Epilepsy can be referred to as a neurological disorder, categorized by intractable seizures with serious consequences. To forecast such seizures, Electroencephalogram (EEG) datasets should gathered continuously. EEG signals were recorded using numerous electrodes fixed on the scalp that cannot worn patients Neurostimulators intervene in advance and ignore seizure rate. Its productivity is incre...

Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis ...

Heart disease is the leading cause of death globally. The most common type heart coronary disease, which occurs when there a build-up plaque inside arteries that supply blood to heart, making circulation difficult. prediction challenge in clinical machine learning. Early detection people at risk vital preventing its progression. This paper proposes deep learning approach achieve improved diseas...

In recent years, information overload has become a phenomenon where it makes people difficult to filter relevant information. To address issues such as high-dimensional data, cold start, and data sparsity, semi-autoencoder is one of the unsupervised deep learning methods used in recommendation systems. It particularly useful for reducing dimensions, capturing latent representations, flexibly re...

Mechanical fault prediction is one of the main problems in condition-based maintenance, and its purpose to predict future working status machine based on collected information machine. However, hand, model health indices by sensors will directly affect evaluation results system. On other because index a continuous time series, effect feature learning data also affects prognosis. This paper make...

This study proposed an efficient ECG preprocessing technique, and the preprocessed data was subsequently used to classify atrial fibrillation (AFib) using end-to-end deep neural networks. is significant since early identification of AFib can help prevent mortality. With this in mind, two-fold proposed, which three denoising autoencoders for signal pre-processing were evaluated compared. Denoisi...