River flow prediction of Hunza River by LSSVR, fuzzy genetic and M5 model tree using nearby station’s meteorological data

2020 ◽  
Author(s):  
Rana Muhammad Adnan Ikram ◽  
Zhongmin Liang ◽  
Ozgur Kisi ◽  
Muhammad Adnan ◽  
Binquan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stream Flow ◽  
Climatic Data ◽  
Test Results ◽  
Learning Models ◽  
M5 Model Tree ◽  
Monthly Runoff ◽  
Fuzzy Genetic ◽  
Hunza River ◽  
Machine Learning Models

<p>River runoff prediction plays a very vital role in water resources planning, hydropower designing and agricultural water management. In the current study, the prediction capability of three machine learning models, least square support vector regression (LSSVR), fuzzy genetic (FG) and M5 model tree (M5Tree), in modeling daily and monthly runoffs of Hunza River catchment (HRC) using own and nearby Gilgit climatic station data is examined. The prediction performances of three machine learning models are compared using three statistical indexes, namely, root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R<sup>2</sup>). Firstly, four previous time lagged values of runoff, rainfall and atmospheric temperature are used as inputs on basis of correlation analysis to validate and test the accuracy of three machine learning models. After analyzing the performance of various input combinations, optimal one is selected for each variable and then these optimal inputs are employed together to see the forecasting performance. In the first part of study, monthly runoff of HRC are predicted using inputs consisting of local previous monthly runoff values and monthly meteorological values of Gilgit station. The test results show that LSSVR provides more accurate prediction results than the other two machine learning models. In the second part, daily runoffs of HRC are predicted using own previous daily runoff and Gilgit station’s climatic values. In the test results, a better accuracy is obtained from LSSVR models in relative to the FG and M5Tree models. In the last part of study, daily runoffs of HRC are predicted using own runoff and climatic data of HRC. In the results, it is found that local climatic data slightly improved the all model’s prediction accuracy in comparison of other scenario which also uses nearby station’s climatic data. The LSSVR models again are found to be better than the FGA and M5Tree models. LSSVM generally performs superior to the FGA and M5Tree in forecasting daily stream flow of Hunza River using local stream flow and climatic inputs. Based on the results of study, LSSVR model is recommended for monthly and daily runoff prediction of HRC with or without local climatic data.</p>

THE IMPACT OF ARTIFICIAL NEURAL NETWORK’S STRUCTURE ON ITS EFFICIENCY FOR FINANCIAL INDICATORS FORECASTING

2021 ◽  
Vol 2 (28) ◽  
pp. 44-51
Author(s):  
B. S. Ermakov ◽  
Keyword(s):  
Machine Learning ◽  
The Other ◽  
Financial Indicators ◽  
Test Results ◽  
Learning Models ◽  
Multiple Tests ◽  
Artificial Neural ◽  
The Impact ◽  
Overfitting Problem ◽  
Machine Learning Models

The article investigates the influence of artificial neural network’s structure on the results, with example of multlayer perceptron for forecasting some of the financial indicators. Multiple tests were made with various networks structures: different numbers of hidden layers and different numbers of neurons in these layers. Based on tests results, the increase of network’s size is effective to a certain extent, but at some point the further size increase is unreasonable. Also, the test results demonstrate that overfitting problem for multilayer perceptron is not as crucial as for the other machine learning models, such as regression. Key words: artificial neural networks, forecasting, multlayer perceptron, overfitting, artificial neural netwok’s size.

scholarly journals Efficient Prediction of Vitamin B Deficiencies via Machine-learning Using Routine Blood Test Results in Patients With Intense Psychiatric Episode

2019 ◽  
Author(s):  
Hidetaka Tamune ◽  
Jumpei Ukita ◽  
Yu Hamamoto ◽  
Hiroko Tanaka ◽  
Kenji Narushima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Test ◽  
Psychiatric Symptoms ◽  
Vitamin B ◽  
Test Results ◽  
Learning Models ◽  
Routine Blood ◽  
Routine Blood Test ◽  
Vitamin B Deficiency ◽  
Machine Learning Models

AbstractBackgroundVitamin B deficiency is common worldwide and may lead to psychiatric symptoms; however, vitamin B deficiency epidemiology in patients with intense psychiatric episode has rarely been examined. Moreover, vitamin deficiency testing is costly and time-consuming, which has hampered effectively ruling out vitamin deficiency-induced intense psychiatric symptoms. In this study, we aimed to clarify the epidemiology of these deficiencies and efficiently predict them using machine-learning models from patient characteristics and routine blood test results that can be obtained within one hour.MethodsWe reviewed 497 consecutive patients deemed to be at imminent risk of seriously harming themselves or others over 2 years in a single psychiatric tertiary-care center. Machine-learning models (k-nearest neighbors, logistic regression, support vector machine, and random forest) were trained to predict each deficiency from age, sex, and 29 routine blood test results gathered in the period from September 2015 to December 2016. The models were validated using a dataset collected from January 2017 through August 2017.ResultsWe found that 112 (22.5%), 80 (16.1%), and 72 (14.5%) patients had vitamin B1, vitamin B12, and folate (vitamin B9) deficiency, respectively. Further, the machine-learning models were well generalized to predict deficiency in the future unseen data, especially using random forest; areas under the receiver operating characteristic curves for the validation dataset (i.e. the dataset not used for training the models) were 0.716, 0.599, and 0.796, respectively. The Gini importance of these vitamins provided further evidence of a relationship between these vitamins and the complete blood count, while also indicating a hitherto rarely considered, potential association between these vitamins and alkaline phosphatase (ALP) or thyroid stimulating hormone (TSH).DiscussionThis study demonstrates that machine-learning can efficiently predict some vitamin deficiencies in patients with active psychiatric symptoms, based on the largest cohort to date with intense psychiatric episode. The prediction method may expedite risk stratification and clinical decision-making regarding whether replacement therapy should be prescribed. Further research includes validating its external generalizability in other clinical situations and clarify whether interventions based on this method could improve patient care and cost-effectiveness.

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>

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