The rise of Artificial Intelligence (AI)-generated faces that are identical to actual ones is both a technological innovation and a major concern. While considering the implications, some of the existing security syst...
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The rise of Artificial Intelligence (AI)-generated faces that are identical to actual ones is both a technological innovation and a major concern. While considering the implications, some of the existing security systems are unable to differentiate between a high-quality deep-fake and an actual intruder's face. For instance, the risks are quite high at an airport security checkpoint, where facial recognition is the first line of security against unauthorized entry. The primary concern here is how trustworthy the computer programs and algorithms will be in recognizing counterfeits among a plethora of actual and artificially generated faces. This necessitates the need to introduce machine learning approaches to differentiate between actual and fraudulent faces, particularly when AI-generated faces are involved. Effective artificial intelligence systems must be adaptive and change quickly in the face of more complex threats rather than simply recognizing them. AI-generated faces are becoming more convincing by the day, increasing the risk of their exploitation. This poses the need to ensure that the technology on which people rely should be robust and trustworthy in critical situations, whether it is a security checkpoint or an e-commerce site. In this perspective, this study has attempted to develop and implement Artificial Intelligence-powered solutions to detect the artificial faces while ensuring reliability for critical functions in an age where reality constantly blends with fiction.
In recent years, the surveillance of the health of electrical appliances has been transformed by the Internet of Things (IoT). This investigation offers a comprehensive account of a system that employs the Internet of...
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—Using Convolutional Neural Networks (CNNs) and Random Forest algorithms, the authors of this study present a powerful hybrid model for the automated identification of peppermint illnesses. The CNN architecture consi...
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In recent years, deep learning has been widely used for human activity recognition (HAR) based on wearable sensor data due to its excellent performance. However, deep learning approaches often need a large amount of l...
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YouTube is one of the most widely used and technically advanced commercial systems of recommendations. It provides an overview of the system and highlights the remarkable gains in efficiency made possible by deep lear...
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Facial recognition technology has become increasingly prevalent in today's world, with applications ranging from mobile phones to banking and workplace security. This research proposes a facial recognition-based s...
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Internet of things enabled devices are considered one of the technologies for the future and at the same time, the mass use of IoT devices has revolutionised the way of using modern technology. Hence, with the mass im...
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The advent and widespread adoption of AI-powered trip planners have revolutionized how travelers engage in travel planning. These intelligent systems leverage artificial intelligence algorithms and data to provide use...
The advent and widespread adoption of AI-powered trip planners have revolutionized how travelers engage in travel planning. These intelligent systems leverage artificial intelligence algorithms and data to provide users with personalized, efficient, and immersive planning experiences. In this research the indispensable role played by immersive street view experiences within AI trip planners. Integrating immersive street view experiences within AI trip planners empowers travelers to make well-informed choices, as they can virtually navigate and assess potential destinations, accommodations, and attractions. These intelligent systems leverage artificial intelligence algorithms and data to provide users with personalized, efficient, and immersive planning experiences. The findings reveal that immersive street view experiences significantly elevate the effectiveness of travel planning and decision-making processes.
With the booming development of fifth-generation network technology and Internet of Things,the number of end-user devices(EDs)and diverse applications is surging,resulting in massive data generated at the edge of *** ...
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With the booming development of fifth-generation network technology and Internet of Things,the number of end-user devices(EDs)and diverse applications is surging,resulting in massive data generated at the edge of *** process these data eficiently,the innovative mobile edge computing(MEC)framework has emerged to guarantee low latency and enable eficient computing close to the user ***,federated learning(FL)has demonstrated its empirical success in edge computing due to its privacy-preserving ***,it becomes a promising solution for analyzing and processing distributed data on EDs in various machine learning tasks,which are the major workloads in ***,EDs are typically powered by batteries with limited capacity,which brings challenges when performing energy-intensive FL *** address these challenges,many strategies have been proposed to save energy in *** the absence of a survey that thoroughly summarizes and classifies these strategies,in this paper,we provide a comprehensive survey of recent advances in energy-efficient strategies for FL in ***,we first introduce the system model and energy consumption models in FL,in terms of computation and *** we analyze the challenges regarding improving energy efficiency and summarize the energy-efficient strategies from three perspectives:learning-based,resource allocation,and client *** conduct a detailed analysis of these strategies,comparing their advantages and ***,we visually illustrate the impact of these strategies on the performance of FL by showcasing experimental ***,several potential future research directions for energy-efficient FL are discussed.
This comprehensive research paper thoroughly investigates the profound implications of environmental factors on skin health. It meticulously examines the multifaceted contributions of pollution, UV radiation, climate ...
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