Applied Technology for Evaluation Index Modeling on Sports-Related Museum

2014 ◽  
Vol 1078 ◽  
pp. 388-391
Author(s):  
Xiu Ping Song
Keyword(s):  
Information Processing ◽  
Evaluation System ◽  
Original Data ◽  
Processing Technology ◽  
Evaluation Index ◽  
Material Resources ◽  
Applied Technology ◽  
Near Future

In this paper, the author investigated the evaluation factors of sport-related museum using information processing technology. After analyzing some original data and research the existing literature, and the author construct an evaluation index equation. The author believed that the evaluation of sport-related museum can be performed by means of using the evaluation equation. This evaluation system has an objective impartially significance. However, the author revealed the computerized evaluation system should be developed in the near future, in doing so, it can save a lot of manpower and material resources.

scholarly journals A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i787-i794
Author(s):  
Gian Marco Messa ◽  
Francesco Napolitano ◽  
Sarah H. Elsea ◽  
Diego di Bernardo ◽  
Xin Gao
Keyword(s):  
Predictive Accuracy ◽  
Hybrid Approach ◽  
Simulated Data ◽  
Original Data ◽  
Diagnostic Process ◽  
Great Promise ◽  
Metabolic Profiles ◽  
Inborn Errors ◽  
Metabolomic Data ◽  
Near Future

Abstract Motivation Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN). Results The proposed SNN model is able to perform disease prioritization for the metabolic profiles of IEM patients even for diseases that it was not trained to identify. To the best of our knowledge, this has not been attempted before. The developed model is able to significantly outperform a baseline model that relies on metabolic simulations only. The prioritization performances demonstrate the feasibility of the method, suggesting that the integration of metabolic models and data could significantly aid the IEM diagnosis process in the near future. Availability and implementation Metabolic datasets used in this study are publicly available from the cited sources. The original data produced in this study, including the trained models and the simulated metabolic profiles, are also publicly available (Messa et al., 2020).

Evaluation Indexes of Sand Liquefaction Analysed by Rough Set

2011 ◽  
Vol 243-249 ◽  
pp. 3087-3091
Author(s):  
Nian Ping Liu ◽  
Hong Tu Wang ◽  
Zhi Gang Yuan
Keyword(s):  
Rough Set ◽  
Groundwater Level ◽  
Analytical Method ◽  
Evaluation System ◽  
Evaluation Index ◽  
Determination Method ◽  
Sand Liquefaction ◽  
Evaluation Indexes ◽  
Result Show ◽  
The Evaluation System

Sand liquefaction is a problem of complex evolution of the disaster, there is no accurate way to judge at present, this study put forward an analytical method to improve and optimize the evaluation system of sand liquefaction based on rough set. The significance of indexes are confirmed by calculating rough dependability between indexes and result for appraisement, the result show that SPT blow count has the greatest impact on the evaluation system, the groundwater level has greater impact, followed by the sand depth, epicenteral distance and duration. The proposed approach overcame the subjectivity of traditional weight determination method, so it is more objective and accurate, and it is reasonable and effective to optimize the evaluation index of sand liquefaction.

The Research of Word Similarity in Semantic Retrieval

2014 ◽  
Vol 701-702 ◽  
pp. 413-417
Author(s):  
Jie Ran ◽  
Ji Ya Huang ◽  
Zu Xiao
Keyword(s):  
Information Processing ◽  
High Efficiency ◽  
Processing Technology ◽  
Semantic Retrieval ◽  
Word Similarity ◽  
Crucial Question ◽  
Computing Method

Word similarity computing is a crucial question in information processing technology. In this paper, an integrated word similarity computing method is proposed by analyzed morpheme's similarity, word order's similarity and word length's similarity, and parameters of the method are decided by experiments. The experiments show that this method has high efficiency.

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