A Normalization Methods for Backpropagation: A Comparative Study

Eesa, Adel Sabry; Arabo, Wahab Kh.

doi:10.25271/2017.5.4.381

Cited by 61 publications

(30 citation statements)

References 10 publications

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“…The common denominator of our study with Shanker et al (1996) is that experimental results show how data standardization methods affect neural network performance in terms of predictive accuracy, computation time and number of iterations. In the literature, there are different scientific publications on applied sciences that are compatible with the results of our study (Sola and Sevilla, 1997;Panigrahi and Behera, 2013;Nayak et al, 2014;Eesa and Arabo, 2017).…”

Section: Resultssupporting

confidence: 91%

Evaluation of Normalization Techniques on Neural Networks for the Prediction of 305-Day Milk Yield

Akıllı

Atil

2020

Turk J Agr Eng Res, TURKAGER

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In this study, the impact of data preprocessing on the prediction of 305-day milk yield using neural networks were investigated with regard to the effect of different normalization techniques. Eight normalization techniques “Z-Score, Min-Max, D-Min-Max, Median, Sigmoid, Decimal Scaling, Median and MAD, TanhEstimators" and five different back propagation algorithms “Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), Conjugate Gradient Back propagation with Powell-Beale Restarts (CGB) and Brayde Fletcher Gold Farlo Shanno Quasi Newton Back propagation (BFG)” were examined and tested comparatively for the analysis. Neural network architecture was optimized and tested with several experiments. Results of the analysis show that applying different normalization techniques affect the performance and the distribution of outputs influences the learning process of the neural network. The magnitude of the effects varied with the type of back propagation algorithms, activation functions, and network's architectural structure. According to the results of the analysis, the most successful performance value in the 305-day milk yield estimation was obtained by using the neural network structured by using the Decimal Scaling normalization technique with the Bayesian Regulation algorithm (R2Adj = 0.8181, RMSE= 0.0068, MAPE= 160.42 for test set; R2Adj =0.8141, RMSE= 0.0067, MAPE= 114.12 for validation set).

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Section: Resultssupporting

confidence: 91%

Evaluation of Normalization Techniques on Neural Networks for the Prediction of 305-Day Milk Yield

Akıllı

Atil

2020

Turk J Agr Eng Res, TURKAGER

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“…The authors elaborate on the model building process and applied a similar data splitting procedure when constructing another model. This study implemented normalization using min-max techniques [ 53 ] and it accelerates the model learning by simplifying the input to a certain range [ 54 ]. Identifying the number of training sizes and using multiple diagnostic parameters were performed to select the best model.…”

Section: Discussionmentioning

confidence: 99%

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

et al. 2021

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Background and objectives Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop and evaluate the CUHAS-ROBUST model performance, an artificial-intelligence-based RR-TB screening tool. Methods A cross-sectional study involved suspected all type of RR-TB patients with complete sputum Lowenstein Jensen DST (reference) and 19 clinical, laboratory, and radiology parameter results, retrieved from medical records in hospitals under the Faculty of Medicine, Hasanuddin University Indonesia, from January 2015-December 2019. The Artificial Neural Network (ANN) models were built along with other classifiers. The model was tested on participants recruited from January 2020-October 2020 and deployed into CUHAS-ROBUST (index test) application. Sensitivity, specificity, and accuracy were obtained for assessment. Results A total of 487 participants (32 Multidrug-Resistant/MDR 57 RR-TB, 398 drug-sensitive) were recruited for model building and 157 participants (23 MDR and 21 RR) in prospective testing. The ANN full model yields the highest values of accuracy (88% (95% CI 85–91)), and sensitivity (84% (95% CI 76–89)) compare to other models that show sensitivity below 80% (Logistic Regression 32%, Decision Tree 44%, Random Forest 25%, Extreme Gradient Boost 25%). However, this ANN has lower specificity among other models (90% (95% CI 86–93)) where Logistic Regression demonstrates the highest (99% (95% CI 97–99)). This ANN model was selected for the CUHAS-ROBUST application, although still lower than the sensitivity of global GeneXpert results (87.5%). Conclusion The ANN-CUHAS ROBUST outperforms other AI classifiers model in detecting all type of RR-TB, and by deploying into the application, the health staff can utilize the tool for screening purposes particularly at the primary care level where the GeneXpert examination is not available. Trial registration NCT04208789.

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“… Mean-MAD Metode ini mirip dengan metode z-score hanya saja nilai simpangan baku diganti dengan nilai Mean Absolute Deviation. Adapun rumusnya sebagai berikut [3]:…”

Section: Pre-processingunclassified

Pengaruh Transformasi Data Pada Metode Learning Vector Quantization Terhadap Akurasi Klasifikasi Diagnosis Penyakit Jantung

Aziz

Warsito

Prahutama

2021

J.Gauss

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Learning Vector Quantization (LVQ) is a type of Artificial Neural Network with a supervised learning process based on competitive learning. Despite the absence of assumptions in LVQ is an advantage, it can be a problem when the predictor variables have big different ranges.This problems can be overcome by equalizing the range of all variables by data transformation so that all variables have relatively same effect. Heart Disease UCI dataset which used in this study is transformed by several transformation methods, such as minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax. The result show that the six transformed data can provide better LVQ classification accuracy than the raw data which has 75.99% for training performance accuracy. LVQ classification accuracy with data transformation of minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax are 89.16%, 88.22%, 89.7%, 90.1%, 88.17% and 92.18%. Based on the One-way ANOVA test and DMRT post hoc test known that there are significant differences between the results of the classification with data transformations and raw data in 0,05 significant level of α. It is also known that the best data transformation methods are softmax for training and sigmoid for testing. Keywords: heart disease, neural network, learning vector quantization, classification, data transformation

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A Normalization Methods for Backpropagation: A Comparative Study

Cited by 61 publications

References 10 publications

Evaluation of Normalization Techniques on Neural Networks for the Prediction of 305-Day Milk Yield

Evaluation of Normalization Techniques on Neural Networks for the Prediction of 305-Day Milk Yield

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

Pengaruh Transformasi Data Pada Metode Learning Vector Quantization Terhadap Akurasi Klasifikasi Diagnosis Penyakit Jantung

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