Healthcare

2019 ◽  
Vol 10 (2) ◽  
pp. 63-85
Author(s):  
Ramani Selvanambi ◽  
Jaisankar N.
Keyword(s):  
Breast Cancer ◽  
Optimization Techniques ◽  
Quality Analysis ◽  
Cancer Information ◽  
Training Algorithms ◽  
Social Spider ◽  
Marquardt Algorithm ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Quality analysis of the treatment of cancer has been an objective of e-health services for quite some time. The objective is to predict the stage of breast cancer by using diverse input parameters. Breast cancer is one of the main causes of death in women when compared to other tumors. The classification of breast cancer information can be profitable to anticipate diseases or track the hereditary of tumors. For classification, an artificial neural network (ANN) structure was carried out. In the structure, nine training algorithms are used and the proposed is the Levenberg-Marquardt algorithm. For optimizing the hidden layer and neuron, three optimization techniques are used. In the result, the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched. The correlation execution diagram for an accuracy of 95%, a sensitivity of 98%, and a specificity of 89% of a social spider optimization (SSO) algorithm are shown.

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 35-43
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Multilayer Perceptron ◽  
Artificial Neural ◽  
Artery Disease ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

Effective Implementation of Value Engineering Using Artificial Neural Network Aid of Optimization Techniques

2019 ◽  
Vol 27 (03) ◽  
pp. 1950022 ◽  
Author(s):  
M. Prem Swarup ◽  
A. Prabhu Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Research Work ◽  
Optimization Techniques ◽  
Value Engineering ◽  
Training Algorithms ◽  
Air Conditioner ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Value Engineering (VE) is a method for characterizing the developed requirements of a product, and it is concerned with the selection of less excessive conditions. VE can understand and improve the optimal outcome such as quantity, security, unwavering quality and convertibility of each managerial unit. It is an incredible solving tool that can diminish costs while preserving or improving performance and quality requirements. In this research work, VE is presented to calculate the heating cost and cooling cost of the air conditioner with the assistance of an Artificial Neural Network (ANN) with an optimization model. This ANN model effectively chooses the maximum number of sources obtainable and the source respective method with low functional cost and energy consumption. For improving the prediction accuracy of VE in the ANN model, we have incorporated some training algorithms and optimized the network hidden layer and hidden neuron by Opposition Genetic Algorithm (OGA). From the results, trained ANN with OGA predicts the output with 96.02% accuracy and also minimum errors compared with the existing GA process.

Classification of Breast Cancer Patients Using Neural Network Technique

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Putri Marhida Badarudin ◽  
◽  
Rozaida Ghazali ◽  
Abdullah Alahdal ◽  
N.A.M. Alduais ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Classification Model ◽  
Breast Cancer Patients ◽  
Ann Model ◽  
Average Accuracy ◽  
Artificial Neural Network Ann ◽  
The One ◽  
Detect Breast Cancer

This work develops an Artificial Neural Network (ANN) model for performing Breast Cancer (BC) classification tasks. The design of the model considers studying different ANN architectures from the literature and chooses the one with the best performance. This ANN model aims to classify BC cases more systematically and more quickly. It provides facilities in the field of medicine to detect breast cancer among women. The ANN classification model is able to achieve an average accuracy of 98.88 % with an average run time of 0.182 seconds. Using this model, the classification of BC can be carried out much more faster than manual diagnosis and with good enough accuracy.

Premature interlude detection and classification of breast cancer using ANN classifier

2017 ◽  
Vol 7 (1.1) ◽  
pp. 587
Author(s):  
A N. Sruthi ◽  
M Shyamala Devi ◽  
P Balamurugan
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
High Speed ◽  
Early Stage ◽  
Extraction Methods ◽  
Mean Power ◽  
Intellectual System ◽  
Artificial Neural Network Ann ◽  
Applied Math

Breast cancer has emerged as the main reason behind most cancers deaths amoungwomen. To decrease the emerging issue, cancer should be handled at the early stage, however it's extremely complicated to discover associated diagnose tumors at a premature stage. Manual analysis of cancer is found to be extremely time consumingprocess andincompetent in several scenarios. As a result, there exists a choice for sensibleschemes that identifies the cancerous cell,simultaneouslydeprived of any participation of people and with excessive accuracy. Here, formulated automatic method victimization Artificial Neural Network (ANN)as better intellectual system for breast cancer classification. Image Processingtakes part avitalplace in cancer recognition once input document is inside the style of pixels. Feature extraction of image could be very vital in Mammogram classification. Alternatives feature extraction methods have been developed recently. An absolutely distinctive function extraction method isused for classification of conventional and Normal cancer image classification. This methodology can offer maximum accuracy at a high speed. The applied math parameter encompass entropy, mean, power, correlation, texture, variance .This constraints can act as a inputs to ANN which is adequate enough to identify and provides the outcome whether or not patient is suffering from cancerous or not.

scholarly journals Predicting Hydraulic Conductivity (k) of Tropical Soils by using Artificial Neural Network (ANN)

2009 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Lim D.K.H ◽  
Kolay P.K.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Conjugate Gradient ◽  
Field Tests ◽  
Tropical Soils ◽  
Training Algorithms ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Hydraulic conductivity of tropical soils is very complex. Several hydraulic conductivity prediction methods have focused on laboratory and field tests, such as the Constant Head Test, Falling Head Test, Ring Infiltrometer, Instantaneous profile method and Test Basins. In the present study, Artificial Neural Network (ANN) has been used as a tool for predicting the hydraulic conductivity (k) of some tropical soils. ANN is potentially useful in situations where the underlying physical process relationships are not fully understood and well-suited in modeling dynamic systems on a real-time basis. The hydraulic conductivity of tropical soil can be predicted by using ANN, if the physical properties of the soil e.g., moisture content, specific gravity, void ratio etc. are known. This study demonstrates the comparison between the conventional estimation of k by using Shepard's equation for approximating k and the predicted k from ANN. A programme was written by using MATLAB 6.5.1 and eight different training algorithms, namely Resilient Backpropagation (rp), Levenberg-Marquardt algorithm (lm), Conjugate Gradient Polak-Ribiere algorithm (cgp), Scale Conjugate Gradient (scg), BFGS Quasi-Newton (bfg), Conjugate Gradient with Powell/Beale Restarts (cgb), Fletcher-Powell Conjugate Gradient (cgf), and One-step Secant (oss) have been compared to produce the best prediction of k. The result shows that the network trained with Resilient Backpropagation (rp) consistently produces the most accurate results with a value of R = 0.8493 and E2 = 0.7209.

Classification of Breast Cancer Patients Using Neural Network Technique

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Putri Marhida Badarudin ◽  
◽  
Rozaida Ghazali ◽  
Abdullah Alahdal ◽  
N.A.M. Alduais ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Classification Model ◽  
Breast Cancer Patients ◽  
Ann Model ◽  
Average Accuracy ◽  
Artificial Neural Network Ann ◽  
The One ◽  
Detect Breast Cancer

This work develops an Artificial Neural Network (ANN) model for performing Breast Cancer (BC) classification tasks. The design of the model considers studying different ANN architectures from the literature and chooses the one with the best performance. This ANN model aims to classify BC cases more systematically and more quickly. It provides facilities in the field of medicine to detect breast cancer among women. The ANN classification model is able to achieve an average accuracy of 98.88 % with an average run time of 0.182 seconds. Using this model, the classification of BC can be carried out much more faster than manual diagnosis and with good enough accuracy.

scholarly journals Plant Classification based on Leaves using Artificial Neural Network

2021 ◽  
Vol 13 (6) ◽  
Author(s):  
Vishwad Desai ◽  
◽  
Vijay Savani ◽  
Rutul Patel ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Computationally Efficient ◽  
Manual Methods ◽  
Computationally Expensive ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Manual methods to examine leaf for plant classification can be tedious, therefore, automation is desired. Existing methods try distinctive approaches to accomplish this task. Nowadays, Convolution Neural Networks (CNN) are widely used for such application which achieves higher accuracy. However, CNN's are computationally expensive and require extensive dataset for training. Other existing methods are far less resource expensive but they also have their shortcomings for example, some features cannot be processed accurately with automation, some necessary differentiators are left out. To overcome this, we have proposed a simple Artificial Neural Network (ANN) for automatic classification of plants based on their leaf features. Experimental results show that the proposed algorithm able to achieve an accuracy of 96% by incorporating only a single hidden layer of ANN. Hence, our approach is computationally efficient compared to existing CNN based methods.

scholarly journals Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Petr Maca ◽  
Pavel Pech ◽  
Jiri Pavlasek
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Optimization Methods ◽  
Local Optimization ◽  
Multilayer Perceptrons ◽  
Training Algorithms ◽  
Activation Functions ◽  
Marquardt Algorithm ◽  
Runoff Forecast ◽  
Hidden Layer

The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.

scholarly journals Pengenalan Simbol Jarimatika Menggunakan Orientasi Histogram dan Multi-layer Perceptron

2015 ◽  
Vol 1 (4) ◽  
pp. 326
Author(s):  
Andi Sunyoto ◽  
Agus Harjoko
Keyword(s):  
Back Propagation ◽  
Optimal Number ◽  
Correct Classification ◽  
Multi Layer Perceptron ◽  
Output Layer ◽  
Input Layer ◽  
Static Images ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Makalah ini membahas tentang pengenalan simbol-simbol Jarimatika menggunakan Jaringan Syaraf Tiruan (JST). Hasil penelitian ini dapat digunakan untuk pengembangan aplikasi perhitungan Jarimatika dan interaksi antara manusia dan komputer yang lebih natural. Segmentasi yang digunakan adalah orientasi histogram, algoritma JST yang digunakan adalah back propagation multi-layer perceptron. Layer-layer JST tersebut adalah satu layer input, satu hidden layer dan satu output layer. Penelitian ini betujuan untuk implementasi pengenalan pola simbol Jarimatika menggunakan JST multi-layer perceptron, implementasi harus mampu menghasilkan klasifikasi dengan benar, sistem harus mampu melakukan klasifikasi dari gambar statis, sehingga dapat menganalisa pengenalan gestur tangan dari simbol-simbol Jarimatika.Penelitian ini menggunakan 18 simbol dasar Jarimatika. Total citra yang digunakan adalah 360 yang terbagi atas 270 citra untuk training dan 90 citra untuk testing. Hasil penelitian ini menunjukkan bahwa JST multi-perceptron dapat digunakan untuk pengenalan simbol Jarimatika dengan akurasi 93.33%. Jumlah neuron yang optimal pada hidden layer adalah 725. Implementasi penelitian ini menggunakan Matlab versi 7 (R2010a).This paper focuses on the recognition of Jarimatika symbols using Artificial Neural Network (ANN). The results of this research can be used to develop applications for the Jarimatika and to make interaction between humans and computers more natural. The Segmentation used is orientation histograms, the ANN algorithm used is back propagation multi-layer perceptron. Th layers of the ANN are one input layer with 19 data, one hidden layer and one output layer. This research aims to implement Jarimatika symbols with pattern recognition and multi-layer perceptron algoritm, the implementation must be able to produce the correct classification, the system must be able to perform the classification of static images, so can analyze the recognition of hand gestures from Jarimatika symbols. This research uses 18 basic Jarimatika symbols. Total image used were 360, consisting of 270 images for training and 90 images for testing. The results of this study indicate that the multi-layer perceptron ANN can be used for recognition of Jarimatika symbols with accuracy 93.33%. The optimal number of neurons in the hidden layer is 725. Implementation of this research using Matlab version 7 (R2010a).

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