Ibrahim Alzamil

Accurate weed detection is essential for site-specific weed management because uncontrolled weeds compete with crops for water, nutrients, light, and growing space, while uniform herbicide application increases production costs and environmental pressure. Existing UAV-based weed detection methods remain limited by three core gaps: inadequate representation of heterogeneous and imbalanced UAV-derived agricultural records, insufficient joint modeling of crop–weed visual similarity and weed-scale variation, and evaluation protocols that rarely assess complete multi-label agreement together with field perturbations and cross-dataset transfer. To address these gaps, this study proposes AgroWeed-MCANet, a multi-scale convolutional attention network for UAV-based multi-label weed detection. Robust feature refinement and compact feature-map arrangement stabilize heterogeneous inputs; convolutional patch encoding, multi-scale ConvNeXt extraction, dilated context aggregation, channel–spatial attention, and cross-scale fusion address local ambiguity, scale variation, and noisy sensing conditions; and the Multi-Label Agreement Score (MLAS), robustness analysis, and cross-dataset evaluation provide field-oriented performance validation. Experiments were conducted on the UAV-UndesirablePlant-UK dataset containing 209,600 labeled field observations with naturally imbalanced crop and weed categories. AgroWeed-MCANet achieved 96.9% accuracy, 95.8% precision, 95.2% recall, 95.5% F1-score, and a 0.912 multi-label agreement score, outperforming twelve recent UAV-based weed detection baselines. Robustness analysis showed stable performance under missing observations, illumination shifts, altitude variations, and 20% noise, with the model maintaining 95.3% accuracy and 93.8% F1-score. Cross-dataset evaluation on DeepWeeds and CWFID further confirmed its transferability, with accuracies of 91.8% and 90.9%, respectively. These findings demonstrate that AgroWeed-MCANet supports reliable UAV-assisted weed monitoring and contributes to sustainable, site-specific weed management in precision agriculture.

Multimodal forecasting is increasingly needed to maintain energy levels, storage capacity, and compute efficiency in off-grid, renewable-powered cloud environments. Variable sensor quality, uncertain interactions with renewable energy, and rapidly changing weather patterns make real-time forecasting difficult. Current transformer, GNN, and CNN systems suffer from sensor noise instability, multimodal temporal–spectral correlation issues, and challenges in the interpretability of operational decision-making. In this research, Q-RCANeX, a quantum-guided residual convolutional attention network for off-grid cloud infrastructures, estimates battery state of charge, renewable energy sources, and microgrid efficiency to overcome these restrictions. The system uses a Hybrid Quantum–Bayesian Evolutionary Optimizer, quantum feature embedding, temporal–spectral attention, residual convolutional encoding, and signal decomposition preprocessing. These parameters reinforce features, reduce noise, and align forecasting behavior with microgrid dynamics. Q-RCANeX obtains 98.6% accuracy, 0.992 AUC, and 0.986 R³ values for REAF, WGF, SOC-F, and EEIF forecasting tasks, according to a statistical study. Additionally, it determines inference latency to 4.9 ms and model size to 18.5 MB. Even with 20% of sensor data missing or noisy, the model outperforms 12 state-of-the-art baselines and maintains 96.8% accuracy using ANOVA, Wilcoxon, Nemenyi, and Holm tests. The findings indicate that the forecasting framework has high accuracy, clarity, and resilience to failures. This makes it useful for real-time, off-grid management of renewable cloud microgrids.

As smart cities evolve, energy infrastructures are becoming more decentralized and dynamic due to the increased integration of renewables, electric vehicles, and consumer-driven usage behaviors. These shifts introduce significant complexity into electricity load forecasting, challenging conventional centralized AI models. Such models often face limitations in scalability, generalization across heterogeneous environments, and preserving user data privacy. Furthermore, their lack of interpretability impedes transparent decision-making, an essential requirement in critical energy management and policy planning contexts. To address these issues, this study proposes HHCTE-FL, a Hierarchical Hybrid Convolutional Transformer Extractor embedded within a federated learning framework. The architecture supports multi-horizon electricity load forecasting while ensuring model transparency through Layer-wise Interpretative Attention Maps (LIAM) and robustness via personalized federated optimization. Functional modules such as Dynamic Temporal Refinement (DTR) and Multi-Stage Adaptive Feature Selection (MSAFS) enable adaptive temporal modeling and feature prioritization across non-IID client data. Evaluation on real-world smart grid datasets demonstrates that HHCTE-FL consistently surpasses 12 benchmark methods, attaining 98.7% accuracy, with the lowest forecast errors (MAE: 0.081, RMSE: 0.114). The model achieves convergence within just 28 communication rounds and operates with low overhead, while attaining a Federated Stability Index (FSI) of 0.96, indicating high training consistency. Statistical significance analysis further affirms its superiority in performance and reliability. By integrating explainable deep learning with privacy-preserving federated learning, HHCTE-FL establishes a scalable and trustworthy paradigm for intelligent electricity load forecasting—aligned with the demands of modern urban energy systems and smart city sustainability goals.

Precision agriculture is necessary for dealing with problems like pest outbreaks, a lack of water, and declining crop health. Manual inspections and broad-spectrum pesticide application are inefficient, time-consuming, and dangerous. New drone photography and IoT sensors offer quick, high-resolution, multimodal agricultural data collecting. Regional diversity, data heterogeneity, and privacy problems make it hard to conclude these data. This study proposes a lightweight, hybrid deep learning architecture called federated LeViT-ResUNet that combines the spatial efficiency of LeViT transformers with ResUNet’s exact pixel-level segmentation to address these issues. The system uses multispectral drone footage and IoT sensor data to identify real-time insect hotspots, crop health, and yield prediction. The dynamic relevance and sparsity-based feature selector (DRS-FS) improves feature ranking and reduces redundancy. Spectral normalization, spatial–temporal alignment, and dimensionality reduction provide reliable input representation. Unlike centralized models, our platform trains over-dispersed client datasets using federated learning to preserve privacy and capture regional trends. A huge, open-access agricultural dataset from varied environmental circumstances was used for simulation experiments. The suggested approach improves on conventional models like ResNet, DenseNet, and the vision transformer with a 98.9% classification accuracy and 99.3% AUC. The LeViT-ResUNet system is scalable and sustainable for privacy-preserving precision agriculture because of its high generalization, low latency, and communication efficiency. This study lays the groundwork for real-time, intelligent agricultural monitoring systems in diverse, resource-constrained farming situations.

Due to Internet of Drones (IoD) technology, drone networks have proliferated, transforming surveillance, logistics, and disaster management. Distributed Denial of Service (DDoS) attacks, malware infections, and communication abnormalities increase cybersecurity dangers to these networks, threatening operational safety and efficiency. Current Intrusion Detection Systems (IDSs) fail to handle drone transmission data’s dynamic, high-dimensional nature, resulting in inadequate real-time anomaly identification and mitigation. This study presents the Cross-Layer Convolutional Attention Network (CLCAN), a new IDS architecture for IoD networks. CLCAN accurately detects complex cyber threats using multi-scale convolutional processing, hierarchical contextual attention, and dynamic feature fusion. Preprocessing methods like weighted differential scaling and gradient-based adaptive resampling improve data quality and reduce class imbalances. Contextual attribute transformation captures the nuanced network behaviors needed for anomaly identification. The proposed technique is shown to be necessary and effective by real-world drone communication dataset evaluations. CLCAN outperforms CNN, LSTM, and XGBoost with 98.4% accuracy, 98.7% recall, and 98.1% F1-score. The model has a remarkable AUC of 0.991. CLCAN can handle datasets of over 118,000 balanced data records in 85 s, compared to 180 s for comparable frameworks. This study pioneers a unified security solution for Drone-to-Drone (D2D) and Drone-to-Base Station (D2BS) communications, filling a crucial IoD security gap. It protects mission-critical drone operations with a strong, efficient, and scalable IDS from emerging cyber threats.