Arabic text classification methods: Systematic literature review of primary studies

Recent research on Big Data proposed and evaluated a number of advanced techniques to gain meaningful information from the complex and large volume of data available on the World Wide Web. To achieve accurate text analysis, a process is usually initiated with a Text Classification (TC) method. Reviewing the very recent literature in this area shows that most studies are focused on English (and other scripts) while attempts on classifying Arabic texts remain relatively very limited. Hence, we intend to contribute the first Systematic Literature Review (SLR) utilizing a search protocol strictly to summarize key characteristics of the different TC techniques and methods used to classify Arabic text, this work also aims to identify and share a scientific evidence of the gap in current literature to help suggesting areas for further research. Our SLR explicitly investigates empirical evidence as a decision factor to include studies, then conclude which classifier produced more accurate results. Further, our findings identify the lack of standardized corpuses for Arabic text; authors compile their own, and most of the work is focused on Modern Arabic with very little done on Colloquial Arabic despite its wide use in Social Media Networks such as Twitter. In total, 1464 papers were surveyed from which 48 primary studies were included and analyzed

Improved Arabic characters recognition by combining multiple machine learning classifiers.

In this paper, we investigate a range of strategies for combining multiple machine learning techniques for recognizing Arabic characters, where we are faced with imperfect and dimensionally variable input characters. Experimental results show that combined confidence-based backoff strategies can produce more accurate results than each technique produces by itself and even the ones exhibited by the majority voting combination

Classification of colloquial Arabic tweets in real-time to detect high-risk floods

Twitter has eased real-time information flow for decision makers, it is also one of the key enablers for Open-source Intelligence (OSINT). Tweets mining has recently been used in the context of incident response to estimate the location and damage caused by hurricanes and earthquakes. We aim to research the detection of a specific type of high-risk natural disasters frequently occurring and causing casualties in the Arabian Peninsula, namely `floods'. Researching how we could achieve accurate classification suitable for short informal (colloquial) Arabic text (usually used on Twitter), which is highly inconsistent and received very little attention in this field. First, we provide a thorough technical demonstration consisting of the following stages: data collection (Twitter REST API), labelling, text pre-processing, data division and representation, and training models. This has been deployed using `R' in our experiment. We then evaluate classifiers' performance via four experiments conducted to measure the impact of different stemming techniques on the following classifiers SVM, J48, C5.0, NNET, NB and k-NN. The dataset used consisted of 1434 tweets in total. Our findings show that Support Vector Machine (SVM) was prominent in terms of accuracy (F1=0.933). Furthermore, applying McNemar's test shows that using SVM without stemming on Colloquial Arabic is significantly better than using stemming techniques

Merleau-Ponty\u27s Interpretation of Machiavelli: Nationalism, Dynamic Perspectives, and the Fabric of Society

This essay examines how Niccolò Machiavelli\u27s political philosophy and Maurice Merleau-Ponty\u27s phenomenology overlap, emphasizing how they both offer valuable perspectives on nationalism, government, and social cohesiveness. Merleau-Ponty frames nationalism as a relational and anticipatory construct shaped by dynamic encounters, emphasizing perception, embodiment, and the lived experience. Renowned for his pragmatism, Machiavelli sees nationalism as a tool for strategy, supporting measures like assimilation and eradication to maintain peace and consolidate power. By contrasting these viewpoints, the essay looks at how their theories handle the difficulties of contemporary leadership in a divided and globalized society. Shared elements like flexibility, foresight, and the function of symbols in promoting unity are highlighted in the analysis. Additionally, it assesses their applicability to current concerns such as technological disruption, international cooperation, and populism. Both scholars emphasize how leaders must carefully balance inclusion and coercion in order to preserve social harmony. In addition to broadening our knowledge of political theory, this synthesis provides practical advice for negotiating the challenges of governance in a changing global environment

Elderly Care System by Using ARDUINO (NODE MCU)

The basic idea of this paper is to design a simple patient  room  containing a number of sensors like : heat and humidity sensor , distance sensor and  fire sensor , where all these sensors are connected to node mcu esp8266, these devices monitored the patient status by internet of things technology (IOT) , and this project aims to make the nurse control a number of rooms at the same time from her room by monitoring the computer screen that display all the immediate patient’s information , and the nurses will here a warnning alarm when the patient  will be at  risk or make unwanted movement  where he some times can not talk  when the asthma attack  . Also this monitoring system provides us with update medical records. Patients’ data are seen and advice is given  from doctors to nurse to do what needed. So the data that will be collected is sent to the server there for the nurse and doctor will enter easily to the patient’s medical  file and in this architecture we use a very available and not expensive equipments .This monitoring system is able to do monitoring patient’s that stay in hospital  for a long time , by mobile , and that enable both doctor and nurse to avoid the repeatedly unnecessary visits to the patients and go to the most risky case through the monitoring screen

Reinforcement Learning based Gateway Selection in VANETs

In vehicular ad hoc networks (VANETs), providing the Internet has become an urgent necessity, where mobile gateways are used to ensure network connection to all customer vehicles in the network. However, the highly dynamic topology and bandwidth limitations of the network represent a significant issue in the gateway selection process. Two objectives are defined to overcome these challenges. The first objective aims to maximize the number of vehicles connected to the Internet by finding a suitable gateway for them depending on the connection lifetime. The second objective seeks to minimize the number of connected vehicles to the same gateway to overcome the limitation of gateways\u27 bandwidth and distribute the load in the network. For this purpose, A gateway discovery system assisted by the vehicular cloud is implemented to find a fair trade-off between the two conflicting objectives. Proximal Policy Optimization, a well-known reinforcement learning strategy, is used to define and train the agent. The trained agent was evaluated and compared with other multi-objective optimization methods under different conditions. The obtained results show that the proposed algorithm has better performance in terms of the number of connected vehicles, load distribution over the mobile gateways, link connectivity duration, and execution time

Optimal k-means clustering using artificial bee colony algorithm with variable food sources length

Clustering is a robust machine learning task that involves dividing data points into a set of groups with similar traits. One of the widely used methods in this regard is the k-means clustering algorithm due to its simplicity and effectiveness. However, this algorithm suffers from the problem of predicting the number and coordinates of the initial clustering centers. In this paper, a method based on the first artificial bee colony algorithm with variable-length individuals is proposed to overcome the limitations of the k-means algorithm. Therefore, the proposed technique will automatically predict the clusters number (the value of k) and determine the most suitable coordinates for the initial centers of clustering instead of manually presetting them. The results were encouraging compared with the traditional k-means algorithm on three real-life clustering datasets. The proposed algorithm outperforms the traditional k-means algorithm for all tested real-life datasets

Diagnosing of some hepatic lesions from light microscope images based on morphological and texture features

One of the common problems observed in medicines is hepatotoxicity as liver play mainly role in metabolizes the herbal medicines. Although, the acceptance of herbal medicines is growing nowadays still there is an absence of knowledge about their toxicological properties and the right use being a hepatotoxic.This paper presents method to detect and diagnoses liver lesions in four types: necrotic cells, fatty degenerative cells, hepatocellular hypertrophic cells and congested cells using image processing techniques. The method is proposed to perform two tasks the first is conclude whether the liver image is normal or abnormal the second if abnormal state is detected then diagnosis lesions type must performs. The method progresses in many steps are preprocessing, features extraction, classification and lesion diagnosing. Grey level co-occurrence Matrix (GLCM) technique is utilize to concentrate features to distinguish between normal and abnormal case using neural network classifier if abnormal state is detected the method feedback with colour image to analyse cells shape and image intensity colour to determine which type of diseases founded in image based on statistical and morphological features of cells. The method tested on 107 images it is got on the accuracy 100% in classification and 95% in diagnosing