Lung Disease Classification Using Different Deep Learning Architectures and Principal Component Analysis

2018 ◽  
Author(s):  
Joel Than Chia Ming ◽  
Norliza Mohd Noor ◽  
Omar Mohd Rijal ◽  
Rosminah M. Kassim ◽  
Ashari Yunus
Keyword(s):  
Principal Component Analysis ◽  
Deep Learning ◽  
Lung Disease ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Classification ◽  
Learning Architectures

scholarly journals A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2229 ◽  
Author(s):  
Mansoor Khan ◽  
Tianqi Liu ◽  
Farhan Ullah
Keyword(s):  
Principal Component Analysis ◽  
Renewable Energy ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Wind Data ◽  
Deep Learning Algorithm

Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results.

scholarly journals PREDICTING LAND PRICES AND MEASURING UNCERTAINTY BY COMBINING SUPERVISED AND UNSUPERVISED LEARNING

2021 ◽  
Vol 25 (2) ◽  
pp. 169-178
Author(s):  
Changro Lee
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Deep Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Hybrid Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Land Prices ◽  
Property Valuation

Despite the popularity deep learning has been gaining, measuring the uncertainty within the result has not met expectations in many deep learning applications and this includes property valuation. In real-world tasks, however, rather than simply requiring predictions, assurance of the certainty of the predictions is also demanded. In this study, supervised learning is combined with unsupervised learning to bridge this gap. A method based on principal component analysis, a popular tool of unsupervised learning, was developed and used to represent the uncertainty in property valuation. Then, a neural network, a representative algorithm to implement supervised learning, was constructed, and trained to predict land prices. Finally, the uncertainty that was measured using principal component analysis was incorporated into the price predicted by the neural network. This hybrid approach is shown to be likely to improve the credibility of the valuation work. The findings of this study are expected to generate interest in the integration of the two learning approaches, thereby promoting the rapid adoption of deep learning tools in the property valuation industry.

A soft computing approach for diabetes disease classification

2016 ◽  
Vol 24 (4) ◽  
pp. 379-393 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Othman Bin Ibrahim ◽  
Abbas Mardani ◽  
Ali Ahani ◽  
Ahmad Jusoh
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Intelligent System ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Removal ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Support Vector

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.

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