Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study

Curvature generation based on weight-updated boosting using shoe last point-cloud measurements

Lasts are foot-shaped forms made of plastic, wood, aluminum, or 3D-printed plastic. The last of a shoe determines not only its shape and style but also how well it fits and protects the foot. A weight-updated boosting-based ensemble learning (WUBEL) algorithm is presented in this paper to extract critical features (points) from plantar pressure imaging to optimize the shoe's last surface to satisfy a comfortable shoe's last surface optimization design. An enhanced last design is constructed from the foot measurement data of the bottom surface of the base last, the critical control lines (points) of the shoe's last body, and the running-in degree of the pressure-sensitive area lattice data. Using a Likert scale (LS) and relevant evaluation indicators, we conducted an experimental evaluation and comparative study of our enhanced last design. With a point-cloud dataset, the proposed method performs highly effectively in constructing shoes, which will help diabetes patients find comfortable and customized shoes.

An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm

High dimensionality data have become common in neuroimaging fields, especially group‐level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data‐driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity. A key contributing factor to this instability is the information loss that occurs during fMRI data reduction. Data reduction of high dimensionality fMRI data in the temporal domain to identify the important information within group datasets is necessary for such analyses and is crucial to ensure the accuracy and stability of the outputs. In this study, we describe an fMRI data reduction strategy based on an adapted neighborhood preserving embedding (NPE) algorithm. Both simulated and real data results indicate that, compared with the widely used data reduction method, principal component analysis, the NPE‐based data reduction method (a) shows superior performance on efficient data reduction, while enhancing group‐level information, (b) develops a unique stratagem for selecting components based on an adjacency graph of eigenvectors, (c) generates more reliable and reproducible brain networks under different model orders when the outputs of NPE are used for ICA, (d) is more sensitive to revealing task‐evoked activation for task fMRI, and (e) is extremely attractive and powerful for the increasingly popular fast fMRI and very large datasets.

Social Group Optimization-Assisted Kapur's Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning-based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19-affected CTI using social group optimization-based Kapur's entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis-based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.