Medical data of the patients includes sensitive information and needs security while transmit over the internet. The double security approach based on watermarking and encryption method has been purposed in this work ...
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The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of *** study utilizes the historical and final learning behav...
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The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of *** study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of Technology during the 2012-2013 academic *** have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these *** goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures *** results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.
As the basic parts of workshop processing, the life of the tool will affect the processing efficiency and processing quality, and the life of the tool will be affected by a variety of uncertain factors in the processi...
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Precise diagnosis and immunity to viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and Middle East respiratory syndrome coronavirus(MERS-CoV)is achieved by the detection of the viral antigens...
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Precise diagnosis and immunity to viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and Middle East respiratory syndrome coronavirus(MERS-CoV)is achieved by the detection of the viral antigens and/or corresponding antibodies,***,a widely used antigen detection methods,such as polymerase chain reaction(PCR),are complex,expensive,and time-consuming Furthermore,the antibody test that detects an asymptomatic infection and immunity is usually performed separately and exhibits relatively low *** achieve a simplified,rapid,and accurate diagnosis,we have demonstrated an indium gallium zinc oxide(IGZO)-based biosensor field-effect transistor(bio-FET)that can simultaneously detect spike proteins and antibodies with a limit of detection(LOD)of 1 pg mL–1 and 200 ng mL–1,respectively using a single assay in less than 20 min by integrat-ing microfluidic channels and artificial neural networks(ANNs).The near-sensor ANN-aided classification provides high diagnosis accuracy(>93%)with significantly reduced processing time(0.62%)and energy consumption(5.64%)compared to the software-based *** believe that the development of rapid and accurate diagnosis system for the viral antigens and antibodies detec-tion will play a crucial role in preventing global viral outbreaks.
The "Smart Roads: Lighting the Way to Safety and Efficiency"project introduces an innovative solution to improve the efficiency and sustainability of outdoor lighting systems. This research focuses on develo...
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In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory. We present mean-field chaos diffusi...
In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory. We present mean-field chaos diffusion models (MF-CDMs), which address the curse of dimensionality inherent in high-cardinality data by utilizing the propagation of chaos property of interacting particles. By treating high-cardinality data as a large stochastic system of interacting particles, we develop a novel score-matching method for infinitedimensional chaotic particle systems and propose an approximation scheme that employs a subdivision strategy for efficient training. Our theoretical and empirical results demonstrate the scalability and effectiveness of MF-CDMs for managing large high-cardinality data structures, such as 3D point clouds.
In recent works, using voice transformation functions (VTF) in optimal shifting of formants has improved near-end speech intelligibility. Though these VTFs are promising, they are computationally expensive to optimize...
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Classification is used in many fields today, and for most of them machine learning algorithms can be used to make a decision. This article investigates the effects of different sizes of training and test datasets on t...
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Modern power systems are experiencing a rapid movement from fossil-based generations to renewable energy resources (RERs) due to concerns about the environment and the dependence on fossil fuel sources. However, the r...
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Battery energy storage systems (BESS) play an essential role in modern grids by supporting renewable power systems, improving grid power quality through voltage and frequency regulation, and supporting electric vehicl...
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