2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)最新文献_第6页

Numerical Solution of Initial Value Problems of Time-Fractional Order via a Novel Fractional 4-Stage Runge-Kutta Method 用一种新的分数阶四阶龙格-库塔法数值解时间-分数阶初值问题

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677713

A. Al-Shimmary

引用次数: 0

Credit Card Fraud Detection in Financial Transactions Using Data Mining Techniques 利用数据挖掘技术检测金融交易中的信用卡欺诈

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677867

R. H. Alwan, Murtadha M. Hamad, O. Dawood

{"title":"Credit Card Fraud Detection in Financial Transactions Using Data Mining Techniques","authors":"R. H. Alwan, Murtadha M. Hamad, O. Dawood","doi":"10.1109/ICCITM53167.2021.9677867","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677867","url":null,"abstract":"Every year, fraudulent credit card transactions result in the loss of billions of dollars. The development of effective fraud detection algorithms is critical for lowering this loss, and more algorithms are turning to advanced data mining approaches to help in fraud detection. Due to the unstable distribution of the data, the design of fraud detection algorithms is very difficult, and the distribution of the categories is highly unbalanced, yet there are many transactions that are categorized by fraud detection system. This paper proposes a system for detection fraud in financial transactions by using some types of data mining models which are logistic regression, random forest, naïve bayes and support vector machine. This is done through suggested basic steps: the first step is to use European cardholder dataset which contains 284.807 transactions that split into two groups. First one contains 199.3649 transactions which is used for training the models, while 85.4421 transactions remained for testing the models. This dataset is highly imbalanced, therefore by using SMOTE technique it will transform to a balanced one. The Second step is preparing the data and apply the Correlation function on training dataset, then implementing the used models on it. The results are compared by evaluation metrics to show which model is the best for detecting fraud. From these results, it is concluded that the Random Forest classifier is the best for fraud detection, which achieved accuracy with 99.15% in testing data.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122307823","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

Estimating the Rate of Occurrence of Geometric Process With Exponential Distribution 指数分布几何过程发生率的估计

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677786

Noor Hani Idrees, M. Sulaiman

引用次数: 2

Deep Learning Model For IDS In the Internet of Things 物联网IDS的深度学习模型

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677571

M. Mohammed, K. Alheeti

{"title":"Deep Learning Model For IDS In the Internet of Things","authors":"M. Mohammed, K. Alheeti","doi":"10.1109/ICCITM53167.2021.9677571","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677571","url":null,"abstract":"Emerging technology makes one's life more comfortable; however, in the Internet of Things, there are a lot of weaknesses like infrastructure, connectivity, network, etc, due to the presence of millions of networked devices that make it difficult to implement safety on each device. Security threats are one of the most important issues recently gaining popularity in IoT, attacks that can cause major disruptions and loss of information within the IoT network. Intrusion Detection System (IDS) has a substantial role in protecting and securing an IoT network through detecting and preventing malicious activities. To develop IDS for timely detection and categorization of cyber threats at the network level, classical machine learning techniques are commonly utilized. However, because malicious attacks are continuously evolving and occurring at extremely large sizes, various problems arise, necessitating a scalable solution. In this paper, a convolutional neural network (CNN) approach, which is a kind of deep learning model for IDS discovery, is developed that is flexible and efficient for detecting and classifying cyber-attacks in IoT networks. The well-applied CNN model on the UNSW-NB15 dataset obtained 100% precision results.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373050","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

Integration of Big Data, IoT and Cloud Computing 大数据、物联网、云计算融合

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/iccitm53167.2021.9677813

Dhuha Albazaz

引用次数: 0

Real-Time Routing for Internet of Things: A Survey on Techniques and Protocols 物联网实时路由:技术与协议综述

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677821

D. Abdullah, Mohammed Tariq Salih

{"title":"Real-Time Routing for Internet of Things: A Survey on Techniques and Protocols","authors":"D. Abdullah, Mohammed Tariq Salih","doi":"10.1109/ICCITM53167.2021.9677821","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677821","url":null,"abstract":"The Internet of Things (IoT) is a wireless network consisting of interconnected objects. Lately, IoT has become the inevitable future for most if not all technologies related to human life. In IoT, various heterogeneous devices are connected in a Wireless Sensor Network. For that energy-efficient routing, optimization plays a very important aspect for network performance in IoT. The technique and devices in IoT require routing protocols that fit in an environment like low power and lossy networks (LLNs), which rise great routing challenges. Most of the applications of the IoT are real-time applications with time constraints that work on sensing the environment, processing the data, and sending back the results sufficiently quickly to affect the environment before a deadline. Unfortunately, in many emergency cases, like in the case of large data traffic, which leads to network congestion and also causes delay and packet loss. That is why there is a vital need to analyze the routing protocols for IoT under real-time constraints. The purpose of this paper is to provide insight into the various state of the art work of the worldwide researchers about routing protocols for the Internet of Things (IoT) under real-time environment, and we explain extensively used routing protocols in real-time IoT, by first state the routing challenges in a real-time IoT, followed by a complete survey of the variant routing protocols and techniques.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115728805","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

Modifications of Hestenes and Stiefel CG Method for Solving Unconstrained Optimization Problems 求解无约束优化问题的Hestenes和Stiefel CG方法的改进

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677756

Isam H. Halil, K. Abbo, Hassan H. Ebrahim

引用次数: 0

Employing the Social Group Optimization Algorithm to Find the Best Hyper parameters of Support Vector Machine 利用社会群体优化算法寻找支持向量机的最佳超参数

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677774

Raad Ahmad Ayoob Al-Salami, Ghazwan Alsoufi

{"title":"Employing the Social Group Optimization Algorithm to Find the Best Hyper parameters of Support Vector Machine","authors":"Raad Ahmad Ayoob Al-Salami, Ghazwan Alsoufi","doi":"10.1109/ICCITM53167.2021.9677774","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677774","url":null,"abstract":"Support Vector Machine (SVM) is one of the most widely used algorithms for solving classification and regression problems. SVM parameters such as the kernel and the penalty (C) parameters greatly affect the classification accuracy. For the purpose of improving classification accuracy within a record implementation time, a Social Group Optimization (SGO) algorithm was proposed to find the best combination of SVM parameters through which to improve the performance and to obtain the highest classification accuracy and speed of execution. Five different types of datasets (Iris, Wine, Glass, Stat log and Car) were used that all of them were taken from the (UCI) repository. Moreover, a medical dataset, which is taken from (Dread, Alia, 2019), was used to verify the proposed algorithm. The results of the proposed algorithm were compared with the Grid Search algorithm (GS). The comparison results showed a preference for the (SGO) algorithm compared to the (GS) algorithm in terms of classification accuracy and speed of implementation for all the used datasets in this work.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128414361","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

Enhancing Spatial Characteristics of Satellite Images 增强卫星图像的空间特征

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677703

Alhan Anwr Younis Al-Safar

引用次数: 0

Finding the Fuzzy Critical Path with Octagonal Fuzzy Numbers using Linear Programming model 用线性规划模型求解八角模糊数模糊关键路径

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM) Pub Date : 2021-08-25 DOI: 10.1109/ICCITM53167.2021.9677823

Sanar Mazin Younis, A. Yousif

引用次数: 0