payment channel network 支付通道网络 Swift 路由的设计与评估

IF 5.6 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-06-01 DOI: 10.1016/j.bcra.2023.100179

Neeraj Sharma , Kalpesh Kapoor , V. Anirudh

{"title":"Design and evaluation of Swift routing for payment channel network","authors":"Neeraj Sharma , Kalpesh Kapoor , V. Anirudh","doi":"10.1016/j.bcra.2023.100179","DOIUrl":"10.1016/j.bcra.2023.100179","url":null,"abstract":"<div><p>Payment Channel Networks (PCNs) are a promising alternative to improve the scalability of a blockchain network. A PCN employs off-chain micropayment channels that do not need a global block confirmation procedure, thereby sacrificing the ability to confirm transactions instantaneously. PCN uses a routing algorithm to identify a path between two users who do not have a direct channel between them to settle a transaction. The performance of most of the existing centralized path-finding algorithms does not scale with network size. The rapid growth of Bitcoin PCN necessitates considering distributed algorithms. However, the existing decentralized algorithms suffer from resource underutilization. We present a decentralized routing algorithm, Swift, focusing on fee optimization. The concept of a secret path is used to reduce the path length between a sender and a receiver to optimize the fees. Furthermore, we reduce a network structure into combinations of cycles to theoretically study fee optimization with changes in cloud size. The secret path also helps in edge load sharing, which results in an improvement of throughput. Swift routing achieves up to 21% and 63% in fee and throughput optimization, respectively. The results from the simulations follow the trends identified in the theoretical analysis.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096720923000544/pdfft?md5=7b1d5eb08e2f11797584988bf124ed9f&pid=1-s2.0-S2096720923000544-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139191289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Temperature Informed Streaming for Optimising Large-Scale Multi-Tiered Storage 优化大规模多层存储的数据温度信息流

IF 13.6 1区计算机科学

Big Data Mining and Analytics Pub Date : 2024-06-01 DOI: 10.26599/bdma.2023.9020039

Dominic Davies-Tagg, Ashiq Anjum, Ali Zahir, Lu Liu, Muhammad Usman Yaseen, Nick Antonopoulos

引用次数: 0

Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning 利用基于集合的在线机器学习提高电话营销的成功率

IF 13.6 1区计算机科学

Big Data Mining and Analytics Pub Date : 2024-06-01 DOI: 10.26599/bdma.2023.9020041

Shahriar Kaisar, Md. Mamunur Rashid, Abdullahi Chowdhury, S. S. Shafin, J. Kamruzzaman, A. Diro

引用次数: 0

AI/ML Enabled Automation System for Software Defined Disaggregated Open Radio Access Networks: Transforming Telecommunication Business 面向软件定义的分列式开放无线接入网络的 AI/ML 自动化系统：电信业务转型

IF 13.6 1区计算机科学

Big Data Mining and Analytics Pub Date : 2024-06-01 DOI: 10.26599/bdma.2023.9020033

Sunil Kumar

引用次数: 1

Erratum regarding missing informed consents and ethic approval in previously published articles 关于以前发表的文章中缺少知情同意书和伦理批准的更正

Journal of Biosafety and Biosecurity Pub Date : 2024-06-01 DOI: 10.1016/j.jobb.2024.05.003

引用次数: 0

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