Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.

Download Full-text

Development of Neural Network Models for a Crude Oil Distillation Column

Jurnal Teknologi ◽

10.11113/jt.v38.509 ◽

2012 ◽

Author(s):

Khairiyah Mohd. Yusof ◽

Fakhri Karray ◽

Peter L. Douglas

Keyword(s):

Neural Network ◽

Steady State ◽

Crude Oil ◽

Distillation Column ◽

Network Models ◽

Ann Model ◽

Neural Network Models ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

Download Full-text

Neural Network MIMO Model for Production of Isopropyl Myristate in a Semibatch Reactive Distillation Column

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.403 ◽

2013 ◽

Vol 284-287 ◽

pp. 403-408

Author(s):

Nur Alwani Ali Bashah ◽

Mohd Roslee Othman ◽

Norashid Aziz

Keyword(s):

Neural Network ◽

Reactive Distillation ◽

Batch Reactor ◽

Isopropyl Myristate ◽

Ann Model ◽

Validation Data ◽

Testing Data ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Mimo Model

Batch reactive distillation is an integrated unit of batch reactor and distillation. It provides benefits of having higher conversion and yield by continuous removal of side product. The aim of this paper is to develop an artificial neural network (ANN) based model for production of isopropyl myristate in an industrial scaled semibatch reactive distillation. Two cases of the MIMO model were developed. Case 1 does not consider historical data as inputs while case 2 does. The trained ANN for both cases was validated with independent validation data and the best architecture was optimized. Case 1 resulted to 8 inputs, 14 hidden nodes and 2 outputs [8-14-2] ANN while Case 2 resulted to [12-13-2] ANN. The results show that both ANN models have ability to predict the unknown validation and testing data very well. However, the [8-14-2] ANN model produce higher accuracy than [12-13-2] ANN model with MSE of 0.00094 and 0.0013, respectively.

Download Full-text

Prediction of Locations in Medical Images Using Orthogonal Neural Networks

10.21203/rs.3.rs-649890/v1 ◽

2021 ◽

Author(s):

Jong Soo Kim ◽

Yongil Cho ◽

Tae Ho Lim

Keyword(s):

Neural Network ◽

Diagnostic Performance ◽

Medical Images ◽

Selection Criterion ◽

Activation Function ◽

X Rays ◽

Ann Model ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Image Localization

Abstract An orthogonal neural network (ONN), a new deep-learning structure for medical image localization, is developed and presented in this paper. This method is simple, efficient, and completely different from a convolution neural network (CNN). The diagnostic performance of ONN for detecting the location of pneumothorax in chest X-rays was assessed and compared to that of CNN. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.870, an accuracy of 85.3%, a sensitivity of 75.0%, and a specificity of 86.5% were achieved; the ONN outperformed the CNN. The diagnostic performance of the ONN with a sigmoid activation function for all the nodes obviously outperformed the ONN with the rectified linear unit (RELU) activation function for all the nodes other than the output nodes. In addition, by applying ONN and CNN to predict the location of the glottis in laryngeal images, we achieved accurate and adjacent prediction rates of 70.5% and 20.5%, respectively, with the ONN. The prediction accuracy of the ONN was compared favorably with that of the CNN. Compared to a CNN, an ONN required only approximately 10% of the computations using a CNN trained on images with an input resolution of 256 × 256 pixels. A fully-connected small artificial neural network (ANN), selected by comparing the test results of several dozens of small ANN models, achieved the best location prediction performance on medical images. This study demonstrated that an ONN can be used as a quick selection criterion to compare ANN models for image localization since an ONN performed well compared decently with the selected ANN model.

Download Full-text

Forecasting Inflation in India

International Journal of Asian Business and Information Management ◽

10.4018/jabim.2011040105 ◽

2011 ◽

Vol 2 (2) ◽

pp. 64-73

Author(s):

Rudra P. Pradhan

Keyword(s):

Neural Network ◽

The Other ◽

Ann Model ◽

Multivariate Models ◽

Mean Squared Errors ◽

Forecasting Performance ◽

Ann Models ◽

Univariate Model ◽

Forecasting Inflation ◽

Artificial Neural Network Ann

This paper presents an application of Artificial Neural Network (ANN) to forecast inflation in India during the period 1994-2009. The study presents four different ANN models on the basis of inflation (WPI), economic growth (IIP), and money supply (MS). The first model is a univariate model based on past WPI only. The other three are multivariate models based on WPI and IIP, WPI and MS, WPI, and IIP and MS. In each case, the forecasting performance is measured by mean squared errors and mean absolute deviations. The paper finally concludes that multivariate models show better forecasting performance over the univariate model. In particular, the multivariate ANN model using WPI, IIP, and MS resulted in better performance than the rest of other models to forecast inflation in India.

Download Full-text

Live to learn: learning rules-based artificial neural network

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v21.i1.pp558-565 ◽

2021 ◽

Vol 21 (1) ◽

pp. 558

Author(s):

Aseel Shakir I. Hilaiwah ◽

Hanan Abed Alwally Abed Allah ◽

Basim Akhudir Abbas ◽

Tole Sutikno

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ann Model ◽

Output Layer ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer ◽

Intelligent Model ◽

Human Nervous System

<span>An extensive review of the artificial neural network (ANN) is presented in this paper. Previous studies review the artificial neural network (ANN) based on the approaches (algorithms) used or based on the types of the artificial neural network (ANN). The presented paper reviews the ANN based on the goal of the ANN (methods, and layers), which become the main objective of this paper. As a famous artificial intelligent model, ANN mimics the human nervous system in handling the information transmited by different nodes (also known as neurons) in this model. These nodes are stacked in layers and work collectively to bring about solution to complex problems. Numerous structures exist for ANN and each of these structures is designed to addressa a specific task. Basically, the ANN architecture is comprised of 3 different layers wherein the first layer rpresents the input layer that consist of several input nodes that represent the input parameterfor the model. The hidden layer is te second layer and consists of a hidden layer of neurons. The neurons in this layer are directly connected to the neurons in the output layer. The third layer is the output layer which is the models’ response layer. The output layer neurons have the activation functions for the calculation of the ANN final output. The connection between the nodes in the ANN model is mediated by synaptic weights. This paper is a comprehensive study of ANN models and their layers.</span>

Download Full-text

Artificial Neural Network- (ANN-) Based Proxy Model for Fast Performances’ Forecast and Inverse Schedule Design of Steam-Flooding Reservoirs

Mathematical Problems in Engineering ◽

10.1155/2021/5527259 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yuhui Zhou ◽

Yunfeng Xu ◽

Xiang Rao ◽

Yujie Hu ◽

Deng Liu ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Inverse Design ◽

Ann Model ◽

Production Schedule ◽

Steam Flooding ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Forward Looking

Steam flooding is one of the most effective and mature technology in heavy oil development. In this paper, a numerical simulation technology of steam flooding reservoir based on the finite volume method is firstly established. Combined with the phase change of steam phase, the fully implicit solution for steam flooding is carried out by using adaptive-time-step Newton iteration method. The Kriging method is used for stochastically to generate 4250 geological model samples by considering reservoir heterogeneity, and corresponding production schedule parameters are randomly given; then, these reservoir model samples are handled by the numerical simulation technology to obtain corresponding dynamic production data, which constitute the data for artificial neural network (ANN) training. By using the highly nonlinear global effect of artificial neural network and its powerful self-adaptive and self-learning functions, the forward-looking and inverse design ANN models of steam-flooding reservoirs are established, which provides a new method for rapid prediction of steam-flooding production performance and production schedule parameter design. In 4250 samples, the error of the forward-looking model is basically less than 0.1%, and the error of the inverse design model is generally less than 15%. It fully shows that the ANN models developed in this paper can quickly and effectively predict oil production and design production parameters and have an important guiding role in the implementation of the steam flooding technology. Finally, the forward-looking ANN model is applied to efficiently analyze the influencing factors of steam flooding process, and uncertainty analysis of the inverse design ANN model is conducted by Monte Carlo Simulation to illustrate its robustness. Besides, this paper may provide a reference for the application of neural network models to underground oil and gas reservoir, which is a typical invisible black box.

Download Full-text

Forecasting Inflation in India

Business, Technology, and Knowledge Management in Asia ◽

10.4018/978-1-4666-2652-2.ch003 ◽

2013 ◽

pp. 32-42

Author(s):

Rudra P. Pradhan

Keyword(s):

Neural Network ◽

Economic Growth ◽

The Other ◽

Ann Model ◽

Multivariate Models ◽

Forecasting Performance ◽

Ann Models ◽

Univariate Model ◽

Forecasting Inflation ◽

Artificial Neural Network Ann

This paper presents an application of Artificial Neural Network (ANN) to forecast inflation in India during the period 1994-2009. The study presents four different ANN models on the basis of inflation (WPI), economic growth (IIP), and money supply (MS). The first model is a univariate model based on past WPI only. The other three are multivariate models based on WPI and IIP, WPI and MS, WPI, and IIP and MS. In each case, the forecasting performance is measured by mean squared errors and mean absolute deviations. The paper finally concludes that multivariate models show better forecasting performance over the univariate model. In particular, the multivariate ANN model using WPI, IIP, and MS resulted in better performance than the rest of other models to forecast inflation in India.

Download Full-text

Monitoring-System Development of Gillnet Using Artificial Neural Network

Volume 6B: Ocean Engineering ◽

10.1115/omae2020-19011 ◽

2020 ◽

Author(s):

Chungkuk Jin ◽

HanSung Kim ◽

JeongYong Park ◽

MooHyun Kim ◽

Kiseon Kim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Big Data ◽

Learning Algorithm ◽

System Development ◽

Ann Model ◽

Ann Models ◽

Artificial Neural ◽

Input Variables ◽

Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

Download Full-text

Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Energies ◽

10.3390/en11092216 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2216 ◽

Cited By ~ 4

Author(s):

Ravi Kishore ◽

Roop Mahajan ◽

Shashank Priya

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Electrical Energy ◽

Optimization Techniques ◽

Operating Conditions ◽

Ann Model ◽

Predicting Performance ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

Download Full-text