Artificial plant optimization algorithm is a novel stochastic population-based evolutionary algorithm by simulating the plant growing process. In this paper, a new variant, which is called APOA-DC is proposed by incor...
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Artificial plant optimization algorithm is a novel stochastic population-based evolutionary algorithm by simulating the plant growing process. In this paper, a new variant, which is called APOA-DC is proposed by incorporating with dynamic population size and cluster methods, furthermore, to investigate the performance, APOA-DC is applied to optimize the DV-Hop localization algorithm, simulation results show it achieves better performance than the DV-Hop algorithm.
This paper presents a saliency based bag-of-phrases (Saliency-BoP for short) method for image retrieval. It combines saliency detection with visual phrase construction to extract bag-of-phrase features. To achieve thi...
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Whole genome sequences are generally accepted as excellent tools for studying evolutionary relationships. Due to the problems caused by the uncertainty in alignment, existing tools for phylogenetic analysis based on m...
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In this paper, we propose a novel queue-based privacy-preserving data aggregation scheme for additive aggregation function. In the scheme, sensor nodes are divided into clusters in a distributed way first, and then, i...
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In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein struc...
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The aim of this paper is to develop an improved AP clustering algorithm based on the quotient space granularity selection. Firstly, we give the characteristics of the quotient space granularity and Affinity Propagatio...
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Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is c...
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Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. This thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real Time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. The results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.
Given a budget and a network where different nodes have different costs to be selected, the budgeted influence maximization is to select seeds on budget so that the number of final influenced nodes can be maximized. I...
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An axiomatic system is presented in this paper, which has a modal operator such that φ ≡ 1 φ ˄ 2φ,where 1 and 2 are the modal operators of the language for the axiom system S5. The axiomatic system for is proved t...
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