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

    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

Localization is an essential demand in wireless sensor networks (WSNs). It relies on several types of measurements. This paper focuses on positioning in 3-D space using time-of-arrival- (TOA-) based distance measurements between the target node and a number of anchor nodes. Central localization is assumed and either RF, acoustic or UWB signals are used for distance measurements. This problem is treated by using iterative gradient descent (GD), and an iterative GD-based algorithm for localization of moving sensors in a WSN has been proposed. To localize a node in 3-D space, at least four anchors are needed. In this work, however, five anchors are used to get better accuracy. In GD localization of a moving sensor, the algo

           The synthesis of the MBIB ligand by the reaction of the BIB ligand with methionine in 1:1 ratio, and the metal complexes with Ni(II), Cu(II), and Pt(IV) were described. All synthesized compounds were characterized using spectroscopic methods such as FT-IR, ¹H NMR, UV-VIS, thermal analysis (TG and DSC), atomic absorption (AAS), elemental microanalysis (C.H.N.S), melting point (m.p.), magnetic susceptibility, molar conductivity measurements, and chloride content. All the complexes were electrolytes, and the suggested geometric shapes for the complexes were octahedral. The magnetic properties of the platinum complex were diamagnetic, while those of the nickel and copper complexes were paramagnetic. All synthes

Chemical bath deposition was used to synthesize ZnO nanorods (NRs) on glass and fluorine^_doped tin oxide (FTO) substrates. X-ray diffraction was performed to examine the crystallinity of ZnO nanorod. Results showed that ZnO NRs had a wurtzite crystal structure. Field emission scanning electron microscopy images showed that glass sample had rod-like structure distribution with (50 nm) diameter and average length of approximately (700 nm), whereas the FTO-coated glass sample had 25 nm diameter and average length of approximately 950 nm. The direct optical transition band gaps of the glass and FTO^_coated glass samples were( 4 and 4.43 eV), respectively. The structural and optical properties of the synthesized ZnO p

Ethanol production were evaluated by many strains with varing

degree of flocculation in fermentation medium of date extract withl 0

Brix, PHS in 30C0Ø¢  Ø¢ for Ø¢ 48hr.lt was found that ethanol production decrease with increase of flocculation degree and non-flocculant strain is Ø¢ more efficient in Ø¢ producing ethanol from flocculant strain,then

ethanol sensitivity were examined for the same strains, in liquid medium YE, it was found thatØ¢  Ø¢ strain is more sensitive from nonآ­ flocculant and ethanol sensitivity depends upon flocculation degree.

A medical- service platform is a mobile application through which patients are provided with doctor’s diagnoses based on information gleaned from medical images. The content of these diagnostic results must not be illegitimately altered during transmission and must be returned to the correct patient. In this paper, we present a solution to these problems using blind, reversible, and fragile watermarking based on authentication of the host image. In our proposed algorithm, the binary version of the Bose_Chaudhuri_Hocquengham (BCH) code for patient medical report (PMR) and binary patient medical image (PMI) after fuzzy exclusive or (F-XoR) are used to produce the patient's unique mark using secret sharing schema (SSS). The patient’s un

Printed Arabic document image retrieval is a very important and needed system for many companies, governments and various users. In this paper, a printed Arabic document images retrieval system based on spotting the header words of official Arabic documents is proposed. The proposed system uses an efficient segmentation, preprocessing methods and an accurate proposed feature extraction method in order to prepare the document for classification process. Besides that, Support Vector Machine (SVM) is used for classification. The experiments show the system achieved best results of accuracy that is 96.8% by using polynomial kernel of SVM classifier.

The use of remote sensing technologies was gained more attention due to an increasing need to collect data for the environmental changes. Satellite image classification is a relatively recent type of remote sensing uses satellite imagery to indicate many key environment characteristics. This study aims at classifying and extracting vacant lands from high resolution satellite images of Baghdad city by supervised Classification tool in ENVI 5.3 program. The classification accuracy was 15%, which can be regarded as fairly acceptable given the difficulty of differentiating vacant land surfaces from other surfaces such as roof tops of buildings.

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med