The scheme of water resources management is necessary for reducing water scarcity in arid areas and improving water availability in general. However, water leak detection and irrigation scheduling traditional AI models are often computationally intensive and require complex hyperparameter tuning, making them less scalable. This study presents an artificial intelligence-based optimization framework that improves forecasting accuracy, computational speed, and real-time adaptability. The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. Moreover, ontology-based feature structuring enhances interpretability, while hyperparameter tuning (GridSearchCV, Bayesian Optimization) and model compression techniques (pruning, quantization, knowledge distillation) ensure computational efficiency. A large number of experiments on real-world IoT sensor data testify to the effectiveness of the framework. It achieves 0.992 AUC-ROC scores for leak detection, an RMSE of 0.227 hours for irrigation scheduling, and an overall accuracy of 94.8%. Additional performance measures comprise precision (89.0%), recall (95.2%), F1-score (0.92), and inference speed (0.003 ms/sample). Although quantization has reduced the computational overhead, we still see a 13.02% increase in the model size as seen in Experiment 6, leading to a trade-off that needs to be optimized further. This study offers a deployable AI-based model for sustainable water management by tackling the issues of scalability, computational cost, and limitations in benchmark evaluation. By virtue of the empirical validation and comparative analysis of the framework, it has been shown to perform better than the regular methods, proving that the methodology can act as a step forward in the field of real-time, AI-assisted irrigation and leak detection systems.

Saudi Arabic Sign Language (SArSL) recognition poses significant challenges due to its complex spatio-temporal structure and the scarcity of annotated datasets. This paper introduces a self-supervised learning framework built upon the Video Momentum Contrast (VideoMoCo) paradigm integrated with a 3D ResNet-50 backbone, designed to jointly capture spatial and temporal gesture dependencies. The proposed model is pretrained on 18,000 unlabeled gesture videos and subsequently fine-tuned on the KARSL-502 dataset containing 15,400 labeled samples covering 502 distinct classes. Experimental evaluation shows that the model attains an F1-score of 92.7%, outperforming CNN-LSTM (86.0%) and Two-Stream CNN (84.5%) baselines—an improvement of nearly 9% points. Beyond accuracy, the framework demonstrates strong robustness to class imbalance, motion variation, and visual noise, while maintaining efficient deployment performance with an inference latency of 12 ms per batch. The ablation study verifies the contribution of the momentum encoder and large negative sample queue in achieving stable and discriminative feature learning. Overall, the VideoMoCo–ResNet-50 framework establishes a scalable and inclusive foundation for real-time SArSL recognition, advancing accessibility for the Saudi Deaf community and supporting future multimodal extensions.

Ensemble learning, which involves combining the opinions of multiple experts to arrive at a better result, has been used for centuries. In this work, a review of the major voting methods in ensemble learning is explored. This work will focus on a new method for combining the results of individual learners. The method depends on the relative accuracy and diversity of teams. Instead of trying to assign weight to each different trainer, the concept of diversity teams is presented. Each team will vote as one player; however, the individual accuracies of each learner still be implemented. The concept of relaxing parameters that deal with each team as one player is presented. Our experiments demonstrate that traditional ensemble voting methods outperform individual learners. There is a limit to the superiority of the ensemble learner that any ensemble learner cannot go beyond. The proposed voting method gives the same results as the traditional ensemble voting methods, however, a different diversity of the proposed method from the traditional voting method or for different values of the relaxing parameter can be achieved.