scholarly journals Least Squares Support Vector Mechanics to Predict the Stability Number of Rubble-Mound Breakwaters

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1452 ◽  
Author(s):  
Nuray Gedik
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Least Squares ◽  
Soft Computing ◽  
Support Vector ◽  
Stability Number ◽  
Experimental Works ◽  
Rubble Mound ◽  
The Stability ◽  
Rubble Mound Breakwaters

In coastal engineering, empirical formulas grounded on experimental works regarding the stability of breakwaters have been developed. In recent years, soft computing tools such as artificial neural networks and fuzzy models have started to be employed to diminish the time and cost spent in these mentioned experimental works. To predict the stability number of rubble-mound breakwaters, the least squares version of support vector machines (LSSVM) method is used because it can be assessed as an alternative one to diverse soft computing techniques. The LSSVM models have been operated through the selected seven parameters, which are determined by Mallows’ Cp approach, that are, namely, breakwater permeability, damage level, wave number, slope angle, water depth, significant wave heights in front of the structure, and peak wave period. The performances of the LSSVM models have shown superior accuracy (correlation coefficients (CC) of 0.997) than that of artificial neural networks (ANN), fuzzy logic (FL), and genetic programming (GP), that are all implemented in the related literature. As a result, it is thought that this study will provide a practical way for readers to estimate the stability number of rubble-mound breakwaters with more accuracy.

scholarly journals STABILITY OF HIGH DENSITY CUBES IN RUBBLE MOUND BREAKWATERS

2020 ◽  
pp. 4
Author(s):  
Yalcin Yuksel ◽  
Marcel van Gent ◽  
Esin Cevik ◽  
H. Alper Kaya ◽  
Irem Gumuscu ◽  
...  
Keyword(s):  
Relative Density ◽  
Slope Angle ◽  
High Density ◽  
Experimental Results ◽  
Normal Density ◽  
Stability Number ◽  
Damage Initiation ◽  
Rubble Mound ◽  
The Stability ◽  
Rubble Mound Breakwaters

The stability number for rubble mound breakwaters is a function of several parameters and depends on unit shape, placing method, slope angle, relative density, etc. In this study two different densities for cubes in breakwater armour layers were tested to determine the influence of the density on the stability. The experimental results show that the stability of high density blocks were found to be more stable and the damage initiation for high density blocks started at higher stability numbers compared to normal density cubes.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

Sign in / Sign up

Export Citation Format

Share Document