Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs) is not only constitute an encouraging research domain but also represent a promising industrial trend that permits the development of various IoT-based ...
详细信息
In this fast processing world, we need fast processing programs with maximum accuracy. This can be achieved when computer vision is connected with optimized deep learning models and neural networks. The goal of this p...
详细信息
The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial *** radiation cost and quality factor of the antenna are influenced by the size of the *** antennas allow for the ...
详细信息
The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial *** radiation cost and quality factor of the antenna are influenced by the size of the *** antennas allow for the circumvention of the bandwidth restriction for small *** parameters have recently been predicted using machine learning algorithms in existing *** learning can take the place of the manual process of experimenting to find the ideal simulated antenna *** accuracy of the prediction will be primarily dependent on the model that is *** this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard *** with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this *** prediction results of the proposed work are better when compared to the existing models in the literature.
Adversarial attacks in the Artificial Intelligence have received extensive coverage in the latest years. Such attacks have affected deep learning models and neural networks due to the weaknesses that are part of the A...
详细信息
Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surv...
详细信息
Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.
Nighttime detection of pedestrians brings important problems such as low light conditions, limitations of sensors, and environmental noise. To tackle this problem, in this study, we propose a novel visual-radar data f...
详细信息
Intention adoption represents a migration from an imperative to a declarative paradigm, which is expected to significantly improve the user experience in blockchain. While the development of account abstraction has ex...
详细信息
IoT is becoming increasingly popular due to its quick expansion and variety of applications. In addition, 5G technology helps with communication and network connectivity. This work integrates C-RAN with IoT networks t...
详细信息
The advent of Healthcare 5.0 heralds a groundbreaking revolution in digital healthcare, superseding the achievements of its predecessor, Healthcare 4.0. Integrating cutting-edge technologies such as the Internet of Me...
详细信息
The advent of Healthcare 5.0 heralds a groundbreaking revolution in digital healthcare, superseding the achievements of its predecessor, Healthcare 4.0. Integrating cutting-edge technologies such as the Internet of Medical Things (IoMT), smart wearables, and the extraordinary capabilities of Artificial Intelligence (AI), Healthcare 5.0 envisions a unified framework that grants seamless access to health records, fosters interconnectedness among individuals, resources, and institutions, and empowers intelligent responses to medical concerns. However, the realization of Healthcare 5.0 faces a significant challenge in the form of high-speed data transmission using smart devices. Conventional AI approaches relying on centralized data processing raise compelling concerns surrounding information privacy and scalability within the Healthcare 5.0 context. Amidst this backdrop, federated learning emerges as a beacon of hope, offering a decentralized AI paradigm that facilitates on-device machine learning without compromising end-user privacy through centralized data export. Safeguarding data integrity, federated learning holds the key to unlocking the full potential of Healthcare 5.0. In this pioneering study, we conduct an extensive survey, exploring the transformative implications of federated learning within the realm of Healthcare 5.0. By shedding light on recent advancements tailored to this paradigm, we delve into the fundamental concepts of resource-awareness, privacy preservation, incentivization, and personalization, all within the framework of federated learning. Moreover, we meticulously scrutinize key parameters including security, sparsification, quantization, robustness, scalability, and privacy, providing an authentic evaluation of the current progress in federated learning for Healthcare 5.0. This comprehensive survey serves as an indispensable cornerstone for the evolution of Healthcare 5.0, offering invaluable insights into its unique requirements and untapp
Effective management of electricity consumption (EC) in smart buildings (SBs) is crucial for optimizing operational efficiency, cost savings, and ensuring sustainable resource utilization. Accurate EC prediction enabl...
详细信息
暂无评论