scholarly journals Modeling the British Pound Sterling to Nigerian Naira Exchange Rate During the Covid-19 Pandemic

2021 ◽  
Vol 2 (2) ◽  
pp. 25-35
Author(s):  
Chukwudi Paul Obite ◽  
Ugochinyere Ihuoma Nwosu ◽  
Prince Henry Osuagwu ◽  
Obioma Gertrude Onukwube
Keyword(s):  
Machine Learning ◽  
Exchange Rate ◽  
Random Forest ◽  
Model Comparison ◽  
Arima Model ◽  
Secondary Data ◽  
Ann Model ◽  
Learning Models ◽  
The Government ◽  
Machine Learning Models

The British Pound Sterling (GBP) to Nigerian Naira (NGN) exchange rate has been grossly affected by the Coronavirus 2019 (Covid-19) pandemic. It has become pertinent to identify robust models that will help to cope with the variability associated with the pandemic. Many original studies found the ARIMA method to be highly useful in modeling and forecasting exchange rates. However, not much work has been done on modeling the GBP and NGN exchange rate during the covid-19 pandemic using machine learning models. This study focuses on modeling the exchange rate between the GPB and NGN during the period of the Covid-19 pandemic by adopting the process of model comparison using the Artificial Neural Network (ANN), Autoregressive Integrated Moving Average (ARIMA), and Random Forest models to obtain an optimal model and forecasts from the model. Secondary data of the GBP to NGN exchange rate within the period of the Covid-19 pandemic from exchangerate.org.uk were used. The two machine learning models (ANN and random forest) performed better than the ARIMA model. The RF, though performed well in the training set, was outperformed in the test set by the ANN model. The ANN model was chosen to model and forecast the GBP and NGN exchange rate during the Covid-19 pandemic. The predicted fall in the GBP to NGN exchange rate to 570 by December 2021 and 575 by September 2022 using the ANN model will have a huge effect on the economy of the country as the country depends largely on imported goods. The Government and policymakers must put in place structural measures that will avoid the looming crisis.

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

scholarly journals AB0652 MACHINE LEARNING TO PREDICT EARLY TNF INHIBITOR USERS IN PATIENTS WITH ANKYLOSING SPONDYLITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1620.1-1621
Author(s):  
J. Lee ◽  
H. Kim ◽  
S. Y. Kang ◽  
S. Lee ◽  
Y. H. Eun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ankylosing Spondylitis ◽  
Tnf Inhibitors ◽  
Tnf Inhibitor ◽  
Ann Model ◽  
Learning Models ◽  
Feature Importance ◽  
Importance Analysis ◽  
Baseline Characteristics ◽  
Machine Learning Models

Background:Tumor necrosis factor (TNF) inhibitors are important drugs in treating patients with ankylosing spondylitis (AS). However, they are not used as a first-line treatment for AS. There is an insufficient treatment response to the first-line treatment, non-steroidal anti-inflammatory drugs (NSAIDs), in over 40% of patients. If we can predict who will need TNF inhibitors at an earlier phase, adequate treatment can be provided at an appropriate time and potential damages can be avoided. There is no precise predictive model at present. Recently, various machine learning methods show great performances in predictions using clinical data.Objectives:We aim to generate an artificial neural network (ANN) model to predict early TNF inhibitor users in patients with ankylosing spondylitis.Methods:The baseline demographic and laboratory data of patients who visited Samsung Medical Center rheumatology clinic from Dec. 2003 to Sep. 2018 were analyzed. Patients were divided into two groups: early TNF inhibitor users treated by TNF inhibitors within six months of their follow-up (early-TNF users), and the others (non-early-TNF users). Machine learning models were formulated to predict the early-TNF users using the baseline data. Additionally, feature importance analysis was performed to delineate significant baseline characteristics.Results:The numbers of early-TNF and non-early-TNF users were 90 and 509, respectively. The best performing ANN model utilized 3 hidden layers with 50 hidden nodes each; its performance (area under curve (AUC) = 0.75) was superior to logistic regression model, support vector machine, and random forest model (AUC = 0.72, 0.65, and 0.71, respectively) in predicting early-TNF users. Feature importance analysis revealed erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and height as the top significant baseline characteristics for predicting early-TNF users. Among these characteristics, height was revealed by machine learning models but not by conventional statistical techniques.Conclusion:Our model displayed superior performance in predicting early TNF users compared with logistic regression and other machine learning models. Machine learning can be a vital tool in predicting treatment response in various rheumatologic diseases.Disclosure of Interests:None declared

Model Comparison for Esp Run-Life Prediction: Classic Statistics Vs. Machine Learning

2021 ◽  
Author(s):  
Alejandro Celemín ◽  
Diego A. Estupiñan ◽  
Ricardo Nieto
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Machine Learning Algorithms ◽  
Slight Reduction ◽  
Learning Models ◽  
Validation Data ◽  
Operational Conditions ◽  
Machine Learning Models

Abstract Electrical Submersible Pumps reliability and run-life analysis has been extensively studied since its development. Current machine learning algorithms allow to correlate operational conditions to ESP run-life in order to generate predictions for active and new wells. Four machine learning models are compared to a linear proportional hazards model, used as a baseline for comparison purposes. Proper accuracy metrics for survival analysis problems are calculated on run-life predictions vs. actual values over training and validation data subsets. Results demonstrate that the baseline model is able to produce more consistent predictions with a slight reduction in its accuracy, compared to current machine learning models for small datasets. This study demonstrates that the quality of the date and it pre-processing supports the current shift from model-centric to data-centric approach to machine and deep learning problems.

scholarly journals Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water

2021 ◽  
Vol 9 ◽  
Author(s):  
Daniel Lowell Weller ◽  
Tanzy M. T. Love ◽  
Martin Wiedmann
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Predictive Models ◽  
Training Data ◽  
Agricultural Water ◽  
Learning Models ◽  
Safety Hazards ◽  
E Coli ◽  
Resampling Method ◽  
Machine Learning Models

Recent studies have shown that predictive models can supplement or provide alternatives to E. coli-testing for assessing the potential presence of food safety hazards in water used for produce production. However, these studies used balanced training data and focused on enteric pathogens. As such, research is needed to determine 1) if predictive models can be used to assess Listeria contamination of agricultural water, and 2) how resampling (to deal with imbalanced data) affects performance of these models. To address these knowledge gaps, this study developed models that predict nonpathogenic Listeria spp. (excluding L. monocytogenes) and L. monocytogenes presence in agricultural water using various combinations of learner (e.g., random forest, regression), feature type, and resampling method (none, oversampling, SMOTE). Four feature types were used in model training: microbial, physicochemical, spatial, and weather. “Full models” were trained using all four feature types, while “nested models” used between one and three types. In total, 45 full (15 learners*3 resampling approaches) and 108 nested (5 learners*9 feature sets*3 resampling approaches) models were trained per outcome. Model performance was compared against baseline models where E. coli concentration was the sole predictor. Overall, the machine learning models outperformed the baseline E. coli models, with random forests outperforming models built using other learners (e.g., rule-based learners). Resampling produced more accurate models than not resampling, with SMOTE models outperforming, on average, oversampling models. Regardless of resampling method, spatial and physicochemical water quality features drove accurate predictions for the nonpathogenic Listeria spp. and L. monocytogenes models, respectively. Overall, these findings 1) illustrate the need for alternatives to existing E. coli-based monitoring programs for assessing agricultural water for the presence of potential food safety hazards, and 2) suggest that predictive models may be one such alternative. Moreover, these findings provide a conceptual framework for how such models can be developed in the future with the ultimate aim of developing models that can be integrated into on-farm risk management programs. For example, future studies should consider using random forest learners, SMOTE resampling, and spatial features to develop models to predict the presence of foodborne pathogens, such as L. monocytogenes, in agricultural water when the training data is imbalanced.

scholarly journals Machine learning to predict early TNF inhibitor users in patients with ankylosing spondylitis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Seulkee Lee ◽  
Yeonghee Eun ◽  
Hyungjin Kim ◽  
Hoon-Suk Cha ◽  
Eun-Mi Koh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ankylosing Spondylitis ◽  
Tnf Inhibitor ◽  
Ann Model ◽  
Learning Models ◽  
Feature Importance ◽  
Importance Analysis ◽  
Baseline Characteristics ◽  
Machine Learning Models

AbstractWe aim to generate an artificial neural network (ANN) model to predict early TNF inhibitor users in patients with ankylosing spondylitis. The baseline demographic and laboratory data of patients who visited Samsung Medical Center rheumatology clinic from Dec. 2003 to Sep. 2018 were analyzed. Patients were divided into two groups: early-TNF and non-early-TNF users. Machine learning models were formulated to predict the early-TNF users using the baseline data. Feature importance analysis was performed to delineate significant baseline characteristics. The numbers of early-TNF and non-early-TNF users were 90 and 505, respectively. The performance of the ANN model, based on the area under curve (AUC) for a receiver operating characteristic curve (ROC) of 0.783, was superior to logistic regression, support vector machine, random forest, and XGBoost models (for an ROC curve of 0.719, 0.699, 0.761, and 0.713, respectively) in predicting early-TNF users. Feature importance analysis revealed CRP and ESR as the top significant baseline characteristics for predicting early-TNF users. Our model displayed superior performance in predicting early-TNF users compared with logistic regression and other machine learning models. Machine learning can be a vital tool in predicting treatment response in various rheumatologic diseases.

scholarly journals Three machine learning models for the 2019 Solubility Challenge

ADMET & DMPK ◽  
2020 ◽  
Author(s):  
John Mitchell
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gold Standard ◽  
Challenge Test ◽  
Wisdom Of Crowds ◽  
Learning Models ◽  
The Third ◽  
Aqueous Solubilities ◽  
Machine Learning Models ◽  
Better Than

<p class="ADMETabstracttext">We describe three machine learning models submitted to the 2019 Solubility Challenge. All are founded on tree-like classifiers, with one model being based on Random Forest and another on the related Extra Trees algorithm. The third model is a consensus predictor combining the former two with a Bagging classifier. We call this consensus classifier Vox Machinarum, and here discuss how it benefits from the Wisdom of Crowds. On the first 2019 Solubility Challenge test set of 100 low-variance intrinsic aqueous solubilities, Extra Trees is our best classifier. One the other, a high-variance set of 32 molecules, we find that Vox Machinarum and Random Forest both perform a little better than Extra Trees, and almost equally to one another. We also compare the gold standard solubilities from the 2019 Solubility Challenge with a set of literature-based solubilities for most of the same compounds.</p>

scholarly journals CPT Data Interpretation Employing Different Machine Learning Techniques

Geosciences ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 265
Author(s):  
Stefan Rauter ◽  
Franz Tschuchnigg
Keyword(s):  
Machine Learning ◽  
Grain Size ◽  
Random Forest ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Models ◽  
Cone Penetration ◽  
Tip Resistance ◽  
Machine Learning Models

The classification of soils into categories with a similar range of properties is a fundamental geotechnical engineering procedure. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. These soil investigations are essential for each individual construction site and have to be performed prior to the design of a project. Since Machine Learning could play a key role in reducing the costs and time needed for a suitable site investigation program, the basic ability of Machine Learning models to classify soils from Cone Penetration Tests (CPT) is evaluated. To find an appropriate classification model, 24 different Machine Learning models, based on three different algorithms, are built and trained on a dataset consisting of 1339 CPT. The applied algorithms are a Support Vector Machine, an Artificial Neural Network and a Random Forest. As input features, different combinations of direct cone penetration test data (tip resistance qc, sleeve friction fs, friction ratio Rf, depth d), combined with “defined”, thus, not directly measured data (total vertical stresses σv, effective vertical stresses σ’v and hydrostatic pore pressure u0), are used. Standard soil classes based on grain size distributions and soil classes based on soil behavior types according to Robertson are applied as targets. The different models are compared with respect to their prediction performance and the required learning time. The best results for all targets were obtained with models using a Random Forest classifier. For the soil classes based on grain size distribution, an accuracy of about 75%, and for soil classes according to Robertson, an accuracy of about 97–99%, was reached.

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