Scene classification is a popular and important question in computer vision and has been developed in different areas. Applying computer vision to artworks has become a popular topic in recent years. However, the trad...
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The present research work depicts the in-depth analysis of physiological data through machinelearning technology. The use of machinelearning is helpful in deriving the data regarding patients with personal stress. T...
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With the invention of IoT and its range of smart applications, people’s life has been reformed drastically. IoT infrastructure consists of actuators and sensors that generate a massive volume of data that requires ex...
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A modified Grey Markov prediction model was developed by combining traditional Grey Models with Markov modified prediction models, and further optimizing the weights of intermediate end-points in the Markov model usin...
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ISBN:
(纸本)9798400710353
A modified Grey Markov prediction model was developed by combining traditional Grey Models with Markov modified prediction models, and further optimizing the weights of intermediate end-points in the Markov model using simulated annealing algorithm. This method provides a solution to the problem of poor fitting performance of the original Grey prediction model when dealing with volatility data, and improves the accuracy of prediction. Through empirical analysis of the total fishery output value (in billions of yuan) of Hubei Province from 2002 to 2022, the reliability test results show that the Grey Markov model optimized by simulated annealing exhibits better predictive reliability compared to traditional Grey models and standard Grey Markov models.
Society keeps ever-growing, and new challenges keep arising for the artificial intelligence to solve, fueled by the desire to enable global operations and optimize current deep and machinelearning methodologies. One ...
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This work advances randomized exploration in reinforcement learning (RL) with function approximation modeled by linear mixture MDPs. We establish the first prior-dependent Bayesian regret bound for RL with function ap...
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This work advances randomized exploration in reinforcement learning (RL) with function approximation modeled by linear mixture MDPs. We establish the first prior-dependent Bayesian regret bound for RL with function approximation;and refine the Bayesian regret analysis for posterior sampling reinforcement learning (PSRL), presenting an upper bound of (O) over tilde (d root (HT)-T-3 log T), where d represents the dimensionality of the transition kernel, H the planning horizon, and T the total number of interactions. This signifies a methodological enhancement by optimizing the O( root log T) factor over the previous benchmark (Osband and Van Roy, 2014) specified to linear mixture MDPs. Our approach, leveraging a value-targeted model learning perspective, introduces a decoupling argument and a variance reduction technique, moving beyond traditional analyses reliant on confidence sets and concentration inequalities to formalize Bayesian regret bounds more effectively.
Convolutional Neural Networks (CNNs) are artificial deep learning networks widely used in computer vision and image recognition for their highly efficient capability of extracting input image features. In the literatu...
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ISBN:
(纸本)9798400717185
Convolutional Neural Networks (CNNs) are artificial deep learning networks widely used in computer vision and image recognition for their highly efficient capability of extracting input image features. In the literature, such a successful tool has been leveraged for detection/classification purposes in several application domains where input data are converted into images. In this work, we consider the application of CNN models, developed by employing standard Python libraries, to detect and then classify Android-based malware applications. Different models are tested, even in combination with machinelearning-based classifiers, with respect to two datasets of 5000 applications each. To emphasize the adequacy of the various CNN implementations, several performance metrics are considered, as also stressed by a comprehensive comparison with related work.
Combined with a deep learning-based overloaded vehicle information capture and recognition method, the captured information from bridge surface images is used to establish a mapping relationship between real-time moni...
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Obfuscated malware refers to malicious software that has been deliberately modified to hide its malicious purpose and avoid detection by conventional security mechanisms. Classifying obfuscated malware poses a signifi...
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In the era of big data, software algorithms are improving rapidly, and machinelearning algorithms are also widely used. In doing so, transmitting signals from quantum devices will also contribute to research on machi...
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