Developing Charcot-Marie-Tooth Disease Recognition System Using Bacterial Foraging Optimization Algorithm Based Spiking Neural Network

Artificial Neural Network Modeling of Glass Transition Temperatures for Some Homopolymers with Saturated Carbon Chain Backbone

The glass transition temperature (Tg) is an important decision parameter when synthesizing polymeric compounds or when selecting their applicability domain. In this work, the glass transition temperature of more than 100 homopolymers with saturated backbones was predicted using a neuro-evolutive technique combining Artificial Neural Networks with a modified Bacterial Foraging Optimization Algorithm. In most cases, the selected polymers have a vinyl-type backbone substituted with various groups. A few samples with an oxygen atom in a linear non-vinyl hydrocarbon main chain were also considered. Eight structural, thermophysical, and entanglement properties estimated by the quantitative structure-property relationship (QSPR) method, along with other molecular descriptors reflecting polymer composition, were considered as input data for Artificial Neural Networks. The Tg's neural model has a 7.30% average absolute error for the training data and 12.89% for the testing one. From the sensitivity analysis, it was found that cohesive energy, from all independent parameters, has the highest influence on the modeled output.

Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment

Mild cognitive impairment (MCI) may be caused by Alzheimer's disease, Parkinson's disease (PD), cerebrovascular accident, nutritional or metabolic disorders, or mental disorders. It is important to determine the cause and treatment of dementia as early as possible because dementia may appear in remission. Decline in MCI cognitive function may affect a patient's walking performance. Therefore, all participants in this study participated in an experiment using a portable gait analysis system to perform walk, time up and go, and jump tests. The collected gait parameters are used in a machine learning classification model based on a support vector machine (SVM) and principal component analysis (PCA). The aim of the study is to predict different types of MCI patients based on gait information. It is shown that the machine learning classification model can predict different types of MCI patients. Specifically, the PCA-SVM model demonstrated better classification performance with 91.67% accuracy and 0.9714 area under the receiver operating characteristic curve (ROC AUC) using the polynomial kernel function in classifying PD-MCI and non-PD-MCI patients.