Cardiac Clarity: Harnessing Machine Learning for Accurate Heart-Disease Prediction by Sujith Santhosh, Krishnaraj Chadaga, R.Vijaya Arjunan, Sandra D'Souza

“…The models were fine-tuned using Grid Search, Random Search, and Bayesian Optimization methods, achieving promising results with Random Forest achieving an AUC =0.99 and accuracy of 98.5%, outperforming all the other models and demonstrating robust performance on various metrics such as AUC, PR Curve, Log Loss, Jaccard Score, and MCC. …”
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Inversion analysis of constitutive relations of blade spar laminates with wrinkle defects by Ning Sun, Bo Zhou, Haocheng Zheng, Yingwei Wang

“…Four sets of uniaxial tension tests were conducted on specimens with varying defect sizes to obtain their corresponding load-displacement curves. A hybrid optimization method integrating Bayesian regularized backpropagation neural networks and particle swarm optimization was developed to inversely determine the constitutive parameters of composite laminates. …”
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Spatiotemporal patterns of wet–dry encounters between water source and receiving areas in the South-to-North water transfer project by Yuping Han, Jinhang Li, Mengdie Zhao

“…The risk of water diversion under different scenarios is quantified by using Bayesian network for probabilistic reasoning. The results showed that: (1) During 1959–2023, three abrupt runoff points were detected in both the Yangtze River basin (YTB) and the Yellow River basin (YRB), with periodicism of 27/17/11 years and 12/27/29 years, respectively. …”
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Forecasting Municipal Solid Waste Removal Volume Based on Socioeconomic Indicators for Carbon Reduction Strategy in Beijing’s Waste Management  by Yaxin Cui, Min Yee Chin, Hong Sheng Loh, Chew Tin Lee, Pei Ying Ong, Yee Van Fan, Kok Sin Woon

“…With Beijing as a focal point due to its substantial contribution to MSW generation and greenhouse gas (GHG) emissions, this study employs two-stage Bayesian-optimized Artificial Neural Network models to forecast MSW removal volume and evaluate associated GHG emissions in Beijing. …”
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A Copula-based Method for River Ecological Flow Quantification and Algal Bloom Risk Assessment in the Hanjiang River by NONG Xi-zhi, LAI Cheng, JING Zheng, YE Ye

“…Finally, a joint distribution model of flow and algal density was established, and the Bayesian conditional probability formula was applied to analyze the probability of algal density exceeding the threshold of algal bloom occurrence under different flow scenarios. …”
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Estimation for Parameters of Life of the Marshall-Olkin Generalized-Exponential Distribution Using Progressive Type-II Censored Data by Ahmed Elshahhat, Abdisalam Hassan Muse, Omer Mohamed Egeh, Berihan R. Elemary

“…As expected, the Bayes estimates are not explicitly expressed, thus the Markov chain Monte Carlo techniques are implemented to approximate the Bayes point estimates and to construct the associated highest posterior density credible intervals. …”
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A Multidimensional Analysis Approach Toward Sea Cliff Erosion Forecasting by Maria Krivova, Michael J. Olsen, Ben A. Leshchinsky

“…First, Digital Elevation Model (DEM) rasters are created from multiple epochs of terrestrial lidar point clouds using two approaches: Triangular Irregular Networks (TINs) and Empirical Bayesian Kriging (EBK). …”
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Surface water quality prediction based on BOA-BiLSTM model(基于BOA-BiLSTM模型的地表水水质预测) by 章佩丽(ZHANG Peili), 赵文雅(ZHAO Wenya), 许旭敏(XU Xumin), 包鑫磊(BAO Xinlei)

“…∶为准确评估监测条件有限的平原河网小流域河水水质演变趋势，预知水质变化情况，利用浙江省台州市南官河2021年6月至2023年6月的水质监测数据，基于贝叶斯优化算法（Bayesian optimization algorithm，BOA）和双向长短期记忆神经网络（bi-directional long short-term memory，BiLSTM）建立了地表水水质预测模型。…”
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Information criterion for approximation of unnormalized densities. by John Y Choe, Yen-Chi Chen, Nick Terry

“…This paper considers the problem of approximating an unknown density when it can be evaluated up to a normalizing constant at a finite number of points. This density approximation problem is ubiquitous in statistics, such as approximating a posterior density for Bayesian inference and estimating an optimal density for importance sampling. …”
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Population dynamics and monitoring applied to decision-making by M. J. Conroy, D. C. Lee

“…Their paper provides a good example of why Bayesian analysis is particularly suited to many management problems. …”
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Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy by Ganesh Narasimha, Dejia Kong, Paras Regmi, Rongying Jin, Zheng Gai, Rama Vasudevan, Maxim Ziatdinov

“…Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.…”
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PREDICTION OF STILLBORN PIGLETS FROM MULTIPAROUS SOWS by Daniel Alonso Domínguez-Olvera, José Guadalupe Herrera-Haro, José Ricardo Bárcena-Gama, María Esther Ortega-Cerrilla, Francisco Ernesto Martínez-Castañeda, Antonio José Rouco-Yáñez, María Angélica Ortiz-Heredia, Nathaniel Alec Rogers-Montoya

“…When categorizing using the optimal cutoff point of 0.395, the predictive efficiency as measured by the area under the Receiver Operating Characteristic (ROC) curve was 0.846 for training and 0.813 for testing. …”
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Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…However, the performance improved significantly after integrating the four models with Bayesian optimization (all models had R² > 0.56), which realized quantitative prediction capabilities for nitrate leaching loss concentrations. …”
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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

“…As a result, the use of ML techniques to optimize PV systems at their MPP is highly beneficial. …”
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Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data by Yasuyuki Kawai, Koji Yamamoto, Keisuke Tsuruta, Keita Miyazaki, Hideki Asai, Hidetada Fukushima

“…After data preprocessing, we constructed a deep neural network multi-output regression model using Bayesian optimization. Model performance was assessed by comparing the predicted values with the actual hospital arrival measurements using mean absolute error, R² score, residual standard deviation, and Spearman’s correlation coefficient. …”
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Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review by Claire R. van Genugten, Claire R. van Genugten, Melissa S. Y. Thong, Melissa S. Y. Thong, Melissa S. Y. Thong, Wouter van Ballegooijen, Wouter van Ballegooijen, Wouter van Ballegooijen, Annet M. Kleiboer, Annet M. Kleiboer, Donna Spruijt-Metz, Arnout C. Smit, Mirjam A. G. Sprangers, Mirjam A. G. Sprangers, Yannik Terhorst, Yannik Terhorst, Heleen Riper, Heleen Riper, Heleen Riper

“…For future development, it is recommended that developers utilize complex analytical techniques that can handle real-or near-time data such as machine learning, passive monitoring, and conduct further research into empirical-based decision rules and points for optimization in terms of enhanced effectiveness and user-engagement.…”
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Modelling the effect of bednet coverage on malaria transmission in South Sudan. by Abdulaziz Y A Mukhtar, Justin B Munyakazi, Rachid Ouifki, Allan E Clark

“…Available data on malaria is utilized to determine realistic parameter values of this model using a Bayesian approach via Markov Chain Monte Carlo (MCMC) methods. …”
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The Effect of Heat Stress on the Performance and Genetic Parameters of Milk Production, Reproduction, and Somatic Cell Scores in Iranian Holstein Cows by Reza Seyedsharifi, Fatemeh Ala Noshahr, Jamal Seif Davati, Nemat Hedayat Evrigh

“…Thus, it can be claimed that the optimal THI point for milk production was between 72 and 70 THI (9-13 °C). …”
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MODELING LONGITUDINAL MEASUREMENTS OF CLINICAL AND EPIDEMIOLOGICAL PARAMETERS USING JOINPOINT REGRESSION by Mihaela Moscalu, Ionela-Lăcrămioara Șerban, Norin Forna, Mihaela Pundiche, Loredana Mitran, Madalina-Elena Datcu, Nicolae Șerban Dragomir, Alin Constantin Pînzariu

“…Joinpoint regression accurately identified inflection points, offering a clearer perspective on the underlying processes. …”
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Assessment of Exploited Stock and Management Implications of Tiger Tooth Croaker (<i>Otolithes ruber)</i> in Coastal Waters of Makran, Pakistan by Samroz Majeed, S M Nurul Amin, Asad Ullah Ali Muhammad, Sudheer Ahmed

“…This study is the first to apply three length-based approaches for assessing the stock status of <i>O. ruber</i> in the Makran coast: (1) TropFishR to estimate the mortality, growth parameters, and current exploitation status, reference points based on the yield per recruitment model, (2) the length-based Bayesian biomass method (LBB) to calculate stock biomass, and (3) the length-based spawning potential ratio (LBSPR) to estimate the spawning potential ratio. …”
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