Sensors of different wavelengths in remote sensing field capture data. Each and every sensor has its own capabilities and limitations. Synthetic aperture radar (SAR) collects data that has a high spatial and radiometric resolution. The optical remote sensors capture images with good spectral information. Fused images from these sensors will have high information when implemented with a better algorithm resulting in the proper collection of data to predict weather forecasting, soil exploration, and crop classification. This work encompasses a fusion of optical and radar data of Sentinel series satellites using a deep learning-based convolutional neural network (CNN). The three-fold work of the image fusion approach is performed in CNN as layered architecture covering the image transform in the convolutional layer, followed by the activity level measurement in the max pooling layer. Finally, the decision-making is performed in the fully connected layer. The objective of the work is to show that the proposed deep learning-based CNN fusion approach overcomes some of the difficulties in the traditional image fusion approaches. To show the performance of the CNN-based image fusion, a good number of image quality assessment metrics are analyzed. The consequences demonstrate that the integration of spatial and spectral information is numerically evident in the output image and has high robustness. Finally, the objective assessment results outperform the state-of-the-art fusion methodologies.
Breast cancer survival prediction can have
an extreme effect on selection of best treatment protocols. Many approaches such as statistical or machine learning models have been employed to predict
the survival prospects of patients, but newer algorithms such as deep learning can be tested with the
aim of improving the models and prediction accuracy. In this study, we used machine learning and deep
learning approaches to predict breast cancer survival in 4,902 patient records from the University of
Malaya Medical Centre Breast Cancer Registry. The
results indicated that the multilayer perceptron (MLP),
random forest (RF) and decision tree (DT) classifiers
could predict survivorship, respectively, with 88.2 %,
83.3 % and 82.5 % accuracy in the tested samples.
Support vector machine (SVM) came out to be lower
with 80.5 %. In this study, tumour size turned out to
be the most important feature for breast cancer survivability prediction. Both deep learning and machine learning methods produce desirable prediction
accuracy, but other factors such as parameter configurations and data transformations affect the accuracy of the predictive model.
This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.