Drug discovery hinges on the accurate prediction of binding affinity between prospective drug molecules and target proteins that influence disease progression, which is financially and computationally demanding. Altho...
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ISBN:
(纸本)9798331541378
Drug discovery hinges on the accurate prediction of binding affinity between prospective drug molecules and target proteins that influence disease progression, which is financially and computationally demanding. Although classical and hybrid quantum machine learning models have been employed in previous studies to aid in binding affinity prediction, they encounter several issues related to convergence stability and prediction accuracy. In this regard, this paper introduces a novel hybrid quantum -classical deep learning model tailored for binding affinity prediction in drug discovery. Specifically, the proposed model synergistically integrates 3D and spatial graph convolutional neural networks within an optimized quantum circuit architecture. Simulation results demonstrate a 6% improvement in prediction accuracy relative to existing classical models, as well as a significantly more stable convergence performance compared to previous classical approaches. Moreover, to scalably deploy the proposed framework over today's noisy intermediate-scale quantum (NISQ) devices, a novel quantum error mitigation algorithm is proposed. This algorithm outperforms existing techniques and is capable of mitigating errors with gate noise probabilities, p < 0.05, while resulting in no additional overhead during the training and testing phases.
The paper explores the complexities of personalized breast cancer treatment, by integration of multi-omics data, clinical data, and advanced computational tools. The heterogeneous nature of breast cancer causes challe...
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The detection and recognition of sprockets and springs play an important role in ensuring the optimal functionality and safety of various mechanical systems. This abstract gives an overview of the methodologies and ad...
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The purpose of safeguarding public safety,protecting vital infrastructure, and preventing security breaches, anomaly detection in surveillance camera systems is an essential responsibility. Deep learning techniques ha...
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Floods occur when water overflows onto normally dry land and are a destructive natural disaster. In recent times, deep learning models have demonstrated their remarkable capabilities in identifying objects and classif...
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Advancements in medical education are continuously evolving, with virtual reality (VR) emerging as a groundbreaking technology reshaping the landscape of radiology training. This abstract presents a paradigm shift in ...
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For real-time mission-critical applications such as forest fire detection, oil refinery monitoring, etc., the edge computing paradigm is heavily used to process data fetched from IoT devices spread over a considerably...
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ISBN:
(纸本)9783031639913;9783031639920
For real-time mission-critical applications such as forest fire detection, oil refinery monitoring, etc., the edge computing paradigm is heavily used to process data fetched from IoT devices spread over a considerably large geographical region. For such real-time edge computing applications working under stringent deadlines, the overall retrieval delay, i.e., the delay in fetching the data from the IoT devices to the edge servers, needs to be minimized;Otherwise, the retrieval delay in fetching the data from IoT devices distributed over such a large geographical region can be prohibitively large. To achieve the above goal, each IoT device must be assigned to a particular edge server while considering the relative positioning as per the topology of the edge cluster. We prove that the above assignment of IoT devices to an edge cluster, which we denote as the Edge Assignment Problem (EAP), is NP-Hard. Therefore, obtaining a polynomial time solution is infeasible. For the above EAP problem, instead of performing both exploration and exploitation on the search space, state-of-the-art heuristic algorithms will only exploit the search space. As a result, these algorithms are unable to achieve an appreciably large reduction in the overall retrieval delay. To that end, we propose a Deep Reinforcement learning-based algorithm that is able to produce a near-optimal assignment of IoT devices to the edge cluster while ensuring that none of the edge servers is overloaded. We motivate and demonstrate our proposed algorithm with the use case of federated learning (FL) - a popular distributed machine learning paradigm that is based on the principle of edge computing such that the clients, i.e., edge servers, train local models from the data obtained from local IoT devices. These local models are further aggregated into a global model at an aggregator (the cloud/fog) by exchanging the model parameters instead of raw data. In that case, an optimal assignment of the IoT devices to each
Home automation is a technology that is often viewed as a luxury and not a necessity. The means of achieving home automation is always considered expensive and inconvenient in the long run. Due to this there is no suc...
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Distinguishing counterfeit news is an essential undertaking in the contemporary era of digital technology. Fabricated news refers to information that has been deliberately fabricated or manipulated and is then publish...
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TO address the issue of overfitting in deep learning testing and pattern collapse in traditional generative adversarial networks (Gans),an based on deep learning improved Star Generative Adversarial network (StarGAN) ...
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