Presently, the use of a credit card has become an integral part contemporary banking and financial system. Predicting potential defaulters or debtors is crucial business opportunity for institutions. For now, some machine learning methods have been applied to achieve this task. However, with dynamic imbalanced nature default data, it challenging classical algorithms proffer robust models optima...

Aim Fruit category classification is important in factory packing and transportation, price prediction, dietary intake, so forth. Methods This study proposed a novel artificial intelligence system to classify fruit categories. First, 2D fractional Fourier entropy with rotation angle vector grid was used extract features from images. Afterwards, five-layer stacked sparse autoencoder as the class...

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing ...

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve g...

Training a denoising autoencoder neural network requires access to truly clean data, a requirement which is often impractical. To remedy this, we introduce a method to train an autoencoder using only noisy data, having examples with and without the signal class of interest. The autoencoder learns a partitioned representation of signal and noise, learning to reconstruct each separately. We illus...

Abstract A Stacked Autoencoder Extreme learning machine (SAE-ELM) assessment model is proposed to study the weight of an Unmanned Combat Air Vehicle (UCAV) situation assessment. On basis existing air combat description parameters and their optimized advantage function weights, mapping relationship between weights established by using SAE-ELM achieve a more accurate evaluation. From four error i...

Successful integration of renewable energy sources like wind power into smart grids largely depends on accurate prediction of power from these intermittent sources. Production of wind power cannot be controlled as the wind speed can vary based on weather conditions. Accurate prediction of wind power can assist smart grid that intelligently decides on the usage of alternative power sources based...