Wasit Journal of Computer and Mathematics Science最新文献_第5页

Content-based filtering algorithm in social media 社交媒体中基于内容的过滤算法

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-03-31 DOI: 10.31185/wjcm.112

Siti Hashim, Johan Waden

引用次数: 0

Computer Vision Using Pose Estimation 基于姿态估计的计算机视觉

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-03-31 DOI: 10.31185/wjcm.111

Ghazali Bin Sulong, M . Randles

引用次数: 0

On BDM-Algebras 在BDM-Algebras

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-03-31 DOI: 10.31185/wjcm.101

Mohd Shahoodh

引用次数: 0

Early stage prediction of COVID-19 Using machine learning model 基于机器学习模型的COVID-19早期预测

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-03-31 DOI: 10.31185/wjcm.107

Mohammed Al-Hasnawi, A. Radhi

{"title":"Early stage prediction of COVID-19 Using machine learning model","authors":"Mohammed Al-Hasnawi, A. Radhi","doi":"10.31185/wjcm.107","DOIUrl":"https://doi.org/10.31185/wjcm.107","url":null,"abstract":"The healthcare sector has traditionally been an early use of technological progress and has achieved significant advantages, especially in the field of machine learning like the prediction of diseases. The COVID-19 epidemic is still having an impact on every facet of life and necessitates a fast and accurate diagnosis. Early detection of COVID-19 is exceptionally critical to saving the lives of human beings. The need for an effective, rapid, and precise way to reduce consultants' workload in diagnosing suspected cases has emerged. This paper presents a proposed model that aims to design and implement an automated model to predict COVID-19 with high accuracy in the early stages. The dataset used in this study considers an imbalanced dataset and converted to a balanced one using Synthetic Minority Over Sampling Technique (SMOTE). Filter-based feature selection method and many machine learning algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Logistic Regression, and Random Forest (RF) is used in this model. Since the best classification result was achieved by using the RF algorithm, and this algorithm was optimized by tuning the hyperparameters. The optimized RF enhanced the accuracy from 98.0 to 99.5.","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131810733","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

Image hiding by using spatial domain steganography 利用空间域隐写技术实现图像隐藏

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-03-31 DOI: 10.31185/wjcm.110

Ghazali Bin Sulong, Maria A.Wimmer

引用次数: 1

Autism spectrum Disorder detection Using Face Features based on Deep Neural network 基于深度神经网络的人脸特征检测自闭症谱系障碍

Wasit Journal of Computer and Mathematics Science Pub Date : 2023-03-31 DOI: 10.31185/wjcm.100

Ali Rashid, S. Shaker

{"title":"Autism spectrum Disorder detection Using Face Features based on Deep Neural network","authors":"Ali Rashid, S. Shaker","doi":"10.31185/wjcm.100","DOIUrl":"https://doi.org/10.31185/wjcm.100","url":null,"abstract":"The majority of screening instruments for autism spectrum disorder (ASD) rely on subjective questions given to caregivers. Although behavioral observation is more objective, it is also more expensive, takes longer to complete, and requires a high level of competence. Therefore, there is still a dire need to create workable, scalable, and trustworthy systems that can identify ASD risk behaviors. Since there are no known causes of autism, early detection and intense therapy can significantly alter the behavior of children and people with the disorder. Artificial intelligence has made this possible, saving many lives in the process. Utilizing biological pictures, autism spectrum disorder (ASD) can be defined as a mental illness type which can be identified. The neurological condition known as ASD is linked to brain development and affects later appearance of the flask framework, a convolutional neural network (CNN) with transfer learning, and physical impression of the face. Xception, Visual Geometry Group Network (VGG16) the classification job was carried out using the previously trained models. 2,940 face photos made up the dataset utilized for the testing of those models, which was obtained via Kaggle platform. Outputs of the 3 models of deep learning have been evaluated with the use of common measures of assessment, including accuracy, sensitivity and specificity. With a 91% accuracy rate, Xception model had the greatest results. And theVGG16 models came next with (75%).","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124141001","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

Detection of Deep Fake in Face Images Using Deep Learning 基于深度学习的人脸图像深度造假检测

Wasit Journal of Computer and Mathematics Science Pub Date : 2022-12-31 DOI: 10.31185/wjcm.92

Hanady Sabah

{"title":"Detection of Deep Fake in Face Images Using Deep Learning","authors":"Hanady Sabah","doi":"10.31185/wjcm.92","DOIUrl":"https://doi.org/10.31185/wjcm.92","url":null,"abstract":"Fake images are one of the most widespread phenomena that have a significant influence on our social life, particularly in the world of politics and celeb. Nowadays, generating fake images has become very easy due to the powerful yet simple applications in mobile devices that navigate in the social media world and with the emergence of the Generative Adversarial Network (GAN) that produces images which are indistinguishable to the human eye. Which makes fake images and fake videos easy to perform, difficult to detect, and fast to spread. As a result, image processing and artificial intelligence play an important role in solving such issues. Thus, detecting fake images is a critical problem that must be controlled and to prevent these numerous harmful effects. This research proposed utilizing the most popular algorithm in deep learning is (Convolution Neural Network) to detect the fake images. \u0000The first steps includes a preprocessing which start with converting images from RGB to YCbCr color space, after that entering the Gamma correction. finally extract edge detection by entering the Canny filter on them. After that, utilizing two different method of detection by applying (Convolution Neural Network with Principal Component Analysis) and (Convolution Neural Network without Principal Component Analysis) as a classifiers. \u0000The results reveal that the use of CNN with PCA in this research results in acceptable accuracy. In contrast, using CNN only gave the highest level of accuracy in detecting manipulated images.","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"5 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134583507","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: 2

On Hyperbolic Differential Equation with Periodic Control Initial Condition 具有周期控制初始条件的双曲型微分方程

Wasit Journal of Computer and Mathematics Science Pub Date : 2022-12-31 DOI: 10.31185/wjcm.97

Zainab Abdel Ameer, Sameer Qasim Hasan

引用次数: 0

Lie Detection: Truth Identification from EEG Signal Using Frequency and Time Features with SVM Classifier 测谎:基于SVM分类器的频率和时间特征的脑电信号真值识别

Wasit Journal of Computer and Mathematics Science Pub Date : 2022-12-31 DOI: 10.31185/wjcm.78

Israa Jalal

引用次数: 0

Artificial Intelligence and Its Role In The Development Of Personalized Medicine And Drug Control 人工智能及其在个性化医疗和药物控制发展中的作用

Wasit Journal of Computer and Mathematics Science Pub Date : 2022-12-31 DOI: 10.31185/wjcm.85

J. Wadén

{"title":"Artificial Intelligence and Its Role In The Development Of Personalized Medicine And Drug Control","authors":"J. Wadén","doi":"10.31185/wjcm.85","DOIUrl":"https://doi.org/10.31185/wjcm.85","url":null,"abstract":"DNA sequencing, imaging procedures, and wireless healthcare monitoring devices are all examples of high-throughput, data-intensive precision medicine assays and technologies that have necessitated new methods for analysing, integrating, and interpreting the enormous volumes of data they produce. While several statistical approaches have been developed to deal with the \"big data\" generated by such tests, previous experience with artificial intelligence (AI) techniques suggests that they may be especially well-suited. Furthermore, data-intensive biomedical technologies applied to study have shown that people differ greatly at the genetic, biochemical, physiological, exposure, and behavioural levels, particularly with regards to disease processes and treatment receptivity. This indicates the need to 'personalise' medications so that they better suit the complex and often individual needs of each patient. AI can play a significant role in the clinical research and development of new personalised health products, from selecting relevant contribute to sustainable to testing their utility, because of the importance of data-intensive assays in revealing appropriate intervention objectives and approaches for personalising medicines. The work here presents a variety of ways in which AI can contribute to the progress of personalised medicine, and we argue that the success of this endeavour is critically dependent on the improvement of appropriate assays and methods for storing, aggregating, accessing, and ultimately combining the data they generate. In addition, the manuscript also discusses the potential future research directions and highlights the shortcomings of various AI methods.","PeriodicalId":224730,"journal":{"name":"Wasit Journal of Computer and Mathematics Science","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115856431","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