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Journal articles on the topic 'RMSE'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature." Geoscientific Model Development 7, no. 3 (2014): 1247–50. http://dx.doi.org/10.5194/gmd-7-1247-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MA

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Mentaschi, L., G. Besio, F. Cassola, and A. Mazzino. "Problems in RMSE-based wave model validations." Ocean Modelling 72 (December 2013): 53–58. http://dx.doi.org/10.1016/j.ocemod.2013.08.003.

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3

Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, no. 1 (2014): 1525–34. http://dx.doi.org/10.5194/gmdd-7-1525-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to t

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4

Hussain, Ruba Yousif, Abd ul-Razzak T. Ziboon, and Khalid I. Hassun. "STANDARD MBTHOD OF RMSE CALCULATION FROM POLYNOMIAL RECTIFICATION." Journal of Engineering 8, no. 04 (2002): 361–70. http://dx.doi.org/10.31026/j.eng.2002.04.07.

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Image quality and geometric accuracy of SPOT data are essential elements in cartographi: applications. The evaluation of geometric accuracy of SPOT data is carried out by analysis and quantification of the errors in geometric correction. For this purpose, many programs are designed to correct the image geometrically (polynomial transformation) and to calculate the root mea 1 square error (RMSE) by the standard method. The standard method of calculating the RMSE is shown to be capable of providing accurate estimates of geometric error when a modest number of control points is available (b

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Pranata, Adrian, Joshua Farragher, Luke Perraton, et al. "Impaired Lumbar Extensor Force Control Is Associated with Increased Lifting Knee Velocity in People with Chronic Low-Back Pain." Sensors 23, no. 21 (2023): 8855. http://dx.doi.org/10.3390/s23218855.

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The ability of the lumbar extensor muscles to accurately control static and dynamic forces is important during daily activities such as lifting. Lumbar extensor force control is impaired in low-back pain patients and may therefore explain the variances in lifting kinematics. Thirty-three chronic low-back pain participants were instructed to lift weight using a self-selected technique. Participants also performed an isometric lumbar extension task where they increased and decreased their lumbar extensor force output to match a variable target force within 20–50% lumbar extensor maximal voluntar

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Malik Bayu Aji. "Analisis Perbandingan RMSE Algoritma Machine Learning dalam Memprediksi Harga Saham." Jurnal Informatika dan Teknologi Komputer ( J-ICOM) 5, no. 2 (2024): 113–19. https://doi.org/10.55377/j-icom.v5i2.8265.

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Perkembangan teknologi bisnis membuat masyarakat tertarik dengan mengikuti bisnis berbasis daring salah satunya adalah perdagangan saham. Dalam melakukan perdagangan saham, diperlukan analisis mendalam terkait kondisi pasar agar tidak mengalami kerugian. Akan tetapi tidak semua orang mampu melakukan analisis pasar saham, pembelajaran mesin berperan penting dalam membantu analisis ini. Peneliti menggunakan metode simulasi dengan data historis saham PT. Telkom Indonesia periode 2018-2022 untuk membandingkan performa algoritma tersebut. Pada tahap percobaan, dilakukan pemodelan dan simulasi mengg

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7

Sugandi, Budi, and Yuniatmi Syamsudin. "Deteksi Tepi Canny dan RMSE untuk Identifikasi Kerusakan pada Kemasan Minuman." JURNAL INTEGRASI 14, no. 2 (2022): 110–13. http://dx.doi.org/10.30871/ji.v14i2.4420.

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Salah satu minuman kemasan yang banyak dipakai adalah kemasan kaleng. Kekurangan kemasan minuman kaleng adalah sifatnya yang mudah rusak akibat benturan dengan benda lain maupun terjatuh. Kemasan yang rusak mengakibatkan produk menjadi tidak sempurna. Sehingga proses identifikasi kerusakan kemasan kaleng menjadi sangat penting sebagai proses penjamin kualitas produk. Penelitian ini ditujukan sebagai salah satu solusi untuk mengidentifikasi kerusakan pada kemasan minuman kaleng. Metode deteksi yang diusulkan berdasarkan pada deteksi tepi Canny dan RMSE (Root Mean Square Error). Proses awal dete

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Lv, Xian Qiang, Song Yang, Xin Zhang, Ying Wang, Yun Feng Shi, and Liu Wei. "Fast Fractal Image Coding Method Based on RMSE and DCT Classification." Applied Mechanics and Materials 241-244 (December 2012): 3034–39. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.3034.

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To solve the problem of long time consuming in the fractal encoding process, a fast fractal encoding algorithm based on RMSE (Root mean square error) and DCT (Discrete Cosine Transform) classification is proposed. During the encoding process, firstly, the image is divided into range blocks and domain blocks by quadtree partition according to RMSE, then, according to DCT coefficients of image block, three classes of image blocks are defined, which are smooth class, horizontal/vertical edge class, diagonal/sub-diagonal class. At last, every range block is limited to search the best matched block

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Hindmarsh, Diane, and David Steel. "Estimating the RMSE of Small Area Estimates without the Tears." Stats 4, no. 4 (2021): 931–42. http://dx.doi.org/10.3390/stats4040054.

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Small area estimation (SAE) methods can provide information that conventional direct survey estimation methods cannot. The use of small area estimates based on linear and generalized linear mixed models is still very limited, possibly because of the perceived complexity of estimating the root mean square errors (RMSEs) of the estimates. This paper outlines a study used to determine the conditions under which the estimated RMSEs, produced as part of statistical output (‘plug-in’ estimates of RMSEs) could be considered appropriate for a practical application of SAE methods where one of the main

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Babu,, S. K. Khadar. "Mathematical Modelling of RMSE Approach on Agricultural Financial Data Sets." International Journal of Pure & Applied Bioscience 5, no. 6 (2017): 942–47. http://dx.doi.org/10.18782/2320-7051.5802.

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11

Beveridge, C. A., J. J. Ross, and I. C. Murfet. "Branching in Pea (Action of Genes Rms3 and Rms4)." Plant Physiology 110, no. 3 (1996): 859–65. http://dx.doi.org/10.1104/pp.110.3.859.

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Cunha, Nicolas Cabral, Arissa Ikeda Suzuki, Fernanda Ferreira da Silva Lima, et al. "Alterações Citogenético-Moleculares no Gene FOXO1 em uma Criança com Rabdomiossarcoma Alveolar: Relato de Caso." Revista Brasileira de Cancerologia 64, no. 3 (2019): 415–19. http://dx.doi.org/10.32635/2176-9745.rbc.2018v64n3.51.

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Introdução: O rabdomiossarcoma (RMS) é o tumor de tecidos moles mais comum da infância. Pode ser classificado em dois subtipos principais: o rabdomiossarcoma alveolar (RMSa) e o embrionário (RMSe). No RMSa, o prognóstico é desfavorável quando comparado ao RMSe, necessitando de tratamento intensificado; dessa forma, a distinção entre ambos os subtipos é fundamental. Citogeneticamente, o RMSa apresenta translocações cromossômicas envolvendo o gene FOXO1 em 80% dos casos. A metodologia de hibridização in situ por fluorescência (FISH) tem sido muito utilizada para caracterizar o RMSa. Relato do ca

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Kamble, V. B., and S. N. Deshmukh. "Comparision Between Accuracy and MSE,RMSE by Using Proposed Method with Imputation Technique." Oriental journal of computer science and technology 10, no. 04 (2017): 773–79. http://dx.doi.org/10.13005/ojcst/10.04.11.

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Presence of missing values in the dataset leads to difficult for data analysis in data mining task. In this research work, student dataset is taken contains marks of four different subjects in engineering college. Mean, Mode, Median Imputation were used to deal with challenges of incomplete data. By using MSE and RMSE on dataset using with proposed Method and imputation methods like Mean, Mode, and Median Imputation on the dataset and found out to be values of Mean Squared Error and Root Mean Squared Error for the dataset. Accuracy also found out to be using Proposed Method with Imputation Tec

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Hendarwati, Emy Khairil, Piter Lepong, and Suyitno Suyitno. "Pemilihan Semivariogram Terbaik Berdasarkan Root Mean Square Error (RMSE) pada Data Spasial Eksplorasi Emas Awak Mas." GEOSAINS KUTAI BASIN 6, no. 1 (2023): 47. http://dx.doi.org/10.30872/geofisunmul.v6i1.1072.

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Semivariogram merupakan perangkat dasar geostatistik yang digunakan untuk memvisualisasi, memodelkan, dan menghitung autokorelasi spasial dari antar data dalam suatu variabel. Semivariogram dibedakan menjadi dua, yaitu semivariogram eksperimental dan semivariogram teoritis. Terdapat tiga jenis model semivariogram teoritis, yaitu model spherical, model eksponensial, dan model gaussian. Penelitian ini bertujuan untuk menentukan model semivariogram terbaik berdasarkan nilai RMSE terkecil. Data penelitian ini adalah data sekunder eksplorasi emas yang terdiri dari data drillhole sebanyak 101 data.

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Aviantoro, Kevin, and Yulia Darnita. "IMPLEMENTASI WIENER, CONTRAST STRETCHING, SHARPENING FILTER PADA CITRA SEMANGKA MENGGUNAKAN MSE,RMSE, DAN PSNR." Djtechno: Jurnal Teknologi Informasi 5, no. 2 (2024): 195–205. http://dx.doi.org/10.46576/djtechno.v5i2.4613.

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Penelitian ini mengkaji tiga metode pemrosesan citra Wiener Filter, Contrast Stretching, dan Sharpening Filter untuk meningkatkan kualitas citra semangka. Evaluasi kinerja dilakukan menggunakan Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), dan Root Mean Square Error (RMSE). Wiener Filter efektif mengurangi noise, Contrast Stretching meningkatkan kontras, dan Sharpening Filter menonjolkan detail. MSE mengukur rata-rata kesalahan kuadrat antara citra asli dan citra yang diproses, dengan nilai < 1 menunjukkan kualitas bagus dan > 1 kualitas kurang bagus. PSNR mengukur rasio s

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Kovács, Dávid Péter, Cas van der Oord, Jiri Kucera, et al. "Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE." Journal of Chemical Theory and Computation 17, no. 12 (2021): 7696–711. http://dx.doi.org/10.1021/acs.jctc.1c00647.

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17

Fortin, V., M. Abaza, F. Anctil, and R. Turcotte. "Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?" Journal of Hydrometeorology 15, no. 4 (2014): 1708–13. http://dx.doi.org/10.1175/jhm-d-14-0008.1.

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Abstract When evaluating the reliability of an ensemble prediction system, it is common to compare the root-mean-square error of the ensemble mean to the average ensemble spread. While this is indeed good practice, two different and inconsistent methodologies have been used over the last few years in the meteorology and hydrology literature to compute the average ensemble spread. In some cases, the square root of average ensemble variance is used, and in other cases, the average of ensemble standard deviation is computed instead. The second option is incorrect. To avoid the perpetuation of pra

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18

Julkarnaen, Agus, Ade Irma Purnamasari, and Irfan Ali. "ANALISIS PENJUALAN ROTI PADA DISTRIBUTOR MY ROTI MENGGUNAKAN METODE REGRESI LINEAR BERDASARKAN NILAI RMSE." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 3 (2024): 3225–29. http://dx.doi.org/10.36040/jati.v8i3.9426.

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Pada saat ini roti menjadi salah satu kebutuhan pokok dalam kehidupan. Komposisi gizi roti bervariasi bergantung pada jenis tepung yang dipakai serta bahan tambahan lainnya. My roti menjual roti dari berbagai macam merk. Tujuan dari penelitian ini adalah untuk memprediksi penjualan roti pada 3 bulan berikutnya pada data penjualan roti merk My roti distributor bandung timur. Regresi linear digunakan sebagai metode prediksi dengan jumlah roti yang terjual sebagai variabel Y dan periode penjualan roti sebagai variabel X. RMSE (Root Mean Squared Error) dan Relative Error digunakan untuk memutar ha

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19

Yersaw, Babur Tesfaye, Edmealem Temesgen Ebstu, Destaw Akili Areru, and Ligalem Agegn Asres. "Performance Evaluation of AquaCrop Model of Tomato under Stage Wise Deficit Drip Irrigation at Southern Ethiopia." Advances in Agriculture 2024 (May 28, 2024): 1–30. http://dx.doi.org/10.1155/2024/7201523.

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Crop modeling is a powerful tool for predicting yield and water productivity. The aim of the study was to calibrate and validate the AquaCrop model for tomato under staged deficit drip irrigation in Ethiopia. The AquaCrop model was calibrated and validated by using the observed data of canopy cover, biomass, dry yield, and soil water content. The results showed that the model was accurate in predicting canopy cover, biomass, and dry yield under different water levels. The overall performance in simulating canopy cover of AquaCrop, biomass, and soil water content showed a good match between mea

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20

Aprilia, Ira, Wahyu Nur Achmadin, Zuwidatul Masruroh, and Abdul Ghofur. "ANALISIS RMSE DALAM HOLT-WINTERS EXPONENTIAL SMOOTHING METHODS PADA FORECASTING EKSPOR MIGAS PROVINSI JAWA TIMUR." ESTIMATOR : Journal of Applied Statistics, Mathematics, and Data Science 2, no. 1 (2024): 10–21. https://doi.org/10.31537/estimator.v2i1.1911.

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Penelitian ini dimulai dengan memvisualisasikan semua data dalam bentuk grafik untuk memudahkan analisis lebih lanjut. Data yang digunakan adalah nilai ekspor minyak dan gas (migas) Provinsi Jawa Timur dari Januari 2021 hingga Desember 2023, dengan pencatatan bulanan. Analisis ini menyoroti pentingnya mempertimbangkan faktor musiman dalam peramalan untuk memahami dinamika data dengan lebih baik. Grafik ini menampilkan pola fluktuatif yang terlihat dalam data, dengan nilai yang naik-turun tidak stabil, yang disebabkan oleh pengaruh musiman yang terjadi setiap bulan. Dalam forecasting, struktur

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21

Hananto, April Lia, Sarina Sulaiman, Sigit Widiyanto, and Aviv Yuniar Rahman. "Evaluation comparison of wave amount measurement results in brass-plated tire steel cord using RMSE and cosine similarity." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 207–14. https://doi.org/10.11591/ijeecs.v22.i1.pp207-214.

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In the production process, quality checking is very important, one of which is on the wire. In the process of making brass-coated steel tire straps sometimes produce quality goods not in accordance with the desired standard values. Checks that are carried out manually have low efficiency and quite high errors occur. So it is necessary to check by measuring the wavelength on the brass plated steel cord automatically. In this study, used 3 automatic measurement methods using 2 evaluations, namely RMSE and cosine similarity. The results showed the best measurement using RMSE with method 2. Wherea

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Airlangga, Gregorius. "Forecasting Climate Change Impacts Using Machine Learning and Deep Learning: A Comparative Analysis." J-SAKTI (Jurnal Sains Komputer dan Informatika) 8, no. 1 (2024): 255. https://doi.org/10.30645/j-sakti.v8i1.784.

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This study undertakes a comparative analysis of machine learning and deep learning models for forecasting the impacts of climate change, utilizing Cross-Validation Root Mean Squared Error (CV RMSE) to gauge performance. Analyzed models include Long Short-Term Memory (LSTM) networks (CV RMSE: 0.155), Linear Regression (CV RMSE: 5647815244.91), Random Forest (CV RMSE: 0.159), Gradient Boosting Machine (GBM) (CV RMSE: 0.164), Support Vector Regressor (SVR) (CV RMSE: 0.159), Decision Tree Regressor (CV RMSE: 0.199), and K-Nearest Neighbors (KNN) Regressor (CV RMSE: 0.166). The study rigorously pro

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23

Scotto, Carlo, and Dario Sabbagh. "The Accuracy of Real-Time hmF2 Estimation from Ionosondes." Remote Sensing 12, no. 17 (2020): 2671. http://dx.doi.org/10.3390/rs12172671.

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A total of 4991 ionograms recorded from April 1997 to December 2017 by the Millstone Hill Digisonde (42.6°N, 288.5°E) were considered, with simultaneous Ne(h)[ISR] profiles recorded by the co-located Incoherent Scatter Radar (ISR). The entire ionogram dataset was scaled with both the Autoscala and ARTIST programs. The reliability of the hmF2 values obtained by ARTIST and Autoscala was assessed using the corresponding ISR values as a reference. Average errors Δ and the root mean square errors RMSE were computed for the whole dataset. Data analysis shows that both the Autoscala and ARTIST system

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Lia Hananto, April, Sarina Sulaiman, Sigit Widiyanto, and Aviv Yuniar Rahman. "Evaluation comparison of wave amount measurement results in brass-plated tire steel cord using RMSE and cosine similarity." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 207. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp207-214.

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<span lang="IN">In the production process, quality checking is very important, one of which is on the wire. In the process of making brass-coated steel tire straps sometimes produce quality goods not in accordance with the desired standard values. Checks that are carried out manually have low efficiency and quite high errors occur. So it is necessary to check by measuring the wavelength on the brass plated steel cord automatically. In this study, used 3 automatic measurement methods using 2 evaluations, namely RMSE and Cosine Similarity. The results showed the best measurement using RMSE

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25

Tziachris, Panagiotis, Panagiota Louka, Eirini Metaxa, Miltiadis Iatrou, and Konstantinos Tsiouplakis. "A Comparative Analysis of Machine Learning and Pedotransfer Functions Under Varying Data Availability in Two Greek Regions." Agriculture 15, no. 11 (2025): 1134. https://doi.org/10.3390/agriculture15111134.

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The current study evaluates the performance of pedotransfer functions (PTFs) and machine learning (ML) algorithms in predicting the soil bulk density (BD) across two distinct regions in Greece—Kozani and Veroia—using both limited and extended sets of soil parameters. The results reveal significant regional differences in prediction accuracy. In the full dataset scenario, Veroia consistently exhibits superior predictive performance across all models (PDF RMSE: 0.104, ML RMSE: 0.095) compared to Kozani (PDF RMSE: 0.133, ML RMSE: 0.122). Generally, ML models outperform PTFs in terms of the RMSE a

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Dharmaratne, P. P., A. S. A. Salgadoe, W. M. U. K. Rathnayake, and A. D. A. J. K. Weerasinghe. "Investigation of Accuracy for Rice Crop Parameters Predicted Using UAV Multispectral Imagery." Tropical Agricultural Research 36, no. 1 (2025): 82–98. https://doi.org/10.4038/tar.v36i1.8885.

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Remote measurement of rice crop parameters; Leaf- Chlorophyll, Above-ground biomass, Plant Height, Leaf Moisture and Rice Yield of rice plants before the actual harvest are vital for the early management of rice crops. This study was conducted in controlled rice fields and extended to farmer rice fields in the Maha season in Sri Lanka. The multispectral aerial images of the field were acquired by an Unmanned Ariel Vehicle (UAV) and processed. Vegetation indices (VIs) were then derived and selected the best combination of VIs explaining the respective groundmeasured parameters. The combination

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Nurholipah, Titin, Rudi Kurniawan, and Yudhistira Arie Wijaya. "EVALUASI PERFORMA MODEL REGRESI LINEAR DENGAN RMSE PADA JUMLAH PENUMPANG BUS TRANSJAKARTA." JIKA (Jurnal Informatika) 8, no. 2 (2024): 180. http://dx.doi.org/10.31000/jika.v8i2.10405.

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Dennison, Philip E., and Dar A. Roberts. "Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE." Remote Sensing of Environment 87, no. 2-3 (2003): 123–35. http://dx.doi.org/10.1016/s0034-4257(03)00135-4.

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Tri Wijaya, Sandy, Indyah Hartami Santi, and Zunita Wulansari. "PENERAPAN METODE K-NEAREST NEIGHBOR UNTUK PREDIKSI HARGA JAGUNG DENGAN PENGUJIAN RMSE." JATI (Jurnal Mahasiswa Teknik Informatika) 7, no. 2 (2023): 1255–60. http://dx.doi.org/10.36040/jati.v7i2.7391.

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Jagung merupakan salah satu komoditas pertanian yang penting di Indonesia, baik sebagai bahan pangan maupun sebagai bahan industri. Namun, pada tahun 2022, produksi jagung mengalami penurunan menjadi sebesar 339.788,4 ton, sedangkan untuk permintaan jagung pada tahun 2022 sebesar 36.527 ton dan penawaran sebesar 298.508 ton. Penurunan produksi dapat berdampak pada pasokan jagung yang tersedia, maka harga jagung juga akan mengalami fluktuasi yang dapat mempengaruhi ekonomi petani, peternak maupun industri yang menggunakan bahan dasar jagung. Adapun tujuan dari penelitian ini yaitu memprediksi h

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Chen, Yanlu, Ruijie Wang, Puming Zong, and Da Chen. "Image Processing for Denoising Using Composite Adaptive Filtering Methods Based on RMSE." Open Journal of Applied Sciences 14, no. 03 (2024): 660–75. http://dx.doi.org/10.4236/ojapps.2024.143047.

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Villasante, Antonio, Álvaro Fernández-Serrano, Carlos Osuna-Sequera, and Eva Hermoso. "Methodology for stiffness prediction in structural timber using cross-validation RMSE analysis." Journal of Building Engineering 107 (August 2025): 112767. https://doi.org/10.1016/j.jobe.2025.112767.

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Hastomo, Widi, Adhitio Satyo Bayangkari Karno, Nawang Kalbuana, Ervina Nisfiani, and Lussiana ETP. "Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 7, no. 2 (2021): 133. http://dx.doi.org/10.26418/jp.v7i2.47411.

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Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ),

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Mokhtar, N. M., N. Darwin, M. F. M. Ariff, Z. Majid, and K. M. Idris. "THE CAPABILITIES OF UNMANNED AERIAL VEHICLE FOR SLOPE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 451–59. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-451-2019.

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Abstract. Slope classification mapping is an important component of land suitability analysis for preventing landslides. This study aim to investigate the capabilities and application of Unmanned Aerial Vehicle (UAV) platform for slope classification. The objectives of this study such as investigating the capabilities of UAV for slope classification, generating Digital Elevation Model (DEM) and orthophoto from the image acquired and assessing the accuracy of DEM and orthophoto produced for slope classification. In this study, the aerial image was acquired using UAV at 60 m and 40 m altitude wi

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Selçuk, Eray, and Ergül Demir. "Comparison of item response theory ability and item parameters according to classical and Bayesian estimation methods." International Journal of Assessment Tools in Education 11, no. 2 (2024): 213–48. http://dx.doi.org/10.21449/ijate.1290831.

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This research aims to compare the ability and item parameter estimations of Item Response Theory according to Maximum likelihood and Bayesian approaches in different Monte Carlo simulation conditions. For this purpose, depending on the changes in the priori distribution type, sample size, test length, and logistics model, the ability and item parameters estimated according to the maximum likelihood and Bayesian method and the differences in the RMSE of these parameters were examined. The priori distribution (normal, left-skewed, right-skewed, leptokurtic, and platykurtic), test length (10, 20,

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Mohammed, Mohammed Ali. "Investigation of financial applications with blockchain technology." Journal of Computer & Electrical and Electronics Engineering Sciences 1, no. 1 (2023): 10–14. http://dx.doi.org/10.51271/jceees-0003.

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Aims: This article investigates recent advancements in machine learning and blockchain technology for cryptocurrency price prediction. The study presents a ML system using various techniques applied to six different datasets. The findings highlight that simpler models can outperform complex ones in predicting cryptocurrency prices. Methods: The methods used in this study include applying diverse ML techniques such as LSTM, CNN, SVM, KNN, XGBoost, Astro ML, LASSO, RIDGE, linear regression, DT, and GP on six cryptocurrency datasets to predict prices. Results: The research evaluated various machi

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Paca, Victor Hugo da Motta, Gonzalo E. Espinoza-Dávalos, Rodrigo da Silva, Raphael Tapajós, and Avner Brasileiro dos Santos Gaspar. "Remote Sensing Products Validated by Flux Tower Data in Amazon Rain Forest." Remote Sensing 14, no. 5 (2022): 1259. http://dx.doi.org/10.3390/rs14051259.

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This work compares methods of climate measurements, such as those used to measure evapotranspiration, precipitation, net radiation, and temperature. The satellite products used were compared and evaluated against flux tower data. Evapotranspiration was validated against the SSEBop monthly and GLEAM daily and monthly products, respectively, and the results were RMSE = 24.144 mm/month, NRMSE = 0.223, r2 = 0.163, slope = 0.411; RMSE = 1.781 mm/day, NRMSE = 0.599, r2 = 0.000, slope = 0.006; RMSE = 36.17 mm/month, NRMSE = 0.401, r2 = 0.002, and slope = 0.026. Precipitation was compared with the CHI

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Chen, Yuan, and Abdul Khaliq. "Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems." Algorithms 17, no. 4 (2024): 163. http://dx.doi.org/10.3390/a17040163.

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In this study, we investigate Quantum Long Short-Term Memory and Quantum Gated Recurrent Unit integrated with Variational Quantum Circuits in modeling complex dynamical systems, including the Van der Pol oscillator, coupled oscillators, and the Lorenz system. We implement these advanced quantum machine learning techniques and compare their performance with traditional Long Short-Term Memory and Gated Recurrent Unit models. The results of our study reveal that the quantum-based models deliver superior precision and more stable loss metrics throughout 100 epochs for both the Van der Pol oscillat

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Elsebaie, Ibrahim H., Atef Q. Kawara, Raied Alharbi, and Ali O. Alnahit. "Bias Correction Methods Applied to Satellite Rainfall Products over the Western Part of Saudi Arabia." Atmosphere 16, no. 7 (2025): 772. https://doi.org/10.3390/atmos16070772.

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Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This study evaluates five satellite-based rainfall products—GPM, GPCP, CHIRPS, PERSIANN-CDR and PERSIANN—against observed monthly rainfall at 28-gauge stations, using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB) and mean absolute error (MAE). Among uncorrected products, GPM achieved the highest mean CC (0.52), and lowes

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Hasekamp, Otto, Pavel Litvinov, Guangliang Fu, Cheng Chen, and Oleg Dubovik. "Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols." Atmospheric Measurement Techniques 17, no. 5 (2024): 1497–525. http://dx.doi.org/10.5194/amt-17-1497-2024.

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Abstract. From a passive satellite remote sensing point of view, the richest set of information on aerosol properties can be obtained from instruments that measure both intensity and polarization of backscattered sunlight at multiple wavelengths and multiple viewing angles for one ground pixel. However, it is challenging to exploit this information at a global scale because complex algorithms are needed with many fit parameters (aerosol and land/ocean reflection), based on online radiative transfer models. So far, two such algorithms have demonstrated this capability at a global scale: the Gen

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Karno, Adhitio Satyo Bayangkari. "Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory)." Journal of Informatic and Information Security 1, no. 1 (2020): 1–8. http://dx.doi.org/10.31599/jiforty.v1i1.133.

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Abstract  This study aims to measure the accuracy in predicting time series data using the LSTM (Long Short-Term Memory) machine learning method, and determine the number of epochs needed to produce a small RMSE (Root Mean Square Error) value. The result of this research is a high level of variation in RMSE value to the number of epochs needed in the data processing. This variation is quite difficult to obtain the right epoch value. By doing an iteration of the LSTM process on the number of different epochs (visualized in the graph), then the number of epochs with a minimum RMSE va

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Andiani, Andiani, Yoel Simanjuntak, and Ninuk Wiliani. "Performance Assessment of ARIMA and LSTM Models in Prediction Using Root Mean Square Error (RMSE)." Journal of Applied Research In Computer Science and Information Systems 2, no. 1 (2024): 149–58. https://doi.org/10.61098/jarcis.v2i1.181.

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Cryptocurrency is a digital financial asset that serves as a medium of exchange, with its ownership guaranteed using decentralized cryptographic technology, and it has become a growing investment tool. Solana is one of the highly sought-after Cryptocurrencies by investors. The market price of Solana exhibits highly volatile movements, which are considered risky for investment purposes, as it offers both high potential profits and losses. In this regard, time series data prediction models are used to analyze and forecast the price movements of Solana. By comparing the performance of ARIMA and L

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Asten, Philipus. "Analisis Kinerja Mikrokomputer Raspberry Pi Pada Smart Greenhouse Berbasis Internet Of Things (IoT) Menggunakan Algoritma Naive Baye." Journal of Information and Technology 3, no. 2 (2023): 55–60. http://dx.doi.org/10.32938/jitu.v3i2.5231.

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Smart Greenhouse adalah sistem yang menggunakan teknologi Internet of Things (IoT) untuk mengontrol dan memantau lingkungan pertumbuhan tanaman secara otomatis. Dalam melakukan analisis kinerja mikrokomputer Raspberry Pi dalam Smart Greenhouse berbasis IoT, dengan menerapkan algoritma Naive Bayes untuk menganalisis data dan membuat keputusan berdasarkan kondisi lingkungan tanaman. Hasil analisis terhadap mikrokomputer Raspberry Pi pada Smart Greenhouse menggunakan algoritma Naive Bayes dapat digunakan untuk memprediksi variabel lingkungan, seperti suhu dan kelembapan, sesuai data yang diperole

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Fang, Jingjing, Yining Wang, Peng Jiang, et al. "Evaluation of Different Methods on the Estimation of the Daily Crop Coefficient of Winter Wheat." Water 15, no. 7 (2023): 1395. http://dx.doi.org/10.3390/w15071395.

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Various methods have been developed to estimate daily crop coefficients, but their performance varies. In this paper, a comprehensive evaluation was conducted to estimate the crop coefficient of winter wheat in four growth stages based on the observed data of weighing-type lysimeters and the high-precision automatic weather station in the Wudaogou Hydrological Experimental Station from 2018 to 2019. The three methods include the temperature effect method, the cumulative crop coefficient method, and the radiative soil temperature method. Our results suggest that the performance of these methods

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Dal Maso, Fabien, Mickaël Begon, and Maxime Raison. "Methodology to Customize Maximal Isometric Forces for Hill-Type Muscle Models." Journal of Applied Biomechanics 33, no. 1 (2017): 80–86. http://dx.doi.org/10.1123/jab.2016-0062.

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One approach to increasing the confidence of muscle force estimation via musculoskeletal models is to minimize the root mean square error (RMSE) between joint torques estimated from electromyographic-driven musculoskeletal models and those computed using inverse dynamics. We propose a method that reduces RMSE by selecting subsets of combinations of maximal voluntary isometric contraction (MVIC) trials that minimize RMSE. Twelve participants performed 3 elbow MVIC in flexion and in extension. An upper-limb electromyographic-driven musculoskeletal model was created to optimize maximum muscle str

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Jamil, Basharat, and Lucía Serrano-Luján. "Hybrid Metaheuristic Algorithms for Optimization of Countrywide Primary Energy: Analysing Estimation and Year-Ahead Prediction." Energies 17, no. 7 (2024): 1697. http://dx.doi.org/10.3390/en17071697.

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In the present work, India’s primary energy use is analysed in terms of four socio-economic variables, including Gross Domestic Product, population, and the amounts of exports and imports. Historical data were obtained from the World Bank database for 44 years as annual values (1971–2014). Energy use is analysed as an optimisation problem, where a unique ensemble of two metaheuristic algorithms, Grammatical Evolution (GE), and Differential Evolution (DE), is applied. The energy optimisation problem has been investigated in two ways: estimation and a year-ahead prediction. Models are compared u

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Yang, Meihua, Dongyun Xu, Songchao Chen, Hongyi Li, and Zhou Shi. "Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra." Sensors 19, no. 2 (2019): 263. http://dx.doi.org/10.3390/s19020263.

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Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector

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Ndulue, Emeka, Ikenna Onyekwelu, Kingsley Nnaemeka Ogbu, and Vintus Ogwo. "Performance evaluation of solar radiation equations for estimating reference evapotranspiration (ETo) in a humid tropical environment." Journal of Water and Land Development 42, no. 1 (2019): 124–35. http://dx.doi.org/10.2478/jwld-2019-0053.

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Abstract Solar radiation (Rs) is an essential input for estimating reference crop evapotranspiration, ETo. An accurate estimate of ETo is the first step involved in determining water demand of field crops. The objective of this study was to assess the accuracy of fifteen empirical solar radiations (Rs) models and determine its effects on ETo estimates for three sites in humid tropical environment (Abakaliki, Nsukka, and Awka). Meteorological data from the archives of NASA (from 1983 to 2005) was used to derive empirical constants (calibration) for the different models at each location while da

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Lubis, Laila Sari, and Agus Buono. "Pemodelan Jaringan Syaraf Tiruan untuk Memprediksi Awal Musim Hujan Berdasarkan Suhu Permukaan Laut." Jurnal Ilmu Komputer dan Agri-Informatika 1, no. 2 (2012): 52. http://dx.doi.org/10.29244/jika.1.2.52-61.

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<p>Anjatan, Indramayu adalah salah satu daerah pertanian di Indonesia. Keberhasilan atau kegagalan panen setiap tahun tergantung pada ketersediaan air di wilayah tersebut. Oleh karena itu, diperlukan suatu metode yang akurat untuk memprediksi awal musim hujan. Metode yang digunakan untuk prediksi dalam penelitian ini adalah jaringan saraf tiruan (JST) back-propagation. Hasil akurasi prediksi JST diukur dengan R2 dan RMSE. Penelitian ini menggunakan suhu permukaan laut (SST) ECHAM4p5_CA yang merupakan salah satu model suhu permukaan laut di bulan Juni, Juli, dan Agustus. Domain SST dipili

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Romanuke, Vadim. "Maximum-versus-mean absolute error in selecting criteria of time series forecasting quality." Bionics of Intelligence 1, no. 96 (2021): 3–9. https://doi.org/10.30837/bi.2021.1(96).01.

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In time series forecasting, a commonly accepted criterion of the forecasting quality is the root-mean-square error (RMSE). Sometimes only RMSE is used. In other cases, another measure of forecasting accuracy is used along with RMSE. It is the mean absolute error (MAE). Although RMSE and MAE are the common criteria of time series forecasting quality, they both register information about averaged errors. However, averaging may remove information about volatility, which is typical for time series, in a few points (outliers) or narrow intervals. Information about outliers in time series forecasts

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Ramelan, Fitrah Amelia, and Lukman Hakim. "Perbandingan Prediksi terhadap Peningkatan Jumlah Pelanggan Iconnet dengan Algoritma Regresi Linear dan Random Forest pada Wilayah Jabodetabek dan Banten." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 9, no. 3 (2025): 790–801. https://doi.org/10.35870/jtik.v9i3.3490.

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This study compares the linear regression and random forest algorithms in predicting the number of Iconnet service customers in the Jabodetabek and Banten regions. The dataset comprises two years of sales data processed through filtering, cleaning, and labeling. Evaluation metrics include MAE, MAPE, and RMSE. The results show that linear regression performs better in predicting customer numbers, achieving MAE 369.85, MAPE 8.80%, and RMSE 388.89, compared to random forest with MAE 679.37, MAPE 16.95%, and RMSE 794.26. Conversely, random forest outperforms linear regression in bandwidth predicti

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