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A Survey on Arabic Text Classification Using Deep and Machine Learning Algorithms

    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 they got. Deep Learning (DL) and Machine Learning (ML) models were used to enhance text classification for Arabic language. Remarks for future work were concluded.

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Publication Date
Mon Jan 02 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
Publication Date
Fri Nov 11 2022
Journal Name
Al-mansour Journal
Text Cryptography Based on Three Different Keys

Secure information transmission over the internet is becoming an important requirement in data communication. These days, authenticity, secrecy, and confidentiality are the most important concerns in securing data communication. For that reason, information hiding methods are used, such as Cryptography, Steganography and Watermarking methods, to secure data transmission, where cryptography method is used to encrypt the information in an unreadable form. At the same time, steganography covers the information within images, audio or video. Finally, watermarking is used to protect information from intruders. This paper proposed a new cryptography method by using thre

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Publication Date
Thu Jun 18 2020
Journal Name
Rimak International Journal Of Humanities And Social Sciences
STRESS IN ENGLISH AND ARABIC: A CONTRASTIVE STUDY

DBN Rashid, Rimak International Journal of Humanities and Social Sciences, 2020

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Publication Date
Thu Jun 01 2006
Journal Name
Journal Of The College Of Languages (jcl)
Demonstratives in English and Arabic : A Contrastive Study

This paper studies the demonstratives as deictic expressions in Standard Arabic and English by outlining their phonological, syntactic and semantic properties in the two languages. On the basis of the outcome of this outline, a contrastive study of the linguistic properties of this group of deictic expressions in the two languages is conducted next. The aim is to find out what generalizations could be made from the results of this contrastive study.

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Publication Date
Wed Aug 11 2021
Journal Name
International Journal Of Interactive Mobile Technologies (ijim)
Survey on 3D Content Encryption

<p class="0abstract">The rapidly growing 3D content exchange over the internet makes securing 3D content became a very important issue. The solution for this issue is to encrypting data of 3D content, which included two main parts texture map and 3D models. The standard encryption methods such as AES and DES are not a suitable solution for 3D applications due to the structure of 3D content, which must maintain dimensionality and spatial stability. So, these problems are overcome by using chaotic maps in cryptography, which provide confusion and diffusion by providing uncorrelated numbers and randomness. Various works have been applied in the field of 3D content-encryption based on the chaotic system. This survey will attempt t

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Publication Date
Wed Aug 17 2022
Journal Name
Aip Conference Proceedings
The effect of using Gaussian, Kurtosis and LogCosh as kernels in ICA on the satellite classification accuracy

This study focusses on the effect of using ICA transform on the classification accuracy of satellite images using the maximum likelihood classifier. The study area represents an agricultural area north of the capital Baghdad - Iraq, as it was captured by the Landsat 8 satellite on 12 January 2021, where the bands of the OLI sensor were used. A field visit was made to a variety of classes that represent the landcover of the study area and the geographical location of these classes was recorded. Gaussian, Kurtosis, and LogCosh kernels were used to perform the ICA transform of the OLI Landsat 8 image. Different training sets were made for each of the ICA and Landsat 8 images separately that used in the classification phase, and used to calcula

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Publication Date
Mon Oct 30 2023
Journal Name
Iraqi Journal Of Science
Review on Hybrid Swarm Algorithms for Feature Selection

    Feature selection represents one of the critical processes in machine learning (ML). The fundamental aim of the problem of feature selection is to maintain performance accuracy while reducing the dimension of feature selection. Different approaches were created for classifying the datasets. In a range of optimization problems, swarming techniques produced better outcomes. At the same time, hybrid algorithms have gotten a lot of attention recently when it comes to solving optimization problems. As a result, this study provides a thorough assessment of the literature on feature selection problems using hybrid swarm algorithms that have been developed over time (2018-2021). Lastly, when compared with current feature selection procedu

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Publication Date
Sun Nov 01 2020
Journal Name
Journal Of Physics: Conference Series
Improve topic modeling algorithms based on Twitter hashtags
Abstract<p>Today with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned</p> ... Show More
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Publication Date
Thu Sep 14 2023
Journal Name
Al-khwarizmi Engineering Journal
Applying Scikit-learn of Machine Learning to Predict Consumed Energy in Al-Khwarizmi College of Engineering, Baghdad, Iraq

Globally, buildings use about 40% of energy. Many elements, such as the physical properties of the structure, the efficiency of the cooling and heating systems, the activity of the occupants, and the building’s sustainability, affect the energy consumption of a building. It is really difficult to predict how much energy a building will need. To improve the building’s sustainability and create sustainable energy sources to reduce carbon dioxide emissions from fossil fuel combustion, estimating the building's energy use is necessary. This paper explains the energy consumed in the lecture building of the Al-Khwarizmi College of Engineering, University of Baghdad (UOB), Baghdad, Iraq. The weather data and the building construction informati

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Publication Date
Fri Aug 13 2021
Journal Name
Neural Computing And Applications
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