The rapid progress in sensor technology and computational capabilities has significantly improved real-time data collection, enabling precise monitoring of various phenomena and industrial processes. However, the volume and complexity of heterogeneous data present substantial processing challenges. Traditional data-processing techniques, such as data aggregation, filtering, and statistical analysis, are increasingly supplemented by data fusion methods. These methods can be broadly categorised into traditional analytics-based approaches, like the Kalman Filter and Particle Filter, and learning-based approaches, utilising machine learning and deep learning techniques such as Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). These techniques combine data from multiple sources to provide a comprehensive and accurate representation of information, which is critical in number of fields. Despite this, a comprehensive review of learning-based, particularly deep learning-based, data fusion strategies is lacking. This paper presents a thorough review of deep learning-based data fusion methodologies across various fields, examining their evolution over the past five years. It highlights applications in remote sensing, healthcare, industrial fault diagnosis, intelligent transportation, and other domains. The paper categories fusion strategies into early-level, intermediate-level, late-level, and hybrid fusion, emphasising their synergies, challenges, and suitability. It outlines significant advancements, the comparative advantages of deep learning-based methods over traditional approaches, and emerging trends and future directions. To ensure a comprehensive analysis, the review is structured using the ProKnow-C methodology, a rigorous selection process that focuses on relevant literature from recent years.

Autonomous unmanned aerial vehicles (UAVs) are essential for detecting and tracking specific events, such as automatic navigation. The intelligent monitoring of people's social distances in crowds is one of the most significant events caused by the coronavirus. The virus is spreading more quickly among the crowds, and the disease cycle continues in congested areas. Due to the error that occurs when humans monitor their activity, an automated model is required to alert to social distance violations in crowds. As a result, this article proposes a two-step framework based on autonomous UAV videos, including human tracking and deep learning-based recognition of the crowd's social distance. The deep architecture is a modified-fast and lightweight ShuffleNet learning structure. First, the Kalman filter is used to determine the positions of individuals, and then the modified ShuffleNet is used to refine the bounding boxes obtained and determine the social distance. The social distance is calculated using the initial refinement of the bounding box obtained during the tracking step and the scale in frames of the human body. The observed average accuracy, average processing time (APT), and processed frame per second (FPS) for three congestion datasets were 97.5%, 84 milliseconds, and 11.5 FPS, respectively. Real-time decision-making was achieved by reducing the size and resolution of the frames. Additionally, the frames were re-labeled to reduce the computational complexity associated with detecting social distancing. The experimental results demonstrated that the proposed method could operate more quickly and accurately on various resolution frames of UAV videos with difficult conditions.

People of all demographics are impacted by mental illness, which has become a widespread and international health problem. Effective treatment and support for mental illnesses depend on early discovery and precise diagnosis. Notably, delayed diagnosis may lead to suicidal thoughts, destructive behaviour, and death. Manual diagnosis is time-consuming and laborious. With the advent of AI, this research aims to develop a novel mental health disorder detection network with the objective of maximum accuracy and early discovery. For this reason, this study presents a novel framework for the early detection of mental illness disorders using a multi-modal approach combining speech and behavioral data. This framework preprocesses and analyzes two distinct datasets to handle missing values, normalize data, and eliminate outliers. The proposed NeuroVibeNet combines Improved Random Forest (IRF) and Light Gradient-Boosting Machine (LightGBM) for behavioral data and Hybrid Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for voice data. Finally, a weighted voting mechanism is applied to consolidate predictions. The proposed model achieves robust performance and a competitive accuracy of 99.06% in distinguishing normal and pathological conditions. This framework validates the feasibility of multi-modal data integration for reliable and early mental illness detection.