A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. the aim is to build a fast and efficient suppo...
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A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. the aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. the selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times.
MOVICAB-IDS enables the more interesting projections of a massive traffic data set to be analysed, thereby providing an overview of any possible anomalous situations taking place on a computer network. this IDS respon...
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the purpose of this paper is to solve Quality-of-Service (QoS) multicast routing problem by Particle Swarm Optimization (PSO). the QoS multicast routing optimization problem was transformed into a quasicontinuous prob...
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Random Walks (RW) search technique can greatly reduce bandwidth production but generally fails to adapt to different workloads and environments. A Random Walker can't learn anything from its previous successes or ...
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the intuitionistic fuzzy set, developed by Atanassov [1], is a useful tool to deal with vagueness and uncertainty. Correlation analysis of intuitionistic fuzzy sets is an important research topic in the intuitionistic...
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Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and Au...
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ISBN:
(纸本)9781728189956
Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and automated Vehicles (CAVs) with carefully designed longitudinal control have the potential to dampen the stop-and-go wave based on simulated vehicle trajectories. In this study, Deep Reinforcement learning (DRL) is adopted to control the longitudinal behavior of CAVs and real-world vehicle trajectory data is utilized to train the DRL controller. It considers a Human-Driven (HD) vehicle tailed by a CAV, which are then followed by a platoon of HD vehicles. Such an experimental design is to test how the CAV can help to dampen the stop-and-go wave generated by the lead HD vehicle and contribute to smoothing the following HD vehicles' speed profiles. the DRL control is trained using real-world vehicle trajectories, and eventually evaluated using SUMO simulation. the results show that the DRL control decreases the speed oscillation of the CAV by 54% and 8%-28% for those following HD vehicles. Significant fuel consumption savings are also observed. Additionally, the results suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically.
As an Internet application, smart tourism has greatly enriched the tourism information. In this paper, we propose a unified modeling and expression method of attraction texts and images based on the text deep represen...
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ISBN:
(数字)9783319689357
ISBN:
(纸本)9783319689357;9783319689340
As an Internet application, smart tourism has greatly enriched the tourism information. In this paper, we propose a unified modeling and expression method of attraction texts and images based on the text deep representation model and convolution neural network. According to the cross-media characteristics of tourism big data, we propose a semantic learning and analysis method for cross-media data, and correlate tourism texts with images based on deep features and topic semantics. Experimental results show that the proposed method can achieve better results for semantic analysis and cross-media retrieval of tourism big data.
In this paper, the problem of routing multicast demands in elastic optical network (EON) is studied. In order to solve the problem in an efficient way, the tabu search algorithm with move prioritization mechanism is p...
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ISBN:
(纸本)9783319248349;9783319248332
In this paper, the problem of routing multicast demands in elastic optical network (EON) is studied. In order to solve the problem in an efficient way, the tabu search algorithm with move prioritization mechanism is proposed. the algorithm is considered in 4 versions, which differ in the move generation process. Next, the numerical experiments are conducted in order to evaluate the algorithm performance and compare its different versions. According to the results, the proposed TS method achieves very good results and significantly outperforms the reference algorithms.
In this paper we apply Biased Minimax Probability Machine (BMPM) to address the problem of relevance feedback in Content-based Image Retrieval (CBIR). In our proposed methodology we treat relevance feedback task in CB...
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this paper illustrates how to compare different agent-based models and how to compare an agent-based model with real data. As examples we investigate ARFIMA models, the probability density function, and the spectral d...
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ISBN:
(纸本)9783540772255
this paper illustrates how to compare different agent-based models and how to compare an agent-based model with real data. As examples we investigate ARFIMA models, the probability density function, and the spectral density function. We illustrate the methodology in an analysis of the agent-based model developed by Levy, Levy, Solomon (2000), and confront it withthe S&P 500 for a comparison with real life data.
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