A novel deep neural network structure for software fault prediction.

Software fault prediction is crucial to compute the potential occurrence of faults within the software components, before code testing or execution. Machine learning, especially deep learning, has been applied to predict faults, but both encounter challenges such as insufficient accuracy, imbalanced data, overfitting, and complex structure. Moreover, deep learning yields superior predictions when working with large datasets.

Joint mechanical properties estimation with a novel EMG-based knee rehabilitation robot: A machine learning approach.

Joint dynamic properties play essential roles in a wide range of biomechanical movement control. This paper develops a device with a novel mechatronic design to apply small-amplitude perturbations to the human knee. Surface Electromyography is employed to record such information; at the same time, force and position sensors collect measurements to be sent to identify human joint dynamics.

Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network.

Recent years evidenced an increase in the total number of skin cancer cases, and it is projected to grow exponentially. This paper proposes a computer-aided diagnosis system for the classification of a malignant lesion, where the acquired image is primarily pre-processed using novel methods. Digital artifacts such as hair follicles and blood vessels are removed, and thereafter, the image is enhanced using a novel method of histogram equalization.

Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques.

The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.

ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation.

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy.

Spatio-temporal deep learning for EEG-fNIRS brain computer interface.

In this paper the classification of motor imagery brain signals is addressed. The innovative idea is to use both temporal and spatial knowledge of the input data to increase the performance. Definitely, the electrode locations on the scalp is as important as the acquired temporal signals from every individual electrode.

Semiblind Spectral Factorization Approach for Magnetic Resonance Spectroscopy Quantification.

An observed magnetic resonance (MR) spectrum is composed of a set of metabolites spectrum, baseline, and noise. Quantification of metabolites of interest in the MR spectrum provides great opportunity for early diagnosis of dangerous disease such as brain tumors. In this paper, a novel spectral factorization approach based on singular spectrum analysis (SSA) is proposed to quantify magnetic resonance spectroscopy (MRS).

Simultaneous BOLD detection and incomplete fMRI data reconstruction.

The problem of simultaneous blood oxygenation level dependent (BOLD) detection and data completion is addressed in this paper. It is assumed that a set of fMRI data with significant number of missing samples is available and the aim is to recover those samples with least possible quality degradation. At the same time, BOLD should be detected.

Investigating Intra-Tablet Coating Uniformity With Spectral-Domain Optical Coherence Tomography.

Spectral domain optical coherence tomography (SD-OCT) has recently attracted a lot of interest in the pharmaceutical industry as a fast and non-destructive modality for direct quantification of thin film coatings that cannot easily be resolved with other techniques. While previous studies with SD-OCT have estimated the intra-tablet coating uniformity, the estimates were based on limited number of B-scans. In order to obtain a more accurate estimate, a greater number of B-scans are required that can quickly lead to an overwhelming amount of data.

A new informed tensor factorization approach to EEG-fMRI fusion.

Background: In this paper exploitation of correlation between post-movement beta rebound in EEG and blood oxygenation level dependent (BOLD) in fMRI is addressed. Brain studies do not reveal any clear relationship between synchronous neuronal activity and BOLD signal. Simultaneous recording of EEG and fMRI provides a great opportunity to recognize different areas of the brain involved in EEG events.

A predictive modeling approach to analyze data in EEG-fMRI experiments.

In this paper, a novel technique based on blind source extraction (BSE) using linear prediction is proposed to extract rolandic beta rhythm from electroencephalogram (EEG) recorded in a simultaneous EEG-fMRI experiment. We call this method CLP-BSE standing for constrained-linear-prediction BSE. Extracting event-related oscillations is a crucial task due to nonphase-locked nature and inter-trial variability of this event.

Removing ballistocardiogram artifact from EEG using short- and long-term linear predictor.

In this paper, a novel source extraction method is proposed for removing ballistocardiogram (BCG) artifact from EEG. BCG appears in EEG signals recorded simultaneously with functional magnetic resonance imaging. The proposed method is a semiblind source extraction algorithm based on linear prediction technique.

Blind separation of image sources via adaptive dictionary learning.

Sparsity has been shown to be very useful in source separation of multichannel observations. However, in most cases, the sources of interest are not sparse in their current domain and one needs to sparsify them using a known transform or dictionary. If such a priori about the underlying sparse domain of the sources is not available, then the current algorithms will fail to successfully recover the sources.

A new spatially constrained NMF with application to fMRI.

In this paper the problem of BOLD detection is addressed. The focus here is on non-negative matrix factorization (NMF), which is a data driven method and able to provide part-based representation of data. A new constrained optimization problem is proposed for the purpose of BOLD detection.

A new adaptive line enhancer based on singular spectrum analysis.

Original adaptive line enhancer (ALE) is used for denoising periodic signals from white noise. ALE, however, relies mainly on second order similarity between the signal and its delayed version and is more effective when the signal is narrowband. A new ALE based on singular spectrum analysis (SSA) is proposed here.