Document classification is a classical problem in information retrieval, and plays an important role in a variety of applications. Automatic document classification can be defined as content-based assignment of one or more predefined categories to documents. Many algorithms have been proposed and implemented to solve this problem in general, however, classifying Arabic documents is lagging behind similar works in other languages. In this paper, we present seven deep learning-based algorithms to classify the Arabic documents. These are: Convolutional Neural Network (CNN), CNN-LSTM (LSTM = Long Short-Term Memory), CNN-GRU (GRU = Gated Recurrent Units), BiLSTM (Bidirectional LSTM), BiGRU, Att-LSTM (Attention-based LSTM), and Att-GRU. And for word representation, we applied the word embedding technique (Word2Vec). We tested our approach on two large datasets–with six and eight categories–using ten-fold cross-validation. Our objective was to study how the classification is affected by the stemming strategies and word embedding. First, we looked into the effects of different stemming algorithms on the document classification with different deep learning models. We experimented with eleven different stemming algorithms, broadly falling into: root-based and stem-based, and no stemming. We performed ANOVA test on the classification results using the different stemmers, which helps assure if the results are significant. The results of our study indicate that stem-based algorithms perform slightly better compared to root-based algorithms. Among the deep learning models, the Attention mechanism and the Bidirectional learning gave outstanding performance with Arabic text categorization. Our best performance is $F\text {-score} = 97.96\%$ , achieved using the Att-GRU model with stem-based algorithm. Next, we looked into different controlling parameters for word embedding. For Word2Vec, both skip-gram and bag-of-words (CBOW) perform well with either stemming strategies. However, when using a stem-based algorithm, skip-gram achieves good results with a vector of smaller dimension, while CBOW requires a larger dimension vector to achieve a similar performance.

Automated classification of Arabic legal texts presents unique challenges stemming from the formal register of judicial language, domain-specific Sharī‘a terminology, and the severe class imbalance inherent in hierarchical legal taxonomies. This paper addresses these challenges through a systematic investigation of hierarchical multi-class classification applied to a dataset of 1146 Arabic judicial cases sourced from the Saudi Ministry of Justice open data portal. Cases are annotated at two hierarchical levels: a binary main-class distinction between criminal and civil cases ($L_1$), and a fine-grained sub-category classification across 63 legal topics ($L_2$), exhibiting a class imbalance ratio of 43:1. We evaluate three methodological families under four classification architectures. For encoder-only models, we fine-tune AraBERT, CAMeL-BERT, MARBERTv2, and XLM-RoBERTa under Flat (Bottom-Up), Hierarchical Multi-Task, Two-Stage, and Hierarchy-Aware configurations. For encoder–decoder models, we adapt AraT5v2 under the same four architectures. Finally, we conduct a prompt-based evaluation of GPT-4o under zero-shot and targeted few-shot settings. The experimental results demonstrate that the Hierarchy-Aware architecture consistently achieves the strongest fine-grained classification performance across both model families. AraBERTv2 with Hierarchy-Aware training achieves the best overall $L_2$ accuracy of 83.04% and a Macro-F1 of 76.07%, while the Single Multi-Task configuration achieves the highest $L_1$ accuracy of 99.57%. GPT-4o under 5-shot prompting achieves 99.13% $L_1$ accuracy and a competitive $L_2$ Macro-F1 of 74.30% without task-specific fine-tuning, though supervised models maintain stronger overall fine-grained performance. These findings highlight the importance of explicit hierarchical supervision and domain-adapted pre-training for Arabic legal text classification, and establish strong baselines for future research in this underexplored domain.

Forest fires remain a major threat to biodiversity, human settlements, and the climate. This study presents Nuthur, an intelligent wildfire prediction and early-warning system for Al-Soudah, Saudi Arabia, integrating near-real-time environmental data with artificial intelligence models. The system used the Algerian Forest Fire dataset and a newly created local Saudi Arabian dataset. L1 regularization and Recursive Feature Elimination with Cross-Validation (RFECV) were used to examine relevant environmental variables, while oversampling, undersampling, and (Conditional Tabular Generative Adversarial Network) CTGAN-based synthetic augmentation were evaluated to address class imbalance. Multiple ML and DL models were evaluated, including Random Forest (RF), SVM, XGBoost, CatBoost, ensemble models, MLP, TabNet, and exploratory LSTM and CNN models, which were not interpreted as temporal or spatial models. Under random five-fold cross-validation, ML models achieved accuracy values from 0.89 to 0.97, with XGBoost with oversampling achieving the highest accuracy of 0.97. Deep-learning models achieved accuracy values from 0.75 to 0.94, with TabNet using RFECV achieving the best deep-learning result. A separate spatial cross-validation analysis of the Saudi dataset showed lower geographic generalization performance. CatBoost without oversampling achieved the highest mean spatial accuracy (0.8317) and weighted F1-score (0.7782), while logistic regression with oversampling achieved the highest fire-class recall (0.5616). In contrast, XGBoost with oversampling had a fire-class recall of 0.1096. These results highlight the need for further geographically and temporally diverse Saudi data before operational deployment.

Car accidents are a frequent occurrence in modern urban environments, often followed by disputes regarding fault attribution. Traditional methods of determining responsibility rely heavily on human judgment, which can be time‐consuming, inconsistent and subject to bias. This paper introduces an AI‐powered system designed to analyse car accident descriptions and determine fault responsibility automatically. Leveraging advanced natural language processing techniques and deep learning models, the system processes unstructured textual reports submitted by both parties involved in an accident. It extracts key incident details and delivers fault percentage assessments based on contextual understanding. The study formulates fault estimation as a direct prediction of bilateral fault‐percentage splits under a constrained responsibility framework (0/25/50/75/100). A Saudi‐aligned labelling protocol combining expert input with controlled GPT‐assisted annotation is introduced to ensure consistency with local traffic‐assessment practices. Furthermore, a systematic comparison of three modelling strategies: flat, dual‐model and hierarchical across multiple deep‐learning backbones is conducted to identify the optimal configuration. Among the models evaluated, RoBERTa demonstrated the highest performance, achieving an overall F 1 score (the harmonic mean of precision and recall) of 94%, significantly outperforming baseline approaches. This solution not only enhances the efficiency and fairness of accident investigations but also represents a step forward in the application of AI in legal and insurance‐related domains.