Modern smart grids encounter several challenges, including the variability of renewable energy sources with power fluctuations of ±35% in solar farms across the Arabian Gulf countries, delays in centralized control, with latencies of 150–200 ms, including all time needed from sending data to receiving control commands, and increased susceptibility to cyber threats, with a 30% annual increase in grid intrusions. Incorporating renewable energy into smart grids presents significant challenges, including intermittent generation, characterized by ±30% solar volatility, slow fault detection, and response times exceeding 500 ms in traditional systems, as well as centralized system weaknesses. This paper presents SwarmNet-5G, a bio-inspired neural network that integrates stigmergic learning and spiking neural networks (SNNs) for decentralized grid management to resolve existing issues. It merges Internet of Things (IoT)-edge intelligence, spiking neuromorphic computing, and swarm-based stigmergy to enable grid optimization. Its design decreases latency by 40% compared to typical deep reinforcement learning (DRL). It achieves a fault detection accuracy of 99.62% within 8.17 ms and requires 50 ms for repair, aligning with Saudi Arabia's Vision 2030 goal of achieving 50% renewable energy. Simulation results performed on the IEEE 39-bus using a dataset show that the solution allocated power during peak demand, such as a 20% load increase, with an efficiency of 92%, while typical DRL reached 78%. Additionally, the model projected annual savings of.2B in maintenance costs and a reduction of 4.8 megatons in CO₂ emissions per year.

Artificial intelligence (AI) and machine learning (ML) have revolutionized assistive technologies, particularly for individuals with hearing and speech impairments. This systematic review critically examines recent innovations in next-generation neural network architectures for sign language recognition (SLR), emphasizing their mathematical and computational foundations. Following PRISMA guidelines, we analyze state-of-the-art models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and hybrid approaches integrating classical machine learning methods such as support vector machines (SVMs). We explore strategies for feature extraction, data augmentation, multimodal fusion, and optimization, highlighting their roles in improving accuracy, robustness, and real-time adaptability. Persistent challenges include dataset scarcity, limited generalizability, and computational trade-offs. From a mathematical perspective, optimization techniques, probabilistic modeling, and explainable AI frameworks are emerging as key enablers for safe and trustworthy SLR systems. This review identifies research gaps and proposes future directions toward responsible, mathematically grounded, and computationally efficient AI-powered assistive technologies.

Monitoring and reducing carbon footprints are crucial for achieving sustainability and effectively tackling climate change. Given the urgent global need to address climate change and to participate in Saudi Arabia's Vision 2030, we present DeepCarbonNet (EcoNet), a novel deep learning (DL) framework to monitor, analyze, and reduce carbon emissions. The framework is built using a new Dual Encounter Logarithmic Path Neural Network (DELPNN) architecture, including a novel Spatial Encounter Pathway (SEP), which processes high-resolution satellite images through a Logarithmic Convolutional Encoder (LCE) to extract multi-scale spatial features, and a new Temporal Encounter Pathway (TEP), which processes sequential Internet of Things (IoT) sensor and energy consumption data via a Gated Logarithmic Recurrent Unit (GLRU) and a central Feature Fusion Operator (FFO) that integrates the spatial and temporal features using cross-attention mechanisms and projects them into a logarithmic latent space to capture intricate, non-linear emission dynamics. This approach enables the precise capture of spatial and temporal dependencies within carbon emission data, achieving an outstanding level of accuracy. The simulation experimental results demonstrated that EcoNet attains a high accuracy of 98.7% in estimating carbon footprints after training the model on two public datasets. Furthermore, the model employed a reinforcement learning (RL)-based optimization strategy, enabling a 29.4% reduction in emissions through adaptive mitigation techniques. EcoNet was designed to adapt to changing conditions and promote environmental sustainability continuously. Beyond monitoring, EcoNet achieved a 32.8% improvement in energy efficiency. Additionally, the framework demonstrated robust performance across weather conditions, with 97.0-98.7% accuracy and an accuracy of emission intensities between 94.2–99.1%. These results showed that EcoNet is a solution for artificial intelligence (AI)-driven environmental sustainability, which offers immediate practical value for industrial monitoring, smart city management, logistic services to reduce fuel consumption, and national sustainability programs.