A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings by Dylan Wald, Olga Doronina, Kathryn Johnson, Ryan King, Michael Sinner, Kevin Griffin, Rohit Chintala, Deepthi Vaidhynathan, Jibonananda Sanyal, Marc Day

Subjects: “…Bayesian neural networks…”
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Evaluation and optimization of adaptive cruise control in autonomous vehicles using the car learning to act simulator: A performance evaluation under various weather conditions by Roza Al-Hindawi, Taqwa Alhadidi

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A Proactive and Time-Sensitive Cyber Risk Assessment Model Integrating Markov Chains and Bayesian Networks by Pavlos Cheimonidis, Konstantinos Rantos

“…To improve accuracy, Bayesian networks are employed to capture both system vulnerabilities and asset interdependencies within the network. …”
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Exploring the predictive role of personal identity in trait anxiety: network and Bayesian evidence from Chinese college students by Ruizhi Huang, Ye Yuan, Ye Yuan, Ye Yuan, Huiqing Shen, Yuqi Sun, Zhilin Wang, Huilin Qiu, Jaesik Jeong, Wenjing Yan, Wenjing Yan, Ke Jiang, Ke Jiang

“…Data were analyzed using adaptive LASSO network analysis and Bayesian modeling.ResultsPersonal identity showed strong predictive power for trait anxiety in both network and Bayesian models. …”
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Improving the Performance of Bayesian Decision Networks for Water Quality Sensor Deployment in UDNs through a Reduced Search Domain by Mariacrocetta Sambito, Antonietta Simone

“…The first phase involves reducing the search domain of the system using a complex network theory (CNT) topological metric adapted to infrastructure systems; the second phase implements the Bayesian approach to the new search space to optimize the position of the sensors in the network. …”
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Addressing Distribution Discrepancies in Pulsar Candidate Identification via Bayesian-neural-network-based Multimodal Incremental Learning by Yi Liu, Jing Jin, Hongyang Zhao, Zhenyi Wang, Yi Shen

“…To address this, our study introduces a multimodal incremental learning approach utilizing Bayesian neural networks. This method enables the model to adapt to new data distributions while preserving the knowledge of previous data. …”
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Layer wise Scaled Gaussian Priors for Markov Chain Monte Carlo Sampled deep Bayesian neural networks by Devesh Jawla, John Kelleher

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Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks: Recent Advances and Key Insights by Fatemeh Salboukh, Yashar Mousavi, Ibrahim Beklan Kucukdemiral, Afef Fekih, Umit Cali

“…This paper provides a comprehensive review of Bayesian Networks (BNs) as a robust probabilistic framework for enhancing fault detection, risk assessment, and condition monitoring in WECSs. …”
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Development of a Web System utilizing Bayesian networks for teaching English in the Zoila Alvarado de Jaramillo primary school by Katherine Ivanova Ramón Campoverde, Luis Roberto Jácome Galarza, Iván Siguencia Larreátegui

“…The proposal is based on a web application aimed at children from 5-6 years to teach words with computerized adaptive test using Bayesian networks as a method of adaptation, for which the library is used OpenMarkov. …”
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Development of a Web System utilizing Bayesian networks for teaching English in the Zoila Alvarado de Jaramillo primary school by Katherine Ivanova Ramón Campoverde, Luis Roberto Jácome Galarza, Iván Siguencia Larreátegui

“…The proposal is based on a web application aimed at children from 5-6 years to teach words with computerized adaptive test using Bayesian networks as a method of adaptation, for which the library is used OpenMarkov. …”
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Development of a Web System utilizing Bayesian networks for teaching English in the Zoila Alvarado de Jaramillo primary school by Katherine Ivanova Ramón Campoverde, Luis Roberto Jácome Galarza, Iván Siguencia Larreátegui

“…The proposal is based on a web application aimed at children from 5-6 years to teach words with computerized adaptive test using Bayesian networks as a method of adaptation, for which the library is used OpenMarkov. …”
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Future shapes present: autonomous goal-directed and sensory-focused mode switching in a Bayesian allostatic network model by Hayato Idei, Jun Tani, Tetsuya Ogata, Yuichi Yamashita

“…In this study, we propose a Bayesian recurrent neural network framework for homeostatic behavior adaptation via hierarchical multimodal integration. …”
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High-accuracy PM2.5 prediction via mutual information filtering and Bayesian-Optimized Spatio-Temporal Convolutional Networks by Wanyu Wang

“…To address these challenges, this paper presents an advanced PM2.5 prediction framework incorporating three key innovations.First, in contrast to conventional static threshold-based feature selection, a dynamic framework integrating Mutual Information (MI) and Adaptive Information Distance (AID) is proposed. By quantifying nonlinear feature correlations (via MI) and spatial redundancies (via AID), the framework adaptively prunes redundant inputs, thereby enhancing the information utility of the feature space for subsequent modeling.Second, a Bayesian optimizer guided by multimodal Gaussian distributions is designed to overcome the limitation of traditional unimodal optimization, which often stagnates at local optima. …”
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Intelligent Clustering and Adaptive Energy Management in Wireless Sensor Networks with KDE-Based Deployment by Mainak Kundu, Ria Kanjilal, Ismail Uysal

“…This work proposes a novel clustering framework that integrates kernel density estimation (KDE)-based adaptive node deployment, silhouette-optimized K-means clustering, Bayesian cluster head (CH) selection with Gaussian noise-based energy uncertainty modeling, energy-efficient coverage control, and carrier sense multiple access with collision avoidance-based data transmission. …”
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Adaptive Anomaly Detection in Network Flows With Low-Rank Tensor Decompositions and Deep Unrolling by Lukas Schynol, Marius Pesavento

“…Extensive experiments on synthetic and real-world data demonstrate that our proposed deep network architecture exhibits a high training data efficiency, outperforms reference methods, and adapts seamlessly to varying network topologies.…”
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A hybrid Bayesian network-based deep learning approach combining climatic and reliability factors to forecast electric vehicle charging capacity by David Chunhu Li

“…The integration of queuing theory and Bayesian network models with deep learning techniques results in a robust system adaptable to various conditions. …”
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GAN-based unsupervised domain adaptive person re-identification by Shengsheng ZHENG, Haibing YIN, Xiaofeng HUANG, Tianjie ZHANG

“…Aiming at the problem that the inaccurate clustering in the unsupervised domain adaptive pedestrian re-recognition results in low network recognition accuracy, an unsupervised domain adaptive pedestrian re-recognition method based on generative confrontation network was proposed.Firstly, the CNN model was optimized by using the batch normalization layer after the pooling layer, deleting a fully connected layer and adopting the Adam optimizer.Secondly, the cluster error was analyzed and the important parameter in the cluster was decided based on minimum error rate Bayesian decision theory.Finally, the generative adversarial network was utilized to adjust the cluster.These steps effectively improved the recognition accuracy of unsupervised domain adaptive person re-identification.In the case of the source domain Market-1501 and the target domain DukeMTMC-reID, experimental results show that mAP and Rank-1 can reach 53.7% and 71.6%, respectively.…”
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Automated knowledge graphs for complex systems (AutoGraCS): Applications to management of bridge networks by Minghui Cheng, Syed M.H. Shah, Antonio Nanni, H. Oliver Gao

“…The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling, Bayesian analysis, and adaptive decision supports. …”
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Probabilistic, Multi‐Sensor Eruption Forecasting by Y. Behr, A. Christophersen, C. Miller

“…Abstract We developed an eruption forecasting model using data from multiple sensors or data streams with the Bayesian network method. The model generates probabilistic forecasts that are interpretable and resilient against sensor outage. …”
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A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. …”
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