This paper provides a review of unsupervised and unsupervised categorization algorithms using safety surrogate data to predict the severity of traffic conflicts using processed traffic video data at conflict sites as ...
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According to the characteristics of large throughput and strong effectiveness of carbon emissions information in urban transportation, people can manage this type of information more effectively. People have studied a...
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The ability to design and synthesize proteins with specific functions holds great significance for drug development, drug delivery, targeted therapy, and materials science. With the advancement of deep learning techno...
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ISBN:
(纸本)9789819756919;9789819756926
The ability to design and synthesize proteins with specific functions holds great significance for drug development, drug delivery, targeted therapy, and materials science. With the advancement of deep learning technology, computational protein design has flourished. This approach allows for the design of amino acid sequences based on the desired protein function and structure, and can even generate proteins not found in nature. Deep learning algorithms can process large amounts of protein structure data and extract useful information to help scientists better understand protein expression and function, thereby accelerating the protein design process.
Aiming at the problem of random generation of Extreme learningmachine (ELM) parameters, an intrusion detection model based on GA-ELM of K-fold stratified cross-validation is proposed. Genetic Algorithm (GA) is used t...
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The kernel trick in supervised learning signifies transformations of an inner product by a feature map, which then restructures training data in a larger Hilbert space according to an endowed inner product. A quantum ...
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Real estate valuation is a complex process conditioned by real estate's legal, economic, and social environment. Difficulties faced by the real estate appraiser in the valuation process become the reason for resea...
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With the increasing number of ships on the sea, the frequency multi-ship encounters situation was becoming more common than two-ship encounter. The complexity and risk of the navigation will exponentially increase wit...
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With the increasing number of ships on the sea, the frequency multi-ship encounters situation was becoming more common than two-ship encounter. The complexity and risk of the navigation will exponentially increase with the more ships involved. Relying solely on the international Regulations for Preventing Collisions at Sea (COLREGS) (objective knowledge) was insufficient to handle the multi-ship intelligent collision avoidance problem, also needed the ship officer's good seamanship (subjective knowledge). In this study, we propose a methodology that combines subjective insights from AIS big data with objective analysis through multi-ship encounters recognition with graph convolutional networks (GCN). (1) The ship encounter 8-azimuths map was utilized to identify the two-ship encounter situation (25 types) from the AIS data. (2) Identify the multi-ship encounters trajectory data by cross-matching the two-ship encounter data. (3) To handle the intricate relationship information between the multiple ships which were transformed into a graph structure using graph theory. (4) Finally, a spatial-temporal edge and node attention graph convolutional network (ST-ENAGCN) was proposed with the graph convolutional unit and long short-term memory (LSTM) unit. The 2022 Ningbo Sea area AIS big data was utilized to achieve graph-structured learning regarding human experiences during the multiship encounters situation. The results indicate that the ST-ENAGCN model can understand the complex marine traffic situation to make the own ship collision avoidance decisions. This study contributes significantly to the increased efficiency and safety of sea operations in complex marine traffic conditions and support autonomous navigation of swarm of MASSs under the human-machine hybrid (HMH) conditions to better understand the collision avoidance intention of the ship officer in the multi-ship encounters situation.
As machinelearning technology penetrates into various fields, how to ensure the quality of machinelearning systems becomes an urgent problem. Current requirements modeling methods for machinelearning systems are st...
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The Long Short-Term Gradient (LSTG) architecture innovatively combines the time series analysis capability of Long short-term memory network (LSTM) with the efficient training strategy of Mini-batch Gradient Descent (...
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Accuracy of a classifier is important for the success of any prediction model. The more accuracy a classifier possesses, the more robust the system is made on it. In this paper, a disease prediction model is developed...
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