Journal of Kufa for Mathematics and Computer最新文献_第4页

Study of Efficient Cloud Storage Architectures for the Security Environment 面向安全环境的高效云存储架构研究

Journal of Kufa for Mathematics and Computer Pub Date : 2023-03-31 DOI: 10.31642/jokmc/2018/100108

Ayad Hasan

{"title":"Study of Efficient Cloud Storage Architectures for the Security Environment","authors":"Ayad Hasan","doi":"10.31642/jokmc/2018/100108","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100108","url":null,"abstract":"Lately, cloud computing has acquired notoriety. Data storage is an essential and beneficial area of concentrate in cloud computing. The term \"cloud data storage\" describes the process of transferring data to an external, up-to-date storage system. You store data to a far-off database instead of your PC's hard plate or another nearby storage gadget. Your PC and the database are associated over the web. The user's requirements for hardware and software are reduced via cloud storage. The security issues associated with accessing and storing data on cloud storage render the cloud information inconsistent and untrustworthy. Through the internet, the cloud computing provides service with dynamically scalable resources. Users gain from cloud computing services in terms of cost and usability. Security issues must be addressed by cloud computing services when sending sensitive data and important applications to shared and public cloud environments. For the needs of data processing and storage, cloud environments are expanding up significantly. The use of cloud computing has a number of benefits as well as drawbacks for service users' data security. The primary security concerns that exist in cloud computing systems are highlighted in this study. The essential security worries with cloud computing are examined in this paper, alongside a straightforward, secure, and protection saving architecture for between cloud data sharing that depends on an encryption/unscrambling calculation that expects to shield data put away in the cloud from unapproved access.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"25 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":"121845625","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 influencers in social networks: A Survey 社交网络中影响者的检测:一项调查

Journal of Kufa for Mathematics and Computer Pub Date : 2023-03-31 DOI: 10.31642/jokmc/2018/100103

Ansam Ali

引用次数: 0

Bayesian Binary reciprocal LASSO quantile regression (with practical application) 贝叶斯二元倒数LASSO分位数回归(附实际应用)

Journal of Kufa for Mathematics and Computer Pub Date : 2023-03-31 DOI: 10.31642/jokmc/2018/100102

Mohammet T. Kahnger Al-mayali

引用次数: 0

Genetic Algorithm Based Clustering Optimization A Survey 基于遗传算法的聚类优化

Journal of Kufa for Mathematics and Computer Pub Date : 2023-03-31 DOI: 10.31642/jokmc/2018/100105

Rawaa Nadhum

引用次数: 0

Covid-19 in Pseudo BH-algebra 伪bh代数中的Covid-19

Journal of Kufa for Mathematics and Computer Pub Date : 2023-03-31 DOI: 10.31642/jokmc/2018/100106

Oday I. Al-Shaher

引用次数: 0

Perturbations of Weyl’s Theorems for Unbounded Class-A Operators 无界a类算子Weyl定理的摄动

Journal of Kufa for Mathematics and Computer Pub Date : 2023-03-31 DOI: 10.31642/jokmc/2018/100113

Dalia Sami

引用次数: 3

Predicting Class Label Using Clustering-Classification Technique: A Comparative Study 用聚类分类技术预测类标号的比较研究

Journal of Kufa for Mathematics and Computer Pub Date : 2023-03-31 DOI: 10.31642/jokmc/2018/100101

Aseel Alshaibanee

{"title":"Predicting Class Label Using Clustering-Classification Technique: A Comparative Study","authors":"Aseel Alshaibanee","doi":"10.31642/jokmc/2018/100101","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100101","url":null,"abstract":"Among different techniques, algorithms and applications of Data Mining, predicting the class label of unlabeled objects(undefined class label) is a crucial term in the field. The most common approaches in this area is the use of classification technique (DT, Bayes, SVM, KNN and others) that represent what is known as supervised learning. However, in many cases no target class labels and the boundaries are available to perform the prediction, so the new approach Clustering-classification technique is used. \u0000The work in this paper presents a survey of the most common researches conducted in this field and discuss their experiments, the algorithms they used, the types of data they utilized, the data sizes used, and the results they discovered. \u0000According to the results, applying the clustering techniques before classification improved classification accuracy and reduced experiment execution time. The Cluster Classifier was proven to be a suitable approach to summarize data by some of the researchers. It achieves a summarization rate of over 50%, which represents a considerable reduction in the size of the test datasets.. \u0000The findings of the researches indicated that, in addition to feature selection and feature extraction, data preprocessing (handled missing data and effective outlier detection techniques) enhanced the classifier performance and accuracy while reducing the classification error. \u0000 ","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"148 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":"123392931","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

Generating and Detecting Face Morphing Using Texture Techniques 使用纹理技术生成和检测人脸变形

Journal of Kufa for Mathematics and Computer Pub Date : 2023-03-31 DOI: 10.31642/jokmc/2018/100115

Iman Saleem, Bahja Khudair Shukr

{"title":"Generating and Detecting Face Morphing Using Texture Techniques","authors":"Iman Saleem, Bahja Khudair Shukr","doi":"10.31642/jokmc/2018/100115","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100115","url":null,"abstract":"Biometric forms major and very effective role nowadays in many fields such as health, reliability, devices, phones, banking, airport security, and others because of its unique characteristics for each person that cannot be replicated in another person. Therefore, most security systems rely and verify biometric properties. Airport security systems rely directly on facial recognition, but these systems may be exposed to attacks by the use of morphing faces in the passport image that allows multiple users to use the same passport. This paper presents a complete system consist of three stage, the first stage generating morphing faces based on edge detection to determine landmark and combine between landmarks to produce morphing. The second stage passing images on to the face recognition system that using Local Binary Pattern to features extraction, the final stage how to detect image bona fide or morph using texture techniques represented by each Local binary pattern and Gray-Level Co-Occurrence Matrix. With the use of the Wasserstein Distance measure, which has not previously been used in this field. The method gave effective results showing the mechanism of reducing morphing attack. ","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"129 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":"128795883","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

Reduced-Complexity Estimation of FM Instantaneous Parameters via Deep-Learning 基于深度学习的FM瞬时参数的低复杂度估计

Journal of Kufa for Mathematics and Computer Pub Date : 2023-03-31 DOI: 10.31642/jokmc/2018/100107

Huda Saleem, Zahir M. Hussain

{"title":"Reduced-Complexity Estimation of FM Instantaneous Parameters via Deep-Learning","authors":"Huda Saleem, Zahir M. Hussain","doi":"10.31642/jokmc/2018/100107","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100107","url":null,"abstract":"Signal frequency estimation is a fundamental problem in signal processing. Deep learning is a fundamental method to solve this problem. This paper used five deep learning methods and three datasets including different singles Single Tone (ST), Linear- Frequency-Modulated (LFM), and Quadratic-Frequency-Modulated (QFM). This signal is affected by Additive White Gaussian (AWG) noise and Additive Symmetric alpha Stable (SαS) noise. Geometric SNR (GSNR) is used to determine the impulsiveness of noise in a Gaussian and SαS noise mixture. Deep learning methods are Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bi-Direction Long Short-Term Memory (BiLSTM), and Convolution Neural Network (1D-CNN & 2D-CNN). When compared to a deep learning classifier with few layers to get on high accuracy and complexity reduces for Instantaneous Frequency (IF) estimation, Linear Chirp Rate (LCR) estimation, and Quadratic Chirp Rate (QCR) estimation. IF estimation of ST signals, IF and LCR estimation of LFM signals, and IF, LCR, and QCR estimation of QFM signals. The accuracy of the ST dataset in GRU is 58.09, LSTM is 46.61, BiLSTM is 45.95, 1D-CNN is 51.48, and 2D-CNN is 54.13. The accuracy of the LFM dataset in GRU is 82.89, LSTM is 66.28, BiLSTM is 20%, 1D-CNN is 74.79, and 2D-CNN is 98.26. The accuracy of the QFM dataset in GRU is 78.76, LSTM is 67.8, BiLSTM is 69.91, 1D-CNN is 75.8, and 2D-CNN is 98.2. The results show that 2D-CNN is better than other methods for parameter estimation in LFM signals and QFM signals, and the GRU is better for parameter estimation in ST signals.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"34 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":"132281304","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

Masked Face Recognition Using Convolutional Neural Networks 基于卷积神经网络的蒙面人脸识别

Journal of Kufa for Mathematics and Computer Pub Date : 2023-03-31 DOI: 10.31642/jokmc/2018/100111

Saja Mohsen Abass

{"title":"Masked Face Recognition Using Convolutional Neural Networks","authors":"Saja Mohsen Abass","doi":"10.31642/jokmc/2018/100111","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100111","url":null,"abstract":"Since the COVID-19 epidemic's rise in 2020, Cover face recognize achieve advanced significantly in the range of computer vision. Face cover is important to stop or limit the COVID-19 disease's spread due to the global outbreak. Face recognize is among of the most commonly used biometric recognition approach, because it can beutilized for monitoring systems, identity management, security verifying, and a lot of applications. The majority features of faces were hidden by mask, leaving just a quite some, including eyes plus head-region, that’s utilized for recognize. This challenge may reduce the recognition percentage because of the limited area to extract features. Due to the popularity of deep learning to extract and recognize deep features in many research areas especially computer vision,In this work, a covered face recognize system is introduced. utilizing Convolutional neural network (CNN), one of the most widely common deep learning algorithms. The final layer in the CNN architecture, the softmax activation function, was utilized to identify the facial characteristics after they had been extracted using CNN from the masked face's eyes, forehead, and brow regions. In the Study employ the \"Extended Yale B database,\" which has issues with changes in placement and lighting. additionally, they covered faces in Dataset with medical masks. In comparison to other approaches to solving this problem, our strategy showed to be successful and promising with a recognition accuracy for \"Extended Yale B\" of 95%.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"7 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":"125601313","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