Hyperspectral Image (HSI) classification methods that use Deep Learning (DL) have proven to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have demonstrated extremely powerful performance in such tasks. However, the lack of training samples is one of the main contributors to low classification performance. Traditional CNN-based techniques under-utilize the inter-band correlations of HSI because they primarily use 2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial information using the same operation. While this overcomes the limitation of 2D-CNNs, it may lead to insufficient extraction of features. In order to overcome this issue, we propose an HSI classification approach named Tri-CNN which is based on a multi-scale 3D-CNN and three-branch feature fusion. We first extract HSI features using 3D-CNN at various scales. The three different features are then flattened and concatenated. To obtain the classification results, the fused features then traverse a number of fully connected layers and eventually a softmax layer. Experimental results are conducted on three datasets, Pavia University (PU), Salinas scene (SA) and GulfPort (GP) datasets, respectively. Classification results indicate that our proposed methodology shows remarkable performance in terms of the Overall Accuracy (OA), Average Accuracy (AA), and Kappa metrics when compared against existing methods.

Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.

The escalating complexity of cyber threats, coupled with the rapid evolution of digital landscapes, poses significant challenges to traditional cybersecurity mechanisms. This review explores the transformative role of LLMs in addressing critical challenges in cybersecurity. With the rapid evolution of digital landscapes and the increasing sophistication of cyber threats, traditional security mechanisms often fall short in detecting, mitigating, and responding to complex risks. LLMs, such as GPT, BERT, and PaLM, demonstrate unparalleled capabilities in natural language processing, enabling them to parse vast datasets, identify vulnerabilities, and automate threat detection. Their applications extend to phishing detection, malware analysis, drafting security policies, and even incident response. By leveraging advanced features like context awareness and real-time adaptability, LLMs enhance organizational resilience against cyberattacks while also facilitating more informed decision-making. However, deploying LLMs in cybersecurity is not without challenges, including issues of interpretability, scalability, ethical concerns, and susceptibility to adversarial attacks. This review critically examines the foundational elements, real-world applications, and limitations of LLMs in cybersecurity while also highlighting key advancements in their integration into security frameworks. Through detailed analysis and case studies, this paper identifies emerging trends and proposes future research directions, such as improving robustness, addressing privacy concerns, and automating incident management. The study concludes by emphasizing the potential of LLMs to redefine cybersecurity, driving innovation and enhancing digital security ecosystems.

Federated Learning (FL) is a transformative decentralized approach in machine learning and deep learning, offering enhanced privacy, scalability, and data security. This review paper explores the foundational concepts, and architectural variations of FL, prominent aggregation algorithms like FedAvg, FedProx, and FedMA, and diverse innovative applications in thermal comfort optimization, energy prediction, healthcare, and anomaly detection within smart buildings. By enabling collaborative model training without centralizing sensitive data, FL ensures privacy and robust performance across heterogeneous environments. We further discuss the integration of FL with advanced technologies, including digital twins and 5G/6G networks, and demonstrate its potential to revolutionize real-time monitoring, and optimize resources. Despite these advances, FL still faces challenges, such as communication overhead, security issues, and non-IID data handling. Future research directions highlight the development of adaptive learning methods, robust privacy measures, and hybrid architectures to fully leverage FL’s potential in driving innovative, secure, and efficient intelligence for the next generation of smart buildings.

Detecting plant diseases is vital for maintaining agricultural productivity and ensuring food security. Advances in computer vision, particularly with Vision Transformers (ViTs), have shown significant potential in improving the accuracy and efficiency of plant disease identification. This study provides a comprehensive evaluation of various ViT parameters to determine the most effective configuration for this purpose. Using the extensive PlantVillage dataset, we systematically analyzed the effects of patch sizes, image resolutions, embedding dimensions, the number of transformer blocks (depth), the number of heads in the multi-head attention layer, and the dimension of the MLP (FeedForward) layer on model performance. We introduced saliency map visualizations to enhance interpretability and evaluate the critical regions contributing to classification decisions, ensuring the approach’s transparency and robustness. Our experiments identified the optimal ViT configuration as follows: image size = 224×224, patch size = 16, embedding dimension = 512, depth = 6, number of heads = 8, and MLP dimension = 1024. This configuration achieved an impressive accuracy of 99.77% on the PlantVillage dataset. In addition, we incorporated a novel cross-dataset transferability evaluation to validate the generalizability of the proposed model. Comparative analysis with traditional convolutional neural network architectures, such as VGG19 and AlexNet, revealed that our optimized ViT model not only surpasses these models in accuracy but also requires significantly fewer trainable parameters and storage space. The incorporation of a lightweight, domain-specific fine-tuning process ensures the model’s adaptability to new datasets with minimal computational overhead. Our findings highlight the scalability and adaptability of ViTs, emphasizing their ability to effectively handle varying image sizes and resolutions. Moreover, our approach outperforms recent state-of-the-art methods across multiple databases, underscoring the efficacy of the chosen ViT parameters.

Sea level rise (SLR) is one of the most pressing challenges of climate change and has drawn noticeable research interest over the past few decades. Factors induced by global climate change, such as temperature increase, have resulted in both direct and indirect changes in sea levels at different spatial scales. Various climatic and non-climatic events contribute to sea level changes, posing risks to coastal and low-lying areas. Nevertheless, changes in sea level are not uniformly distributed globally due to several regional factors such as wave actions, storm surge frequencies, and tectonic land movement. The high exposure to those factors increases the vulnerability of subjected areas to SLR impacts. The impacts of events induced by climate change and SLR are reflected in biophysical, socioeconomic, and environmental aspects. Different indicator-based and model-based approaches are used to assess coastal areas’ vulnerabilities, response to impacts, and implementation of adaptation and mitigation measures. Various studies have been conducted to project future SLR impacts and evaluate implemented protection and adaptation approaches, aiding policymakers in planning effective adaptation and mitigation measures to reduce damage. This paper provides an overview of SLR and its key elements, encompassing contributing factors, impacts, and mitigation and adaptation measures, featuring a dedicated section on the Arabian Gulf, a semi-enclosed sea.