scholarly journals An Evaluation Strategy for Commercial Precision Marketing Based on Artificial Neural Network

2020 ◽  
Vol 34 (5) ◽  
pp. 637-644
Author(s):  
Jianfeng Cheng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Principal Component ◽  
Principal Evaluation ◽  
Evaluation Index System ◽  
Benefit Sharing ◽  
Optimized Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Smart Mobile

With the proliferation of the Internet and smart mobile terminals, great progress has been made in the precision placement and benefit-sharing mechanism of commercial advertisements. Meanwhile, media marketing has become increasingly in-depth and precise. So far, mature theories have been proposed on consumer value and precision marketing. But further research is needed to mine the value from the big data on commercial precision marketing. To improve the accuracy of commercial precision marketing, this paper presents an evaluation index system (EIS) for commercial precision marketing based on improved attention-interest-desire-memory-action (ADIMA) model, and determines the principal evaluation indices through principal component analysis (PCA). Next, an artificial neural network (ANN) was established to evaluate commercial precision marketing, and optimized through k-means clustering (KMC). Finally, the optimized model was realized on MATLAB. The proposed EIS and ANN were proved scientific and effectiveness through simulations. The research results provide a reference for the application of the ANN in other fields of marketing.

scholarly journals Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

2019 ◽  
Vol 245 (11) ◽  
pp. 2539-2547 ◽  
Author(s):  
J. Stangierski ◽  
D. Weiss ◽  
A. Kaczmarek
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Regression Models ◽  
Principal Component ◽  
Multiple Regression Models ◽  
Gouda Cheese ◽  
Artificial Neural ◽  
Validation Set ◽  
Artificial Neural Network Ann

Abstract The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C.

scholarly journals Evaluation of the Motivation Status of Enterprises and Higher Vocational Schools Participating in Modern Apprenticeship and Its Key Influencing Factors Based on Artificial Neural Network

2021 ◽  
Vol 16 (08) ◽  
pp. 188
Author(s):  
Shu Ji ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Evaluation Model ◽  
Intelligent Manufacturing ◽  
Evaluation Index System ◽  
Vocational Schools ◽  
Participation Motivation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

During the reform of talent training mode, higher vocational schools must promote and apply modern apprenticeship to meet the needs of intelligent manufacturing. However, most enterprises and schools differ greatly in the participation enthusiasm and implementation motivation for modern apprenticeship. To enhance the participation motivation, it is critical to correctly evaluate the motivation status of enterprises and schools participating in modern apprenticeship, and analyze its key influencing factors. For this reason, this paper employs the Artificial Neural Network (ANN) to evaluate such motivation status. Firstly, a Modern Apprenticeship Motivation Status (MAMS) evaluation model was established, along with its evaluation index system (EIS). Then, differences in the motivation status were compared from seven aspects. After that, an improved backpropagation (BP) neural network was built to construct and optimize the MAMS prediction model. Finally, the constructed model was proved valid through experiments.

scholarly journals Artificial Neural Network (ANN) based Object Recognition Using Multiple Feature Sets

2011 ◽  
pp. 32-36
Author(s):  
Manami Barthakur ◽  
Tapashi Thakuria ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Multi Layer Perceptron ◽  
Multiple Features ◽  
Error Back Propagation ◽  
Multiple Feature ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied components to form a unique hybrid feature set so as to capture relevant details of objects for recognition using a ANN which for the work is a Multi Layer Perceptron (MLP) trained with (error) Back Propagation learning.

scholarly journals Sample Reduction for Physiological Data Analysis Using Principal Component Analysis in Artificial Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8240
Author(s):  
Cid Mathew Santiago Adolfo ◽  
Hassan Chizari ◽  
Thu Yein Win ◽  
Salah Al-Majeed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Biomedical Applications ◽  
Reduction Process ◽  
Principal Component ◽  
Classification Problem ◽  
Multidimensional Datasets ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

With its potential, extensive data analysis is a vital part of biomedical applications and of medical practitioner interpretations, as data analysis ensures the integrity of multidimensional datasets and improves classification accuracy; however, with machine learning, the integrity of the sources is compromised when the acquired data pose a significant threat in diagnosing and analysing such information, such as by including noisy and biased samples in the multidimensional datasets. Removing noisy samples in dirty datasets is integral to and crucial in biomedical applications, such as the classification and prediction problems using artificial neural networks (ANNs) in the body’s physiological signal analysis. In this study, we developed a methodology to identify and remove noisy data from a dataset before addressing the classification problem of an artificial neural network (ANN) by proposing the use of the principal component analysis–sample reduction process (PCA–SRP) to improve its performance as a data-cleaning agent. We first discuss the theoretical background to this data-cleansing methodology in the classification problem of an artificial neural network (ANN). Then, we discuss how the PCA is used in data-cleansing techniques through a sample reduction process (SRP) using various publicly available biomedical datasets with different samples and feature sizes. Lastly, the cleaned datasets were tested through the following: PCA–SRP in ANN accuracy comparison testing, sensitivity vs. specificity testing, receiver operating characteristic (ROC) curve testing, and accuracy vs. additional random sample testing. The results show a significant improvement in the classification of ANNs using the developed methodology and suggested a recommended range of selectivity (Sc) factors for typical cleaning and ANN applications. Our approach successfully cleaned the noisy biomedical multidimensional datasets and yielded up to an 8% increase in accuracy with the aid of the Python language.

scholarly journals A New Method for Human Posture Recognition Using Principal Component Analysis and Artificial Neural Network

2015 ◽  
Vol 7 (3) ◽  
pp. 11-19 ◽  
Author(s):  
M. Z. Uddin ◽  
M. A. Yousuf
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Supervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Human Action ◽  
Human Posture ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recognition of human posture from images is currently a very active area of research in computer vision. This paper presents a novel recognition method to determine a human posture is of walking or sitting using Principal Component Analysis (PCA) and Artificial Neural Network (ANN). In this paper, two types of learning are used to recognize the human posture. One is unsupervised and another is supervised learning. We have used PCA for unsupervised learning and ANN for supervised learning. To evaluate the performance of the proposed method, we have considered four types of human posture; walking, sitting, right leg up-down and left leg up-down. The experimental results on the human action of walking, sitting, right leg up-down and left leg up-down database show that our approach produces accurate recognition.

Performance optimization of gas refineries by ANN and DEA based on financial and operational factors

2015 ◽  
Vol 12 (2) ◽  
pp. 109-134 ◽  
Author(s):  
A. Azadeh ◽  
A. Roohani ◽  
S. Motevali Haghighi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Performance Evaluation ◽  
Performance Optimization ◽  
Principal Component ◽  
Integrated Approach ◽  
Financial Indicators ◽  
First Case ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study presents a combined artificial neural network (ANN) and multivariate approach for performance evaluation and optimization of gas refineries. This study introduces standard financial and non-financial indicators for performance evaluation of the gas refineries. Data are collected from gas balance sheets and the detailed statistics of gas refineries. Two cases have been considered for performance evaluation. In the first case the financial indicators and in the second case the financial and non-financial indicators are used and tested over five years period. The refineries are evaluated by data envelopment analysis (DEA), principal component analysis (PCA), numerical taxonomy and artificial neural network (ANN). Finally, a complete sensitivity analysis is performed for each stated method. The results show that DEA is more resistant to noise than other methods. Also, there is slight difference between results of financial and combined financial and operational indicators. This suggests the use of combined financial and operational indicators for future practical studies in gas refineries. This is the first study that presents an integrated approach for combined performance of financial and operational indicators in gas refineries.

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