Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour by Xinyi Dong, Ying Dong, Jinming Liu, Siting Wu

“…The Bayesian optimization algorithm was used to optimize the LSTM parameters. …”
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Research on temporary shipping network model and optimization strategy under partial shipping network disruption by Zhiyi Ye, Xiang Yuan

“…To solve this NP-hard problem, custom operators for meta-heuristic algorithms (SA, GA and PSO) are designed and enhanced through Bayesian hyperparameter optimization, ensuring algorithmic adaptability to real-time disruptions. …”
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A Hybrid Security Framework for Train-to-Ground (T2G) Communication Using DOA-Optimized BPNN Detection, Bayesian Risk Scoring, and RL-Based Response by Chaoyuan Sun, Weijiao Zhang, Peng Sun, Hui Wang, Chunhui Yang

“…A Bayesian risk scoring module then quantifies detection outcomes and classifies risk levels, improving threat detection accuracy. …”
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Neural Correlation Integrated Adaptive Point Process Filtering on Population Spike Trains by Mingdong Li, Shuhang Chen, Xiang Zhang, Yiwen Wang

“…In this paper, we propose a neural correlation integrated adaptive point process filter (CIPPF) that can incorporate the information from functional neural connectivity from population spike trains in a recursive Bayesian framework. …”
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Dynamic Control of Isolated Network Microgrids: A Resilient Backpropagation Neural Network-Based Virtual Inertia Control Approach by Md Asaduzzaman Shobug, Md Alamgir Hossain, Fuwen Yang, Junwei Lu

“…This paper introduces a novel Resilient Back Propagation Bayesian Neural network-based virtual inertia control strategy to enhance frequency response and overall stability of the considered network microgrid. …”
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A Multi-Dimensional Evaluation Model for Epidemic Prevention Policies by Zhoujingming Gao, Zhiyi Tan, Bing-Kun Bao

“…The module also strengthens the robustness of the proposed model with the help of BDL since BDL can adapt the data of different regions better through resampling the probability distribution of network weights. …”
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Attention-based approach of detecting spam in social networks by Qiang QU, Hongtao YU, Ruiyang HUANG

“…In social networks,a large amount of spam has seriously threaten users' information security and the credit system of social websites.Aiming at the noise and sparsity problems,an attention-based CNN method was proposed to detect spam.On the basis of classical CNN,this method added a filter layer in which an attention mechanism based on Naive Bayesian weighting technology was designed to solve the noise issue.What’s more,instead of the original pooling strategy,it adapted an attention-based pooling policy to alleviate the sparsity problem.Compared with other methods,the results show that the accuracy has increased by 1.32%，2.15%，0.07%，1.63% on four different data sets.…”
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A Low-Complexity Approach for Improving the Accuracy of Sensor Networks by Angelo Coluccia

“…An adaptive Bayesian approach is proposed to this aim, which allows improving the accuracy of the delivered estimates with no significant increase in computational complexity. …”
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Ensemble inference of unobserved infections in networks using partial observations. by Renquan Zhang, Jilei Tai, Sen Pei

“…The inference method may support decision-making under uncertainty and be adapted for use for other dynamical models in networks.…”
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Neurocomputational Mechanisms of Sense of Agency: Literature Review for Integrating Predictive Coding and Adaptive Control in Human–Machine Interfaces by Anirban Dutta

“…Objective: This review synthesizes computational models—particularly predictive coding, Bayesian inference, and optimal control theories—to provide a unified framework for understanding the SoA in both healthy and dysfunctional brains. …”
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Deep Reinforcement Learning for Uplink Scheduling in NOMA-URLLC Networks by Benoit-Marie Robaglia, Marceau Coupechoux, Dimitrios Tsilimantos

“…Our approach involves 1) formulating the NOMA-URLLC problem as a Partially Observable Markov Decision Process (POMDP) and the introduction of an agent state, serving as a sufficient statistic of past observations and actions, enabling a transformation of the POMDP into a Markov Decision Process (MDP); 2) adapting the Proximal Policy Optimization (PPO) algorithm to handle the combinatorial action space; 3) incorporating prior knowledge into the learning agent with the introduction of a Bayesian policy. …”
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A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images by Muhammad Attique Khan, Usama Shafiq, Ameer Hamza, Anwar M. Mirza, Jamel Baili, Dina Abdulaziz AlHammadi, Hee-Chan Cho, Byoungchol Chang

“…Bayesian Optimization (BO) is employed for dynamic hyperparameter tuning, and an Entropy-controlled Marine Predators Algorithm (EMPA) selects optimal features. …”
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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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A Federated Learning Model for Detecting Cyberattacks in Internet of Medical Things Networks by Abdallah Ghourabi, Adel Alkhalil

“…The XGBoost models are further optimized using a Bayesian method and integrated with an aggregation algorithm to construct an adaptive global model. …”
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Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients by Su-Ying Guo, Xiu-Jun Gong

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Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel by Nikhil Thapa, Joonwhoan Lee

“…To capture beat intervals of varying lengths and ensure optimal alignment of beat predictions, the model employs a Dynamic Bayesian Network (DBN), followed by Viterbi decoding for effective post-processing. …”
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Computer Vision in Monitoring Fruit Browning: Neural Networks vs. Stochastic Modelling by Maria Kondoyanni, Dimitrios Loukatos, Charalampos Templalexis, Diamanto Lentzou, Georgios Xanthopoulos, Konstantinos G. Arvanitis

“…Thus, this study investigated the application of computer vision techniques and various RGB cameras in the detection and classification of enzymatic browning in cut pears, comparing convolutional neural networks (CNNs) with stochastic modelling. More specifically, light is shed on the potential of CNN-based approaches for high-throughput and easily adapted applications and the potential of stochastic methods for precise, quantitative analyses. …”
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Dynamic Latent Space Model With Position Clusters and Its Application in International Trade Network by Jiajia Wang

“…The model extends the dynamic latent space model by incorporating latent position clustering and accounting for weighted networks. A fully Bayesian method with adaptive Markov chain Monte Carlo sampling is used to estimate the novel model. …”
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Decoding cortical chronotopy—Comparing the influence of different cortical organizational schemes by Falko Mecklenbrauck, Jorge Sepulcre, Jana Fehring, Ricarda I. Schubotz

“…As expected, resting-state timescales were slower in structural network hubs, hierarchically higher areas defined by the functional and spatial gradients, and thicker cortical regions. …”
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Comparing user experiences with traditional ADA paratransit and on-demand mobility services by Jimin Choi, Jordana L. Maisel

“…To overcome many paratransit service barriers, transit agencies in the U.S. have partnered with technology-enabled third-party companies (e.g., transportation network companies [TNCs], adaptive TNCs, taxi companies) to offer new on-demand mobility options. …”
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