Since the outbreak of COVID-19 in 2019, more than 200 million individuals have been infected worldwide, resulting in over four million deaths. Although large-scale nucleic acid test is an effective way to diagnose COV...
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ISBN:
(纸本)9781665426565
Since the outbreak of COVID-19 in 2019, more than 200 million individuals have been infected worldwide, resulting in over four million deaths. Although large-scale nucleic acid test is an effective way to diagnose COVID-19, the possibility of false positives or false negatives means that the chest CT scan remains a necessary tool in COVID-19 diagnosis for cross-validation. A lot of research has been carried out using deep learning methods for COVID-19 diagnosis using CT scans. However, privacy concerns result in very limited datasets being publicly available. In this research, we propose a novel framework based on the centripetal contrastive learning of visual representations (CeCLR) method with stacking ensemble learning to represent features more efficiently so as to achieve better performance on a limited COVID-19 dataset. Experimental results demonstrate that our deep learning system is superior to other baseline models. Our method achieves an F1 score of 0.914, AUC of 0.952, and accuracy of 0.909 when diagnosing COVID-19 on CT scans.
With the rapid development of China High-Speed Rail, massive text data related to railway safety has been accumulated. When analyzing and understanding this data, classifying railway accident report text is essential ...
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ISBN:
(纸本)9781665426565
With the rapid development of China High-Speed Rail, massive text data related to railway safety has been accumulated. When analyzing and understanding this data, classifying railway accident report text is essential and tedious work. Usually, such classification tasks are manually done by experts and workers in the railway safety department. Traditional data mining algorithms have been applied in these tasks to classify the text automatically. However, due to the complexity of the text data, classification algorithms sometimes fail and have insufficient learning ability. Meanwhile, the rise of machine learning enables us to deal with these complex problems effectively. In this paper, we propose an end-to-end multi-layer convolutional neural networks model to classify the railway safety-related text. We update the CNN part of the traditional model by increasing layers and adding a multi-height convolutional kernel. Additionally, we develop a data-preprocessing strategy to obtain the neat input data and reduce the complexity of the task. Experiments show that our proposed method achieves competitive performance and is suitable for railway safety-related text classification problems.
The emerging smart grid uniquely combines two-way communication and energy flow, allowing consumers to become active participants in market-based energy supply and demand strategies. In such a market, Peer-to-Peer (P2...
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ISBN:
(纸本)9781665426565
The emerging smart grid uniquely combines two-way communication and energy flow, allowing consumers to become active participants in market-based energy supply and demand strategies. In such a market, Peer-to-Peer (P2P) energy trading paradigm allows local communities and individuals who generate electricity to freely decide how and with whom they are going to trade it. The greatest challenge of P2P energy trading is how to design efficient mechanisms among rational participants that maximize their monetary benefits. Furthermore, since utility companies own the transmission lines, a key question that yet to be addressed in P2P markets is: how to match between different energy buyers and sellers while taking into account the physical constraints of the underlying grid infrastructure, e.g., capacity, congestion, and line transmission costs. This paper proposes a novel double-sided auction mechanism with a matching algorithm that addresses the aforementioned challenges. In this paper, the social welfare of the participants is modeled as an optimization problem with cost constraints incurred due to energy generation, operating and maintenance, capacity, and line transmission costs. The study provides theoretical analysis of the P2P auction model including mechanism design properties such as individual rationality, computational efficiency, and truthfulness. The results of the experiments indicate that the proposed auction model outperform existing systems and yields better economic incentives for participants.
The article aims to present the GenBank System for managing the gene bank in the Institute for Plant Genetic Resources in the town of Sadovo (IPGR). The functionality and architecture of the system are described. Some...
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With the progress of new technologies, certain areas such as health have seen their functioning modes to be changed to accommodate the trend of the actual world. Then, new concepts have emerged such as telemedicine, e...
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This article presents an intelligent tourist guide known as TG that takes into account various factors, such as the tourist's preferences, location, time available, and the presence and location of cultural and hi...
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This article represents the view of the authors about the needs and the implementation of a distributed system, among all institutes in Bulgaria, for management of plant genetics resources, according the standards def...
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The proceedings contain 146 papers. The topics discussed include: high-dimensional real parameter clonal selection memory algorithm;an empirical research of marine fishery forecasting methods based on the classificati...
ISBN:
(纸本)9781509034833
The proceedings contain 146 papers. The topics discussed include: high-dimensional real parameter clonal selection memory algorithm;an empirical research of marine fishery forecasting methods based on the classification model;refinement of a Newton reciprocal algorithm for arbitrary precision numbers;a time series clustering method based on hypergraph partitioning;sparse coding with sparse dictionaries for credit risk classification;a reduced weighted Wang-Mendel algorithm using the clustering algorithm to build fuzzy system;real time activity recognition on streaming sensor data for smart environments;forecasting house price index of china using dendritic neuron model;training a dendritic neural model with genetic algorithm for classification problems;improving Elman neural network model via fusion of new feedback mechanism and genetic algorithm;a new algorithm of diagnosis strategy based on fault criticality;a new logistic map based chaotic biogeography-based optimization approach for cluster analysis;a search-based approach for test suite generation from extended finite state machines;a novel multi-agent knowledge reasoning method for cooperation and confrontation;fitness and diversity guided particle swarm optimization for global optimization and training artificial neural networks;and chaotic grey wolf optimization.
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