This paper introduces a pioneering approach integrating Advanced Encryption Standard (AES) security algorithms with multi-objective drug design, aimed at personalized medicine and optimized drug discovery. By leveragi...
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This research study presents an optimized wind energy conversion system for increased energy output through the use of Graph Neural Networks (GNNs) and machine learning. In order to increase efficiency, the system inc...
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The loss of melanocytes caused by the chronic autoimmune disease vitiligo leads in depigmented patches of skin that are frequently hard to distinguish from other hypopigmented disorders. Effective therapy depends on e...
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The title is 'Rainfall Prediction Using Machine learning'. The initiative's dataset is written in Python and stored in Microsoft Excel. A wide range of machine learning algorithms are used to discover whic...
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Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors...
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ISBN:
(纸本)9798400706585
Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)-based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.
The prediction of stock prices remains a perpetual challenge and opportunity in the domain of finance and investment. In this study, we delve into the realm of machine learning, employing both Linear Regression and Lo...
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Binary similarity is to identify the semantic similarities of two or more binary code snippets. In recent years, deep learning-based methods have shown promising results. They formalize code similarity as the nearest ...
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ISBN:
(纸本)9781665455336
Binary similarity is to identify the semantic similarities of two or more binary code snippets. In recent years, deep learning-based methods have shown promising results. They formalize code similarity as the nearest neighbor retrieval task, and the overall workflow can be divided into two stages: 1) feeding the code snippets into the embedding model to get the corresponding high-dimensional vectors as fingerprints (i.e., constructing the codebase). 2) using the codebase for nearest neighbor retrieval to get the top-k results. Most existing studies only focus on the first stage (more specifically, the embedding model) while ignoring the overhead of the retrieval stage. In real-world scenarios, the codebase could be quite large and contain massive embeddings, which keeps the precise nearest neighbor retrieval prohibitive expensive. To mitigate the issue above, this paper proposes a novel approach, dubbed BinCH, which can efficiently perform code search without sacrificing accuracy.
Detecting frauds in computing platforms involves identifying malicious user activity sessions. Recently, deep learning models have been employed to design fraud detection approaches. Effective training of these deep l...
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Autonomous vehicles (AVs) are susceptible to cyberattacks due to their reliance on vehicle-to-everything (V2X) communication. This research proposes a Gradient Boosting-based intrusion detection system to safeguard AV...
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Artificial Intelligence (AI) part of Federated learning (FL) builds on distributed data and modelling to deliver learning to the edge of the device. Even though FL has been celebrated as the beginning of AI, it has no...
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