scholarly journals Machine learning informed predictor importance measures of environmental parameters in maritime optical turbulence

2020 ◽  
Vol 59 (21) ◽  
pp. 6379
Author(s):  
Christopher Jellen ◽  
John Burkhardt ◽  
Cody Brownell ◽  
Charles Nelson
Keyword(s):  
Machine Learning ◽  
Environmental Parameters ◽  
Optical Turbulence ◽  
Predictor Importance

scholarly journals Rainfall Prediction for Udaipur, Rajasthan using Machine Learning Models Based on Temperature, Vapour Pressure and Relative Humidity

2020 ◽  
Vol 8 (6S) ◽  
pp. 133-137
Keyword(s):  
Machine Learning ◽  
Relative Humidity ◽  
Vapour Pressure ◽  
Predictor Variables ◽  
Ensemble Model ◽  
Learning Models ◽  
Rainfall Prediction ◽  
Predictor Importance ◽  
The Impact ◽  
Machine Learning Models

The study aims at Rainfall prediction using Machine Learning models using the minimum of features. The prediction here is based on temperature, vapour pressure and relative humidity. Numerous studies carried out earlier used more features than this study. A training-test split of 75-25 was used. The best results were obtained by combining the best of the candidate models into an ensemble model to identify that predictor importance of vapour pressure was 0.89 while that of relative humidity was 0.11 with temperature not seen as a significant predictor for rainfall though the high correlation of temperature (°C) with vapour pressure (Torr) and relative humidity (Percentage) suggests that the two predictor variables subsume the impact of temperature.

scholarly journals Predictors for extubation failure in COVID-19 patients using a machine learning approach

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Lucas M. Fleuren ◽  
Tariq A. Dam ◽  
Michele Tonutti ◽  
Daan P. de Bruin ◽  
Robbert C. A. Lalisang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Body Mass Index ◽  
Intensive Care ◽  
Critically Ill ◽  
Body Mass ◽  
Life Support ◽  
Extubation Failure ◽  
Machine Learning Algorithms ◽  
Optimal Timing ◽  
Predictor Importance

Abstract Introduction Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. Methods We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. Results A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. Conclusion The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.

scholarly journals A Machine Learning approach for personal thermal comfort perception evaluation: experimental campaign under real and virtual scenarios

2020 ◽  
Vol 197 ◽  
pp. 04001
Author(s):  
Francesco Salamone ◽  
Alice Bellazzi ◽  
Lorenzo Belussi ◽  
Gianfranco Damato ◽  
Ludovico Danza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Comfort ◽  
Environmental Parameters ◽  
Smart Devices ◽  
Thermal Perception ◽  
Biometric Data ◽  
Experimental Campaign ◽  
Machine Learning Approach ◽  
Comfort Perception ◽  
External Stimuli

Personal Thermal Comfort models differ from the steady-state methods because they consider personal user feedback as target value. Today, the availability of integrated “smart” devices following the concept of the Internet of Things and Machine Learning (ML) techniques allows developing frameworks reaching optimized indoor thermal comfort conditions. The article investigates the potential of such approach through an experimental campaign in a test cell, involving 25 participants in a Real (R) and Virtual (VR) scenario, aiming at evaluating the effect of external stimuli on personal thermal perception, such as the variation of colours and images of the environment. A dataset with environmental parameters, biometric data and the perceived comfort feedbacks of the participants is defined and managed with ML algorithms in order to identify the most suitable one and the most influential variables that can be used to predict the Personal Thermal Comfort Perception (PTCP). The results identify the Extra Trees classifier as the best algorithm. In both R and VR scenario a different group of variables allows predicting PTCP with high accuracy.

scholarly journals An IoT-Based Smart Building Solution for Indoor Environment Management and Occupants Prediction

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2959
Author(s):  
Alessandro Floris ◽  
Simone Porcu ◽  
Roberto Girau ◽  
Luigi Atzori
Keyword(s):  
Machine Learning ◽  
Indoor Environment ◽  
Prediction Models ◽  
Pearson Correlation ◽  
Environmental Parameters ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Environment Management ◽  
Smart Building

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939).

scholarly journals Solar Power Prediction via Support Vector Machine and Random Forest

2018 ◽  
Vol 69 ◽  
pp. 01004 ◽  
Author(s):  
Chih-Feng Yen ◽  
He-Yen Hsieh ◽  
Kuan-Wu Su ◽  
Min-Chieh Yu ◽  
Jenq-Shiou Leu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Output Power ◽  
Environmental Parameters ◽  
Energy Market ◽  
Support Vector ◽  
Learning Models ◽  
Power Prediction ◽  
Machine Learning Models

Due to the variability and instability of photovoltaic (PV) output, the accurate prediction of PV output power plays a major role in energy market for PV operators to optimize their profits in energy market. In order to predict PV output, environmental parameters such as temperature, humidity, rainfall and win speed are gathered as indicators and different machine learning models are built for each solar panel inverters. In this paper, we propose two different kinds of solar prediction schemes for one-hour ahead forecasting of solar output using Support Vector Machine (SVM) and Random Forest (RF).

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