Stochastic Analysis and Neural Network-Based Yield Prediction with Precision Agriculture

In this paper, we propose a general mathematical model for analyzing yield data. The data analyzed in this paper come from a characteristic corn field in the upper midwestern United States. We derive expressions for statistical moments from the underlying stochastic model. Consequently, we illustrate how a particular feature variable contributes to the statistical moments (and in effect, the characteristic function) of the target variable (i.e., yield). We also analyze the data with neural network techniques and provide two methods of data analysis. This mathematical model and neural network-based data analysis allow for better understanding of the variability within the data set, which is useful to farm managers attempting to make current and future decisions using the yield data. Lenders and risk management consultants may benefit from the insights of this mathematical model and neural network-based data analysis regarding yield expectations.

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Application of the collision mathematical model based on a BP neural network in railway vehicles

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit ◽

10.1177/0954409720959869 ◽

2020 ◽

pp. 095440972095986

Author(s):

Yu-Ru Li ◽

Tao Zhu ◽

Shou-Ne Xiao ◽

Bing Yang ◽

Guang-Wu Yang ◽

...

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Finite Element ◽

Bp Neural Network ◽

Learning Performance ◽

Small Data ◽

Data Set ◽

Computation Efficiency ◽

Simulation Results ◽

Relative Errors

In order to enhance the learning performance of small-data-set models and improve the computation efficiency of finite element simulations of vehicle collision, the collision mathematical model (VCMM) based on the back-propagation (BP) neural network is established to predict the collision response data of a single car and marshalling cars at unknown velocities. The predicted results of VCMM were compared with the simulation results of the finite element method (FEM) to verify the model. The compared results show that the maximum relative errors of deformation, energy absorption and average interfacial force of a single vehicle are all below 8.5%, and the relative errors of the maximum compression of the C0 coupler and the internal energy of the A1 car among the marshalling cars are all less than 5%. In addition, the calculation time of the single car and marshalling cars collisions based on the VCMM are reduced by 24.36 and 61.8 times, respectively, compared with the FEM results, and the simulation calculation efficiency is greatly improved. The prediction result of VCMM will partially replace experimental and simulation results for crashworthiness and safety design of the vehicle structure in future studies.

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Rethinking Data Analysis - Part Two: Some Alternatives to Frequentist Approaches

International Journal of Market Research ◽

10.1177/147078530905100212 ◽

2009 ◽

Vol 51 (2) ◽

pp. 1-16

Author(s):

Ray Kent

Keyword(s):

Neural Network ◽

Data Analysis ◽

Chaos Theory ◽

Data Sets ◽

Neural Network Analysis ◽

Data Set ◽

Total Replacement ◽

Statistical Procedures ◽

Frequentist Statistics ◽

Fuzzy Set Analysis

In ‘Rethinking data analysis – part one: the limitations of frequentist approaches'’ (Kent 2009) it was argued that standard, frequentist statistics were developed for purposes entirely other than for the analysis of survey data; when applied in this context, the assumptions being made and the limitations of the statistical procedures are commonly ignored. This paper examines ways of approaching the analysis of data sets that can be seen as viable alternatives. It reviews Bayesian statistics, configurational and fuzzy set analysis, association rules in data mining, neural network analysis, chaos theory and the theory of the tipping point. Each of these approaches has its own limitations and not one of them can or should be seen as a total replacement for frequentist approaches. Rather, they are alternatives that should be considered when frequentist approaches are not appropriate or when they do not seem to be adequate to the task of finding patterns in a data set.

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Clustering Neutrosophic Data Sets and Neutrosophic Valued Metric Spaces

Symmetry ◽

10.3390/sym10100430 ◽

2018 ◽

Vol 10 (10) ◽

pp. 430 ◽

Cited By ~ 3

Author(s):

Ferhat Taş ◽

Selçuk Topal ◽

Florentin Smarandache

Keyword(s):

Mathematical Model ◽

Big Data ◽

Data Analysis ◽

Metric Spaces ◽

Practical Method ◽

Data Type ◽

Data Sets ◽

Data Types ◽

Data Set ◽

First Time

In this paper, we define the neutrosophic valued (and generalized or G) metric spaces for the first time. Besides, we newly determine a mathematical model for clustering the neutrosophic big data sets using G-metric. Furthermore, relative weighted neutrosophic-valued distance and weighted cohesion measure, is defined for neutrosophic big data set. We offer a very practical method for data analysis of neutrosophic big data although neutrosophic data type (neutrosophic big data) are in massive and detailed form when compared with other data types.

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Classification of the operating state of rolling bearings with the convolutional neural network with variable dilation factors

Bulletin of the National Technical University «KhPI» Series: Dynamics and Strength of Machines ◽

10.20998/2078-9130.2021.2.249274 ◽

2021 ◽

pp. 106-111

Author(s):

Ruslan Babudzhan ◽

Konstantyn Isaienkov ◽

Oleksii Vodka ◽

Danilo Krasiy ◽

Ivan Zadorozhny ◽

...

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Convolutional Neural Network ◽

Binary Classification ◽

Classification Problems ◽

Rolling Bearings ◽

Data Set ◽

Shaft Rotation ◽

The Mathematical Model

The work describes rolling bearings operation data processing, and their use in the problem of constructing a mathematical model of the binary classification of the operating state of bearings by the method of a convolutional neural network with varying factors of dilatation of the kernel of convolutional layers. To classify bearings with defects, we used vibration acceleration data from our own test bench and a publicly available data set. The work also investigated a method for generalizing the classification of bearing signals obtained as a result of fundamentally different experiments and having different standard sizes. To unify signals, the following processing method is proposed: select data areas with displacement, go to the frequency space using fast Fourier transform, cut off frequencies exceeding 10 times the shaft rotation frequency, restore the signal while maintaining 10 shaft rotation periods, scale the received signal by dividing it by its diameter orbits of the rolling body and interpolate the signal at 2048 points. This algorithm also allows to generate a balanced sample for building a mathematical model. This feature is provided by varying the step of splitting the initial signal. The advantage of this algorithm over the classical methods of oversampling or undersampling is the generation of new objects that specify the statistical parameters of the general population. The signal processing algorithm was used both for binary classification problems within one dataset, and for training on one and testing on another. To increase the data set for training and testing the mathematical model, the bootstrapping method is used, based on multiple generation of samples using the Monte Carlo method. The quality of the mathematical model of binary classification was assessed by the proportion of correct answers. The problem is formulated as the problem of minimizing binary cross entropy. The results obtained are presented in the form of graphs demonstrating the neural network training process and graphs of the distribution density of metrics.

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Profitable Algorithmic Trading Strategy

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39101 ◽

2021 ◽

Vol 9 (12) ◽

pp. 2424-2433

Author(s):

Prince Nathan S

Keyword(s):

Neural Network ◽

Machine Learning ◽

Logistic Regression ◽

Data Analysis ◽

Data Science ◽

Principal Component ◽

Machine Learning Techniques ◽

Learning Models ◽

Data Set ◽

Machine Learning Models

Abstract: Cryptocurrency has drastically increased its growth in recent years and Bitcoin (BTC) is a very popular type of currency among all the other types of cryptocurrencies which is been used in most of the sectors nowadays for trading, transactions, bookings, etc. In this paper, we aim to predict the change in bitcoin prices by using machine learning techniques on data from Investing.com. We interpret the output and accuracy rate using various machine learning models. To see whether to buy or sell the bitcoin we created exploratory data analysis from a year of data set and predict the next 5 days change using machine learning models like logistic Regression, Logistic Regression with PCA (Principal Component Analysis), and Neural network. Keywords: Data Science, Machine Learning, Regression, PCA, Neural Network, Data Analysis

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CHEMOMETRIC DATA ANALYSIS WITH AUTOENCODER NEURAL NETWORK

Electronic Imaging ◽

10.2352/issn.2470-1173.2019.1.vda-679 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 679-1-679-6 ◽

Cited By ~ 1

Author(s):

Muhammad Bilal ◽

Mohib Ullah ◽

Habib Ullah

Keyword(s):

Neural Network ◽

Data Analysis

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Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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