Machine Learning Algorithmic Study of the Naira Exchange Rate

In this study, we discuss various machine learning algorithms and architectures suitable for the Nigerian Naira exchange rate forecast. Our analyses were focused on the exchange rates of the British Pounds, US Dollars and the Euro against the Naira. The exchange rate data was sourced from the Central Bank of Nigeria. The performances of the algorithms were evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute Error and the coefficient of determination (R-Squared score). Finally, we compared the performances of these algorithms in forecasting the exchange rates.

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Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

10.2196/preprints.31709 ◽

2021 ◽

Author(s):

Hangsik Shin

Keyword(s):

Machine Learning ◽

Correlation Coefficient ◽

Age Estimation ◽

Mean Squared Error ◽

Mean Absolute Error ◽

Absolute Error ◽

Coefficient Of Determination ◽

Vascular Aging ◽

Squared Error ◽

Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

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Machine learning meets pKa

F1000Research ◽

10.12688/f1000research.22090.2 ◽

2020 ◽

Vol 9 ◽

pp. 113 ◽

Cited By ~ 2

Author(s):

Marcel Baltruschat ◽

Paul Czodrowski

Keyword(s):

Machine Learning ◽

Cross Validation ◽

Mean Squared Error ◽

Mean Absolute Error ◽

External Validation ◽

Absolute Error ◽

Source Model ◽

Squared Error ◽

Fold Cross Validation ◽

Better Than

We present a small molecule pKa prediction tool entirely written in Python. It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

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Predicting and Analysing the Behaviour of COVID-19

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit217213 ◽

2021 ◽

pp. 40-46

Author(s):

Gaurav Singh ◽

Shivam Rai ◽

Himanshu Mishra ◽

Manoj Kumar

Keyword(s):

Machine Learning ◽

Polynomial Regression ◽

Mean Squared Error ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Systems Science ◽

Data Repository ◽

Support Vector ◽

Squared Error

The prime objective of this work is to predicting and analysing the Covid-19 pandemic around the world using Machine Learning algorithms like Polynomial Regression, Support Vector Machine and Ridge Regression. And furthermore, assess and compare the performance of the varied regression algorithms as far as parameters like R squared, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. In this work, we have used the dataset available on Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at John Hopkins University. We have analyzed the covid19 cases from 22/1/2020 till now. We applied a supervised machine learning prediction model to forecast the possible confirmed cases for the next ten days.

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Automated Outlier Detection for Electrical Motors and Transformers

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9304 ◽

2020 ◽

Vol 17 (9) ◽

pp. 4703-4708

Author(s):

K. Anitha Kumari ◽

Avinash Sharma ◽

S. Nivethitha ◽

V. Dharini ◽

V. Sanjith ◽

...

Keyword(s):

Machine Learning ◽

Polynomial Regression ◽

Mean Squared Error ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Squared Error ◽

Electrical Motors

Electrical appliances most commonly consist of two electrical devices, namely, electrical motors and transformers. Typically, electrical motors are normally used in all sort of industrial purposes. Failures of such motors results in serious problems, such as overheat, shut down and even burnt, in their host systems. Thus, more attention have to be paid in detecting the outliers. In a similar way, to avoid the unexpected power reliability problems and system damages, the prediction of the failures in the transformers is expected to quantify the impacts. By predicting the failures, the lifetime of the transformers increases and unnecessary accidents is avoided. Therefore, this paper presents the detection of the outliers in electrical motors and failures in transformers using supervised machine learning algorithms. Machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF) and regression techniques like Support Vector Regression (SVR), Polynomial Regression (PR) are used to analyze the use cases of different motor specifications. Evaluation and the efficiency of findings are proved by considering accuracy, precision, F-measure, and recall for motors. Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and R-squared Error (R2) are considered as metrics for transformers. The proposed approach helps to identify the anomalies like vibration loss, copper loss and overheating in the industrial motor and to determine the abnormal functioning of the transformer that in turn leads to ascertain the lifetime. The proposed system analyses the behaviour of the electrical machines using the energy meter data and reports the outliers to users. It also analyses the abnormalities occurring in the transformer using the parameters involved in the degradation of the paper-oil insulation system and the voltage of operation as a whole leads to the predict the lifetime.

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Price Forecasting for the Balancing Energy Market Using Machine-Learning Regression

Energies ◽

10.3390/en13205420 ◽

2020 ◽

Vol 13 (20) ◽

pp. 5420

Author(s):

Alexandre Lucas ◽

Konstantinos Pegios ◽

Evangelos Kotsakis ◽

Dan Clarke

Keyword(s):

Machine Learning ◽

Electricity Markets ◽

Mean Squared Error ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Energy Market ◽

Gradient Boosting ◽

Price Forecasting ◽

Multiple Variable

The importance of price forecasting has gained attention over the last few years, with the growth of aggregators and the general opening of the European electricity markets. Market participants manage a tradeoff between, bidding in a lower price market (day-ahead), but with typically higher volume, or aiming for a lower volume market but with potentially higher returns (balance energy market). Companies try to forecast the extremes of revenues or prices, in order to manage risk and opportunity, assigning their assets in an optimal way. It is thought that in general, electricity markets have quasi-deterministic principles, rather than being based on speculation, hence the desire to forecast the price based on variables that can describe the outcome of the market. Many studies address this problem from a statistical approach or by performing multiple-variable regressions, but they very often focus only on the time series analysis. In 2019, the Loss of Load Probability (LOLP) was made available in the UK for the first time. Taking this opportunity, this study focusses on five LOLP variables (with different time-ahead estimations) and other quasi-deterministic variables, to explain the price behavior of a multi-variable regression model. These include base production, system load, solar and wind generation, seasonality, day-ahead price and imbalance volume contributions. Three machine-learning algorithms were applied to test for performance, Gradient Boosting (GB), Random Forest (RF) and XGBoost. XGBoost presented higher performance and so it was chosen for the implementation of the real time forecast step. The model returns a Mean Absolute Error (MAE) of 7.89 £/MWh, a coefficient of determination (R2 score) of 76.8% and a Mean Squared Error (MSE) of 124.74. The variables that contribute the most to the model are the Net Imbalance Volume, the LOLP (aggregated), the month and the De-rated margins (aggregated) with 28.6%, 27.5%, 14.0%, and 8.9% of weight on feature importance respectively.

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Machine learning meets pKa

F1000Research ◽

10.12688/f1000research.22090.1 ◽

2020 ◽

Vol 9 ◽

pp. 113

Author(s):

Marcel Baltruschat ◽

Paul Czodrowski

Keyword(s):

Machine Learning ◽

Cross Validation ◽

Mean Squared Error ◽

Mean Absolute Error ◽

External Validation ◽

Absolute Error ◽

Source Model ◽

Squared Error ◽

Fold Cross Validation ◽

Better Than

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Towards improving machine learning algorithms accuracy by benefiting from similarities between cases

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201077 ◽

2021 ◽

Vol 40 (1) ◽

pp. 947-972

Author(s):

Samih M. Mostafa

Keyword(s):

Machine Learning ◽

Missing Values ◽

Mean Absolute Error ◽

Absolute Error ◽

Noise Removal ◽

Point Of View ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Imputation Methods ◽

Machine Learning Methods

Data preprocessing is a necessary core in data mining. Preprocessing involves handling missing values, outlier and noise removal, data normalization, etc. The problem with existing methods which handle missing values is that they deal with the whole data ignoring the characteristics of the data (e.g., similarities and differences between cases). This paper focuses on handling the missing values using machine learning methods taking into account the characteristics of the data. The proposed preprocessing method clusters the data, then imputes the missing values in each cluster depending on the data belong to this cluster rather than the whole data. The author performed a comparative study of the proposed method and ten popular imputation methods namely mean, median, mode, KNN, IterativeImputer, IterativeSVD, Softimpute, Mice, Forimp, and Missforest. The experiments were done on four datasets with different number of clusters, sizes, and shapes. The empirical study showed better effectiveness from the point of view of imputation time, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2 score) (i.e., the similarity of the original removed value to the imputed one).

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A new generic method to improve machine learning applications in official statistics

Statistical Journal of the IAOS ◽

10.3233/sji-210885 ◽

2021 ◽

pp. 1-16

Author(s):

Kevin Kloos

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Statistical Properties ◽

Machine Learning Algorithms ◽

Official Statistics ◽

Academic Literature ◽

Misclassification Bias ◽

Squared Error ◽

Machine Learning Applications ◽

Applications Of Machine Learning

The use of machine learning algorithms at national statistical institutes has increased significantly over the past few years. Applications range from new imputation schemes to new statistical output based entirely on machine learning. The results are promising, but recent studies have shown that the use of machine learning in official statistics always introduces a bias, known as misclassification bias. Misclassification bias does not occur in traditional applications of machine learning and therefore it has received little attention in the academic literature. In earlier work, we have collected existing methods that are able to correct misclassification bias. We have compared their statistical properties, including bias, variance and mean squared error. In this paper, we present a new generic method to correct misclassification bias for time series and we derive its statistical properties. Moreover, we show numerically that it has a lower mean squared error than the existing alternatives in a wide variety of settings. We believe that our new method may improve machine learning applications in official statistics and we aspire that our work will stimulate further methodological research in this area.

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Designing accurate emulators for scientific processes using calibration-driven deep models

Nature Communications ◽

10.1038/s41467-020-19448-8 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Jayaraman J. Thiagarajan ◽

Bindya Venkatesh ◽

Rushil Anirudh ◽

Peer-Timo Bremer ◽

Jim Gaffney ◽

...

Keyword(s):

Mean Squared Error ◽

Mean Absolute Error ◽

Absolute Error ◽

Heterogeneous Data ◽

Small Data ◽

Machine Learning Methods ◽

Squared Error ◽

Noise Structure ◽

The Mean ◽

Modern Machine

Abstract Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study an often overlooked, yet important, problem of choosing loss functions while designing such emulators. Popular choices such as the mean squared error or the mean absolute error are based on a symmetric noise assumption and can be unsuitable for heterogeneous data or asymmetric noise distributions. We propose Learn-by-Calibrating, a novel deep learning approach based on interval calibration for designing emulators that can effectively recover the inherent noise structure without any explicit priors. Using a large suite of use-cases, we demonstrate the efficacy of our approach in providing high-quality emulators, when compared to widely-adopted loss function choices, even in small-data regimes.

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Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

Materials ◽

10.3390/ma13051072 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1072 ◽

Cited By ~ 19

Author(s):

Dong Van Dao ◽

Hai-Bang Ly ◽

Huong-Lan Thi Vu ◽

Tien-Thinh Le ◽

Binh Thai Pham

Keyword(s):

Compressive Strength ◽

Civil Engineering ◽

Mean Squared Error ◽

Model Performance ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Dry Density ◽

Statistical Measures ◽

Foamed Concrete

Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R2 value of 0.976 on the training part and an R2 of 0.972 on the testing part, using the optimized C-ANN-[3–4–5–1] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

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