A wearable computing platform for developing cloud-based machine learning models for health monitoring applications

2016 ◽  
Author(s):  
Shyamal Patel ◽  
Ryan S. McGinnis ◽  
Ikaro Silva ◽  
Steve DiCristofaro ◽  
Nikhil Mahadevan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Wearable Computing ◽  
Learning Models ◽  
Computing Platform ◽  
Monitoring Applications ◽  
Machine Learning Models

scholarly journals Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1932
Author(s):  
Ramyar Saeedi ◽  
Keyvan Sasani ◽  
Assefaw H. Gebremedhin
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Mobile Health ◽  
Health Monitoring ◽  
Learning Models ◽  
Healthcare Applications ◽  
User Data ◽  
Adaptation Cost ◽  
Mobile Health Monitoring ◽  
Machine Learning Models

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.

scholarly journals A Data Cleaning Approach for a Structural Health Monitoring System in a 75 MW Electric Arc Ferronickel Furnace

2020 ◽  
Vol 2 (1) ◽  
pp. 21
Author(s):  
Jaiber Camacho-Olarte ◽  
Julián Esteban Salomón Torres ◽  
Daniel A. Garavito Jimenez ◽  
Jersson X. Leon Medina ◽  
Ricardo C. Gomez Vargas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Health Monitoring ◽  
Data Cleaning ◽  
Electric Arc ◽  
Furnace Operation ◽  
Arc Furnace ◽  
Learning Models ◽  
Operation Parameters ◽  
Machine Learning Models

Within a model of scientific and technical cooperation between the smelting company Cerro Matoso S.A. (CMSA) and the Universidad Nacional de Colombia (UNAL), a project was developed in order to take advantage of the data that were obtained from a sensor network in a ferronickel electric arc furnace at CMSA to improve the structural health monitoring process. Through this sensor network, online data are obtained on the temperature measurement along the refractory lining of the electric furnace, as well as heat fluxes and chemical characterization of the minerals on each stage of the process. These data are stored in a local database, which stores several years of historical data with valuable information for control and analysis purposes. These data reflect the behavior of the industrial process and can be used in the development of machine learning models to predict some of the electric arc furnace operation parameters, and thus improve the decision-making process. Currently, most of the data are analyzed by the experts of the structural control department, but, due to the large amount of data, the development of analytical tools is necessary to support their work. This paper proposes a data cleaning approach for improving data quality by creating a set of rules and filters based on both expert judgment and best practices in data quality. A statistical analysis was also carried out in order to detect variables with anomalies and outliers, which do not reflect real operation parameters and belong to anomalous data that should not be considered for modelling. With the proposed process, the quality of the data was improved and abnormal data were eliminated in order to consolidate a clean data set for later use in the development of machine learning models. This work contributes on understanding data cleansing rules that must be considered in order to reflect the real behavior of the electric furnace operation for further analysis and modeling tasks.

scholarly journals Automated Hyper-parameter Tuning for Machine Learning Models in Machine Health Prognostics

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Wang-Chi Cheung ◽  
Weiwen Zhang ◽  
Yong Liu ◽  
Feng Yang ◽  
Rick-Siow-Mong Goh
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Domain Knowledge ◽  
Parameter Tuning ◽  
Bayesian Optimization ◽  
Learning Models ◽  
Effective Choice ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Machine Learning Models

Recent studies have revealed the success of data-driven machine health monitoring, which motivates the use of machine learning models in machine health prognostic tasks. While the machine learning approach to health monitoring is gaining importance, the construction of machine learning models is often impeded by the difficulty in choosing the underlying hyper-parameter configuration (HP-config), which governs the construction of the machine learning model. While an effective choice of HP-config can be achieved with human effort, such an effort is often time consuming and requires domain knowledge. In this paper, we consider the use of Bayesian optimization algorithms, which automate an effective choice of HP-config by solving the associated hyperparameter optimization problem. Numerical experiments on the data from PHM 2016 Data Challenge demonstrate the salience of the proposed automatic framework, and exhibit improvement over default HP-configs in standard machine learning packages or chosen by a human agent.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Sign in / Sign up

Export Citation Format

Share Document