scholarly journals PENERAPAN ALGORITMA RANDOM FOREST UNTUK IDENTIFIKASI DEHIDRASI BERBASIS CITRA URINE

2020 ◽  
Vol 6 (3) ◽  
pp. 49-54
Author(s):  
Niyalatul Muna ◽  
Faisal Lutfi Afriansyah ◽  
Ameng Bagus Suprayogy
Keyword(s):  
Random Forest ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Root Mean Squared Error ◽  
Squared Error

Tingkat dehidrasi tidak hanya bisa dirasakan secara langsung akan tetapi dapat diamati dan dilihat secara fisik berbasis visual. Secara visual salah satu gejala dari dehidrasi dapat dilihat dari warna urine. Gejala ini biasanya tidak begitu diperhatikan dan dianggap biasa. Padahal gejala hipohidrasi atau dehidrasi merupakan dampak yang merugikan dari asupan air yang tidak memadai sehingga mempengaruhi warna urine yang dihasilkan. Kesulitan panca indra manusia membedakan gejala dehidrasi dan melihat perbedaan warna urine secara visual sering diterjemahkan berbeda-beda, dikarenakan tingkat kemiripan warna yang dihasilkan. Beberapa penelitian menunjukkan adanya pemanfaatan teknologi kamera dengan sistem cerdas dapat membantu kesulitan dan keterbatasan panca indra manusia. Penelitian ini menggunakan citra urine diambil dari sample orang dewasa yang dikelompokkan berdasarkan kategori warna urine hasil penelitian terdahulu. Pengambilan fitur dari setiap citra urine diambil nilai warna dari  YCbCr. Model warna yang dihasilkan dari setiap sampel akan diidentifikasi menggunakan algoritma Random Forest dengan cross-validation. Hasil dari percobaan yang dilakukan menunjukkan akurasi 90% dari 30 dataset yang diujikan dengan nilai precision 90.2%, recall 90%, Mean absolute error 0.2473, dan Root mean squared error sebesar 0.3208.

PREDIKSI HARGA CRYPTOCURRENCY DENGAN METODE K-NEAREST NEIGHBOURS

2018 ◽  
Vol 14 (2) ◽  
pp. 137
Author(s):  
Haerul Fatah ◽  
Agus Subekti
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Search Algorithm ◽  
Absolute Error ◽  
Nearest Neighbour ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Nearest Neighbours

Uang elektronik menjadi pilihan yang mulai ramai digunakan oleh banyak orang, terutama para pengusaha, pebisnis dan investor, karena menganggap bahwa uang elektronik akan menggantikan uang fisik dimasa depan. Cryptocurrency muncul sebagai jawaban atas kendala uang eletronik yang sangat bergantung kepada pihak ketiga. Salah satu jenis Cryptocurrency yaitu Bitcoin. Analogi keuangan Bitcoin sama dengan analogi pasar saham, yakni fluktuasi harga tidak tentu setiap detik. Tujuan dari penelitian yang dilakukan yaitu melakukan prediksi harga Cryptocurrency dengan menggunakan metode KNN (K-Nearest Neighbours). Hasil dari penelitian ini diketahui bahwa model KNN yang paling baik dalam memprediksi harga Cryptocurrency adalah KNN dengan parameter nilai K=3 dan Nearest Neighbour Search Algorithm : Linear NN Search. Dengan nilai Mean Absolute Error (MAE) sebesar 0.0018 dan Root Mean Squared Error (RMSE) sebesar 0.0089.

scholarly journals Machine learning meets pKa

F1000Research ◽  
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

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.

scholarly journals Prediction Of Use Of Electric Kwh Using Support Vector Regression (SVR) Method (Case Study: Pt Pln (Persero) Rayon Seririt)

Repositor ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 525
Author(s):  
Rima Mediana ◽  
Setio Basuki ◽  
Nur Hayatin
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Basis Function ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Radial Basis

AbstrakPeranan listrik sangat penting bagi kehidupan masyarakat, begitu pentingnya peranan listrik tentu saja berdampak pada kebutuhan listrik yang begitu besar, maka PT. PLN (Persero) Rayon Seririt sebagai penyedia tenaga listrik harus bisa memprediksi besarnya peggunaan listrik rumah tangga setiap harinya. Selain itu menyebabkan semakin besar pula pemakian kwh listik, apabila pemakaian kwh listrik tidak diolah dengan baik akan menimbulkan beban energi listrik yang tidak terbendung. Dengan permasalahan yang telah diuraikan, penelitian ini menerapkan algoritma Support Vector Regression dalam Prediksi Pemakain KWH Listrik untuk mengetahui besarnya pemakaian kwh listrik yang akan datang. Berdasarkan hasil pengujian yang dilakukan hasil nilai akurasi terbaik Mean Absolute Error (MAE) sebesar 133560,1, Root Mean Squared Error (RMSE) sebesar 167664,1, dan Koefisien Korelasi sebesar 84,0 pada kernel polynomial. Sehingga algoritma Support Vector Regression dan fungsi kernel Radial Basis Function (RBF) cocok digunakan dalam memprediksi pemakaian kwh listrik.AbstractThe role of electricity is really significant for societies' live and it brings the huge impacts on the needs of electricity. This circumstance makes PT. PLN (Persero) Rayon Seririt as the provider of electricity must be able to predict the amount of household electricity usage steadily. This also causes the greater use of kwh electricity, if the use of kwh electricity is not treated properly, it will cause the burden of electrical energy is unstoppable.  Through the problems that have been elaborated, this study implements the Support Vector Regression algorithm in the prediction of kwh electricity usage to know the amount of  kwh electricity usage that will come.Based on the results of tests that have been conducted,  the result of best accuracy value Mean Absolute Error (MAE) equal to 133560,1, Root Mean Squared Error (RMSE) equal to 133560,1, and Correlation Coefficient equal to 84,0 at Radial Base Function kernel. It means, the Support Vector Regression algorithm and Radial Basis Function kernel function (RBF) are suitable to predict the use of kwh electricity.

scholarly journals Non-invasive Estimation of Haemoglobin Level Using PCA and Artificial Neural Networks

2019 ◽  
Vol 13 (1) ◽  
pp. 114-119 ◽  
Author(s):  
M. Lakshmi ◽  
P. Manimegalai
Keyword(s):  
Principal Components ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Root Mean Squared Error ◽  
Non Invasive ◽  
Squared Error ◽  
Wide Range ◽  
R Value ◽  
Invasive Measurement

Objective: Haemoglobin(Hb) measurement is generally performed by the traditional “fingerstick” test i.e., by invasively drawing blood from the body. Although the conventional laboratory measurement is accurate, it has its own limitations such as time delay, inconvenience of the patient, exposure to biohazards and the lack of real-time monitoring in critical situations. Non-invasive Haemoglobin Measurement (SpHb) has gained enormous attention among researches and can provide an earlier diagnosis to polycythemia, anaemia, various cardiovascular diseases, etc. Currently, Photoplethysmograph signal (PPG) is used for measuring oxygen saturation, to monitor the depth of anesthesia, heart rate and respiration monitoring. But through detailed statistical analysis, PPG signal can provide further information about various blood components. Investigation / Methodology: In this paper, an approach for non-invasive measurement of Hb using PPG, Principal Component Analysis (PCA) and Neural Network is proposed. A transmissive type PPG sensor is developed which is interfaced with Crowduino for the acquisition of PPG. From the obtained PPG signal, Principal Components (PC) are extracted. SpHb is predicted followed by the extraction of features from the PC. The analysis involves the SpHb prediction using a single PC, double PC and finally all the three PC. The predicted SpHb is evaluated with Hblab in terms of R-value, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. Conclusion: An approach for non-invasive measurement of Hb using Principal Components obtained from the PPG signal is discussed. The SpHb value is compared with the Hblab values. Correlation R-value between SpHb and Hblab is 0.77 when three principal components are used. Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) between SpHb and Hblab are 0.3, 0.44 and 0.6633 respectively when SpHb is measured with three principal components. It is evident from the result analysis that SpHb shows the promising result when all the three principal components are used. However, one of the limitations of the work is that the population setting chosen for the work does not include paediatric patients, accurately ill patient, pregnant population and surgical patients. With detailed analysis on a wide range of population setting, Hb prediction using PPG is a promising approach for non-invasive measurement.

scholarly journals Machine learning meets pKa

F1000Research ◽  
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

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.

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

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

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

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.

Electricity Consumption Analysis Using Demographic Variables Case Study – Nakhonratchasima, Thailand

2013 ◽  
Vol 734-737 ◽  
pp. 1679-1682
Author(s):  
Sureeporn Meehom ◽  
Nopphadon Khodpun
Keyword(s):  
High Speed ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Demographic Variables ◽  
Resources Management ◽  
Squared Error ◽  
High Speed Diesel

Electricity energy is vital in social and economic for nation development. The electricity consumption analysis plays an important role for sustainable energy and electricity resources management in the future. In this paper, the influence of demographical variables on the annual electricity consumption in Nakhonratchasima has been investigated by multiple regression analysis. It is founded that the electricity consumption correlated with four demographic variables, which are the number of electricity consumers, the amount of high speed diesel usages, the number of industrial factory and the number of employed labor force. The historical electricity consumption and all variables for the period 20022010 have been analyzed in 8 models for electricity prediction in 2011. In conclusion, the effective model has been selected by comparison of adjusted R2, mean absolute error (MAE) and root mean squared error (RMSE) of the proposed models. Model 8 is acceptable in relation to electricity consumption analysis with adjusted-R2, RMSE and MAE equal to 0.9980, 0.7540% and 0.6095% respectively. The results indicate that the model using all four variables has strong ability to predict future annual electricity consumption with 4,195,837,877 kWh in 2011.

On the mean squared error, the mean absolute error and the like

1999 ◽  
Vol 28 (8) ◽  
pp. 1813-1822 ◽  
Author(s):  
Shaul K. Bar-Lev ◽  
Benzion Boukai ◽  
Peter Enis
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Squared Error ◽  
The Mean

Cross-Validation of Field-Based Assessments of Body Composition for Individuals with Down Syndrome

2005 ◽  
Vol 22 (2) ◽  
pp. 198-206 ◽  
Author(s):  
Phillip C. Usera ◽  
John T. Foley ◽  
Joonkoo Yun
Keyword(s):  
Body Composition ◽  
Down Syndrome ◽  
Body Fat ◽  
Cross Validation ◽  
Mean Squared Error ◽  
The Body ◽  
Anthropometric Measurements ◽  
Prediction Equations ◽  
Root Mean Squared Error ◽  
Squared Error

The purpose of this study was to cross-validate skinfold and anthropometric measurements for individuals with Down syndrome (DS). Estimated body fat of 14 individuals with DS and 13 individuals without DS was compared between criterion measurement (BOP POD®) and three prediction equations. Correlations between criterion and field-based tests for non-DS group and DS groups ranged from .81 – .94 and .11 – .54, respectively. Root-Mean-Squared-Error was employed to examine the amount of error on the field-based measurements. A MANOVA indicated significant differences in accuracy between groups for Jackson’s equation and Lohman’s equation. Based on the results, efforts should now be directed toward developing new equations that can assess the body composition of individuals with DS in a clinically feasible way.

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