Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging by Youngjin Han, Inwhee Joe

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Towards a digital twin: Digitization and model-based optimization of the innovative high-gradient magnetic separatorMendeley Data by Marko Tesanovic, Torben Bardel, Robin Karl, Sonja Berensmeier

“…Optimization results indicated a theoretical 4% productivity gain and a 3 percentage point yield improvement, while exposing critical design constraints in the HGMS chamber.These findings underscore the potential of digital twins to accelerate process optimization and reduce development costs through in-silico experimentation. …”
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Bayesian adaptation in Poisson cognitive systems by Aleksander A. Solodov

“…From a mathematical point of view, such a model of cognitive functioning is the implementation of an adaptive Bayesian approach, which allows to reduce the influence of a priori distribution of an unknown quantity on its evaluation.The described model of the cognitive system is justified by the fact that the value of X is not only random, but also with an unknown a priori distribution, is not observed directly, and in some way must be evaluated by the cognitive system on the basis of the already existing in the unconscious number of events and the last event on the basis of which.The optimal estimation of the random parameter is used to solve the problem of classification of observations, i.e. the optimal verification of the one-sided hypothesis by the Bayesian criterion.As a result of the undertaken consideration the applicability of the developed formal definition of cognitive system for the formulation of various problems of analysis and synthesis of systems is demonstrated. …”
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Bayesian changepoint detection for epidemic models by Peter Johnson, Jesper Lund Pedersen

“…This rate can be observed indirectly via the drift of a Brownian motion, before optimally filtering the transmission rate along with any changepoints using Bayesian stochastic filtering techniques. …”
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Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source by Parisa Sadeghli Dizaji, Hamidreza Habibiyan

“…In this work, we present a knowledge-integrated machine learning framework based on Bayesian Optimization for designing squeezed light sources using microring resonators. …”
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Bayesian Estimation-Based Pedestrian Tracking in Microcells by Yoshiaki Taniguchi, Masahiro Sasabe, Satoshi Aihara, Hirotaka Nakano

“…We consider a pedestrian tracking system where sensor nodes are placed only at specific points so that the monitoring region is divided into multiple smaller regions referred to as microcells. …”
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A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling by Joel Adams, Brendon Cintas, Nakwon Sung, Anthony Abrahao, Leonel Lagos, Dwayne McDaniel

“…The robot uses input from the world with depth cameras to avoid collisions with the robot’s body, and unreachable points for the end effector are addressed by both allowing for a soft collision with the environment to occur, prompting the system to abandon that point, and varying the exploration tendency of the optimization based on consecutive collisions. …”
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Yield Diagnosis and Tuning for Emerging Semiconductors During Research Stage by Chunshan Wang, Zizhao Ma, Yuxuan Zhu, Chensheng Jin, Dongyu Chen, Chuxin Zhang, Yining Chen, Wenzhong Bao, Yufeng Xie

“…In this paper, we propose a yield diagnosis and tuning scheme based on ensemble learning and Bayesian optimization, which demonstrate outstanding performance even with a limited data volume. …”
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Bayesian network for predicting mandibular third molar extraction difficulty by Tian Meng, Zhiyong Zhang, Xiao Zhang, Chao Zhang

“…Abstract Background This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient’s personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications. …”
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Optimizing photocatalytic dye degradation: A machine learning and metaheuristic approach for predicting methylene blue in contaminated water by Yunus Ahmed, Keya Rani Dutta, Sharmin Nahar Chowdhury Nepu, Meherunnesa Prima, Hamad AlMohamadi, Parul Akhtar

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Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition by Yihang Zhang, Hang Zhao

“…The performance of the model based on MOCOA-VMD achieves the best accuracy of 94.35%, much higher than the model constructed by modules of EEMD, VMD and CNN. Moreover, Bayesian optimization was carried out to fine-tune the hyperparameters batch size, learning rate, epochs, and momentum. …”
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Bayesian Uncertainty Quantification of Reflooding Model With PSO–Kriging and PCA Approach by Ziyue Zhang, Dong Li, Nianfeng Wang, Meng Lei

“…In order to solve the problem of large time costs in sampling and inefficient use of transient sample points, particle swarm optimization (PSO)–Kriging model and principal component analysis (PCA) are adopted in this paper. …”
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Maximizing Measurement Visual-Inertial Odometry for Low-Light Environment Through Online Image Pre-Processing by Suyong Lee, Hanyeol Lee, Chan Gook Park

“…We employ two image pre-processing techniques, <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-correction and CLAHE, whose parameters are estimated online using Bayesian Optimization (BO). The objective function of BO is to maximize the number of feature points extracted by the FAST corner detector, effectively increasing the measurements. …”
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Bayesian exploration of the composition space of CuZrAl metallic glasses for mechanical properties by Tero Mäkinen, Anshul D. S. Parmar, Silvia Bonfanti, Mikko J. Alava

“…We exploit an automated loop with an online database, a Bayesian optimization algorithm, and molecular dynamics simulations. …”
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Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis by Mathew Varidel, Victor An, Ian B Hickie, Sally Cripps, Roman Marchant, Jan Scott, Jacob J Crouse, Adam Poulsen, Bridianne O'Dea, Sarah McKenna, Frank Iorfino

“…MethodsWe frame the recommendation problem within a Bayesian decision-theoretic framework, whereby a preference ordering of decisions is estimated using the expected utility of outcomes under interventions. …”
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Exploiting full-duplex opportunities in WLANs via a reinforcement learning-based medium access control protocol by Song Liu, Peng Wei

“…Thus, we develop a Window-Constraint Bayesian (WCB) algorithm to generate optimized scheduling policies online. …”
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Bayesian spatio-temporal conditional autoregressive localized modeling techniques for socioeconomic factors and stunting in Indonesia by Aswi Aswi, Septian Rahardiantoro, Anang Kurnia, Bagus Sartono, Dian Handayani, Nurwan Nurwan

“…The highest risk was observed in Sulawesi Barat in 2022 (RR = 2.768), while DKI Jakarta consistently showed the lowest (RR = 0.004).Some key points of the article are: • Bayesian spatio-temporal models facilitate the classification of distinct area groups • The models were employed to analyze stunting patterns in Indonesia. • The inclusion of covariates influenced the number of groups identified.…”
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Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty by Qinghua Wei, Yuanhao Wang, Guo Yang, Tianyuan Li, Shuting Yu, Ziqiang Dong, Tong-Yi Zhang

“…The active learning strategy demonstrates that a machine learning model will have high generalizability if experimental data uncertainty is included, which greatly improves the model prediction or the material design accuracy. The feature-point-start forward method in multi-objective optimization adopts two Gaussian process regression (GPR) models, one for strength and one for elongation, and their outputs build up the acquisition-function-modified objective space of strength and elongation. …”
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Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation by Xuan Du, Yisha Pan, Xiaoyan Yang, Longgao Chen, Liangchen Liu, Ying Lin

“…This paper proposes a Point of Interest (POI)-weighted scoring method based on Empirical Bayesian Kriging (EBK), which is empirically demonstrated in Xuzhou, China. …”
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Transforming traffic accident investigations: a virtual-real-fusion framework for intelligent 3D traffic accident reconstruction by Yanzhan Chen, Qian Zhang, Fan Yu

“…While visual rendering by 3D-GS struggles under adverse conditions like nighttime or rain, a clustering parameter stochastic optimization model and mixed-integer programming Bayesian optimization (MIPBO) algorithm are proposed to enhance the segmentation of large-scale 3D point clouds. …”
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