Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models by John Fischer, Marko Orescanin, Justin Loomis, Patrick Mcclure

“…Federated learning (FL) is an approach to training machine learning models that takes advantage of multiple distributed datasets while maintaining data privacy and reducing communication costs associated with sharing local datasets. …”
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Modeling the Dynamic Loads Affecting a Bridge Crane during Start-Up by I. R. Antipas

“…The results obtained with the developed mathematical model and its numerical solution are useful for optimizing the crane structures, providing compliance with the operational requirements, and extending the service life of lifting machines.…”
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Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making by Vadim Tynchenko, Alexander Lomazov, Vadim Lomazov, Dmitry Evsyukov, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Ivan Malashin

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Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression by Lindsay L. Benster, Cory R. Weissman, Federico Suprani, Kamryn Toney, Houtan Afshar, Noah Stapper, Vanessa Tello, Louise Stolz, Mohsen Poorganji, Zafiris J. Daskalakis, Lawrence G. Appelbaum, Jordan N. Kohn

“…The best-fit models proved reasonably accurate at discriminating treatment responders (Area under the curve (AUC): 0.689 [0.638, 0.740], p < 0.01) and remitters (AUC 0.745 [0.692, 0.797], p < 0.01), though only the response model was well-calibrated. …”
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Bearing fault diagnosis for high-speed train based on improved VMD and APSO-SVM by ZHANG Qingsong, ZHANG Bing, QIN Yi

“…Aiming at the problem that the fault information of high-speed train wheel bearing is weak and difficult to extract, a fault feature extraction and recognition model for vibration signal of high-speed train bearing based on variational mode decomposition and adaptive particle swarm optimization-support vector machine was proposed. …”
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Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression M... by Ru Yu, Xiaoli Wang, Xiaojun Xu, Zhiwen Zhang

“…Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. …”
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An Explainable Fuzzy Framework for Assessing Preeclampsia Classification by Matías Salinas, Daira Velandia, Leondry Mayeta-Revilla, Ayleen Bertini, Marvin Querales, Fabian Pardo, Rodrigo Salas

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Agent Selection Framework for Federated Learning in Resource-Constrained Wireless Networks by Maria Raftopoulou, Jose Mairton B. da Silva, Remco Litjens, H. Vincent Poor, Piet van Mieghem

“…Federated learning is an effective method to train a machine learning model without requiring to aggregate the potentially sensitive data of agents in a central server. …”
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Different radiomics models in predicting the malignant potential of small intestinal stromal tumors by Yuxin Xie, Chongfeng Duan, Xuzhe Zhou, Xiaoming Zhou, Qiulin Shao, Xin Wang, Shuai Zhang, Fang Liu, Zhenbo Sun, Ruirui Zhao, Gang Wang

“…The DCA of the nomogram model and radiomics model showed optimal clinical efficacy. …”
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Research on Creep Constitutive Model for High Temperature Reactor Based on Neural Network by ZHANG Fan, DAI Shoutong

“…The analysis indicates that the optimized creep constitutive model can describe the creep behavior of materials more accurate than either of the creep theory or neural network training model. …”
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A systematic review and meta-analysis of lung cancer risk prediction models by Ghida Khalife, Matilda Nilsson, Lotta Peltola, Juho Waris, Antti Jekunen, Riikka-Leena Leskelä, Heidi Andersén, Mikko Nuutinen, Eija Heikkilä, Susanna Nurmi-Rantala, Paulus Torkki

“…Results: The review identified 18 models utilising conventional machine learning, six employing neural networks, and 14 comparing different predictive frameworks. …”
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Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification by Abeer saber, Esraa Hassan, Samar Elbedwehy, Wael A. Awad, Tamer Z. Emara

“…The proposed model combines multiple DL models to improve overall performance by leveraging the strengths of different architectures, ensembles can enhance accuracy, robustness, and generalization. …”
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Deep Learning in Finance: A Survey of Applications and Techniques by Ebikella Mienye, Nobert Jere, George Obaido, Ibomoiye Domor Mienye, Kehinde Aruleba

“…Beyond summarizing their mathematical foundations and learning processes, this study offers new insights into how these models are applied in real-world financial contexts, highlighting their specific advantages and limitations in tasks such as algorithmic trading, risk management, and portfolio optimization. …”
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Application of artificial intelligence in the diagnosis and treatment of lacrimal disorders: challenges and opportunities by PENG Xintong, LI Guangyu

“…However, several challenges persist, such as the complexity of multimodal data integration, limitations in model generalization capabilities, and the need for real-time prediction and dynamic adjustments, all of which necessitate continuous technological innovations, algorithm optimization, and interdisciplinary collaborations. …”
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Idiographic Lapse Prediction With State Space Modeling: Algorithm Development and Validation Study by Eric Pulick, John Curtin, Yonatan Mintz

“…Further, SSMs estimate a model for a patient’s time-series behavior, making them ideal for stepping beyond risk prediction to frameworks for optimal treatment selection (eg, administered using a digital therapeutic platform). …”
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Research status of soft anthropomorphic dexterous hands by LIU Yibo, XIAO Huaping, LIU Chuanwang, SUN Zhenhao, HAO Tianze, LIU Shuhai

“…Secondly, based on Adams software, a parameterized virtual prototype model of the unloading mechanism was established to perform kinematic and dynamic simulations of the unloading mechanism. …”
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Application of Discrete Element Method to Potato Harvesting Machinery: A Review by Yuanman Yue, Qian Zhang, Boyang Dong, Jin Li

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A Hybrid MOO, MCGDM, and Sentiment Analysis Methodologies for Enhancing Regional Expansion Planning: A Case Study Luwu - Indonesia by Muhammad Faisal, Irmawati, Titik Kwaha Abd Rahman, Jufri, Sahabuddin, Herlinah, Ida Mulyadi

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ECG‐based epileptic seizure prediction: Challenges of current data‐driven models by Sotirios Kalousios, Jens Müller, Hongliu Yang, Matthias Eberlein, Ortrud Uckermann, Gabriele Schackert, Witold H. Polanski, Georg Leonhardt

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Management of scientific and ancestral knowledge: a decision-making model in mezcal industry in Mexico by Antonia Terán-Bustamante, Antonieta Martínez-Velasco, Sandra Nelly Leyva-Hernández

“…For this purpose, a decision-making model for managing scientific and ancestral knowledge is created to support links with universities, research centers, and rural communities to accelerate innovation and competitiveness in this sector.MethodsThe analysis methods were carried out through decision-making, machine-learning techniques, and fuzzy logic.ResultsThe Bayesian Network model suggests that the preceding variables to optimize the Mezcaleros Knowledge Management are the Mezcaleros Indigenous community, the Denomination of Origin, Scientific and Ancestral Knowledge, Waste Management and Use, and Jima.DiscussionThis knowledge management model aims to guide small producers to be more productive and competitive through the support of a facilitator.…”
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