information security has become a vital issue in contemporary society. With the rapid development of Internet technology and computer network technology, people's demand for information has become higher and highe...
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The objective of this research is to propose an AI based novel augmented reality (AR) software to assist users in operating a wide range of technical and non-technical devices. The project's aim is to make this AR...
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The proposed work employs ns-3, SUMO, and NetAnim to create geographical routing in vehicular ad hoc networks (VANETs). The research attempts to assess the performance of three well-known protocols AODV, DSDV, and OLS...
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The application of Distributed Intrusion Detection System (DIDS) in campus network is a security technology which aims at monitoring and analyzing network attacks. With the increasing number of campus network users an...
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Bearing fault diagnostics is essential for ensuring operational effectiveness and preventing failures across various industries, which often result in substantial financial losses. The development of quantum machine l...
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ISBN:
(纸本)9798350308266;9798350308259
Bearing fault diagnostics is essential for ensuring operational effectiveness and preventing failures across various industries, which often result in substantial financial losses. The development of quantum machine learning has introduced a paradigm shift in defect diagnostic methods, leveraging the computational power of quantum computers. This study investigates the impact of quantum computing and classical computing algorithms on Case Western Reserve University bearing dataset. The proposed methodology encompasses normalization, segmentation, data augmentation, and feature extraction/selection, executed on classical computer. Selected classical features are encoded as quantum state amplitudes, followed by the application of superposition and entanglement to qubits. The classification stage employs a quantum support vector machine, implemented, and simulated using the available IBM quantum resources. The results showcase 99.61% training accuracy and 99.14% testing accuracy with overall F1-score, recall, and precision of 99%. Comparative analysis between classical and quantum machine learning algorithms is conducted perfomace anlysis. These outcomes underscore the potential of quantum machine learning in defect diagnosis, offering heightened precision and reliability in practical applications with fast computing.
The recognition of secure data transmission in a cloud platform requires the integrity of sensitive information with higher confidentiality. This is obtained using the aid of deep learning techniques. The robustness o...
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Performance prediction for students is relatively a new topic of research that aims to apply quantitative and qualitative approaches to evaluate student performance. This paper reviews existing approaches in predictin...
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This work investigates an adaptively updating rule of the penalty in alternating direction method (ADM) for l1 problem. The adjusting criterion is motivated by the iterative behaviors of the objective and residue of c...
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Clustering mixed-type data, which includes both continuous and categorical features, presents significant challenges due to the distinct nature of these data types. Many traditional distance-based and density-based me...
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With the rapid development of informationtechnology, modern network communication is encountering increasing challenges and opportunities. With the popularization and implementation of 5G technology, higher demands h...
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