Depression is a widespread mental health disorder that may remain undiagnosed by conventional clinical methods. The rapidly growing world of social media sites such as Twitter, Reddit, Facebook, Instagram, and Weibo has provided new avenues for depression detection using Machine Learning (ML) as well as Deep Learning (DL), which analyze user behavior patterns and linguistic cues for more accurate detection of depression. Many techniques have been developed for this aim over the years. Identifying relevant publications on this topic using current academic search systems is challenging due to the rapid growth of research publications, unclear or limited search terms, and the complexity of citation networks. Several review papers have been published to ease this task by summarizing the methodologies, key findings, and recommendations for future research. However, most current reviews often do not provide a clear overview of the evolution, latest techniques, and challenges. This paper aims to address that gap by providing a comprehensive review of ML and DL methodologies for detecting depression on social media. We propose a generic architecture for these systems and present a detailed analysis of methodologies and datasets used for evaluation in this field. In addition, we highlight key open research areas, providing a useful starting point for further research and development. By narrowing our focus to social media, this review contributes to advancing the understanding and application of cutting-edge methods for depression detection. While this review highlights advancements in social media-based depression detection, it excludes alternative approaches like graph-based systems and reinforcement learning, and its focus on social media may limit its applicability to other domains.

Renewable energy resources have gained considerable attention in recent years due to their efficiency and economic benefits. Their proportion of total energy use continues to grow over time. Photovoltaic (PV) cell and wind energy generation are the least-expensive new energy sources in most countries. Renewable energy technologies significantly contribute to climate mitigation and provide economic benefits. Apart from these advantages, renewable energy sources, particularly solar energy, have drawbacks, for instance restricted energy supply, reliance on weather conditions, and being affected by several kinds of faults, which cause a high power loss. Usually, the local PV plants are small in size, and it is easy to trace any fault and defect; however, there are many PV cells in the grid-connected PV system where it is difficult to find a fault. Keeping in view the aforedescribed facts, this paper presents an intelligent model to detect faults in the PV panels. The proposed model utilizes the Convolutional Neural Network (CNN), which is trained on historic data. The dataset was preprocessed before being fed to the CNN. The dataset contained different parameters, such as current, voltage, temperature, and irradiance, for five different classes. The simulation results showed that the proposed CNN model achieved a training accuracy of 97.64% and a testing accuracy of 95.20%, which are much better than the previous research performed on this dataset.

Deep learning-driven side-channel analysis (SCA) is a promising approach to side-channel analytic profiling. Recent studies have shown that neural networks can successfully attack defended targets, even with a small number of attack traces. However, developing neural networks requires fine-tuning hyperparameters, which is challenging and time-consuming, especially for complex neural networks. This study proposes an AutoSCA framework that uses Bayesian optimization to automate deep learning hyperparameter tuning for SCA. The framework is implemented using two popular neural network architectures: the multi-layer perceptron (MLP) and convolutional neural network (CNN). The AutoSCA framework improves deep learning performance and side-channel measurements, which has potential applications in 6G communication-based mobile devices. The framework was trained and evaluated using the ASCAD and CHES CTF datasets. The experimental results showed that the CNN-based AutoSCA outperformed the MLP-based AutoSCA and other state-of-the-art models, in terms of low time complexity and higher accuracy. Results suggest that Bayesian optimization is effective regardless of the dataset, neural network architecture, or type of leaky prototype in defeating contemporary attacks. Applying deep learning optimization against side-channel attacks in consumer electronics can significantly enhance the security of user data and privacy in an increasingly connected.

The integration of solar energy into the global energy mix is more prevalent owing to its sustainability and ecological advantages. With the fast expansion of large-scale solar farms, the necessity for efficient and dependable maintenance and monitoring of solar modules has grown significantly. This study advocates for the utilization of unmanned aerial vehicles (UAVs) outfitted with thermal imagers and visible-light cameras as an efficient method for identifying flaws in solar photovoltaic (PV) modules. In contrast to conventional inspection techniques, UAV-based photography provides continuous, real-time surveillance, facilitating the rapid detection of potential defects such as cell malfunctions, fractures, shading, and string failures, which may result in substantial financial losses. The choice to prioritize thermal and visual imaging above mathematical modeling or optimization algorithms arises from the necessity of delivering practical, scalable inspection solutions that can be readily implemented in the field. Our review paper emphasizes instant functionality and practical insights for predictive maintenance, without necessitating complex computing resources or optimization tools. This study emphasizes the significance of early defect identification on solar farm efficiency and delineates a strategy for improving the reliability and cost-effectiveness of photovoltaic module inspections via UAV technology.

The advent of sixth-generation (6G) networks introduces unprecedented challenges in achieving seamless connectivity, ultra-low latency, and efficient resource management in highly dynamic environments. Although fifth-generation (5G) networks transformed mobile broadband and machine-type communications at massive scales, their properties of scaling, interference management, and latency remain a limitation in dense high mobility settings. To overcome these limitations, artificial intelligence (AI) and unmanned aerial vehicles (UAVs) have emerged as potential solutions to develop versatile, dynamic, and energy-efficient communication systems. The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning (CoRL) to manage an autonomous network. The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity, movement directions, allocate power, and resource distribution. Unlike conventional centralized or autonomous methods, CoRL involves joint state sharing and conflict-sensitive reward shaping, which ensures fair coverage, less interference, and enhanced adaptability in a dynamic urban environment. Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%, achieves convergence 40% faster, and reduces latency and energy consumption by 30% compared with centralized and decentralized baselines. Furthermore, the distributed nature of the algorithm ensures scalability and flexibility, making it well-suited for future large-scale 6G deployments. The results highlighted that AI-enabled UAV systems enhance connectivity, support ultra-reliable low-latency communications (URLLC), and improve 6G network efficiency. Future work will extend the framework with adaptive modulation, beamforming-aware positioning, and real-world testbed deployment.