Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System

From the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustment power is determined based on information on major outliers in past forecasts, reducing the maximum forecast error in addition to improving the average forecast accuracy is extremely important from the perspective of grid operation. In the past, researchers have proposed various methods combining the numerical weather prediction (NWP) and machine learning techniques for the one day-ahead solar power forecasting, but the accuracy of NWP has been a bottleneck issue. In recent years, the development of the ensemble prediction system (EPS) forecasts based on probabilistic approaches has been promoted to improve the accuracy of NWP, and in Japan, EPS forecasts in the mesoscale domain, called mesoscale ensemble prediction system (MEPS), have been distributed by the Japan Meteorological Agency (JMA). The use of EPS as a machine learning model is expected to improve the maximum forecast error, as well as the accuracy, since the predictor can utilize various weather scenarios as information. The purpose of this study is to examine the effect of EPS on the GHI prediction and the structure of the machine learning model that can effectively use EPS. In this study, we constructed the support vector regression (SVR)-based predictors with multiple network configurations using MEPS as input and evaluated the forecast error of the Kanto region GHI by each model. Through the comparison of the prediction results, it was shown that the machine learning model can achieve average accuracy improvement while reducing the maximum prediction error by MEPS, and knowledge was obtained on how to effectively provide EPS information to the predictor. In addition, machine learning was found to be useful in improving the systematic error of MEPS.

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MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model

Journal of Clinical Medicine ◽

10.3390/jcm9020343 ◽

2020 ◽

Vol 9 (2) ◽

pp. 343 ◽

Cited By ~ 4

Author(s):

Arash Kia ◽

Prem Timsina ◽

Himanshu N. Joshi ◽

Eyal Klang ◽

Rohit R. Gupta ◽

...

Keyword(s):

Machine Learning ◽

At Risk ◽

Area Under The Curve ◽

Learning Model ◽

Clinical Deterioration ◽

Early Warning Score ◽

Support Vector ◽

Adult Age ◽

Machine Learning Model ◽

Patients At Risk

Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.

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Preoperative prediction of complicated appendicitis using machine learning method

10.21203/rs.3.rs-27341/v1 ◽

2020 ◽

Author(s):

Chunbo Kang ◽

Xubin Li ◽

Xiaoqian Chi ◽

Yabin Yang ◽

Haifeng Shan ◽

...

Keyword(s):

Machine Learning ◽

Clinical Symptoms ◽

Laboratory Data ◽

Learning Model ◽

Complicated Appendicitis ◽

Support Vector ◽

Training Set ◽

Preoperative Prediction ◽

Machine Learning Model ◽

Testing Set

Abstract BACKGROUND Accurate preoperative prediction of complicated appendicitis (CA) could help selecting optimal treatment and reducing risks of postoperative complications. The study aimed to develop a machine learning model based on clinical symptoms and laboratory data for preoperatively predicting CA.METHODS 136 patients with clinicopathological diagnosis of acute appendicitis were retrospectively included in the study. The dataset was randomly divided (94: 42) into training and testing set. Predictive models using individual and combined selected clinical and laboratory data features were built separately. Three combined models were constructed using logistic regression (LR), support vector machine (SVM) and random forest (RF) algorithms. The CA prediction performance was evaluated with Receiver Operating Characteristic (ROC) analysis, using the area under the curve (AUC), sensitivity, specificity and accuracy factors.RESULTS The features of the abdominal pain time, nausea and vomiting, the highest temperature, high sensitivity-CRP (hs-CRP) and procalcitonin (PCT) had significant differences in the CA prediction (P<0.001). The ability to predict CA by individual feature was low (AUC<0.8). The prediction by combined features was significantly improved. The AUC of the three models (LR, SVM and RF) in the training set and the testing set were 0.805, 0.888, 0.908 and 0.794, 0.895, 0.761, respectively. The SVM-based model showed a better performance for CA prediction. RF had a higher AUC in the training set, but its poor efficiency in the testing set indicated a poor generalization ability.CONCLUSIONS The SVM machine learning model applying clinical and laboratory data can well predict CA preoperatively which could assist diagnosis in resource limited settings.

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Moderate to severe OSA screening based on support vector machine of the Chinese population faciocervical measurements dataset: a cross-sectional study

BMJ Open ◽

10.1136/bmjopen-2020-048482 ◽

2021 ◽

Vol 11 (9) ◽

pp. e048482

Author(s):

Liu Zhang ◽

Ya Ru Yan ◽

Shi Qi Li ◽

Hong Peng Li ◽

Ying Ni Lin ◽

...

Keyword(s):

Machine Learning ◽

Chinese Population ◽

Learning Model ◽

Cross Sectional Study ◽

Neck Circumference ◽

Secondary Outcome ◽

Support Vector ◽

Sectional Study ◽

Cross Sectional ◽

Machine Learning Model

ObjectivesObstructive sleep apnoea (OSA) has received much attention as a risk factor for perioperative complications and 68.5% of OSA patients remain undiagnosed before surgery. Faciocervical characteristics may screen OSA for Asians due to smaller upper airways compared with Caucasians. Thus, our study aimed to explore a machine-learning model to screen moderate to severe OSA based on faciocervical and anthropometric measurements.DesignA cross-sectional study.SettingData were collected from the Shanghai Jiao Tong University School of Medicine affiliated Ruijin Hospital between February 2019 and August 2020.ParticipantsA total of 481 Chinese participants were included in the study.Primary and secondary outcome(1) Identification of moderate to severe OSA with apnoea–hypopnoea index 15 events/hour and (2) Verification of the machine-learning model.ResultsSex-Age-Body mass index (BMI)-maximum Interincisal distance-ratio of Height to thyrosternum distance-neck Circumference-waist Circumference (SABIHC2) model was set up. The SABIHC2 model could screen moderate to severe OSA with an area under the curve (AUC)=0.832, the sensitivity of 0.916 and specificity of 0.749, and performed better than the STOP-BANG (snoring, tiredness, observed apnea, high blood pressure, BMI, age, neck circumference, and male gender) questionnaire, which showed AUC=0.631, the sensitivity of 0.487 and specificity of 0.772. Especially for asymptomatic patients (Epworth Sleepiness Scale <10), the SABIHC2 model demonstrated better predictive ability compared with the STOP-BANG questionnaire, with AUC (0.824 vs 0.530), sensitivity (0.892 vs 0.348) and specificity (0.755 vs 0.809).ConclusionThe SABIHC2 machine-learning model provides a simple and accurate assessment of moderate to severe OSA in the Chinese population, especially for those without significant daytime sleepiness.

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1050 – Linear Support Vector Machine Learning Model is Highly Specific for Predicting Low-Grade Intraductal Papillary Mucinous Neoplasm Lesions

Gastroenterology ◽

10.1016/s0016-5085(19)40629-x ◽

2019 ◽

Vol 156 (6) ◽

pp. S-1432

Author(s):

Yasmin G. Hernandez-Barco ◽

Dania Daye ◽

Jessica McGoldrick ◽

Keith D. Lillemoe ◽

Dushyant Sahani ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Intraductal Papillary Mucinous Neoplasm ◽

Learning Model ◽

Support Vector ◽

Low Grade ◽

Linear Support Vector Machine ◽

Mucinous Neoplasm ◽

Machine Learning Model

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An Intelligent Classifier Paradigm for Intrusion Detection Using an Improved Multiclass Support Vector Machine Learning Model

International Journal of Advancements in Computing Technology ◽

10.4156/ijact.vol5.issue8.121 ◽

2013 ◽

Vol 5 (8) ◽

pp. 1090-1099

Author(s):

Sherin M. Youssef ◽

Ahmed AbouElfarag ◽

Eslam M. Gaballa

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Intrusion Detection ◽

Learning Model ◽

Support Vector ◽

Machine Learning Model ◽

Multiclass Support Vector Machine

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Hybrid Machine Learning Model Using Decision Tree and Support Vector Machine for Diabetes Identification

Advances in Intelligent Systems and Computing - Data Engineering and Intelligent Computing ◽

10.1007/978-981-16-0171-2_28 ◽

2021 ◽

pp. 293-305

Author(s):

K. Dhasaradhan ◽

R. Jaichandran ◽

K. L. Shunmuganathan ◽

S. Usha Kiruthika ◽

S. Rajaprakash

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Decision Tree ◽

Learning Model ◽

Support Vector ◽

Machine Learning Model ◽

Hybrid Machine

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FINDING TEMPORAL STRUCTURE IN TEXT: MACHINE LEARNING OF SYNTACTIC TEMPORAL RELATIONS

International Journal of Semantic Computing ◽

10.1142/s1793351x07000238 ◽

2007 ◽

Vol 01 (04) ◽

pp. 441-457 ◽

Cited By ~ 5

Author(s):

STEVEN BETHARD ◽

JAMES H. MARTIN ◽

SARA KLINGENSTEIN

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Temporal Structure ◽

Learning Model ◽

High Accuracy ◽

Support Vector ◽

Automatic Extraction ◽

Temporal Relations ◽

Tense And Aspect ◽

Machine Learning Model

This research proposes and evaluates a linguistically motivated approach to extracting temporal structure from text. Pairs of events in a verb-clause construction were considered, where the first event is a verb and the second event is the head of a clausal argument to that verb. All pairs of events in the TimeBank that participated in verb-clause constructions were selected and annotated with the labels BEFORE, OVERLAP and AFTER. The resulting corpus of 895 event-event temporal relations was then used to train a machine learning model. Using a combination of event-level features like tense and aspect with syntax-level features like the paths through the syntactic tree, support vector machine (SVM) models were trained which could identify new temporal relations with 89.2% accuracy. High accuracy models like these are a first step towards automatic extraction of temporal structure from text.

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A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

Materials ◽

10.3390/ma13245701 ◽

2020 ◽

Vol 13 (24) ◽

pp. 5701

Author(s):

Zhuoying Jiang ◽

Jiajie Hu ◽

Babetta L. Marrone ◽

Ghanshyam Pilania ◽

Xiong (Bill) Yu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Glass Transition ◽

Transition Temperature ◽

Glass Transition Temperature ◽

Deep Neural Network ◽

Learning Model ◽

Support Vector ◽

Model Framework ◽

Machine Learning Model

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

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