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Proposed Hybrid Cryptosystems Based on Modifications of Playfair Cipher and RSA Cryptosystem
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Cipher security is becoming an important step when transmitting important information through networks. The algorithms of cryptography play major roles in providing security and avoiding hacker attacks. In this work two hybrid cryptosystems have been proposed, that combine a modification of the symmetric cryptosystem Playfair cipher called the modified Playfair cipher and two modifications of the asymmetric cryptosystem RSA called the square of RSA technique and the square RSA with Chinese remainder theorem technique. The proposed hybrid cryptosystems have two layers of encryption and decryption. In the first layer the plaintext is encrypted using modified Playfair to get the cipher text, this cipher text will be encrypted using squared RSA to get the final cipher text. This algorithm achieved higher security to data but suffers from a long computational time. So Chinese remainder theorem has been used in the second hybrid cryptosystem to obtain less encryption and decryption time. The simulation results indicated that using the modified Playfair with the proposed square RSA has improved security. Moreover, using the Chinese remainder theorem achieved less encryption and decryption time in comparison to our first proposed and the standard algorithms.

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Publication Date
Tue Oct 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
The impact of the RMB exchange rate on the value of Chinese exports For the period (1978-2017) using the Angel-Granger model
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In this research, we sought to identify the nature of the relationship between the exchange rate of the Chinese yuan and the value of Chinese exports, through the formulation of a standard model based on the model of common integration, and based on the data of the study and using the test "Angel-Granger" It reflects the relationship between the two research variables, through which the relationship between the RMB exchange rate and the value of Chinese exports was estimated during the period 1978-2017.

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Publication Date
Thu Dec 05 2024
Journal Name
Shifaa
Evaluating alveolar bone thickness in upper and lower incisors from orthodontic perspective. A Cone beam computed tomography-based retrospective study
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Purpose: This study aimed to assess the thickness of alveolar bone of maxillary and mandibular incisors from orthodontics perspective. Materials and Method: A total of 73 Cone beam computed tomography for Iraqi patients (47 females and 26 males) were included in this study. The selected images were captured and imported to AutoCAD database software to perform the measurement. To measure alveolar bone thickness, a reference line was drawn through the long axis of each incisor, from the incisal edge to the root apex. Then, labial and lingual/palatal perpendicular lines were drawn to the reference line at 3, 6, and 9mm apically from the cemento-enamel junction (CEJ). Results: The buccal bone is generally thinner than the lingual/palata

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Publication Date
Sun Oct 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
A novel data offloading scheme for QoS optimization in 5G based internet of medical things
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The internet of medical things (IoMT), which is expected the lead to the biggest technology in worldwide distribution. Using 5th generation (5G) transmission, market possibilities and hazards related to IoMT are improved and detected. This framework describes a strategy for proactively addressing worries and offering a forum to promote development, alter attitudes and maintain people's confidence in the broader healthcare system without compromising security. It is combined with a data offloading system to speed up the transmission of medical data and improved the quality of service (QoS). As a result of this development, we suggested the enriched energy efficient fuzzy (EEEF) data offloading technique to enhance the delivery of dat

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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science (ijeecs)
Increasing validation accuracy of a face mask detection by new deep learning model-based classification
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During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

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Publication Date
Mon May 28 2018
Journal Name
Iraqi Journal Of Science
M A Modified Similarity Measure for Improving Accuracy of User-Based Collaborative Filtering: Nadia Fadhil
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Production sites suffer from idle in marketing of their products because of the lack in the efficient systems that analyze and track the evaluation of customers to products; therefore some products remain untargeted despite their good quality. This research aims to build a modest model intended to take two aspects into considerations. The first aspect is diagnosing dependable users on the site depending on the number of products evaluated and the user's positive impact on rating. The second aspect is diagnosing products with low weights (unknown) to be generated and recommended to users depending on logarithm equation and the number of co-rated users. Collaborative filtering is one of the most knowledge discovery techniques used positive

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Publication Date
Sat Jun 01 2024
Journal Name
Iaes International Journal Of Artificial Intelligence (ij-ai)
A novel fusion-based approach for the classification of packets in wireless body area networks
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This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota

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Publication Date
Mon Jan 27 2020
Journal Name
Iraqi Journal Of Science
Localization of the Optic Disc in Retinal Fundus Image using Appearance Based Method andVasculature Convergence
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Optic Disc (OD) localization is a basic step for the screening, identification and appreciation of the risk of diverse ophthalmic pathologies such as glaucoma and diabetic retinopathy.In fact, the fundamental step towards an exact OD segmentation process is the success of OD localization. This paper proposes a fully automatic procedure for OD localization based on two of the OD most relevant features  of high-intensity value and vasculature convergence. Merging ofthese two features renders the proposed method capable of localizing the OD within the variously complicated environments such as the faint disc boundary, unbalanced shading, and the existence of retinal pathologies like cotton wall and exudates,which usually share the same

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Publication Date
Sun Oct 30 2022
Journal Name
Iraqi Journal Of Science
Anticancer Mechanisms of Zoledronic Acid-Based Graphene Oxide Nanoparticles for Prostate Cancer Bone Metastases Treatment
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     Bone metastases are the main reason for death in males suffering from advanced prostate cancer. This study aimed to create zoledronic acid and graphene oxide conjugation for anticancer therapy. The process of conjugation was confirmed by several characterization methods including UV-VIS spectrophotometry, Fourier Transform Infrared Spectroscopy (FTIR), and atomic force microscope (AFM). the cytotoxicity of 400, 600, and 800 μg/ml to each GO, ZOL, and ZOL-GO was evaluated on a human hepatic cell line (WRL 68) and human prostate cancer cell line (PC3) using an MTT assay. The antitumor mechanisms of ZOL-GO were examined by cell cycle analysis. The results demonstrated That ZOL-GO caused a reduction in the cell viability of WRL 68

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Publication Date
Fri Mar 29 2024
Journal Name
Iraqi Journal Of Science
Biological versus Topological Domains in Improving the Reliability of Evolutionary-Based Protein Complex Detection Algorithms
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     By definition, the detection of protein complexes that form protein-protein interaction networks (PPINs) is an NP-hard problem. Evolutionary algorithms (EAs), as global search methods, are proven in the literature to be more successful than greedy methods in detecting protein complexes. However, the design of most of these EA-based approaches relies on the topological information of the proteins in the PPIN. Biological information, as a key resource for molecular profiles, on the other hand, acquired a little interest in the design of the components in these EA-based methods. The main aim of this paper is to redesign two operators in the EA based on the functional domain rather than the graph topological domain. The perturb

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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Increasing validation accuracy of a face mask detection by new deep learning model-based classification
...Show More Authors

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

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