Optimization of classifier ensemble diversity by Leif E. Peterson

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Pengenalan Ekspresi Wajah Menggunakan DCT dan LDA untuk Aplikasi Pemutar Musik (MOODSIC) by I Gede Pasek Suta Wijaya, Asno Azzawagaam Firdaus, Aditya Perwira Joan Dwitama, Mustiari Mustiari

“…MOODSIC is developed using face expression recognition machine based on DCT, LDA and statistical classification algorithm. Based on offline testing result, face expression recognition machine successfully give good performance with accuracy of 100% when DCT features are 144 elements, 6 eigen vectors of LDA and kind of statistical classifier is LDA. …”
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UWB Positioning Integrity Estimation Using Ranging Residuals and ML Augmented Filtering by Mihkel Tommingas, Muhammad Mahtab Alam, Ivo Muursepp, Sander Ulp

“…By using machine learning (ML), the most important features were extracted from the initial set, and then, used to train and validate a model for UWB coordinate error prediction. …”
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Lidar-Binocular Camera-Integrated Navigation System for Underground Parking by Wei He, Rui Li, Wenjie Liao

“…Then, in the field experiment, the 3D cloud point data were collected by the test vehicle that equipped with the proposed navigation system from an underground parking and obtained 199 pairs of feature points’ distances. Finally, four different statistical methods were used to analyze the calculated distance errors. …”
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Forecasting the daily energy load schedule of working days using meteofactors for the central power system of Mongolia by A. G. Rusina, O. Tuvshin, P. V. Matrenin

“…According to the method of statistical analysis, daily load curves were constructed with an absolute percentage error of 2.68%. …”
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Cyber security Enhancements with reinforcement learning: A zero-day vulnerabilityu identification perspective. by Muhammad Rehan Naeem, Rashid Amin, Muhammad Farhan, Faisal S Alsubaei, Eesa Alsolami, Muhammad D Zakaria

“…In the present paper, we analyzed the statistical evaluation of forecasted values for several parameters in a reinforcement learning environment. …”
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Time Series Forecasting Based on Temporal Networks Evolution and Dynamic Constraints by Yunlong Peng, Han Li, Xu Han

“…Statistical tests and error metric results demonstrate that the proposed method achieves superior prediction accuracy compared to existing forecasting approaches under consideration, which confirms that this work not only contributes theoretically but also provides a practical solution for time series forecasting applications.…”
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An Image and State Information-Based PINN with Attention Mechanisms for the Rapid Prediction of Aircraft Aerodynamic Characteristics by Yiduo Kan, Xiangdong Liu, Haikuo Liu

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Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques by Vanessa Steindorf, Hamna Mariyam K. B., Nico Stollenwerk, Aitor Cevidanes, Jesús F. Barandika, Patricia Vazquez, Ana L. García-Pérez, Maíra Aguiar

“…Forecasting models, including random forest (RF) and seasonal autoregressive integrated moving average (SARIMAX), were evaluated using root mean squared error (RMSE) and mean absolute error (MAE) metrics. …”
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The fidelity of dynamic signaling by noisy biomolecular networks. by Clive G Bowsher, Margaritis Voliotis, Peter S Swain

“…We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. …”
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News Classification using Natural Language Processing with TF-IDF and Multinomial Naïve Bayes by Nadira Alifia Ionendri, Feri Candra, Afdi Rizal

“…However, current methods of classifying news are manual, time-consuming, and prone to human error. This study proposes an automated news classification system using Natural Language Processing (NLP) techniques with Term Frequency–Inverse Document Frequency (TF-IDF) for feature extraction and the Multinomial Naïve Bayes algorithm for classification. …”
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Leveraging pre-vaccination antibody titres across multiple influenza H3N2 variants to forecast the post-vaccination responseResearch in context by Hannah Stacey, Michael A. Carlock, James D. Allen, Hannah B. Hanley, Shane Crotty, Ted M. Ross, Tal Einav

“…Model predictions against prior vaccine studies had 2.4-fold error (95% CI: 2.34–2.40x, no large outliers with >4-fold error), yielding more accurate and robust predictions than a null model with 3.2-fold error (95% CI: 3.12–3.21x, 12% large outliers). …”
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Predictive Equations for Estimation of the Slump of Concrete Using GEP and MARS Methods by Ismail Husein, Ramaswamy Sivaraman, Sarwar Hasan Mohmmad, Forqan Ali Hussein Al-Khafaji, Sokaina Issa Kadhim, Yousof Rezakhani

“…The experimental data set contains five input variables, including the water-cement ratio (W/C), water (W), cement (C), river sand (Sa), and Bida Natural Gravel (BNG) used for the estimation of SL. Three common statistical indices, such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the accuracy of the derived equations. …”
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Deep learning-based research on fault warning for marine dual fuel engines by Lingkai Meng, Huibing Gan, Haisheng Liu, Daoyi Lu

“…The results reveal that the model obtained a mean square error (MSE) of 0.000051, a root mean square error (RMSE) of 0.007135, a mean absolute error (MAE) of 0.003185, and a mean absolute percentage error (MAPE) of 0.000386. …”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The study utilized a substantial dataset with a total of 61,584 images, and the most effective model attained an impressive Root Mean Square Error (RMSE) of 0.81, underscoring the model's remarkable capacity to accurately detect and predict casting quality issues. …”
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Comparison оf Digital Relief Models by S. A. Antonov, S. V. Peregudov

“…Despite its popularity and at first glance similarity of the data, a number of features of each of the digital terrain models were identified. …”
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IMAGING THE LYSOSOME IN CELLULAR SYSTEMS FOR DRUG DISCOVERY AND SCREENING

“… Lysosomal storage disorders (LSDs) are a group of rare diseases characterized by a genetic-derived lysosomal metabolism error. Within the complex spectrum of symptoms, the LSDs strongly affect the central nervous system (CNS). …”
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Inverse Gravimetric Problem Solving via Prolate Ellipsoidal Parameterization and Particle Swarm Optimization by Ruben Escudero González, Zulima Fernández Muñiz, Antonio Bernardo Sánchez, Juan Luis Fernández Martínez

“…The best-fitting model includes four ellipsoids (two low- and two high-density), reproducing the main features of the observed Bouguer anomaly with a prediction error of 20–25%. …”
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Modern Epidemic Situation of Parenteral Viral Hepatitis and Drug Addiction in the Russian Federation and Moscow by Yu. B. Novikova, A. A. Asratyan, E. V. Rusakova

“…The forms of federal statistical observation were applied. To assess the significance of differences between the compared values, the statistical significance error (p) were calculated based on the calculation of Student's test (t). …”
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The Heavy‐Tailed Inverse Power Lindley Type‐I Model: Reliability Inference and Actuarial Applications by Amal S. Hassan, Diaa S. Metwally, Mohammed Elgarhy, Rokaya Elmorsy Mohamed

“…Some mathematical and statistical properties of the HTIPL‐TI distribution were examined. …”
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