Iraqi Journal for Computer Science and Mathematics最新文献_第2页

Detecting Abnormal Driving Behavior Using Modified DenseNet 使用修改后的 DenseNet 检测异常驾驶行为

Iraqi Journal for Computer Science and Mathematics Pub Date : 2023-07-23 DOI: 10.52866/ijcsm.2023.02.03.005

Aisha Ayad, Matheel E. Abdulmunim

{"title":"Detecting Abnormal Driving Behavior Using Modified DenseNet","authors":"Aisha Ayad, Matheel E. Abdulmunim","doi":"10.52866/ijcsm.2023.02.03.005","DOIUrl":"https://doi.org/10.52866/ijcsm.2023.02.03.005","url":null,"abstract":"Car accidents have serious consequences, including depletion of resources, harm to human health and well-being, and social problems. The three primary factors contributing to car accidents are driver error, external factors, and vehicle-related factors. The main objective of this paper is to address the issue of car accidents caused by driver error. To achieve this goal, a solution is proposed in the form of a modified version of the Dense model, called the 1Dimention-DenseNet, specifically designed to detect abnormal driving behavior. The model incorporates adapted dense blocks and transition layers, which enable it to accurately identify patterns indicative of abnormal driving behavior. This paper compares the performance of the 1D-DenseNet model to the original DenseNet model in detecting abnormal driving behavior in the Kaggle distracted driver behavior dataset. Results show that the 1D-DenseNet model outperforms the original DenseNet model in classification and validation accuracies, loss, and overhead. The 1D-DenseNet, after 100 epochs of training using Keras on top of TensorFlow, the 1D-DenseNet achieved a categorical cross-entropy loss of 0.19 on the validation set, with classification and validation accuracies of 99.80% and 99.96%, respectively. These findings demonstrate the effectiveness of the 1D-DenseNet model in improving the detection of abnormal driving behavior.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356321","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

Hard Voting Approach using SVM, Naïve Bays and Decision Tree for Kurdish Fake News Detection 使用 SVM、Naïve Bays 和决策树的硬投票法检测库尔德假新闻

Iraqi Journal for Computer Science and Mathematics Pub Date : 2023-07-04 DOI: 10.52866/ijcsm.2023.02.03.003

Rania Azad M. San Ahmed

{"title":"Hard Voting Approach using SVM, Naïve Bays and Decision Tree for Kurdish Fake News Detection","authors":"Rania Azad M. San Ahmed","doi":"10.52866/ijcsm.2023.02.03.003","DOIUrl":"https://doi.org/10.52866/ijcsm.2023.02.03.003","url":null,"abstract":"In today's age of excessive information, the proliferation of fake news has emerged as a significant challenge, eroding the trustworthiness of news sources and posing serious threats to society. With the advent of social media and the internet, false information spreads more easily, necessitating the development of effective methods to identify and prevent its dissemination.While machine learning models have been widely employed to classify text data as authentic or fake, the majority of existing research has primarily focused on well-resourced languages such as English, leaving low-resourced languages like Kurdish largely overlooked. To address this gap, our proposed work introduces a novel approach: a hard voting ensemble method that combines the insights of four weak learners. By fine-tuning these weak learners for optimal performance, our approach achieves enhanced accuracy compared to the current state-of-the-art methods. Specifically, our findings demonstrate the effectiveness of the hard voting approach using a combination of Support Vector Machines (SVM), Decision Trees (DT), and Naive Bayes (NB) classifiers, resulting in an impressive accuracy rate of 89.73%. This outperforms the individual SVM classifier and underscores the potential of ensemble techniques in fake news detection.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139363126","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

Implementing an Automated Inventory Management System for Small and Medium-sized Enterprises 中小企业自动化库存管理系统的实现

Iraqi Journal for Computer Science and Mathematics Pub Date : 2023-05-26 DOI: 10.52866/ijcsm.2023.02.02.021

Hayder Sabah Salih, Mohanad Ghazi, Mohammad Aljanabi

引用次数: 0

Iraqi Journal for Computer Science and Mathematics Pub Date : 2023-05-23 DOI: 10.52866/ijcsm.2023.02.02.020

Munther H. Abed, Mohd Nizam Mohmad Kahar

{"title":"Review on unrelated parallel machine scheduling problem with additional resources","authors":"Munther H. Abed, Mohd Nizam Mohmad Kahar","doi":"10.52866/ijcsm.2023.02.02.020","DOIUrl":"https://doi.org/10.52866/ijcsm.2023.02.02.020","url":null,"abstract":"This study deals with an unrelated parallel machine scheduling problem with additional resources (UPMR). That is one of the important sub-problems in the scheduling. UPMR consists of scheduling a set of jobs on unrelated machines. In addition to that, a number of one or more additional resources are needed. UPMR is very important and its importance comes from the wealth of applications; they are applicable to engineering and scientific situations and manufacturing systems such as industrial robots, nurses, machine operators, bus drivers, tools, assembly plant machines, fixtures, pallets, electricity, mechanics, dies, automated guided vehicles, fuel, and more. The importance also comes from the concern about the limitation of resources that are dedicated for the production process. Therefore, researchers and decision makers are still working on UPMR problem to get an optimum schedule for all instances which have not been obtained to this day. The optimum schedule is able to increase the profits and decrease the costs whilst satisfying the customers’ needs. This research aims to review and discuss studies related to unrelated parallel machines and additional resources. Overall, the review demonstrates the criticality of resolving the UPMR problem. Metaheuristic techniques exhibit significant effectiveness in generating results and surpassing other algorithms. Nevertheless, continued improvement is essential to satisfy the evolving requirements of UPMR, which are subject to operational changes based on customer demand.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115051564","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

Convolutional Neural Networks using FPGA-based Pipelining 基于fpga的流水线卷积神经网络

Iraqi Journal for Computer Science and Mathematics Pub Date : 2023-05-23 DOI: 10.52866/ijcsm.2023.02.02.019

Gheni A. Ali, Ahmed Hussein Ali

{"title":"Convolutional Neural Networks using FPGA-based Pipelining","authors":"Gheni A. Ali, Ahmed Hussein Ali","doi":"10.52866/ijcsm.2023.02.02.019","DOIUrl":"https://doi.org/10.52866/ijcsm.2023.02.02.019","url":null,"abstract":"In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of the use of FPGA-based pipelining for hardware acceleration of CNNs. These days, most people use convolutional neural networks (CNNs) to perform computer vision tasks like picture categorization and object recognition. The processing and memory demands of CNNs, however, can be excessive, especially for real-time applications. In order to speed up CNNs, FPGA-based pipelining has emerged as a viable option thanks to its parallel processing capabilities and low power consumption. The examination describes the fundamentals of FPGA-based pipelining and the basic structure of convolutional neural networks (CNNs). The current best practises for developing pipelined accelerators for CNNs on FPGAs are then reviewed, covering topics like partitioning and pipelining. Area and power limits, memory needs, and latency considerations are only some of the difficulties and trade-offs discussed in the article. In addition, the survey evaluates and contrasts the various pipelined FPGA accelerators for CNNs in terms of performance, energy consumption, and resource utilisation. Future directions and potential research areas are also discussed in the paper, such as the use of approximate computing techniques, the integration of reconfigurable architectures with emerging memory technologies, and the exploration of hybrid architectures that combine FPGAs and other hardware accelerators. This survey was created to aid researchers and practitioners in developing efficient and effective hardware accelerators for neural networks by providing a thorough overview of current trends and issues in FPGA-based pipelining for CNNs.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131174072","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

Analyzing the User's Sentiments of ChatGPT Using Twitter Data 使用Twitter数据分析ChatGPT的用户情绪

Iraqi Journal for Computer Science and Mathematics Pub Date : 2023-05-23 DOI: 10.52866/ijcsm.2023.02.02.018

Adem Korkmaz, Cemal Aktürk, Tarık Talan

引用次数: 6

Impact of Couple Stress with Rotation on Walters, B Fluid in Porous Medium 耦合应力与旋转对多孔介质中Walters, B流体的影响

Iraqi Journal for Computer Science and Mathematics Pub Date : 2023-05-16 DOI: 10.52866/ijcsm.2023.02.02.017

Zainab Ali, M. Thawi, L. Hummady

引用次数: 0

Security Attacks on E-Voting System Using Blockchain 基于区块链的电子投票系统安全攻击

Iraqi Journal for Computer Science and Mathematics Pub Date : 2023-05-04 DOI: 10.52866//ijcsm.2023.02.02.016

S. Salman, Sufyan T. Faraj Al-Janabi, A. Sagheer

引用次数: 1

A Hybrid Integrated Model for Big Data Applications Based on Association Rules and Fuzzy Logic: A Review 基于关联规则和模糊逻辑的大数据应用混合集成模型综述

Iraqi Journal for Computer Science and Mathematics Pub Date : 2023-04-30 DOI: 10.52866/ijcsm.2023.02.02.015

hind raad, Murtadha M. Hamad

{"title":"A Hybrid Integrated Model for Big Data Applications Based on Association Rules and Fuzzy Logic: A Review","authors":"hind raad, Murtadha M. Hamad","doi":"10.52866/ijcsm.2023.02.02.015","DOIUrl":"https://doi.org/10.52866/ijcsm.2023.02.02.015","url":null,"abstract":"There is a real increasing in the generating of data from different sources. Data mining (DM) is a useful method to elicit valuable information. Association rule mining (ARM) can assist in finding patterns and trends in big data. Also fuzzy logic plays a main role as assistance technique in handling big data issues. This review paper produces recent literature on hybridization regarding the association rule mining or other DM methods such as classification and clustering and fuzzy logic techniques in big data. Whereas a hybrid model of association rule and fuzzy logic is suggested to get a valuable knowledge for big data applications at good accuracy and less time, with the aid of distributed framework for big data handling (Hadoop, Spark and MapReduce). Different techniques and algorithms were used in these works and evaluated according to accuracy, sensitivity, recall and run time with a various result as Specificity = 86%, Sensitivity = 80% and F-measure = 2.5, or achieving high accuracy and shorter runtime compared to other methods and 98.5accuraccy of fitness function in pruning redundant rules. At the end of the paper we present the most used and prominent techniques that assist in providing a useful and valuable knowledge in different domains from a huge, unstructured and even heterogeneous data. The paper will be beneficial to the researches who interesting in the field of mining big data.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128751230","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

Computation of the Stabilizing Solution of Nash-Riccati Equations Nash-Riccati方程稳定解的计算

Iraqi Journal for Computer Science and Mathematics Pub Date : 2023-04-28 DOI: 10.52866/ijcsm.2023.02.02.014

I. Ivanov, I. G. Ivanov

引用次数: 0