Smart cities are being developed worldwide with the use of technology to improve the quality of life of citizens and enhance their safety. Video surveillance is a key component of smart city infrastructure, as it involves the installation of cameras at strategic locations throughout the city for monitoring public spaces and providing real-time surveillance footage to law enforcement and other city representatives. Video surveillance systems have evolved rapidly in recent years, and are now integrated with advanced technologies like deep learning, blockchain, edge computing, and cloud computing. This study provides a comprehensive overview of video surveillance systems in smart cities, as well as the functions and challenges of those systems. The aim of this paper is to highlight the importance of video surveillance systems in smart cities and to provide insights into how they could be used to enhance safety, security, and the overall quality of life for citizens.

This study presents a comprehensive exploration of the hyperparameter optimization in one-dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The increasing frequency and complexity of cyberattacks have prompted an urgent need for effective intrusion-detection systems (IDSs). Herein, we focus on optimizing nine hyperparameters within a 1D-CNN model, using two well-established evolutionary computation methods—genetic algorithm (GA) and particle swarm optimization (PSO). The performances of these methods are assessed using three major datasets—UNSW-NB15, CIC-IDS2017, and NSL-KDD. The key performance metrics considered in this study include the accuracy, loss, precision, recall, and F1-score. The results demonstrate considerable improvements in all metrics across all datasets, for both GA- and PSO-optimized models, when compared to those of the original nonoptimized 1D-CNN model. For instance, on the UNSW-NB15 dataset, GA and PSO achieve accuracies of 99.31 and 99.28%, respectively. Both algorithms yield equivalent results in terms of the precision, recall, and F1-score. Similarly, the performances of GA and PSO vary on the CIC-IDS2017 and NSL-KDD datasets, indicating that the efficacy of the optimization algorithm is context-specific and dependent on the nature of the dataset. The findings of this study demonstrate the importance and effects of efficient hyperparameter optimization, greatly contributing to the field of network security. This study serves as a crucial step toward developing advanced, robust, and adaptable IDSs capable of addressing the evolving landscape of cyber threats.

In the modern era of active network throughput and communication, the study of Intrusion Detection Systems (IDS) is a crucial role to ensure safe network resources and information from outside invasion. Recently, IDS has become a needful tool for improving flexibility and efficiency for unexpected and unpredictable invasions of the network. Deep learning (DL) is an essential and well-known tool to solve complex system problems and can learn rich features of enormous data. In this work, we aimed at a DL method for applying the effective and adaptive IDS by applying the architectures such as Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU). CNN models have already proved an incredible performance in computer vision tasks. Moreover, the CNN can be applied to time-sequence data. We implement the DL models such as CNN, LSTM, RNN, GRU by using sequential data in a prearranged time range as a malicious traffic record for developing the IDS. The benign and attack records of network activities are classified, and a label is given for the supervised-learning method. We applied our approaches to three different benchmark data sets which are UNSW NB15, KDDCup ’99, NSL-KDD to show the efficiency of DL approaches. For contrast in performance, we applied CNN and LSTM combination models with varied parameters and architectures. In each implementation, we trained the models until 100 epochs accompanied by a learning rate of 0.0001 for both balanced and imbalanced train data scenarios. The single CNN and combination of LSTM models have overcome compared to others. This is essentially because the CNN model can learn high-level features that characterize the abstract patterns from network traffic records data.

In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats.

A microgrid is characterized by the integration of distributed energy resources and controllable loads in a power distribution network. Such integration introduces new, unique challenges to microgrid management that have never been exposed to traditional power systems. To accommodate these challenges, it is necessary to redesign a conventional Energy Management System (EMS) so that it can cope with intrinsic characteristics of microgrids. While many projects have shown excellent research outcomes, they have either tackled portions of the characteristics or validated their EMSs only via simulations. This paper proposes a Microgrid Platform (MP), an advanced EMS for efficient microgrid operations. We design the MP by taking into consideration (i) all the functional requirements of a microgrid EMS (i.e., optimization, forecast, human–machine interface, and data analysis) and (ii) engineering challenges (i.e., interoperability, extensibility, and flexibility). Moreover, a prototype system is developed and deployed in two smart grid testbeds: UCLA Smart Grid Energy Research Center and Korea Institute of Energy Research. We then conduct experiments to verify the feasibility of the MP design in real-world settings. Our testbeds and experiments demonstrate that the MP is able to communicate with various energy devices and to perform an energy management task efficiently.

This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.