[ad_1] Introduction With the rapid growth of Internet of Things (IoT) devices and the increasing demand for real-time data processing, edge computing has emerged as a promising solution to address the limitations of cloud computing.…
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[ad_1] Introduction Neuromorphic computing has emerged as a promising approach to developing brain-inspired computing systems that mimic the behavior of the human brain. Spiking neural networks, which are based on the principles of biological neural…
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[ad_1] Introduction Distributed ledger technology (DLT), which emerged as the underlying technology of cryptocurrencies like Bitcoin, has gained significant attention in recent years. Particularly, the application of DLT in decentralized finance (DeFi) has the potential…
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[ad_1] Introduction Homomorphic encryption is a revolutionary technology that allows computations to be performed directly on encrypted data without the need to decrypt it. This breakthrough in cryptography has significant implications for secure image processing,…
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[ad_1] Thesis Overview: Title: Generative Adversarial Networks for Video Prediction Introduction: Generative adversarial networks (GANs) have gained significant attention in recent years for their ability to generate realistic data samples. Video prediction is a challenging…
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[ad_1] Introduction In recent years, deep learning has become a popular and powerful approach for solving complex machine learning problems, including time series forecasting. However, one of the main challenges with deep learning models is…
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[ad_1] Introduction: Quantum computing has the potential to revolutionize the field of cryptography by breaking many of the commonly used encryption schemes. As quantum computers become more powerful, it is crucial to develop secure multi-party…
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[ad_1] Introduction Quantum machine learning (QML) has emerged as an interdisciplinary field that combines quantum physics and machine learning techniques to solve complex computational problems. One of the key areas of research in QML is…
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[ad_1] Introduction Quantum-inspired optimization has emerged as a promising approach for solving complex optimization problems in various domains. In the context of network resource allocation, the efficient utilization of resources is crucial for ensuring high…
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[ad_1] Introduction: Zero-shot learning for semantic segmentation is a cutting-edge research topic in the field of computer vision, with the potential to revolutionize the way we approach image segmentation tasks. Traditional semantic segmentation methods require…
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