Improved terrain estimation from spaceborne lidar in tropical peatlands using spatial filtering
Science of Remote Sensing, 2023 Tropical peatlands are estimated to hold carbon stocks of 70 Pg C or more as partly decomposed organic matter, or peat. Peat may accumulate over thousands of years into gently mounded deposits called peat domes with a relief of several meters over ...
Alexander R. Cobb +6 more
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A Comparison of Root Mean Square Errors on Skeletonization Methods [PDF]
International Journal of Applied Physics and Mathematics, 2012 Vectorization is the most fundamental operation in interpretation of line drawings and document analysis. There are several reasons for converting image vectorization. Vector data is normally created from existing natural source image like photographs, scanned images.
Neeti Daryal, Vinod Kumar
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Investigation of the efficiency of support vector machine in predicting changes in water quality parameters (Case study: Choghakhor International Wetland) [PDF]
بومشناسی آبزیان, 2020 Inland waters, such as wetlands, are considered to be sensitive ecosystems, and sustainable productivity can only be achieved by adopting an appropriate environmental approach.
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Forecasting Wind Energy Production Using Machine Learning Techniques [PDF]
E3S Web of Conferences, 2023 Wind energy is an essential source of renewable energy that has gained popularity in recent years. Accurately forecasting wind energy production is crucial for efficient energy management and distribution.
Margarat G. Simi +3 more
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Decomposition of the mean absolute error (MAE) into systematic and unsystematic components.
PLoS ONE, 2023 When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE).
Scott M Robeson, Cort J Willmott
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Use of Machine Learning to Predict the Glycemic Status of Patients with Diabetes
Medical Sciences Forum, 2022 In this work, a machine learning methodology is used to predict the progress of the glycemic values of six patients with diabetes. Eight different algorithms are compared i.e., ANN, PNN, Polynomial Regression, Gradient Boosted Trees Regression, Random ...
Alessandro Massaro +4 more
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Moments and root-mean-square error of the Bayesian MMSE estimator of classification error in the Gaussian model [PDF]
Pattern Recognition, 2014 The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge,
Amin Zollanvari, Edward R. Dougherty
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Multi-region electricity demand prediction with ensemble deep neural networks.
PLoS ONE, 2023 Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions.
Muhammad Irfan +9 more
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Journal of Electrical Engineering and Computer, 2023 Penyakit hipertensi atau tekanan darah tinggi merupakan masalah kesehatan yang signifikan secara global. Prediksi yang akurat tentang risiko hipertensi dapat membantu dalam pencegahan, diagnosa, dan pengobatan dini. Dalam penelitian ini, kami mengusulkan
Sudriyanto Sudriyanto
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Root mean square error (RMSE) or mean absolute error (MAE): when to use them or not
Geoscientific Model Development, 2022 Abstract. The mean absolute error (MAE) and root mean squared error (RMSE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide.
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