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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.

The Arabic Language is the native tongue of more than 400 million people around the world, it is also a language that carries an important religious and international weight. The Arabic language has taken its share of the huge technological explosion that has swept the world, and therefore it needs to be addressed with natural language processing applications and tasks.
This paper aims to survey and gather the most recent research related to Arabic Part of Speech (APoS), pointing to tagger methods used for the Arabic language, which ought to aim to constructing corpus for Arabic tongue. Many AI investigators and researchers have worked and performed POS utilizing various machine-learning methods, such as Hidden-Markov-Model (HMM), Brill, Maximum-Match (MM), decision tree, bee colony, Neural-Network (NN), and other hybrid methods.
This survey groups a number of published papers based on the Arabic Language Applications (ALP) towards tagging related problems utilized and approaches with the difference between types of tags used. It addresses and tries to identify the gaps in the current studies putting a foundation for future studies in this field.

Keywords provide the reader with a summary of the contents of the document and play a significant role in information retrieval systems, especially in search engine optimization and bibliographic databases. Furthermore keywords help to classify the document into the related topic. Keywords extraction included manual extracting depends on the content of the document or article and the judgment of its author. Manual extracting of keywords is costly, consumes effort and time, and error probability. In this research an automatic Arabic keywords extraction model based on deep learning algorithms is proposed. The model consists of three main steps: preprocessing, feature extraction and classification to classify the document tokens into keyword or not, Conventional Neural Networks (CNN) is used as a classifier.
Two types of dataset are building in this research to test the proposed model, the first dataset form Arab Journal for Scientific Publishing (AJSP), the other dataset from Jordan Journal of Social Sciences (JJSS). The experiment results indicate promising results in the field of Arabic keyword extraction; the average accuracy of Conventional Neural Networks is found 0.97 with average precision 0.92.

With the growth of open areas comes an ever-increasing risk of fire. However, there is a problem with the present approaches to fire detection, which rely on smoke sensors for wide regions. The advent of video surveillance systems has greatly improved our ability to detect smoke and flames coming from a distance and reduced this risk. Point sensors are slower at detecting fires than cameras when image processing is used. Moreover, using this video and image data presents processing challenges due to the enormous volume of data involved. Several approaches have recently been put forth to address this issue and distinguish between fire and smoke. Earlier methods included image processing algorithms for flame and smoke detection as well as motion-based estimation of smoke. Recently, a variety of techniques have been put forth using deep learning and convolutional neural networks (CNNs) to predict and automatically identify fire and smoke in videos and images. In this study, we review previous studies of fire/smoke detection systems based on machine vision and deep learning. The foundations of image processing techniques, CNN, and their applicability to video smoke and fire detection are explained. A discussion of current data sets and an overview of recent methods applied in this field The difficulties and potential solutions for advancing the application of CNN in this field are discussed. Then a comparison for researchers in the last years based on the dataset, year, technique, limitation, and accuracy they got The CNNs have been found to have a high potential for detecting open areas, and improved development can help create a system that would significantly reduce the loss of human life and property. Remarks for future work were concluded.

Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning models for a variety of tasks under the control of a unified architecture for each proposed model.