Background:Fractures of patella constitute 1% of all fractures. Various techniques have been described for internal fixation of patella fractures. Superiority of one technique over the other has long been debated. Objective:We reviewed a series of seventy patients with transverse or comminuted fractures of patella treated with cerclage and tension band wiring technique to assess if it had any advantages over k. wires and tension band wiring technique. Type of the study:Retrospective study.Methods; Seventy patients with displaced patella fracture, with a mean age of 47 years (range 13-75) were divided into two groups :group A 36 patients were treated with cerclageand tensi

Background: Fractures of patella constitute 1% of all fractures. Various techniques have been described for internal fixation of patella fractures. Superiority of one technique over the other has long been debated. Objective: We reviewed a series of seventy patients with transverse or comminuted fractures of patella treated with cerclage and tension band wiring technique to assess if it had any advantages over k. wires and tension band wiring technique. Type of the study: Retrospective study. Methods; Seventy patients with displaced patella fracture, with a mean age of 47 years (range 13-75) were divided into two groups :group A 36 patients were treated with cerclage and tension bands technique ,and group B 34 patients were fixed by 2 K.

Multiplicative inverse in GF (2 m ) is a complex step in some important application such as Elliptic Curve Cryptography (ECC) and other applications. It operates by multiplying and squaring operation depending on the number of bits (m) in the field GF (2 m ). In this paper, a fast method is suggested to find inversion in GF (2 m ) using FPGA by reducing the number of multiplication operations in the Fermat's Theorem and transferring the squaring into a fast method to find exponentiation to (2 k ). In the proposed algorithm, the multiplicative inverse in GF(2 m ) is achieved by number of multiplications depending on log 2 (m) and each exponentiation is operates in a single clock cycle by generating a reduction matrix for high power of two ex

    Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th

Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai