Enhancing Machine Learning Performance with Continuous In-Session Ground Truth Scores: Pilot Study on Objective Skeletal Muscle Pain Intensity Prediction

Machine learning (ML) models trained on subjective self-report scores struggle to objectively classify pain accurately due to the significant variance between real-time pain experiences and recorded scores afterwards. This study developed two devices for acquisition of real-time, continuous in-session pain scores and gathering of ANS-modulated endodermal activity (EDA).The experiment recruited N = 24 subjects who underwent a post-exercise circulatory occlusion (PECO) with stretch, inducing discomfort. Subject data were stored in a custom pain platform, facilitating extraction of time-domain EDA features and in-session ground truth scores. Moreover, post-experiment visual analog scale (VAS) scores were collected from each subject. Machine learning models, namely Multi-layer Perceptron (MLP) and Random Forest (RF), were trained using corresponding objective EDA features combined with in-session scores and post-session scores, respectively. Over a 10-fold cross-validation, the macro-averaged geometric mean score revealed MLP and RF models trained with objective EDA features and in-session scores achieved superior performance (75.9% and 78.3%) compared to models trained with post-session scores (70.3% and 74.6%) respectively. This pioneering study demonstrates that using continuous in-session ground truth scores significantly enhances ML performance in pain intensity characterization, overcoming ground truth sparsity-related issues, data imbalance, and high variance. This study informs future objective-based ML pain system training.Comment: 18 pages, 2-page Appendix, 7 figure

Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based Androgen Receptor Bioassay Data

Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p \u3c 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Article originally published International Journal of Machine Learning and ComputingSmartphones are widely used today, and it becomes possible to detect the user's environmental changes by using the smartphone sensors, as demonstrated in this paper where we propose a method to identify human activities with reasonably high accuracy by using smartphone sensor data. First, the raw smartphone sensor data are collected from two categories of human activity: motion-based, e.g., walking and running; and phone movement-based, e.g., left-right, up-down, clockwise and counterclockwise movement. Firstly, two types of features extraction are designed from the raw sensor data, and activity recognition is analyzed using machine learning classification models based on these features. Secondly, the activity recognition performance is analyzed through the Convolutional Neural Network (CNN) model using only the raw data. Our experiments show substantial improvement in the result with the addition of features and the use of CNN model based on smartphone sensor data with judicious learning techniques and good feature designs

Enhancing Interest In Electromagnetics Among EET Students

The distinction between electrical engineering (EE) and electronic engineering technology (EET) programs is obvious. While an EE curriculum is more theoretical, EET curriculum is generally focused on an experience-building learning style and appeals to students who learn best by their hands-on experience. Electronic engineering technology programs prepare students for practical design and production work

PROPAGATION CHARACTERISTICS OF MICROWAVE IN SINGAPORE'S TROPICAL RAINFALL ENVIRONMENT

Master'sMASTER OF ENGINEERIN

An improved id-iq harmonic current detection algorithm for three-phase voltage asymmetry

Due to phase angle measured by phase locked loop has phase difference with grid positive sequence fundamental voltage of traditional id-iq harmonic detection method in grid voltage asymmetry and distortion phase, at the same time, low pass filter performance used in traditional id-iq harmonic detection method will influence accuracy and real-time performance, an improved id-iq harmonic detection algorithm was proposed, namely using of fundamental positive sequence current with good symmetry to instead of instantaneous voltage to lock phase, so as to eliminate measured phase difference of phase angle of power grid, and using of average value theory to replace function of the low pass filter，in order to reduce time delay and improve real-time performance and detection accuracy of the algorithm. The Matlab/Simulink simulation result proves that the algorithm is correctness

Predicting Countermovement Jump Heights By Time Domain, Frequency Domain, and Machine Learning Algorithms

© 2017 IEEE. In this paper, we introduce an experiment evaluating performance of football players in countermovement jumps (CMJs). Three methods including time domain, frequency domain, and machine learning algorithms are proposed for performance evaluation. Correlation coefficients and p-values are given for time domain and frequency domain methods, and prediction errors are given for different machine learning algorithms

Analyses of Countermovement Jump Performance in Time and Frequency Domains

This study aimed to analyze counter-movement jump (CMJ) performance in time and frequency domains. Fortyfour Division I American football players participated in the study. Kinetic variables were collected from both dominant and non-dominant legs using two force plates. Normalized peak power, normalized net impulse, and normalized peak force significantly correlated with jump height (r =.960, r =.998, r =.725, respectively with p \u3c.05). The mean frequency component was significantly correlated with CMJ performance (r =.355 with p \u3c.05). The reliability of the frequency variables was higher than the time domain variables. Frequency domain variables showed weaker correlations with jump height compared with time domain variables. Frequency domain analysis provides frequency components, which represent the rate of energy transmission from the eccentric phase to the end of the push-off phase. Frequency component information may provide additional information for the analyses of CMJ performance for athletes