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Fully Homomorphic Encryption Scheme for Securing Cloud Data
Full text linkOne of the pioneer and important fields in the computer science area is cloud computing. The data within cloud computing are usually transformed to it from local storage; therefore, the security of this data is an important issue. To solve this data security issue, it is important that cloud service providers (CSPs) store encrypted versions of user data. Before transmitting data to the cloud provider, it was encrypted using traditional encryption schemes. Nevertheless, for these schemes, the private key must be provided to the server to be used for the decryption on the other side before any calculations, yielding a security risk and issue for the cloud data. Homomorphic encryption provides a capable solution to this issue since it enables calculations on encrypted data with no need to be decrypted and the private encryption key is not compromised. A new fully homomorphic encryption scheme to protect cloud data is proposed in this paper, it is called NAZUZ. The NAZUZ scheme is based on prime modular operations and encrypts messages by operating on each character without converting them to binary. NAZUZ security relies on the difficulty of factoring large integer numbers and introduces noise complexity to the plaintext through the number of CSP users
COVID-19 Diagnosis Applied DWT and CNN on X-ray Chest Images
Full text linkBackground: Medical images have many important applications, and this importance increased when the emergence of the COVID-19 pandemic. These applications have been focused on computed tomography chest images and X-ray images. This research will focus on special X-ray medical image applications of coronavirus (COVID-19).Methods: Many methods are applied on medical images to achieve certain features. The designed approach is implemented through many steps starting from preprocessing up to classification step. The proposed approach focusing on generating efficient features using discrete wavelet transform (DWT) then applying convolutional neural network (CNN) to classify between normal and abnormal COVID-19.Results: The COVID-19 diagnosis approach is implemented to achieve high performance system. The obtained result of COVID-19 diagnosis applied CNN tool leading to validation accuracy of 92.31%.Conclusion: Hybridizing two technologies (DWT and CNN) is intended to reach the best results in the diagnostic process. In addition, X-ray chest image is an important tool for detection and diagnosis of COVID-19 diseases
Analyzing the Performance of Bitcoin to Gold Prices, the Telecommunications Market, the Stock Price Index, and Insurance Companies’ Performance from (March 1, 2021–September 4, 2023)
Full text linkManaging cryptocurrencies by financial intermediaries offer numerous benefits to global financial markets and the economy. Among all cryptocurrencies, Bitcoin stands out with the highest market capitalization and a weak correlation to other assets, making it an attractive option for portfolio diversification and risk management. This research aims to examine the impact of Bitcoin on the NASDAQ gold price (GC), the telecommunications market (IXUT), and insurance company performance (IXIS) through the analysis of secondary data from March 1, 2021, to September 4, 2023. The data were obtained from https://www.investing.com; statistical software E views applied various econometric methods to the data. The results suggest a positive correlation between Bitcoin and the other variables, indicating that Bitcoin can significantly expand investment opportunities and drive economic growth. This study highlights the importance of considering cryptocurrencies, especially Bitcoin, as a viable option for investment diversification and risk management in financial markets
A Transformer-Based Neural Network Machine Translation Model for the Kurdish Sorani Dialect
Full text linkThe transformer model is one of the most recently developed models for translating texts into another language. The model uses the principle of attention mechanism, surpassing previous models, such as sequence-to-sequence, in terms of performance. It performed well with highly resourced English, French, and German languages. Using the model architecture, we investigate training the modified version of the model in a low-resourced language such as the Kurdish language. This paper presents the first-ever transformer-based neural machine translation model for the Kurdish language by utilizing vocabulary dictionary units that share vocabulary across the dataset. For this purpose, we combine all the existing parallel corpora of Kurdish – English by building a large corpus and training it on the proposed transformer model. The outcome indicated that the suggested transformer model works well with Kurdish texts by scoring (0.45) on bilingual evaluation understudy (BLEU). According to the BLEU standard, the score indicates a high-quality translation
Eye Tracking Technique for Controlling Computer Game Objects
Full text linkThe study explored the employment of associate in accessible eye tracer with keyboard and mouse input devices for video games. An interactive game has been developed using unity with multiple balls objects and by hitting they could collect more point for each player. It has been used different techniques to hit the balls using mouse, keyboard, and mixed. Eye tracker input has been help to increase the performance of collected the player points. The research explains how the eye tacking techniques can be used in widely in video game and it is very interactive. Finally, we examine the use of visual observation in relevancy the keyboard and mouse input control and show the difference. Our results indicate that the employment of a watch huntsman will increase the immersion of a computer game and considerably improve the video game technology
COVID-19 Classification based on Neutrosophic Set Transfer Learning Approach
Full text linkThe COVID-19 virus has a significant impact on individuals around the globe. The early diagnosis of this infectious disease is critical to preventing its global and local spread. In general, scientists have tested numerous ways and methods to detect people and analyze the virus. Interestingly, one of the methods used for COVID-19 diagnosis is X-rays that recognize whether the person is infected or not. Furthermore, the researchers attempted to use deep learning approaches that yielded quicker and more accurate results. This paper used the ResNet-50 module based on the Neutrosophic (NS) domain to diagnose COVID patients over a balanced database collected from a COVID-19 radiography database. The method is a future work of the N. E. M. Khalifa et al.’s method for NS set significance on deep transfer learning. True (T), False (F), and Indeterminate (I) membership sets were used to define chest X-ray images in the NS domain. Experimental results confirmed that the proposed approach achieved a 98.05% accuracy rate outperforming the accuracy value acquired from previously conducted studies within the same database
Real-Time Twitter Data Analysis: A Survey
Full text linkInternet users are used to a steady stream of facts in the contemporary world. Numerous social media platforms, including Twitter, Facebook, and Quora, are plagued with spam accounts, posing a significant problem. These accounts are created to trick unwary real users into clicking on dangerous links or to continue publishing repetitious messages using automated software. This may significantly affect the user experiences on these websites. Effective methods for detecting certain types of spam have been intensively researched and developed. Effectively resolving this issue might be aided by doing sentiment analysis on these postings. Hence, this research provides a background study on Twitter data analysis, and surveys existing papers on Twitter sentiment analysis and fake account detection and classification. The investigation is restricted to the identification of social bots on the Twitter social media network. It examines the methodologies, classifiers, and detection accuracies of the several detection strategies now in use
Rough Set-Based Feature Selection for Predicting Diabetes Using Logistic Regression with Stochastic Gradient Decent Algorithm
Full text linkDisease prediction and decision-making plays an important role in medical diagnosis. Research has shown that cost of disease prediction and diagnosis can be reduced by applying interdisciplinary approaches. Machine learning and data mining techniques in computer science are proven to have high potentials by interdisciplinary researchers in the field of disease prediction and diagnosis. In this research, a new approach is proposed to predict diabetes in patients. The approach utilizes stochastic gradient descent which is a machine learning technique to perform logistic regression on a dataset. The dataset is populated with eight original variables (features) collected from patients before being diagnosed with diabetes. The features are used as input values in the proposed approach to predict diabetes in the patients. To examine the effect of having the right variable in the process of making predictions, five variables are selected from the dataset based on rough set theory (RST). The proposed approach is applied again but this time on the selected features to predict diabetes in the patients. The results obtained from both applications have been documented and compared as part of the approach evaluations. The results show that the proposed approach improves the accuracy of predicting diabetes when RST is used to select variables for making the prediction. This paper contributes toward the ongoing efforts to find innovative ways to improve the prediction of diabetes in patients
Modified Advanced Encryption Standard for Boost Image Encryption
Full text linkCryptography is a field of study that deals with converting data from a readable to an unreadable format. It can provide secrecy, data integrity, authenticity, and non-repudiation services. Security has become a concern for the community because of the technology’s potential use in numerous sectors of any company, market, agency, or governmental body, information. The cryptosystems ensure that data are transported securely and only authorized individuals have access to it. Deeply encrypted data that cannot be deciphered through cryptanalysis are in high demand right now. There are a variety of encryption algorithms that can guarantee the confidentiality of data. For multimedia data, standard symmetric encryption algorithms (AES) can give superior protection. However, using the symmetric key encryption approach on more complicated multimedia data (mainly photos) may result in a computational issue. To address this issue, the AES has been modified to satisfy the high computing requirements due to the complex mathematical operations in MixColumns transformation, which slow down the encryption process. The modified AES uses bit permutation to replace the MixColumns transformation in AES because it is simple to construct and does not require any complex mathematical computation. This research focuses on using the Modified Advanced Encryption Standard (MAES) algorithm with 128 and 256 bit key sizes to encrypt and decrypt image data. The algorithms were implemented using the Python programming language without complex mathematical computation. By comparing the MAES algorithm with the original AES algorithm, the results showed that the MAES requires less encrypting and decryption time with higher efficiency for all file sizes
Performance Assessment of Teaching through Students Evaluations: A Case Study Applied at University of Anbar
Full text linkThe methods of the assessment of faculty member in universities varied. Some of them depend on the assessment of the faculty member on the teaching and research burden. Others assessments focus on how much teaching is provided during the calendar year. There are some ways in which the faculty member’s assessment depends on the qualitative aspect provided by the faculty member. In addition, some of them combine both quantitative and qualitative aspects of the necessities to reach an appropriate evaluation process. This research aimed to consider the student as an essential part in the educational science and it is clear that the role taken by the student is no less important than the other role. This research started, where a questionnaire was prepared which included several paragraphs to measure the extent to which the student can evaluate the professor. The fourth stage students were chosen as the study sample because the students at last stage in the university has a good level of thinking and preparation. That means these students have experience in understanding the level of teaching and the knowledge of teachers. The proposed approach tries to analyze the educational model for evaluation developed in the Computer Science Department of the University of Anbar for learning and teaching of computer science and related scientific disciplines. In general, it is clear that more than 50% of the tested sample are satisfied with the teaching process which indicated good results have been achieved