scholarly journals Peak-Load Forecasting for Small Industries: A Machine Learning Approach

2020 ◽  
Vol 12 (16) ◽  
pp. 6539 ◽  
Author(s):  
Dong-Hoon Kim ◽  
Eun-Kyu Lee ◽  
Naik Bakht Sania Qureshi
Keyword(s):  
Peak Load ◽  
Load Forecasting ◽  
Environmental Issues ◽  
Predictive Performance ◽  
Learning Approach ◽  
Industrial Facilities ◽  
Machine Learning Approach ◽  
The Subject ◽  
Feature Information ◽  
Small Industries

Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, but most small industrial facilities cannot provide such information because of a lack of infrastructure. Therefore, we introduce a series of studies to implement a generalized prediction model that is applicable to these small industrial facilities. On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models. In addition, a new method is proposed to improve prediction performance by providing additional compensation to the prediction results by reflecting the fewest opinions among the prediction results of each model. Actual data from two small industrial facilities were applied to our process, and the results proved the effectiveness of our proposed method.

scholarly journals PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data

2019 ◽  
Vol 33 ◽  
pp. 1118-1125 ◽  
Author(s):  
Patrick Schwab ◽  
Walter Karlen
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Assessment ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Term Data

Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson’s disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson’s disease.

A Machine Learning Approach for One-Stop Learning

2008 ◽  
pp. 333-357 ◽  
Author(s):  
Marco A. Alvarez ◽  
SeungJin Lim
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approach ◽  
Internet Users ◽  
Machine Learning Approach ◽  
Supervised Learning Algorithms ◽  
One Stop ◽  
The Subject

Current search engines impose an overhead to motivated students and Internet users who employ the Web as a valuable resource for education. The user, searching for good educational materials for a technical subject, often spends extra time to filter irrelevant pages or ends up with commercial advertisements. It would be ideal if, given a technical subject by user who is educationally motivated, suitable materials with respect to the given subject are automatically identified by an affordable machine processing of the recommendation set returned by a search engine for the subject. In this scenario, the user can save a significant amount of time in filtering out less useful Web pages, and subsequently the user’s learning goal on the subject can be achieved more efficiently without clicking through numerous pages. This type of convenient learning is called One-Stop Learning (OSL). In this paper, the contributions made by Lim and Ko in (Lim and Ko, 2006) for OSL are redefined and modeled using machine learning algorithms. Four selected supervised learning algorithms: Support Vector Machine (SVM), AdaBoost, Naive Bayes and Neural Networks are evaluated using the same data used in (Lim and Ko, 2006). The results presented in this paper are promising, where the highest precision (98.9%) and overall accuracy (96.7%) obtained by using SVM is superior to the results presented by Lim and Ko. Furthermore, the machine learning approach presented here, demonstrates that the small set of features used to represent each Web page yields a good solution for the OSL problem.

scholarly journals Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Benedikt Lorch ◽  
Ghislain Vaillant ◽  
Christian Baumgartner ◽  
Wenjia Bai ◽  
Daniel Rueckert ◽  
...  
Keyword(s):  
Respiratory Motion ◽  
Diagnostic Value ◽  
Learning Approach ◽  
Motion Artefacts ◽  
Patient Motion ◽  
Random Decision Forests ◽  
Machine Learning Approach ◽  
Image Artefacts ◽  
The Subject ◽  
Space Data

The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.

scholarly journals Mapping of the Canopy Openings in Mixed Beech-Fir Forest at Sentinel-2 Subpixel level Using UAV and Machine Learning Approach

2020 ◽  
Author(s):  
Ivan Pilaš ◽  
Mateo Gašparović ◽  
Alan Novkinić ◽  
Damir Klobučar
Keyword(s):  
Forest Canopy ◽  
Predictive Performance ◽  
Spatial Extent ◽  
Learning Approach ◽  
Training Set ◽  
Multiple Features ◽  
Machine Learning Approach ◽  
Multi Temporal ◽  
Rgb Images ◽  
Sentinel 2

The presented study demonstrates the bi-sensor approach suitable for rapid and precise up-to-date mapping of forest canopy gaps for the larger spatial extent. The approach makes use of UAV RGB images on smaller areas for highly precise forest canopy mask creation. Sentinel-2 was used as a scaling platform for transferring information from UAV to a wider spatial extent. The various approaches of the improvement of the predictive performance were examined: (I) the highest R2 of the single satellite index was up to 0.57, (II) the highest R2 using multiple features obtained from the single date, S-2 image was 0.624 and, (III) the highest R2 on the multi-temporal set of S-2 images, was 0.697. Satellite indices such as ARVI, IPVI, NDI45, PSSRa, MCARI, CI, RI, and NDTI were the dominant predictors in most of the ML algorithms. The more complex ML algorithms such as SVM, Random Forest, GBM, XGBoost, and Catboost that provided the best performance on the training set exhibited weaker generalization capabilities. Therefore, a simpler and more robust Elastic Net algorithm was chosen for the final map creation.

scholarly journals Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

2020 ◽  
Author(s):  
Jan Wolff ◽  
Alexander Gary ◽  
Daniela Jung ◽  
Claus Normann ◽  
Klaus Kaier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Psychiatric Hospital ◽  
Hospital Care ◽  
Routine Data ◽  
Psychiatric Hospitals ◽  
Predictive Performance ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Better Than

Abstract Background: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier. Methods: The study included consecutively discharged patients between 1 st of January 2017 and 31 st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals. Results: The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping. Conclusion: The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.

scholarly journals Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

2020 ◽  
Author(s):  
Jan Wolff ◽  
Alexander Gary ◽  
Daniela Jung ◽  
Claus Normann ◽  
Klaus Kaier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Psychiatric Hospital ◽  
Hospital Care ◽  
Routine Data ◽  
Psychiatric Hospitals ◽  
Predictive Performance ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Better Than

Abstract Background: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier.Methods: The study included consecutively discharged patients between 1st of January 2017 and 31st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals. Results: The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping. Conclusion: The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.

scholarly journals Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

2019 ◽  
Author(s):  
Jan Wolff ◽  
Alexander Gary ◽  
Daniela Jung ◽  
Claus Normann ◽  
Klaus Kaier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Psychiatric Hospital ◽  
Hospital Care ◽  
Routine Data ◽  
Psychiatric Hospitals ◽  
Predictive Performance ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Better Than

Abstract Background:A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier. Methods:The study included consecutively discharged patients between 1stof January 2017 and 31stof December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals. Results: The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping. Conclusion:The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.

scholarly journals Very short-term load forecasting on factory level – A machine learning approach

Procedia CIRP ◽  
2019 ◽  
Vol 80 ◽  
pp. 705-710 ◽  
Author(s):  
Jessica Walther ◽  
Dario Spanier ◽  
Niklas Panten ◽  
Eberhard Abele
Keyword(s):  
Machine Learning ◽  
Load Forecasting ◽  
Learning Approach ◽  
Short Term ◽  
Machine Learning Approach ◽  
Short Term Load Forecasting
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