Predictions of Weekly Soil Movements Using Moving-Average and Support-Vector Methods: A Case-Study in Chamoli, India

Author(s):  
Praveen Kumar ◽  
Priyanka Sihag ◽  
Ankush Pathania ◽  
Shubham Agarwal ◽  
Naresh Mali ◽  
...  
2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  

2020 ◽  
Vol 15 (6) ◽  
pp. 517-527
Author(s):  
Yunyun Liang ◽  
Shengli Zhang

Background: Apoptosis proteins have a key role in the development and the homeostasis of the organism, and are very important to understand the mechanism of cell proliferation and death. The function of apoptosis protein is closely related to its subcellular location. Objective: Prediction of apoptosis protein subcellular localization is a meaningful task. Methods: In this study, we predict the apoptosis protein subcellular location by using the PSSMbased second-order moving average descriptor, nonnegative matrix factorization based on Kullback-Leibler divergence and over-sampling algorithms. This model is named by SOMAPKLNMF- OS and constructed on the ZD98, ZW225 and CL317 benchmark datasets. Then, the support vector machine is adopted as the classifier, and the bias-free jackknife test method is used to evaluate the accuracy. Results: Our prediction system achieves the favorable and promising performance of the overall accuracy on the three datasets and also outperforms the other listed models. Conclusion: The results show that our model offers a high throughput tool for the identification of apoptosis protein subcellular localization.


2021 ◽  
Vol 13 (5) ◽  
pp. 949
Author(s):  
Salman Qureshi ◽  
Saman Nadizadeh Shorabeh ◽  
Najmeh Neysani Samany ◽  
Foad Minaei ◽  
Mehdi Homaee ◽  
...  

Due to irregular and uncontrolled expansion of cities in developing countries, currently operational landfill sites cannot be used in the long-term, as people will be living in proximity to these sites and be exposed to unhygienic circumstances. Hence, this study aims at proposing an integrated approach for determining suitable locations for landfills while considering their physical expansion. The proposed approach utilizes the fuzzy analytical hierarchy process (FAHP) to weigh the sets of identified landfill location criteria. Furthermore, the weighted linear combination (WLC) approach was applied for the elicitation of the proper primary locations. Finally, the support vector machine (SVM) and cellular automation-based Markov chain method were used to predict urban growth. To demonstrate the applicability of the developed approach, it was applied to a case study, namely the city of Mashhad in Iran, where suitable sites for landfills were identified considering the urban growth in different geographical directions for this city by 2048. The proposed approach could be of use for policymakers, urban planners, and other decision-makers to minimize uncertainty arising from long-term resource allocation.


Ecohydrology ◽  
2014 ◽  
Vol 8 (6) ◽  
pp. 1109-1118 ◽  
Author(s):  
C. Díaz Muñiz ◽  
J. R. Alonso Fernández ◽  
P. J. García Nieto ◽  
J. C. Alvarez Antón

2013 ◽  
Vol 291-294 ◽  
pp. 2164-2168 ◽  
Author(s):  
Li Tian ◽  
Qiang Qiang Wang ◽  
An Zhao Cao

With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.


2014 ◽  
Vol 602-605 ◽  
pp. 370-374
Author(s):  
Hong Bo Xu ◽  
Jia Yu Li

Health assessment of the girder is crucial to an overhead traveling crane. This paper presents an intelligent damage identification method for the girder based on stiffness variation index (SVI) and least squares support vector machine (LSSVM). In the method, the SVI indicators, which have high resolution to environmental noise, serve as the damage feature to detect damage locations. Moreover, the SVI indicators are input to the LSSVM classifier for identifying the actual damage level of the girder. A case study on girder damage identification demonstrates that the method could determine the actual conditions of the girder structure accurately.


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