{"title":"Hiding a Secret Message Encrypted by S-DES Algorithm","authors":"Nada Abdul aziz Mustafa","doi":"10.31642/jokmc/2018/100213","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100213","url":null,"abstract":"Nowadays, it is quite usual to transmit data through the internet, making safe online communication essential and transmitting data over internet channels requires maintaining its confidentiality and ensuring the integrity of the transmitted data from unauthorized individuals. The two most common techniques for supplying security are cryptography and steganography. Data is converted from a readable format into an unreadable one using cryptography. Steganography is the technique of hiding sensitive information in digital media including image, audio, and video. In our proposed system, both encryption and hiding techniques will be utilized. This study presents encryption using the S-DES algorithm, which generates a new key in each cycle to increases the complexity of the algorithm detection. An additional shared key between the sender and the receiver is added which is applied before starting the encryption process, that key must be hex-decimal in order to increase the level of security and give enough time to delay the guessing of the secret text by the attackers. The secret message data is concealed using one of the two techniques: either least significant bit (3-LSB) steganography or hiding in green and blue bits. To expedite the concealment process, the cover image is enhanced by applying a median filter, the median filter removes noise from the image while preserving its details. Finally, comparing the results of the two methods to determine which is better for the cover image in terms of PSNR metrics and hiding process time.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114774324","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}
{"title":"Some Shadowing Properties in Fuzzy Dynamical System","authors":"Safa Ali, May Alaa","doi":"10.31642/jokmc/2018/100224","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100224","url":null,"abstract":"In this paper we introduce the notions (L- , two – sided limit shadowing, negative ) for fuzzy dynamical systems and prove that properties (fuzzy , fuzzy L- , fuzzy two – sided limit shadowing) are invariant under fuzzy","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123852900","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}
Ali Moazzam, Zainab Ijaz, Muhammad Hussain, Nimra Maqbool, Emad A. Kuffi
{"title":"Applications of Fractional-Laplace Transformation in the Field of Electrical Engineering","authors":"Ali Moazzam, Zainab Ijaz, Muhammad Hussain, Nimra Maqbool, Emad A. Kuffi","doi":"10.31642/jokmc/2018/100211","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100211","url":null,"abstract":"This study examines the various ways that fractional Laplace transform can be used to solve three different kinds of mathematical equations: the equation of analysis of electric circuits, simultaneous differential equations, and the heat conduction equation. This article how to use the fractional Laplace transform to calculate heat flow in semi-infinite solids in the context of heat conduction. The answers that are developed offer important information about how temperatures vary across time and space. The essay also examines how to analyse electrical circuits using the Fractional Laplace transform. This method allows researchers to measure significant electrical parameters including charge and current, which improves their comprehension of circuit dynamics. Practical examples are included throughout the essay to show how useful the Fractional Laplace transform is in various fields. As a result of the answers found using this methodology, researchers and engineers working in the fields of heat conduction, system dynamics, and circuit analysis can gain important new knowledge. In conclusion, this study explains the applicability and effectiveness of the fractional Laplace transform in resolving a variety of mathematical equations. It is a vital tool for researchers because it may be used in a wide range of scientific and engineering areas.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"47 Suppl 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131592142","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}
{"title":"A Principally Radg-Lifting Modules","authors":"Rasha Najah Mirza","doi":"10.31642/jokmc/2018/100117","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100117","url":null,"abstract":"In this article we present a new class of modules which is named as a principally -lifting modules. This class termed by Principally -lifting in this work which defined as, a module is called Principally -lifting if for every cyclic submodule of with , there is a decomposition such that and is g-small in . Thus, a ring is called Principally -lifting if it is a principally -lifting as -module. We determined it is structure. Several characterizations, properties, and instances are described of these modules'.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"16 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":"114433088","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}
{"title":"Localization in WSN based on Area of field and mobility : A Survey","authors":"Aiadzainabb Ayed","doi":"10.31642/jokmc/2018/100109","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100109","url":null,"abstract":"Abstract— Localization in Wireless Sensor Networks (WSNs) is process to determine the locations of sensors connected within a network. Several applications need to determine the location of sensor nodes in the network to help increasing network performance. Some applications, such as item tracking, military operations, routing protocols, robotics, and inventory management, are made feasible by sensor network’s ability to accurately locate the sensor nodes. For each of these applications, different criteria for position estimation. These are speed, accuracy, and reliability. In this paper we going to classify the localization algorithms with a new perspective based on three criteria (area of fields, cooperation between nodes and node’s mobility). Following along this paper may give’s an idea to the researchers to develop efficient algorithms to localize nodes with accuracy adapting to different techniques with respect to the geographic area and anchor type to be designed","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"17 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":"132596203","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}
{"title":"Siamese Network-Based Palm Print Recognition","authors":"Ebtesam N. Alshemmary","doi":"10.31642/jokmc/2018/100116","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100116","url":null,"abstract":"palm print recognition is a biometric technology used to identify individuals based on their unique comfort patterns. Identifying patterns in computer vision is a challenging and interesting problem. It is an effective and reliable method for authentication and access control. In recent years, deep learning approaches have been used for handprint recognition with very good results. We suggest in this paper, a Siamese network-based approach for handprint recognition. The proposed approach consists of two convolutional neural networks (CNNs) that share weights and are trained to extract features from images of handprints, which are then compared using a loss of variance function to determine whether the two images belong to the same person or not. Among 13,982 input images, 20% are used for testing, 80% for training, and then passing each image over one of two matching subnets (CNN) that transmit weights and parameters. So that, the extracted features become clearer and more prominent. This approach has been tested and implemented using the CASIA PalmprintV1 5502 palm print database, the CASIA Multi-Spectral PalmprintV1 7,200 palm print, and the THUPALMLAB database of 1,280 palm print using MATLAB 2022a. For 13,982 palmprint recognitions in the database, the equal error rate was 0.044, and the accuracy was 95.6% (CASIA palmprintV1, THUPALMLAB, and CASIA Multi-Spectral palmprintV1). The performance of the real-time detecting system is stable and fast enough.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"36 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":"122113847","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}
{"title":"Modules in which every surjective endomorphism has an e-small kernel","authors":"Osama Mohammed, T. Y. Ghawi","doi":"10.31642/jokmc/2018/100118","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100118","url":null,"abstract":"In this paper we introduce the notion of e-gH modules which is a proper generalization of Hopfian modules and defined as, a module is called e-gH if, any surjective -endomorphism of has an e-small kernel, a ring is called e-gH if, is e-gH. We give some characterizations and properties of this modules.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"1 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":"129219623","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}
{"title":"Restrict Nearly Primary Finitely Compactly Packed Modules","authors":"خضير عبيس","doi":"10.31642/jokmc/2018/100112","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100112","url":null,"abstract":"In this work, all rings under consideration will be assumed to be commutative with nonzero identity and all modules will be nonzero unital. We introduce the concept of Restrict Nearly primary finitely compactly packed modules, which generalizes the concept of Restrict Nearly primary compactly packed modules. We find the conditions that make the Restrict Nearly primary finitely compactly packed modules, Restrict Nearly primary compactly packed. Also, several results on the Restrict Nearly primary finitely compactly packed modules are proved. In addition, the necessary and sufficient conditions for an ℛ−module","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"19 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":"116082022","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}
{"title":"Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling","authors":"Douaa S. Alwan, M. Naji","doi":"10.31642/jokmc/2018/100114","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100114","url":null,"abstract":"The rice crop is one of the most important food crops that depend on it globally. Therefore, farmers must preserve the production of this crop from infection with pests and diseases that lead to its destruction through artificial intelligence and deep learning techniques. A hybrid model combining a Residual Network 50 (ResNet50) deep convolutional neural network (CNN) and a support vector machine (SVM) developed diagnoses rice diseases. Farmers or people working in agriculture could use this model to quickly and accurately identify the diseases in their crops and treat them, increasing crop yield and reducing the need for costly and time-consuming manual inspection. ResNet50, a deep learning model effective at image classification tasks, was used to extract features from images of rice plants. SVM was then used to classify the diseases based on these features. The ResNet50 was able to capture complex patterns in the images, while the SVM was able to use these patterns to make accurate classification decisions. This hybrid model allowed for high precision in rice disease diagnosis, achieving an accuracy of approximately 99%.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"9 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":"125417503","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}
Zahraa chaffat Oleiwi, Ebtesam N. Alshemmary, Salam Al-augby
{"title":"Identify Best Learning Method for Heart Diseases Prediction Under impact of Different Datasets Characteristics","authors":"Zahraa chaffat Oleiwi, Ebtesam N. Alshemmary, Salam Al-augby","doi":"10.31642/jokmc/2018/100104","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100104","url":null,"abstract":"This paper introduces an experimental study of the heart disease datasets characteristics impact on the performance of classification algorithms in the aim of identifying the best algorithm for each dataset under its characteristics. The performance of five machine learning algorithms (logistic regression (LR), K-Nearest Neighbor (KNN), Decision tree (DT), Random Forest (RF), and support vector machine (SVM)), single layer neural network (ANN), and deep neural network (DNN), has been evaluated using five heart disease datasets under four data complexity measurement: number of samples (dataset size), number of features (dimension of dataset), Data sparsity measures, and correlation of features. All datasets have been processed and normalized then the mutual information-based feature selection method was used to solve the overfitting problem. The results show that in general, the machine learning especially the Random Forest algorithm achieves high classification accuracy than deep learning network. In other hand, the high sparsity and less mutual information of dataset has large impact on degradation of the performance of classification algorithms than other characteristics of data.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"136 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":"124273636","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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