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Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand workplaces. The development of wearable technologies, such as fitness trackers and smartwatches, has made it possible to continuously analyze physiological signals in real-time to determine a person level of exhaustion. This has allowed for timely insights into preventing hazards associated with fatigue. This review focuses on wearable technology and artificial intelligence (AI) integration for tiredness detection, adhering to the PRISMA principles. Studies that used signal processing methods to extract pertinent aspects from physiological data, such as ECG, EMG, and EEG, among others, were analyzed as part of the systematic review process. Then, to find patterns of weariness and indicators of impending fatigue, these features were examined using machine learning and deep learning models. It was demonstrated that wearable technology and cutting-edge AI methods could accurately identify weariness through multi-modal data analysis. By merging data from several sources, information fusion techniques enhanced the precision and dependability of fatigue evaluation. Significant developments in AI-driven signal analysis were noted in the assessment, which should improve real-time fatigue monitoring while requiring less interference. Wearable solutions powered by AI and multi-source data fusion present a strong option for real-time tiredness monitoring in the workplace and other crucial environments. These developments open the door for more improvements in this field and offer useful tools for enhancing safety and reducing fatigue-related hazards., Comment: 43 pages, 18 figures, 2 tables

For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) has started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to significantly improve clinical outcomes. However, they also present unique challenges related to data integration and ethical issues. This survey reviews the development of ML and DL methods for the early diagnosis and treatment of mental health issues. It examines a range of applications, with a particular emphasis on behavioral assessments, genetic and biomarker analysis, and medical imaging for diagnosing diseases like depression, bipolar disorder, and schizophrenia. Predictive modeling for illness progression is further discussed, focusing on the role of risk prediction models and longitudinal studies. Key findings highlight how ML and DL can improve diagnostic accuracy and treatment outcomes while addressing methodological inconsistencies, data integration challenges, and ethical concerns. The study emphasizes the importance of building real-time monitoring systems for individualized treatment, enhancing data fusion techniques, and fostering interdisciplinary collaboration. Future research should focus on overcoming these obstacles to ensure the valuable and ethical application of ML and DL in mental health services., Comment: 20 pages, 3 figures, 3 tables

In the modern world, our cities and societies face several technological and societal challenges, such as rapid urbanization, global warming & climate change, the digital divide, and social inequalities, increasing the need for more sustainable cities and societies. Addressing these challenges requires a multifaceted approach involving all the stakeholders, sustainable planning, efficient resource management, innovative solutions, and modern technologies. Like other modern technologies, social media informatics also plays its part in developing more sustainable and resilient cities and societies. Despite its limitations, social media informatics has proven very effective in various sustainable cities and society applications. In this paper, we review and analyze the role of social media informatics in sustainable cities and society by providing a detailed overview of its applications, associated challenges, and potential solutions. This work is expected to provide a baseline for future research in the domain., Comment: 35 pages, 3 tables, and 4 figures

Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource allocation, and security attacks. The utilization of deep learning techniques in cognitive radio networks can significantly enhance the network's capability to adapt to changing environments and improve the overall system's efficiency and reliability. As the demand for higher data rates and connectivity increases, B5G/6G wireless networks are expected to enable new services and applications significantly. Therefore, the significance of deep learning in addressing cognitive radio network challenges cannot be overstated. This review article provides valuable insights into potential solutions that can serve as a foundation for the development of future B5G/6G services. By leveraging the power of deep learning, cognitive radio networks can pave the way for the next generation of wireless networks capable of meeting the ever-increasing demands for higher data rates, improved reliability, and security., Comment: The article has been accepted for publication in "Journal of Network and Computer Applications" during October 2024

Robots find extensive applications in industry. In recent years, the influence of robots has also increased rapidly in domestic scenarios. The Q-learning algorithm aims to maximise the reward for reaching the goal. This paper proposes a modified version of the Q-learning algorithm, known as Q-learning with scaled distance metric (Q-SD). This algorithm enhances task learning and makes task completion more meaningful. A robotic manipulator (agent) applies the Q-SD algorithm to the task of table cleaning. Using Q-SD, the agent acquires the sequence of steps necessary to accomplish the task while minimising the manipulator's movement distance. We partition the table into grids of different dimensions. The first has a grid count of 3 times 3, and the second has a grid count of 4 times 4. Using the Q-SD algorithm, the maximum success obtained in these two environments was 86% and 59% respectively. Moreover, Compared to the conventional Q-learning algorithm, the drop in average distance moved by the agent in these two environments using the Q-SD algorithm was 8.61% and 6.7% respectively., Comment: 9 pages, 9 figures, 7 tables

The way we engage with digital spaces and the digital world has undergone rapid changes in recent years, largely due to the emergence of the Metaverse. As technology continues to advance, the demand for sophisticated and immersive interfaces to interact with the Metaverse has become increasingly crucial. Haptic interfaces have been developed to meet this need and provide users with tactile feedback and realistic touch sensations. These interfaces play a vital role in creating a more authentic and immersive experience within the Metaverse. This article introduces the concept of MetaDigiHuman, a groundbreaking framework that combines blended digital humans and haptic interfaces. By harnessing cutting-edge technologies, MetaDigiHuman enables seamless and immersive interaction within the Metaverse. Through this framework, users can simulate the sensation of touching, feeling, and interacting with digital beings as if they were physically present in the environments, offering a more compelling and immersive experience within the Metaverse., Comment: 5 pages, 2 figures

Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent in the Industrial Internet of Things (IIoT) context, where data privacy, security, and efficient utilization of distributed resources are paramount. The essence of FL in IIoT lies in its ability to learn from diverse, distributed data sources without requiring central data storage, thus enhancing privacy and reducing communication overheads. However, despite its potential, several challenges impede the widespread adoption of FL in IIoT, notably in ensuring interpretability and robustness. This article focuses on enabling trustworthy FL in IIoT by bridging the gap between interpretability and robustness, which is crucial for enhancing trust, improving decision-making, and ensuring compliance with regulations. Moreover, the design strategies summarized in this article ensure that FL systems in IIoT are transparent and reliable, vital in industrial settings where decisions have significant safety and economic impacts. The case studies in the IIoT environment driven by trustworthy FL models are provided, wherein the practical insights of trustworthy communications between IIoT systems and their end users are highlighted., Comment: 7 pages, 2 figures

Farmers face various challenges when it comes to identifying diseases in rice leaves during their early stages of growth, which is a major reason for poor produce. Therefore, early and accurate disease identification is important in agriculture to avoid crop loss and improve cultivation. In this research, we propose a novel hybrid deep learning (DL) classifier designed by extending the Squeeze-and-Excitation network architecture with a channel attention mechanism and the Swish ReLU activation function. The channel attention mechanism in our proposed model identifies the most important feature channels required for classification during feature extraction and selection. The dying ReLU problem is mitigated by utilizing the Swish ReLU activation function, and the Squeeze-andExcitation blocks improve information propagation and cross-channel interaction. Upon evaluation, our model achieved a high F1-score of 99.76% and an accuracy of 99.74%, surpassing the performance of existing models. These outcomes demonstrate the potential of state-of-the-art DL techniques in agriculture, contributing to the advancement of more efficient and reliable disease detection systems., Comment: 17 pages, 4 tables, 10 figures

The success of Artificial Intelligence (AI) in multiple disciplines and vertical domains in recent years has promoted the evolution of mobile networking and the future Internet toward an AI-integrated Internet-of-Things (IoT) era. Nevertheless, most AI techniques rely on data generated by physical devices (e.g., mobile devices and network nodes) or specific applications (e.g., fitness trackers and mobile gaming). Therefore, Generative AI (GAI), a.k.a. AI-generated content (AIGC), has emerged as a powerful AI paradigm; thanks to its ability to efficiently learn complex data distributions and generate synthetic data to represent the original data in various forms. This impressive feature is projected to transform the management of mobile networking and diversify the current services and applications provided. On this basis, this work presents a concise tutorial on the role of GAIs in mobile and wireless networking. In particular, this survey first provides the fundamentals of GAI and representative GAI models, serving as an essential preliminary to the understanding of GAI's applications in mobile and wireless networking. Then, this work provides a comprehensive review of state-of-the-art studies and GAI applications in network management, wireless security, semantic communication, and lessons learned from the open literature. Finally, this work summarizes the current research on GAI for mobile and wireless networking by outlining important challenges that need to be resolved to facilitate the development and applicability of GAI in this edge-cutting area., Comment: This work has been accepted for publication in the IEEE Internet of Things Journal under ID number IoT-37996-2024

Unmanned Aerial Vehicles (UAVs), previously favored by enthusiasts, have evolved into indispensable tools for effectively managing disasters and responding to emergencies. For example, one of their most critical applications is to provide seamless wireless communication services in remote rural areas. Thus, it is substantial to identify and consider the different security challenges in the research and development associated with advanced UAV-based B5G/6G architectures. Following this requirement, the present study thoroughly examines the security considerations about UAVs in relation to the architectural framework of the 5G/6G system, the technologies that facilitate its operation, and the concerns surrounding privacy. It exhibits security integration at all the protocol stack layers and analyzes the existing mechanisms to secure UAV-based B5G/6G communications and its energy and power optimization factors. Last, this article also summarizes modern technological trends for establishing security and protecting UAV-based systems, along with the open challenges and strategies for future research work., Comment: 25 pages, 6 figures, 3 tables

The photoinduced switching of Fe(II)-based spin-crossover complexes from singlet to quintet takes place at ultrafast time scales. This a priori spin-forbidden transition triggered numerous time-resolved experiments of solvated samples to elucidate the mechanism at play. The involved intermediate states remain uncertain. We apply ultrafast x-ray spectroscopy in molecular films as a method sensitive to spin, electronic, and nuclear degrees of freedom. Combining the progress in molecule synthesis and film growth with the opportunities at x-ray free-electron lasers, we analyze the transient evolution of the Fe L3 fine structure at room temperature. Our measurements and calculations indicate the involvement of an Fe triplet intermediate state. The high-spin state saturates at half of the available molecules, limited by molecule-molecule interaction within the film.