2022 32nd Conference of Open Innovations Association (FRUCT)最新文献_第4页

Comparative Analysis of Machine Learning Methods Application for Financial Fraud Detection 机器学习方法在金融欺诈检测中的应用比较分析

2022 32nd Conference of Open Innovations Association (FRUCT) Pub Date : 2022-11-09 DOI: 10.23919/FRUCT56874.2022.9953872

A. Menshchikov, V. Perfilev, Denis Roenko, M. Zykin, Maksim Fedosenko

{"title":"Comparative Analysis of Machine Learning Methods Application for Financial Fraud Detection","authors":"A. Menshchikov, V. Perfilev, Denis Roenko, M. Zykin, Maksim Fedosenko","doi":"10.23919/FRUCT56874.2022.9953872","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953872","url":null,"abstract":"This paper addresses the fraud detection problem in the context of Big Data used in remote banking systems. The paper aims to propose a new algorithm for automatic detection of fraudulent transactions using machine learning with a performance that allows to apply it in big data systems. The article identifies promising directions for optimizing the operation of methods for fraudulent transactions detection in anti-fraud systems. Architectural approaches to the operation of anti-fraud systems have been studied. Based on this, an architecture for illegal actions prediction in a near real-time mode was proposed. The research task of the article is to find the most suitable machine learning algorithm, with the least training and prediction time, demonstrating high classification performance. To achieve this goal, an analysis of the supervised and ensemble machine learning algorithms was made. The dataset was preprocessed for the experiment with SMOTE resampling and robust scaling techniques. The chosen methods were compared using different metrics: $F$1 score, AUC and time consumption for training and classification. As a result of a metrics comparison, it was found that multilayer perceptron (MLP) and boosting methods (Adaptive, Gradient, XGBoost) has the highest classification, but MLP outperforms boosting methods in terms of time consumption for classification. Thus, MLP was selected as the most appropriate algorithm for further integration to proposed Big Data architecture. Based on the data obtained during the experiments, the degree of their implementation in fraud detection systems was assessed and architecture for the anti-fraud detection system for big data was proposed.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125265908","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

Database Block Management using Master Index 使用主索引的数据库块管理

2022 32nd Conference of Open Innovations Association (FRUCT) Pub Date : 2022-11-09 DOI: 10.23919/FRUCT56874.2022.9953806

Michal Kvet

引用次数: 1

An Event-Driven Approach to the Recognition Problem in Video Surveillance System Development 视频监控系统开发中的事件驱动识别方法

2022 32nd Conference of Open Innovations Association (FRUCT) Pub Date : 2022-11-09 DOI: 10.23919/FRUCT56874.2022.9953883

Nikita A. Bazhenov, Egor I. Rybin, Dmitry G. Korzun

{"title":"An Event-Driven Approach to the Recognition Problem in Video Surveillance System Development","authors":"Nikita A. Bazhenov, Egor I. Rybin, Dmitry G. Korzun","doi":"10.23919/FRUCT56874.2022.9953883","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953883","url":null,"abstract":"Many video surveillance systems (VSS) have been already developed for various application domains. Such systems are based on well-elaborated recognition algorithms of Artifi-cial Intelligence (AI) and implemented as Ambient Intelligence (AmI) services in Internet of Things (IoT) environments. In particular, algorithms support such smart VSS functions of video data processing as human detection, human identification, object location within an image, human activity recognition. Many software tools have been developed to implement various recognition algorithms for VSS development. In this paper, we consider the following VSS development problems: a) a generic model of events in video data for a given problem domain, b) a hardware-software architecture for data processing with existing recognition algorithms, and c) a model to construct a required smart VSS function using existing software tools. We introduce our event-oriented approach to solve the above VSS development problems. The approach is experienced in several use cases. Our experimental study shows the applicability of the proposed approach in terms of the accuracy and performance.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116988758","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

Preface of the 32nd Conference of Open Innovations Association FRUCT 开放式创新协会第32届会议序言

2022 32nd Conference of Open Innovations Association (FRUCT) Pub Date : 2022-11-09 DOI: 10.23919/FRUCT56874.2022.9953837

引用次数: 0

Model for the Monitoring of Competences of the PISA Test in Peru under a B-Learning Approach 基于b -学习方法的秘鲁PISA测试能力监测模型

2022 32nd Conference of Open Innovations Association (FRUCT) Pub Date : 2022-11-09 DOI: 10.23919/FRUCT56874.2022.9953879

Abel Rodríguez, Daniel Quispe, Lenis Wong

引用次数: 0

Opinion Mining for Modeling User Experience of Online Education: Sentiment Analysis and Keywords Extraction of Student Reviews 面向在线教育用户体验建模的意见挖掘:学生评论的情感分析与关键词提取

2022 32nd Conference of Open Innovations Association (FRUCT) Pub Date : 2022-11-09 DOI: 10.23919/FRUCT56874.2022.9953875

A. Moskvina, M. Kirina, Anastasia Gavrilyuk

引用次数: 0