Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision by Janni Yuval, Paul A. O'Gorman, Chris N. Hill

“…The NN parameterization has a structure that ensures physical constraints are respected, such as by predicting subgrid fluxes instead of tendencies. The NN parameterization leads to stable simulations that replicate the climate of the high‐resolution simulation with similar accuracy to a successful random‐forest parameterization while needing far less memory. …”
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Near-field sound source localization using principal component analysis–multi-output support vector regression by Lanmei Wang, Yao Wang, Guibao Wang, Jianke Jia

“…In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. …”
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A Comparative study of the Envisaged and Definite Stock Prices of BSE SMEs Using RNN during the COVID-19 Pandemic by S. Kaur, A. Munde, A. K. Goyal

“…The stock market is unstable, but the use of machine learning algorithms allows to predict its future dynamics before spending. …”
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Resolving the conformational ensemble of a membrane protein by integrating small-angle scattering with AlphaFold. by Samuel Eriksson Lidbrink, Rebecca J Howard, Nandan Haloi, Erik Lindahl

“…Several strategies have recently been developed to drive the machine learning algorithm AlphaFold2 (AF) to sample multiple conformations, but it is more challenging to a priori predict what states are stabilized in particular conditions and how the transition occurs. …”
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Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images by Cristian Crisosto

“…The results are quantified in terms of the root mean square error (RMSE) and the mean absolute error (MAE). The new algorithm reduced both the RMSE and the MAE of the prediction by approximately 30% compared to the reference persistence model under diverse cloud conditions. …”
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Analysis of Energy Consumption in a Federated Learning-Based Zero-Touch Network by Urooj Yousuf Khan, Musharaf Ali Talpur, Umme Laila, Samar Raza Talpur

“… The current world revolves around data. Internet predicts that there are currently 2.8 million devices connected to the Internet. …”
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CatBoost Optimization Using Recursive Feature Elimination by Agus Hadianto, Wiranto Herry Utomo

“…CatBoost is a powerful machine learning algorithm capable of classification and regression application. …”
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Optimal Over-Frequency Droop Control for DFIG-Based Wind Farms Under Unreliable Communication by Yaxin Wang, Donglian Qi, Jianliang Zhang, Yulin Chen

“…Nowadays, high penetration of wind power is integrated into power grids, and WTs usually adopt the MPPT algorithm to maximize power output, which decouples the rotor speeds of wind turbines (WTs) and system frequency. …”
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Informing antimicrobial stewardship with explainable AI. by Massimo Cavallaro, Ed Moran, Benjamin Collyer, Noel D McCarthy, Christopher Green, Matt J Keeling

“…An appropriately trained gradient boosted decision tree algorithm, supplemented by a Shapley explanation model, predicts the likely antimicrobial drug resistance, with the odds of resistance informed by characteristics of the patient, admission data, and historical drug treatments and culture test results. …”
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Variational quantum Hamiltonian engineering by Benchi Zhao, Keisuke Fujii

“…Furthermore, we show that VQHE is compatible with grouping strategies, enabling further reductions in measurement complexity for expectation value estimation. …”
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A Cloud-Based Intelligence System for Asian Rust Risk Analysis in Soybean Crops by Ricardo Alexandre Neves, Paulo Estevão Cruvinel

“…Rules derived from time-series data are employed to enable scenario prediction for risk evaluation related to disease development. …”
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Transfer learning based feature selection for feedforward neural network for speech emotion classifier by D. V. Krasnoproshin, M. I. Vashkevich

“…This technique allowed us to significantly reduce initial feature vector size while increasing models’ prediction quality. We used TESS and RAVDESS datasets to estimate the performance of proposed method. …”
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Sentiment Analysis using Twitter Dataset by Ibrahim Moge Noor, Metin Turan

“…We perform Multi nominal naïve Bayes classifier algorithm to classify tweets documents. We split our dataset using k-folder validation since we had limited amounts of data, so to achieve unbiased prediction of the model. . …”
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A general framework for active space embedding methods with applications in quantum computing by Stefano Battaglia, Max Rossmannek, Vladimir V. Rybkin, Ivano Tavernelli, Jürg Hutter

“…The application of this scheme to study localized electronic states in materials is showcased through the accurate prediction of the optical properties of the neutral oxygen vacancy in magnesium oxide (MgO). …”
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Development of a Digital Model of the Agricultural Tool Working Element by V. I. Myalenko

“…The digital twin is a virtual model of the agricultural tool working element that allows you to calculate and predict its expected behavior during the entire period of operation.…”
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Design and Verification of Reactor Power Control Based on Stepped Dynamic Matrix Controller by Xu Zhang, Zhiguang Deng, Jun Li, Youwei Yang, Quan Ma, Mingming Liu

“…As key equipment in nuclear power plant, the reactor power control system is adopted to strictly control and regulate the reactor power of a PWR (pressurized water reactor) in a nuclear power plant. A well-optimized predictive control algorithm based on SDMC (stepped dynamic matrix controller) is developed and introduced in this paper and applied to the power regulation of a reactor power model. …”
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Online monitoring data processing methods for railway slopes and its application: A case study of the Shuohuang Railway by Mu GU

“…Subsequently, the CLEAN algorithm, introduced to the field of deformation monitoring, is utilized to suppress noise, minimizing its impact on subsequent deformation trend predictions. …”
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Handling method for GPS outages based on PSO-LSTM and fading adaptive Kalman filtering by Xiaoming Li, Xianchen Wang, Can Pei

“…Considering that the predicted pseudo-position may contain outliers or accumulated errors, a robust algorithm is employed to mitigate its impact on correcting INS errors. …”
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Optimization of State Clustering and Safety Verification in Deep Reinforcement Learning Using KMeans++ and Probabilistic Model Checking by Ryeonggu Kwon, Gihwon Kwon

“…The continuous state space is clustered using the KMeans++ algorithm, enabling efficient state space reduction. …”
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Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning by Pol Suárez, Francisco Alcántara-Ávila, Jean Rabault, Arnau Miró, Bernat Font, Oriol Lehmkuhl, Ricardo Vinuesa

“…The setup involves multiple zero-net-mass-flux jets and couples a computational fluid dynamics solver with a numerical multi-agent reinforcement learning framework based on the proximal policy optimization algorithm. Our results demonstrate up to 16% drag reduction at R e D  = 400, outperforming classical periodic control strategies. …”
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