Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677713
A. Al-Shimmary
In this article, we present a derivation of a novel 4-stage fractional Runge-Kutta method (4sFRKM). Then we apply it to solve time-fractional initial values problems. This method is useful because it provides us with good numerical solutions. When compared with the exact solution, the stability of the proposed method is examined and the corresponding region of stability is depicted. Moreover, the efficiency and accuracy of the prospected method were achieved through illustrative numerical examples, and the results are supported by tables and figures. All the calculations were done using MATLAB.
{"title":"Numerical Solution of Initial Value Problems of Time-Fractional Order via a Novel Fractional 4-Stage Runge-Kutta Method","authors":"A. Al-Shimmary","doi":"10.1109/ICCITM53167.2021.9677713","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677713","url":null,"abstract":"In this article, we present a derivation of a novel 4-stage fractional Runge-Kutta method (4sFRKM). Then we apply it to solve time-fractional initial values problems. This method is useful because it provides us with good numerical solutions. When compared with the exact solution, the stability of the proposed method is examined and the corresponding region of stability is depicted. Moreover, the efficiency and accuracy of the prospected method were achieved through illustrative numerical examples, and the results are supported by tables and figures. All the calculations were done using MATLAB.","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":"114555179","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}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677867
R. H. Alwan, Murtadha M. Hamad, O. Dawood
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.
{"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}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677786
Noor Hani Idrees, M. Sulaiman
Geometric process (GP) is an important alternative to the Non homogeneous Poisson process (NHPP), in which arrival time is modeled with the trend, in this paper, we consider the problem of parameter estimation for geometric process, when the first arrival is distributed as an Exponential distribution (EXP). Parametric estimation methods including Maximum likelihood (ML), nonparametric estimation method including modified moments (MM), and a semi-parametric estimation method including modified least squares (MLS) chat were used in this paper, to achieve the goal of the paper, the model will be simulated and applied to one the important aspects of life, which is stopping of the gas power plant in Mosul.
{"title":"Estimating the Rate of Occurrence of Geometric Process With Exponential Distribution","authors":"Noor Hani Idrees, M. Sulaiman","doi":"10.1109/ICCITM53167.2021.9677786","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677786","url":null,"abstract":"Geometric process (GP) is an important alternative to the Non homogeneous Poisson process (NHPP), in which arrival time is modeled with the trend, in this paper, we consider the problem of parameter estimation for geometric process, when the first arrival is distributed as an Exponential distribution (EXP). Parametric estimation methods including Maximum likelihood (ML), nonparametric estimation method including modified moments (MM), and a semi-parametric estimation method including modified least squares (MLS) chat were used in this paper, to achieve the goal of the paper, the model will be simulated and applied to one the important aspects of life, which is stopping of the gas power plant in Mosul.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"91 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":"116955560","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}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677571
M. Mohammed, K. Alheeti
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.
{"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}
Pub Date : 2021-08-25DOI: 10.1109/iccitm53167.2021.9677813
Dhuha Albazaz
{"title":"Integration of Big Data, IoT and Cloud Computing","authors":"Dhuha Albazaz","doi":"10.1109/iccitm53167.2021.9677813","DOIUrl":"https://doi.org/10.1109/iccitm53167.2021.9677813","url":null,"abstract":"","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"78 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":"114287052","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}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677821
D. Abdullah, Mohammed Tariq Salih
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.
{"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}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677756
Isam H. Halil, K. Abbo, Hassan H. Ebrahim
Nonlinear conjugate gradient methods have a very nice theory, with a lot of important results on their convergence. This is the main argument for which these methods are intensely used in solving practical unconstrained optimization applications. There are plenty of conjugate gradient methods and can be divided to the standard conjugate gradients, hybrid and parameterized and others. This paper concerned with parameterized type conjugate gradient methods, a new search direction for nonlinear conjugate gradient algorithms is presented in this study, which is based on the Hestenes-Stefel approach and conjugacy condition, the descent property and global convergence for convex functions is proved. Numerical experiments show that the proposed algorithm is promising.
{"title":"Modifications of Hestenes and Stiefel CG Method for Solving Unconstrained Optimization Problems","authors":"Isam H. Halil, K. Abbo, Hassan H. Ebrahim","doi":"10.1109/ICCITM53167.2021.9677756","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677756","url":null,"abstract":"Nonlinear conjugate gradient methods have a very nice theory, with a lot of important results on their convergence. This is the main argument for which these methods are intensely used in solving practical unconstrained optimization applications. There are plenty of conjugate gradient methods and can be divided to the standard conjugate gradients, hybrid and parameterized and others. This paper concerned with parameterized type conjugate gradient methods, a new search direction for nonlinear conjugate gradient algorithms is presented in this study, which is based on the Hestenes-Stefel approach and conjugacy condition, the descent property and global convergence for convex functions is proved. Numerical experiments show that the proposed algorithm is promising.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"13 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":"121481501","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}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677774
Raad Ahmad Ayoob Al-Salami, Ghazwan Alsoufi
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.
支持向量机(SVM)是解决分类和回归问题最广泛使用的算法之一。支持向量机的核参数和惩罚(C)参数等参数对分类精度影响很大。为了在记录实现时间内提高分类精度，提出了一种社会群体优化(Social Group Optimization, SGO)算法，通过SVM参数的最佳组合来提高性能，获得最高的分类精度和执行速度。使用了五种不同类型的数据集(Iris, Wine, Glass, Stat log和Car)，它们都来自(UCI)存储库。此外，使用取自(Dread, Alia, 2019)的医疗数据集来验证所提出的算法。将该算法的结果与网格搜索算法(GS)进行了比较。对比结果表明，在本研究使用的所有数据集上，(SGO)算法在分类精度和实现速度方面优于(GS)算法。
{"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}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677703
Alhan Anwr Younis Al-Safar
Several research disciplines rely on satellite imagery because it possesses high quality; nevertheless, it is possible to enhance satellite images using digital image enhancement techniques that facilitate better noise reduction, contrast, and brightness. Smooth, crisp, and focused images obtained after image processing are used for assessing and demonstrating image characteristics. The present study uses several low-contrast images recorded using the Landsat sensor; multi-step spatial domain image processing is conducted for image enhancement. Initially, the Wiener Filter is employed for noise reduction; subsequently, Gamma Correction is employed for varying γ values, i.e., (0.3, 0.7, 1.1) and constant C = 1. Logarithmic transformation using C = 0.3 is the other image enhancement method. Algorithmic performance was assessed using standard values of AMBE, MSE, and PSNR. to check the signal and brightness power respectively. This outcome is so because the gamma transformation expression considers only the present mean and standard deviation for the working pixel.
{"title":"Enhancing Spatial Characteristics of Satellite Images","authors":"Alhan Anwr Younis Al-Safar","doi":"10.1109/ICCITM53167.2021.9677703","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677703","url":null,"abstract":"Several research disciplines rely on satellite imagery because it possesses high quality; nevertheless, it is possible to enhance satellite images using digital image enhancement techniques that facilitate better noise reduction, contrast, and brightness. Smooth, crisp, and focused images obtained after image processing are used for assessing and demonstrating image characteristics. The present study uses several low-contrast images recorded using the Landsat sensor; multi-step spatial domain image processing is conducted for image enhancement. Initially, the Wiener Filter is employed for noise reduction; subsequently, Gamma Correction is employed for varying γ values, i.e., (0.3, 0.7, 1.1) and constant C = 1. Logarithmic transformation using C = 0.3 is the other image enhancement method. Algorithmic performance was assessed using standard values of AMBE, MSE, and PSNR. to check the signal and brightness power respectively. This outcome is so because the gamma transformation expression considers only the present mean and standard deviation for the working pixel.","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":"126486824","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}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677823
Sanar Mazin Younis, A. Yousif
A Binary Integer Linear Programing (BILP) model was used to find the Fuzzy Critical Path (FCP) of a fuzzy project network, when the lengths of all activities are represented as Octagonal Fuzzy Numbers (OFN). Although there are many methods to solve the fuzzy network problems, this paper presents the simplest method for the purpose of estimating the CP, especially, when every path activity is expressed by an OFN in the fuzzy network problems. The OFN of each activity converted to the crisp one using a modified ranking approach. A numerical example of a fuzzy network problem is given to illustrated the steps of the method were the OFN of each activity represented the time needed to complete implementation of that activity. The same example is solved by using CP Method (CPM). This is considered to be one of the leading standard methods of solving such problems. This paper presents a comparison of results of the two methods.
{"title":"Finding the Fuzzy Critical Path with Octagonal Fuzzy Numbers using Linear Programming model","authors":"Sanar Mazin Younis, A. Yousif","doi":"10.1109/ICCITM53167.2021.9677823","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677823","url":null,"abstract":"A Binary Integer Linear Programing (BILP) model was used to find the Fuzzy Critical Path (FCP) of a fuzzy project network, when the lengths of all activities are represented as Octagonal Fuzzy Numbers (OFN). Although there are many methods to solve the fuzzy network problems, this paper presents the simplest method for the purpose of estimating the CP, especially, when every path activity is expressed by an OFN in the fuzzy network problems. The OFN of each activity converted to the crisp one using a modified ranking approach. A numerical example of a fuzzy network problem is given to illustrated the steps of the method were the OFN of each activity represented the time needed to complete implementation of that activity. The same example is solved by using CP Method (CPM). This is considered to be one of the leading standard methods of solving such problems. This paper presents a comparison of results of the two methods.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"78 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":"125476042","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}
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