Solar irradiation (Rs) is the electromagnetic radiation energy emitted by the Sun. It plays a crucial role in sustaining life on Earth by providing light, heat, and energy. Furthermore, it serves as a key driver of Earth’s climate and weather systems, influencing the distribution of heat across the planet, shaping global air and ocean currents, and determining weather patterns. Variations in Rs levels have significant implications for climate change and long-term climate trends. Moreover, Rs represents an abundant and renewable energy resource, offering a clean and sustainable alternative to fossil fuels. By harnessing solar energy, we can actively reduce greenhouse gas emissions. However, the utilization of Rs comes with its own challenges that must be addressed. One problem is its variability, which makes it difficult to predict and plan for consistent solar energy generation. Its intermittent nature also poses difficulties in meeting continuous energy demand unless appropriate energy storage or backup systems are in place. Integrating large-scale solar energy systems into existing power grids can present technical challenges. Rs levels are influenced by various factors; understanding these factors is crucial for various applications, such as renewable energy planning, climate modeling, and environmental studies. Overcoming the associated challenges requires advancements in technology and innovative solutions. Measuring and harnessing Rs for various applications can be achieved using various devices; however, the expense and scarcity of measuring equipment pose challenges in accurately assessing and monitoring Rs levels. In order to address this, alternative methods have been developed with which to estimate Rs, including artificial intelligence and machine learning (ML) models, like neural networks, kernel algorithms, tree-based models, and ensemble methods. To demonstrate the impact of feature selection methods on Rs predictions, we propose a Multivariate Time Series (MVTS) model using Recursive Feature Elimination (RFE) with a decision tree (DT), Pearson correlation (Pr), logistic regression (LR), Gradient Boosting Models (GBM), and a random forest (RF). Our article introduces a novel framework that integrates various models and incorporates overlooked factors. This framework offers a more comprehensive understanding of Recursive Feature Elimination and its integrations with different models in multivariate solar radiation forecasting. Our research delves into unexplored aspects and challenges existing theories related to solar radiation forecasting. Our results show reliable predictions based on essential criteria. The feature ranking may vary depending on the model used, with the RF Regressor algorithm selecting features such as maximum temperature, minimum temperature, precipitation, wind speed, and relative humidity for specific months. The DT algorithm may yield a slightly different set of selected features. Despite the variations, all of the models exhibit impressive performance, with the LR model demonstrating outstanding performance with low RMSE (0.003) and the highest R2 score (0.002). The other models also show promising results, with RMSE scores ranging from 0.006 to 0.007 and a consistent R2 score of 0.999.

Vehicular ad hoc networks (VANETs) are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. They are a special type of mobile ad hoc networks (MANETs) that can share useful information to improve road traffic and safety. In VANETs, vehicles are interconnected through a wireless medium, making the network susceptible to various attacks, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), or even black hole attacks that exploit the wireless medium to disrupt the network. These attacks degrade the network performance of VANETs and prevent legitimate users from accessing resources. VANETs face unique challenges due to the fast mobility of vehicles and dynamic changes in network topology. The high-speed movement of vehicles results in frequent alterations in the network structure, posing difficulties in establishing and maintaining stable communication. Moreover, the dynamic nature of VANETs, with vehicles joining and leaving the network regularly, adds complexity to implementing effective security measures. These inherent constraints necessitate the development of robust and efficient solutions tailored to VANETs, ensuring secure and reliable communication in dynamic and rapidly evolving environments. Therefore, securing communication in VANETs is a crucial requirement. Traditional security countermeasures are not pertinent to autonomous vehicles. However, many machine learning (ML) technologies are being utilized to classify malicious packet information and a variety of solutions have been suggested to improve security in VANETs. In this paper, we propose an enhanced intrusion detection framework for VANETs that leverages mutual information to select the most relevant features for building an effective model and synthetic minority oversampling (SMOTE) to deal with the class imbalance problem. Random Forest (RF) is applied as our classifier, and the proposed method is compared with different ML techniques such as logistic regression (LR), K-Nearest Neighbor (KNN), decision tree (DT), and Support Vector Machine (SVM). The model is tested on three datasets, namely ToN-IoT, NSL-KDD, and CICIDS2017, addressing challenges such as missing values, unbalanced data, and categorical values. Our model demonstrated great performance in comparison to other models. It achieved high accuracy, precision, recall, and f1 score, with a 100% accuracy rate on the ToN-IoT dataset and 99.9% on both NSL-KDD and CICIDS2017 datasets. Furthermore, the ROC curve analysis demonstrated our model’s exceptional performance, achieving a 100% AUC score.

Moroccan installations of solar photovoltaic panels experience operational difficulties due to shading and vegetation-related soiling, which reduce energy output by 15–30%. Most monitoring systems depend upon a single vegetation index, which can reduce the accuracy of detecting even moderate anomalies. This paper presents a novel integration of multi-criteria hierarchical labeling with dual-branch deep learning for enhanced vegetation anomaly detection. We combined MODIS (2000–2015) and Sentinel-2 (2015–2025) images and NASA POWER weather records to study a 25-year vegetation record using multi-source satellite data in 5 of Morocco’s ecologically diverse zones. We introduced a three-class hierarchical labeling scheme (normal, moderate, severe) for dynamic vegetation models based on combined vegetation indices (NDVI, EVI, NDWI) and meteorological thresholds. The proposed dual-branch architecture uses independent data streams for unfused data, which include temporal multi-scale CNNs (TMSCNN) for spatiotemporal modeling and bidirectional LSTMs for weather-integrated vegetation data. Systematic ablation studies show improvements from using NDVI (68.98%) to multispectral indices (77.74%), meteorological integration (81.02%), and a final accuracy of 82.34% ± 0.88%. The moderate anomaly class exhibits lower precision (65%), demonstrating the challenge of operationalizing severity-based anomaly classification. This work integrates hierarchical, multi-criteria labeling and hybrid deep learning for solar photovoltaic vegetation monitoring.