The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomains within UAV research, newcomers may find themselves overwhelmed by the numerous options available. It is therefore crucial for those involved in UAV research to recognize its interdisciplinary nature and its connections with other disciplines. This paper presents a comprehensive overview of the UAV field, highlighting recent trends and advancements. Drawing on recent literature reviews and surveys, the review begins by classifying UAVs based on their flight characteristics. It then provides an overview of current research trends in UAVs, utilizing data from the Scopus database to quantify the number of scientific documents associated with each research direction and their interconnections. This paper also explores potential areas for further development in UAVs, including communication, artificial intelligence, remote sensing, miniaturization, swarming and cooperative control, and transformability. Additionally, it discusses the development of aircraft control, commonly used control techniques, and appropriate control algorithms in UAV research. Furthermore, this paper addresses the general hardware and software architecture of UAVs, their applications, and the key issues associated with them. It also provides an overview of current open source software and hardware projects in the UAV field. By presenting a comprehensive view of the UAV field, this paper aims to enhance our understanding of this rapidly evolving and highly interdisciplinary area of research.

Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years, offering advanced tools and methodologies that promise to revolutionize patient outcomes. This review provides an exhaustive overview of the contemporary frameworks employed in the field, focusing on the objective of AI-driven analysis and dissecting methodologies across supervised, unsupervised, and ensemble learning. Specifically, we delve into techniques such as deep learning, artificial neural networks, traditional classification, and probabilistic models (PMs) under supervised learning. With its prowess in clustering and dimensionality reduction, unsupervised learning (USL) is explored alongside ensemble methods, including bagging and potent boosting algorithms. The thyroid cancer datasets (TCDs) are integral to our discussion, shedding light on vital features and elucidating feature selection and extraction techniques critical for AI-driven diagnostic systems. We lay out the standard assessment criteria across classification, regression, statistical, computer vision, and ranking metrics, punctuating the discourse with a real-world example of thyroid cancer detection using AI. Additionally, this study culminates in a critical analysis, elucidating current limitations and delineating the path forward by highlighting open challenges and prospective research avenues. Through this comprehensive exploration, we aim to offer readers a panoramic view of AI’s transformative role in thyroid cancer diagnosis, underscoring its potential and pointing toward an optimistic future.

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.

We propose a novel approach for face verification by encoding 2D and 3D face images as a high order tensor. To perform tensor dimensionality reduction for both the unsupervised and supervised cases, we propose Multilinear Whitened Principal Component Analysis (MWPCA) and Tensor Exponential Discriminant Analysis (TEDA), respectively. MWPCA is utilized to solve the Small Sample Size (SSS) problem in the high-dimensional space and to improve the discrimination power achieved by classical Multilinear Principal Component Analysis (MPCA). In the supervised case, we extend Multilinear Discriminant Analysis (MDA) to TEDA in order to emphasize the discriminant data included in the null space of the within-class scatter matrix of each tensor’s mode. Additionally, TEDA enlarges the margin between samples belonging to different classes via distance diffusion mappings. Our proposed approach can be seen as a novel data fusion method based on tensor representation. Indeed, the histograms of different local descriptors extracted from both 2D and 3D face modalities are combined through different tensor modes. The extensive experimental evaluation carried out on FRGC v2.0, Bosphorus and CASIA 2D and 3D face databases indicates that the proposed approach performs significantly better than state-of-the-art approaches.