The Extreme Learning Machine (ELM) is a single-hidden layer feedforward neural network (SLFN) learning algorithm that can learn effectively and quickly. The ELM training phase assigns the input weights and bias random...
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Strong story interactive narratives (IN) are stories that branch based on participant actions where all branches conform to a set of predefined constraints. However, participants in these systems may create branches w...
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A goal of Experience Managers (EM) is to guide users through a space of narrative trajectories, or story branches, in an Interactive Narrative (IN). When a user performs an action that deviates from the intended traje...
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Modeling preference time in triathlons means predicting the intermediate times of particular sports disciplines by a given overall finish time in a specific triathlon course for the athlete with the known personal bes...
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Existing input methods for inserting text into a digital device are slow and create a steep contrast of information exchange difference between a human and a computer. While programmers are fast at getting information...
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In order to improve the preclinical diagnose of Alzheimer's disease (AD), there is a great deal of interest in analyzing the AD related brain structural changes with magnetic resonance image (MRI) analyses. As the...
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ISBN:
(纸本)9781509011735
In order to improve the preclinical diagnose of Alzheimer's disease (AD), there is a great deal of interest in analyzing the AD related brain structural changes with magnetic resonance image (MRI) analyses. As the major features, variation of the structural connectivity and the cortical surface morphometry provide different views of structural changes to determine whether AD is present on presymptomatic patients. However, the large scale tensor-valued information and relatively low imaging resolution in diffusion MRI (dMRI) have created huge challenges for analysis. In this paper, we propose a novel framework that improves dMRI analysis power by fusing cortical surface morphometry features from structural MRI (sMRI). We first compute the hyperbolic harmonic maps between cortical surfaces with the landmark constraints thus to precisely evaluate surface tensor-based morphometry. Meanwhile, the graph-based analysis of structural connectivity derived from dMRI is conducted. Next, we fuse these two features via the optimal mass transportation (OMT) and eventually the Wasserstein distance (WD) based single image index is computed as a potential clinical multimodality imaging score. We apply our framework to brain images of 20 AD patients and 20 matched healthy controls, randomly chosen from the Alzheimer's Disease Neuroimaging Initiative (ADNI2) dataset. Our preliminary experimental results of group classification outperformed those of some other single dMRI-based features, such as regional hippocampal volume, mean scores of fractional anisotropy (FA) and mean axial (MD). The novel image fusion pipeline and simple imaging score of structural changes may benefit the preclinical AD and AD prevention research.
The Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) has obtained very good results on various multi-objective optimization problems in the past few years. ...
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The Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) has obtained very good results on various multi-objective optimization problems in the past few years. This paper focuses on an attempt to improve even more its performance by introducing a hyper-heuristic mechanism to select the best set of its operators and parameters. In this paper we use Upper Confidence Bound (UCB) as the basis of the hyper-heuristic, and test three versions of the proposed approach. Four well known benchmarks (CEC 2009, WFG, DTLZ and ZDT) and a quality indicator (hypervolume) are used to analyze the performance of the three variants. The proposed approach is compared with the original MOEA/D-DRA and the results show that tuning the parameters via UCB is an interesting alternative for a hyper-heuristic based version of MOEA/D-DRA on the addressed problems.
Bogor regency consists of 40 districts, 23 are prone tonatural disasters. Klapanunggal district is listed in 10 districts declared as most vulnerable to natural disasters. Natural disasters could lead to loss of prope...
Bogor regency consists of 40 districts, 23 are prone tonatural disasters. Klapanunggal district is listed in 10 districts declared as most vulnerable to natural disasters. Natural disasters could lead to loss of property and infrastructure damage and will affect the food security in the region. Food shortages is one example of the condition which causes food insecurity. The aim of this research is to analyze the government's food security strategy in anticipation of disaster with a case study of food insecurity in Klapanunggal district. The analysis suggested that; 1) FSVA is an appropriate program to identify food shortage areas, 2) Food Shortage Relief program (program Penanganan Daerah Rawan Pangan-PDRP) is the optimal efforts in reducing food shortages in the region, 3)The mismatch between FSVA indicators and Food Shortage Relief program makes Klapanunggal district difficult in achievingfree status food-shortage. Based on the analysis, it is suggested that the implementation of Food Shortages Relief program could be carried out based on the priority issues and implemented with integrated coordination and assistance among stakeholders. Such priority issues, integrated coordination and assistance are fully analyzed in this study.
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