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 accu

Ultrasound imaging is often preferred over other medical imaging modalities because it is non-invasive, non-ionizing, and low-cost. However, the main weakness of medical ultrasound image is the poor quality of images, due to presence of speckle noise and blurring. Speckle is characteristic phenomenon in ultrasound images, which can be described as random multiplicative noise that occurrence is often undesirable, since it affects the tasks of human interpretation and diagnosis. Blurring is a form of bandwidth reduction of an ideal image owing to the imperfect image formation process. Image denoising involves processing of the image data to produce a visually high quality image. The denoising algorithms may be classified into two categorie

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed

The subject matter of the article Prediction of COVID-19 disease and infection rate based on a pre-trained model that supports deep learning. The goal is to build a system to diagnose people as infected or not with covid disease with the percentage of infection and the affected site and to present it with interactive interfaces to facilitate the use of the system for anyone not specialized in the software field. The task is to detect or predict the Coronavirus that affects the airways, lungs, and breathing. It is the cause of many deaths and is still in the process of transformation and development, but with less media exposure. From this standpoint, a medical system was proposed to detect the presence of the Coronavirus in the lung based o

Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can pre

Boltzmann mach ine neural network bas been used to recognize the Arabic speech.  Fast Fourier transl(>lmation algorithm has been used t() extract speciral 'features from an a caustic signal .

The  spectral  feature size is reduced by series of operations in

order to make it salable as input for a neural network which is used as a recogni zer by Boltzmann Machine Neural  network which has been used as a recognizer for phonemes . A training set consist of a number of Arabic phoneme repesentations, is used to train lhe neuntl network.

The neural network recognized Arabic. After Boltzmann Machine Neura l    network   training  the  system   with