D2 shape distribution and artificial neural networks for  3D objects recognition

In this paper, we propose a 3D object recognition approach, based on the shape distribution D2 and artificial neural networks. The challenge is to discriminate between similar and dissimilar shapes by finding a shape signature that can be constructed and classified quickly. We propose a connectionist system to recognize 3D objects in VRML (Virtual Reality Modeling Language) format. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The proposed strategy is the following: from a polygon object to be recognized, a triangulation is performed. Then, distances are calculated between two random points of the triangulated surface of the 3D object. The frequency of these distances will be represented by a normalized histogram. The values of these histograms feed a multi-layer neural network with back- propagation training. We demonstrate the potential of this approach in a set of experiments, which proved our system could achieve above 91.7% recognition rate. In addition, to evaluate the efficiency of our method, we compare our classifier with Support vector machine and k- nearest neighbours. The simulation results highlight the performance of the proposed approach.

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Artificial Neural Networks and the Mass Appraisal of Real Estate

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v14i03.8420 ◽

2018 ◽

Vol 14 (03) ◽

pp. 180 ◽

Cited By ~ 4

Author(s):

Gang Zhou ◽

Yicheng Ji ◽

Xiding Chen ◽

Fangfang Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Real Estate ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Support Vector ◽

The Real ◽

The Real Estate ◽

Artificial Neural

<p>With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.</p>

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Predicting the strength of Populus spp. clones using artificial neural networks and ε-regression support vector machines (ε-rSVM)

Holzforschung ◽

10.1515/hf.2011.107 ◽

2011 ◽

Vol 65 (6) ◽

pp. 855-863 ◽

Cited By ~ 10

Author(s):

Shawn D. Mansfield ◽

Kyu-Young Kang ◽

Lazaros Iliadis ◽

Stavros Tachos ◽

Stavros Avramidis

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector Machines ◽

Input Data ◽

Back Propagation ◽

Gaussian Kernel ◽

Support Vector ◽

Data Set ◽

Vector Machines ◽

Artificial Neural

Abstract Wood properties, including bending stiffness and strength, basic density and microfibril angle were experimentally obtained for six aspen and six hybrid poplar clones grown in Western Canada. Data analysis attempted to establish a relationship between wood mechanical properties and intrinsic wood attributes by means of artificial neural networks (ANN) and ε-regression support vector machines (ε-rSVM) employing a 5-fold cross validation approach (5-fold CV). Initial results for strength were acceptable, but require further improvement. Estimations of stiffness results (MOE) were inferior to those of strength (MOR) due to the fact that in several regression cases, the developed model worked well for narrow windows of data, but failed on a large scale due to the high variations in the values of the input data vectors. In such cases, the result is probably the development of regression with uneven performance throughout the input data set, and therefore the modeling capacity is poor. To avoid this predicament, different neural networks with one output neuron were developed in order to estimate either the stiffness or the strength, and at the same time the approximation capabilities of ε-rSVM were employed. In both methods, 5-fold CV was carried out in order to attain a more generalized solution by eliminating the boundary effect phenomena and by avoiding local behavior of the global support vector regression. The resultant models were evaluated by common metrics. The best ANN for the estimation of strength in combination with 5-fold CV, was a modular back propagation with average R2=0.70, and mean root mean square error (MRMSE) equal to 0.19 and mean average percent error (MAPE) equal to 12.5%. The Gaussian kernel 5-fold CV ε-rSVM estimated MOR with similar accuracy. The best 5-fold CV ANN for MOE estimation was a feed forward back propagation one, with average R2=0.60, MRMSE equal to 0.23 and MAPE equal to 41.5%, which was better than all other kernel methods employed.

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An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i9.404 ◽

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.

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The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽

10.3390/biom11040500 ◽

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.

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The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2017.11.056 ◽

2018 ◽

Vol 193 ◽

pp. 297-304 ◽

Cited By ~ 5

Author(s):

Maryam Valizadeh ◽

Mahmoud Reza Sohrabi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector Regression ◽

Spectrophotometric Determination ◽

Support Vector ◽

Artificial Neural

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New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.145534 ◽

2021 ◽

Vol 772 ◽

pp. 145534 ◽

Cited By ~ 2

Author(s):

Ying Deng ◽

Xiaoling Zhou ◽

Jiao Shen ◽

Ge Xiao ◽

Huachang Hong ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Tap Water ◽

Back Propagation ◽

New Methods ◽

Radial Basis ◽

Artificial Neural

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A comparison of artificial neural networks and support vector machines for short-term load forecasting using various load types

2017 IEEE Manchester PowerTech ◽

10.1109/ptc.2017.7980814 ◽

2017 ◽

Cited By ~ 6

Author(s):

Glen Mitchell ◽

Sanjay Bahadoorsingh ◽

Neil Ramsamooj ◽

Chandrabhan Sharma

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector Machines ◽

Load Forecasting ◽

Support Vector ◽

Short Term ◽

Vector Machines ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Load Types

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Prediction of Ferrite-Martensite Dual-Phase Steels Mechanical Properties by Use of Artificial Neural Networks

Materials Science Forum ◽

10.4028/www.scientific.net/msf.773-774.268 ◽

2013 ◽

Vol 773-774 ◽

pp. 268-274

Author(s):

Amir Ghiami ◽

Ramin Khamedi

Keyword(s):

Mechanical Properties ◽

Neural Networks ◽

Artificial Neural Networks ◽

Back Propagation ◽

Total Elongation ◽

Ultimate Tensile Stress ◽

Dual Phase ◽

Dual Phase Steels ◽

Good Ability ◽

Artificial Neural

This paper presents an investigation of the capabilities of artificial neural networks (ANN) in predicting some mechanical properties of Ferrite-Martensite dual-phase steels applicable for different industries like auto-making. Using ANNs instead of different destructive and non-destructive tests to determine the material properties, reduces costs and reduces the need for special testing facilities. Networks were trained with use of a back propagation (BP) error algorithm. In order to provide data for training the ANNs, mechanical properties, inter-critical annealing temperature and information about the microstructures of many specimens were examined. After the ANNs were trained, the four parameters of yield stress, ultimate tensile stress, total elongation and the work hardening exponent were simulated. Finally a comparison of the predicted and experimental values indicates that the results obtained from the given input data reveal a good ability of the well-trained ANN to predict the described mechanical properties.

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Rainfall Prediction by Artificial Neural Networks Trained using Different Climate Variables

10.21203/rs.3.rs-569733/v1 ◽

2021 ◽

Author(s):

Mateus Alexandre da Silva ◽

Marina Neves Merlo ◽

Michael Silveira Thebaldi ◽

Danton Diego Ferreira ◽

Felipe Schwerz ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Input Data ◽

Back Propagation ◽

Metropolitan Region ◽

Climate Variables ◽

Back Propagation Algorithm ◽

Rainfall Prediction ◽

Artificial Neural ◽

Belo Horizonte

Abstract Predicting rainfall can prevent and mitigate damages caused by its deficit or excess, besides providing necessary tools for adequate planning for the use of water. This research aimed to predict the monthly rainfall, one month in advance, in four municipalities in the metropolitan region of Belo Horizonte, using artificial neural networks (ANN) trained with different climate variables, and to indicate the suitability of such variables as inputs to these models. The models were developed through the MATLAB® software version R2011a, using the NNTOOL toolbox. The ANN’s were trained by the multilayer perceptron architecture and the Feedforward and Back propagation algorithm, using two combinations of input data were used, with 2 and 6 variables, and one combination of input data with 3 of the 6 variables most correlated to observed rainfall from 1970 to 1999, to predict the rainfall from 2000 to 2009. The most correlated variables to the rainfall of the following month are the sequential number corresponding to the month, total rainfall and average compensated temperature, and the best performance was obtained with these variables. Furthermore, it was concluded that the performance of the models was satisfactory; however, they presented limitations for predicting months with high rainfall.

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