Phenology-Based Maize and Soybean Yield Potential Prediction Using Machine Learning and Sentinel-2 Imagery Time-Series by Dorijan Radočaj, Ivan Plaščak, Mladen Jurišić

“…Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). …”
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Research on the Key Issues of Big Data Quality Management, Evaluation, and Testing for Automotive Application Scenarios by Yingzi Wang, Ce Yu, Jue Hou, Yongjia Zhang, Xiangyi Fang, Shuyue Wu

“…Through the comprehensive detection process of data importance, network busyness, duration of transmission process, and failure situation, the efficiency has been increased by 20%, and an adaptive data integrity detection method based on random algorithm and encryption algorithm is designed. …”
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Probabilistic and deep learning approaches for conductivity-driven nanocomposite classification by Wejden Gazehi, Rania Loukil, Mongi Besbes

“…The proposed framework begins with a Bayesian Network (BN) model, which provides probabilistic insights into the conductive behavior of nanocomposites by analyzing the distribution and interaction of their constituent nanoparticles. …”
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A Nonlinear Multiparameters Temperature Error Modeling and Compensation of POS Applied in Airborne Remote Sensing System by Jianli Li, Wenjian Wang, Feng Jiao, Jiancheng Fang, Tao Yu

“…In order to improve the precision and generalization ability of the temperature error compensation for POS, a nonlinear multiparameters temperature error modeling and compensation method based on Bayesian regularization neural network was proposed. …”
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Interpretable AI-driven causal inference to uncover the time-varying effects of PM2.5 and public health interventions on COVID-19 infection rates by Yang Han, Jacqueline C. K. Lam, Victor O. K. Li, Jon Crowcroft

“…It capitalizes on an encoder-decoder architecture for causal inference, where the encoder captures the time-varying causal relationships using a graph neural network, and the decoder provides time-series prediction based on the identified causal structures using a recurrent neural network. …”
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Constraint-aware wind power forecasting with an optimized hybrid machine learning model by Md. Omer Faruque, Md. Alamgir Hossain, S.M. Mahfuz Alam, Muhammad Khalid

“…In response, this paper introduces a novel constraint aware forecasting framework formed by a convolutional neural network (CNN) integrated with a double layer of gated recurrent unit (GRU) and fully connected layers. …”
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Current status and outlook of UWB radar personnel localization for mine rescue by ZHENG Xuezhao, MA Jiawen, HUANG Yuan, LI Qiang, REN Jing, LIU Yu

“…Future research directions of UWB radar personnel localization technology for mine rescue operations are proposed: ① optimizing the UWB radar localization system by constructing cross-modal information fusion models and developing highly adaptive signal processing methods to enhance the system's adaptability to post-mining disaster environments; ② improving the applicability of combined static and dynamic target localization by developing hybrid localization algorithms that integrate Bayesian networks or deep belief networks to fuse static and dynamic target features and establishing state-switching-based comprehensive models; ③ improving UWB radar echo processing algorithms, combining adaptive beamforming technology, Multiple Input Multiple Output (MIMO) technology, and optimized K-means++ or entropy-based hierarchical analysis algorithms, effectively distinguishing multi-target position information, and validating their adaptability and reliability in complex environments through extensive simulation experiments.…”
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Design and Optimization of Stacked Wideband On-Body Antenna with Parasitic Elements and Defected Ground Structure for Biomedical Applications Using SB-SADEA Method by Mariana Amador, Mobayode O. Akinsolu, Qiang Hua, João Cardoso, Daniel Albuquerque, Pedro Pinho

“…By using an AI-driven design approach, a self-adaptive Bayesian neural network surrogate-model-assisted differential evolution for antenna optimization (SB-SADEA) method to be specific, and a stacked structure having parasitic elements and a defected ground structure with 27 tuneable design parameters, the simulated impedance bandwidth of the on-body antenna was successfully enhanced from 150 MHz to 1.3 GHz, while employing a single and simplified body model in the simulation process. …”
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Supervised machine learning applied in nursing notes for identifying the need of childhood cancer patients for psychosocial support by Akseli Reunamo, Hans Moen, Sanna Salanterä, Sanna Salanterä, Päivi M. Lähteenmäki

“…Patients with the latter label were identified by having an outpatient appointment reservation in a mental health–related care unit at least 1 year after their primary diagnosis.ResultsThe random forest classification model trained on both cancer and diabetes patients performed best for the cancer patient population in three-times repeated nested cross-validation with 0.798 mean area under the receiver operating characteristics curve and was better with 99% probability (credibility interval −0.2840 to −0.0422) than the neural network–based model using only cancer patients in training when comparing all classifiers pairwise by using the Bayesian correlated t-test.ConclusionsUsing machine learning to predict childhood cancer patients needing psychosocial support was possible using nursing notes with a good area under the receiver operating characteristics curve. …”
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Probabilistic linkages of propagation from meteorological to agricultural drought in the North African semi-arid region by Younes Dahhane, Victor Ongoma, Abdessamad Hadri, Mohamed Hakim Kharrou, Oualid Hakam, Abdelghani Chehbouni

“…The propagation time from meteorological drought to agricultural drought was identified, and probabilistic linkages between the two types of droughts were investigated using the copula function and Bayesian network. Results show that a combination of SPEI3 as meteorological drought index and VHI as agricultural drought index has the highest correlation coefficient of 0.65 and the lowest RMSE and MAE of 1.5 and 1.5, respectively. …”
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Different Selection Levels of Mitogenomes: New Insights into Species Differentiation of the <i>Triops longicaudatus</i> (LeConte, 1846) Complex (Branchiopoda: Notostraca) by Xiaoyan Sun, Takeshi Kozai

“…To gain insights into their stress adaptations and species differentiation, we explored the genetic diversity of populations of the <i>T. longicaudatus</i> complex and constructed their haplotype networks. …”
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Dynamic Recognition Model of Driver’s Propensity under Multilane Traffic Environments by Xiaoyuan Wang, Jin Liu, Jinglei Zhang

“…Then dynamic recognition model of driver’s propensity can be established in time-varying environment through Dynamic Bayesian Network (DBN). Physiology-psychology experiments and real vehicle tests are designed to collect characteristic data of driver’s propensity in different situations. …”
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Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf by Bonyad Ahmadi, Mehdi Gholamalifard, Seyed Mahmoud Ghasempouri, Tiit Kutser

“…Machine learning models further enhanced retrievals, with the Mixture Density Network (MDN) emerging as the superior framework. …”
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Analysis of Single-Pilot Intention Modeling in Commercial Aviation by Lei Dong, Hongbing Chen, Changxiao Zhao, Peng Wang

“…The deep information in the feature vector of a single-pilot operation item is captured by the BiLSTM network, and the neural weight is adaptively assigned by the training mechanism. …”
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Discrete choice experiment to evaluate preferences of patients with cystic fibrosis among alternative treatment-related health outcomes: a protocol by Charlie McLeod, Richard Norman, Andre Schultz, Steven Mascaro, Steve Webb, Tom Snelling

“…Weighted preference information from the DCE will be used to develop a multiattribute utility instrument as a measure of treatment success in the upcoming Bayesian Evidence-Adaptive Trial to optimise management of CF. …”
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Shifts in seasonal influenza patterns in Australia during and after COVID-19: A comprehensive analysis by Cynthia Lu, Ian G. Barr, Stephen Lambert, Kerrie Mengersen, Liping Wang, Weizhong Yang, Zhongjie Li, Sotiris Vardoulakis, Hilary Bambrick, Wenbiao Hu

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Modelling flood losses of micro-businesses in Ho Chi Minh City, Vietnam by A. Buch, A. Buch, A. Buch, D. Paprotny, K. Rafiezadeh Shahi, K. Rafiezadeh Shahi, H. Kreibich, N. Sairam

“…Based on the identified drivers, probabilistic loss models (nonparametric Bayesian networks) were developed using a combination of data-driven and expert-based model formulation. …”
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Resilience assessment in process industries: A review of literature by Maryam Ghaljahi, Leila Omidi, Ali Karimi

“…As a result of the review of published literature, the most commonly used method to assess resilience in process industries is Dynamic Bayesian Network (DBN). DBN may be used for the estimation of uncertainty and probability of resilience in chemical processes. …”
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In hot water: Uncertainties in projecting marine heatwaves impacts on seagrass meadows. by Paula S Hatum, Kathryn McMahon, Kerrie Mengersen, Jennifer K McWhorter, Paul P-Y Wu

“…This study, through its novel integration of climate models, Dynamic Bayesian Networks, and Monte Carlo methods, offers a groundbreaking approach to ecological forecasting, significantly enhancing seagrass resilience assessment and supporting climate adaptation strategies under changing climatic conditions. …”
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US-CoastEX: Observation-based probabilistic reanalysis of storm surge and sea level extremes for the United States by Joao Morim, D. J. Rasmussen, Thomas Wahl, Francisco M. Calafat, Robert E. Kopp, Michael Oppenheimer, Soenke Dangendorf

“…Non-stationary extreme storm surge distributions are generated for gauged and ungauged sites by applying Bayesian methods to the U.S. tide gauge network, complemented with additional storm data unavailable in commonly used tide gauge data. …”
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