Application of the BP neural network classification model in the value-added services of telecom customers

This study investigates the use of artificial neural networks (ANNs) to classify reasons for medication nonadherence. A survey method is used to collect individual reasons for nonadherence to treatment plans. Seven reasons for nonadherence are identified from the survey. ANNs using backpropagation learning are trained and validated to produce a nonadherence classification model. Most patients identified multiple reasons for nonadherence. The ANN models were able to accurately predict almost 63 percent of the reasons identified for each patient. After removal of two highly common nonadherence reasons, new ANN models are able to identify 73 percent of the remaining nonadherence reasons. ANN models of nonadherence are validated as a reliable medical informatics tool for assisting healthcare providers in identifying the most likely reasons for treatment nonadherence. Physicians may use the identified nonadherence reasons to help overcome the causes of nonadherence for each patient.

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Aluminum Plate Surface Defects Classification Based on the BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.734.543 ◽

2015 ◽

Vol 734 ◽

pp. 543-547 ◽

Cited By ~ 5

Author(s):

Qing Hua Li ◽

Di Liu

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Surface Defects ◽

Image Feature ◽

Classification Model ◽

Aluminum Plate ◽

Geometric Feature ◽

Plate Surface ◽

Target Image ◽

Defects Classification

The aluminum plate surface defects recognition method of BP neural network is studied based on target detection .In order to detect the defects, the target image is binaried by adaptive threshold method. After binarizing the target image, three kinds of image feature, including geometric feature, grayscale feature and shape feature, are extracted from the target image and its corresponding binary image. The defects classification model based on back-propagation neural network utilizes three layers neural network structure model and the hyperbolic tangent function of S function as the activation function, the number of neurons in hidden layer is confirmed by experiments. The experimental results show that the classification accuracy of BP neural network classification model as high as 94%, this can meet our requirements.

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BP neural network classification for bleeding detection in wireless capsule endoscopy

Journal of Medical Engineering & Technology ◽

10.1080/03091900903111974 ◽

2009 ◽

Vol 33 (7) ◽

pp. 575-581 ◽

Cited By ~ 14

Author(s):

G. Pan ◽

G. Yan ◽

X. Song ◽

X. Qiu

Keyword(s):

Neural Network ◽

Capsule Endoscopy ◽

Bp Neural Network ◽

Wireless Capsule Endoscopy ◽

Neural Network Classification ◽

Bleeding Detection ◽

Wireless Capsule

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Sports Action Recognition Based on GB-BP Neural Network and Big Data Analysis

Computational Intelligence and Neuroscience ◽

10.1155/2021/1678123 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Lidong Wang ◽

Kai Qiu ◽

Wang Li

Keyword(s):

Neural Network ◽

Big Data ◽

Data Analysis ◽

Action Recognition ◽

Bp Neural Network ◽

Big Data Analysis ◽

Classification Model ◽

Gradient Boosting ◽

Network Algorithm ◽

Neural Network Algorithm

In recent years, the application of the gradient boosting-back propagation (GB-BP) neural network algorithm in many industries has brought huge benefits, so how to combine the GB-BP neural network algorithm with sports has become a research hotspot. Based on this, this paper studies the application of the GB-BP neural network algorithm in wrestling, designs the sports athletes action recognition and classification model based on the GB-BP neural network algorithm, first analyzes the research status of wrestling action recognition, and then optimizes and improves the shortcomings of action recognition and big data analysis technology. The GB-BP neural network algorithm can realize the accurate recognition and classification of wrestlers’ training actions and carry out big data mining analysis with known action recognition, so as to achieve accurate classification. The experimental results show that the model can play a good role in wrestling and effectively improve the efficiency of wrestlers in training.

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A Neural Network Classification Model Based on Covering and Immune Clustering Algorithm

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2020.1.3739 ◽

2020 ◽

Vol 15 (1) ◽

Author(s):

Fang Zhao

Keyword(s):

Neural Network ◽

Information Processing ◽

Clustering Algorithm ◽

Distributed Storage ◽

Recognition Rate ◽

Original Data ◽

Classification Model ◽

Computing Technique ◽

Neural Network Classification ◽

Hidden Layer

Inspired by the information processing mechanism of the human brain, the artificial neural network (ANN) is a classic data mining method and a powerful soft computing technique. The ANN provides a valuable tool for information processing and pattern recognition, thanks to its advantages in distributed storage, parallel processing, fast problem-solving and adaptive learning. The constructive neural network (CNN) is a popular emerging neural network model suitable for processing largescale data. In this paper, a novel neural network classification model was established based on the covering algorithm (CA) and the immune clustering algorithm (ICA). The CA is highly comprehensible, and enjoys fast computing speed, and high recognition rate. However, the learning effect of this algorithm is rather poor, because the training set is randomly selected from the original data, and the number of nodes (covering number) and area being covered are greatly affected by the learning sequence. To solve the problem, the ICA was introduced to preprocess the data samples, and calculate the cluster centers based on the antibody-antigen affinity. The CA and the ICA work together to determine the covering center and radius automatically, and convert them into the weights and thresholds of the hidden layer of neural network. The number of hidden layer neurons equals the number of covering. In addition, the McCulloch-Pitts (M-P) neurons were adopted for the output layer. Based on the input feature of the hidden layer, the output feature completes the mapping from input to output, creating the final classes of the original data. The introduction of the ICA fully solves the defect of the CA. Finally, our neural network classification model was verified through experiments on real-world datasets.

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An Implementation of First and Second Order Neural Network Classification on Potential Drug Addict Repetition

Emerging Advances in Integrated Technology ◽

10.30880/emait.2021.02.01.003 ◽

2021 ◽

Vol 02 (01) ◽

Author(s):

Nazri Mohd Nawi ◽

◽

Eneng Tita Tosida ◽

Hamiza Hasbi ◽

Norhamreeza Abdul Hamid ◽

...

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Gradient Descent ◽

Back Propagation ◽

Drug Addict ◽

Second Order ◽

Learning Performance ◽

Neural Network Classification ◽

Minimization Methods ◽

Bp Networks

Back propagation (BP) neural network is known for its popularity and its capability in prediction and classification. BP used gradient descent (GD) method as one of the most widely used error minimization methods used to train back propagation (BP) networks. Besides its popularity BP still faces some limitation such as very slow in learning as well as easily get stuck at local minima. Many techniques have been introduced to improve BP performance. This research implements second order method together with gradient descent in order to improve its performance. The efficiency of both methods are verified and compared by means of simulations on classifying drug addict repetition. The results show that the second order methods are more reliable and significantly improves the learning performance of BP.

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Region Classification of Forestry Ecological-Economy System with BP Neural Network

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.1864 ◽

2011 ◽

Vol 467-469 ◽

pp. 1864-1869

Author(s):

Mei Zhang ◽

Jing Hua Wen ◽

Zu Xun Zhang

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Sichuan Province ◽

Classification Model ◽

Classification Result ◽

Region Classification ◽

Pass Through ◽

The City ◽

Economy System

First the principle of BP Net Neural Works was introduced, and the Region Classification model based on BP Net Neural Works of Forestry Ecological-Economy System was built. Then the City Forestry Ecological-Economy System of Sichuan province was classified with the model, moreover it was simulated with platform of MATLAB, and the Classification result was perfect, its Classification precision could arrive at above 90%.The emulator result indicated that the BP Net Neural Works pass through training could recognize region character of the Forestry Ecological-Economy System effectively,and could realize auto classification of the Forestry Ecological-Economy System.

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