P.0093 Optimizing prediction of response to antidepressant medications using machine learning and environmental data

2021 ◽  
Vol 53 ◽  
pp. S66-S67
Author(s):  
A. Spinrad ◽  
S. Darki-Morag ◽  
R. Zoller ◽  
D. Taliaz
Keyword(s):  
Machine Learning ◽  
Environmental Data ◽  
Prediction Of Response ◽  
Antidepressant Medications

scholarly journals A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3953 ◽  
Author(s):  
Bruno Abade ◽  
David Perez Abreu ◽  
Marilia Curado
Keyword(s):  
Machine Learning ◽  
Environmental Data ◽  
Machine Learning Techniques ◽  
Smart Environments ◽  
Prototype System ◽  
Indoor Environments ◽  
Process Data ◽  
Indoor Space ◽  
Occupancy Detection ◽  
Real Scenario

Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user’s experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments.

scholarly journals Prediction of Atmospheric Corrosivity from Environmental Data by Machine Learning

2020 ◽  
Vol 71 (2) ◽  
pp. 193-193
Author(s):  
Hideki KATAYAMA ◽  
Shinjiro YAGYU ◽  
Shigeyuki MATSUNAMI
Keyword(s):  
Machine Learning ◽  
Environmental Data

scholarly journals Predictive modelling of turbofan engine components condition using machine and deep learning methods

2021 ◽  
Vol 23 (2) ◽  
pp. 359-370
Author(s):  
Michał Matuszczak ◽  
Mateusz Żbikowski ◽  
Andrzej Teodorczyk
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Predictive Modelling ◽  
Continuous Variable ◽  
Environmental Data ◽  
Bayesian Optimization ◽  
Aviation Industry ◽  
Learning Models ◽  
Turbofan Engine ◽  
Machine Learning Models

The article proposes an approach based on deep and machine learning models to predict a component failure as an enhancement of condition based maintenance scheme of a turbofan engine and reviews currently used prognostics approaches in the aviation industry. Component degradation scale representing its life consumption is proposed and such collected condition data are combined with engines sensors and environmental data. With use of data manipulation techniques, a framework for models training is created and models' hyperparameters obtained through Bayesian optimization. Models predict the continuous variable representing condition based on the input. Best performed model is identified by detemining its score on the holdout set. Deep learning models achieved 0.71 MSE score (ensemble meta-model of neural networks) and outperformed significantly machine learning models with their best score at 1.75. The deep learning models shown their feasibility to predict the component condition within less than 1 unit of the error in the rank scale.

scholarly journals Efficient gravitational-wave glitch identification from environmental data through machine learning

2020 ◽  
Vol 101 (10) ◽  
Author(s):  
Robert E. Colgan ◽  
K. Rainer Corley ◽  
Yenson Lau ◽  
Imre Bartos ◽  
John N. Wright ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gravitational Wave ◽  
Environmental Data

scholarly journals On-farm welfare monitoring system for goats based on Internet of Things and machine learning

2020 ◽  
Vol 16 (7) ◽  
pp. 155014772094403
Author(s):  
Yuan Rao ◽  
Min Jiang ◽  
Wen Wang ◽  
Wu Zhang ◽  
Ruchuan Wang
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Monitoring System ◽  
Animal Husbandry ◽  
Environmental Data ◽  
Software Systems ◽  
Data Detection ◽  
Livestock Farming ◽  
On Farm

Intensive animal husbandry is becoming more and more popular with the adoption of modern livestock farming technologies. In such circumstances, it is required that the welfare of animals be continuously monitored in a real-time way. To this end, this study describes one on-farm welfare monitoring system for goats, with a combination of Internet of Things and machine learning. First, the system was designed for uninterruptedly monitoring goat growth in a multifaceted and multilevel manner, by means of collecting on-farm videos and representative environmental data. Second, the monitoring hardware and software systems were presented in detail, aiming at supporting remote operation and maintenance, and convenience for further development. Third, several key approaches were put forward, including goat behavior analysis, anomaly data detection, and processing based on machine learning. Through practical deployment in the real situation, it was demonstrated that the developed system performed well and had good potential for offering real-time monitoring service for goats’ welfare, with the help of accurate environmental data and analysis of goat behavior.

scholarly journals Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

2019 ◽  
Vol 12 (1) ◽  
pp. 21 ◽  
Author(s):  
Liangliang Zhang ◽  
Zhao Zhang ◽  
Yuchuan Luo ◽  
Juan Cao ◽  
Fulu Tao
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Satellite Data ◽  
Maize Yield ◽  
Environmental Data ◽  
Yield Prediction ◽  
County Level ◽  
Climate Data ◽  
Source Data ◽  
Optical Fluorescence

Maize is an extremely important grain crop, and the demand has increased sharply throughout the world. China contributes nearly one-fifth of the total production alone with its decreasing arable land. Timely and accurate prediction of maize yield in China is critical for ensuring global food security. Previous studies primarily used either visible or near-infrared (NIR) based vegetation indices (VIs), or climate data, or both to predict crop yield. However, other satellite data from different spectral bands have been underutilized, which contain unique information on crop growth and yield. In addition, although a joint application of multi-source data significantly improves crop yield prediction, the combinations of input variables that could achieve the best results have not been well investigated. Here we integrated optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield across four agro-ecological zones (AEZs) in China using a regression-based method (LASSO), two machine learning (ML) methods (RF and XGBoost), and deep learning (DL) network (LSTM). The results showed that combining multi-source data explained more than 75% of yield variation. Satellite data at the silking stage contributed more information than other variables, and solar-induced chlorophyll fluorescence (SIF) had an almost equivalent performance with the enhanced vegetation index (EVI) largely due to the low signal to noise ratio and coarse spatial resolution. The extremely high temperature and vapor pressure deficit during the reproductive period were the most important climate variables affecting maize production in China. Soil properties and management factors contained extra information on crop growth conditions that cannot be fully captured by satellite and climate data. We found that ML and DL approaches definitely outperformed regression-based methods, and ML had more computational efficiency and easier generalizations relative to DL. Our study is an important effort to combine multi-source remote sensed and environmental data for large-scale yield prediction. The proposed methodology provides a paradigm for other crop yield predictions and in other regions.

scholarly journals Deep learning for brain disorders: from data processing to disease treatment

2020 ◽  
Author(s):  
Ninon Burgos ◽  
Simona Bottani ◽  
Johann Faouzi ◽  
Elina Thibeau-Sutre ◽  
Olivier Colliot
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Language Processing ◽  
State Of The Art ◽  
Imaging Genetics ◽  
Environmental Data ◽  
Brain Disorders ◽  
Disease Treatment ◽  
Clinical Routine

Abstract In order to reach precision medicine and improve patients’ quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

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