## 2 Literature Review  
Arabic dialectal languages have received attention due to the mounting demand for social media and internet connectivity, posing opportunities and obstacles in Arabic language processing and machine learning. Applications such as machine translation and sentiment analysis depend on these unofficial data. A dialectal text on social media can be meritoriously processed using deep learning mechanisms. Numerous deep learning representations of the automated classification of Arabic dialect text were examined using the Arabic Online Commentary dataset. The outcomes are commendable, based on the results obtained for each pair of dialects [2]. Dialect identification is a crucial task required for applications such as e-health and automatic speech recognition. Fundamental frequency, energy, and Mel-frequency cepstrum coefficient parameters were previously extended as methods for automatically identifying Arabic dialects. The effectiveness of these methods was demonstrated by their outperformance of contemporary techniques on five dialect datasets using a convolutional neural network architecture [5]. Two transformer models, BERT (using deep learning) and DistillBERT (employing machine learning), were also implemented to classify texts based on attention mechanisms [6]. A method for recognizing and categorizing Arabic speech dialects based on the speaking nation is presented with an accuracy of 83%, where the system extracts features from 672 audio inputs using convolutional neural network (CNN) techniques. The system is more effective than machine learning models because it uses spectrogram features to translate speech into images, thus improving Arabic speech dialect classification, which is important for machine translation and communication [7]. This systematic literature review identifies research gaps in computational studies of dialectal Arabic, with a focus on popular research topics, dialects, machine learning methodologies, neural grid input features, data types, datasets, system evaluation criteria, publication venues, and trends. It exposes prejudices that prioritize regional dialects over individual vernaculars, text over speech, and Egyptian over all other vernaculars. This review also identifies research gaps, including the dearth of resources for city dialects, the lack of linguistic studies, the neglect of Mauritanian and Bahraini, and the dearth of deep machine learning experiments [8]. SYSTRAN used a variety of neural network models to compete in the Arabic dialect identification subtask of the DSL shared task. A CNN-biLSTM network, a binary CNN-biLSTM system, and a multi-input CNN made up the top-performing systems. Despite a lack of training data, the network generated competitive results [4]. The engraved system of Arabic makes Modern Standard Arabic (MSA) the most commonly used language in Arabic datasets. The Arabic Online Commentary Dataset is a new Arabic resource with dialect annotations. The data were crowdsourced to determine the dialect in more than 100,000 sentences. Automatic classifiers for dialect identification were trained using the data, and the classification accuracy was almost human-like. The classifiers were used to identify dialectical data using a massive web crawl of 3.5 million Arabic newspaper pages [9]. The ADI17 corpus, which comprises 1717 wave files, was used in this study to recommend machine learning models for Arabic dialect identification. Features were extracted using Mel-Frequency Cepstral Coefficients (MFCCs) and Triangular Filter Bank Cepstral Coefficients (TFCCs). KNN, Random Forest, Multi-Layer Perceptron, and Artificial Neural Networks were used to automatically identify extracted features. The accuracy of the results was high [10]. An article, outlined by [PERSON] et al., mentions the techniques applied in the multi-label country-level dialect identification (MLDID) task, which is the first NADI 2024 Subtask.