Multi-Objective Optimization Design of Dual-Spindle Component Based on Coupled Thermal–Mechanical–Vibration Collaborative Analysis by Xiaoliang Lin, Yiming Xie, Xiaolei Deng, Jing Tian, Yue Han, Peng Wang

“…The dual-spindle component of a CNC machine tool is taken as the parameterized model. …”
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A Reinforcement Learning Approach to Personalized Asthma Exacerbation Prediction Using Proximal Policy Optimization by Dahiru Adamu Aliyu, Emelia Akashah Patah Akhir, Maryam Omar Abdullah Sawad, Jameel Shehu Yalli, Yahaya Saidu

“…Traditional predictive models rely on static machine learning approaches, which lack adaptability to evolving patient conditions and environmental changes. …”
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An efficient bearing fault detection strategy based on a hybrid machine learning technique by Khalid Alqunun, Mohammed Bachir Bechiri, Mohamed Naoui, Abderrahmane Khechekhouche, Ismail Marouani, Tawfik Guesmi, Badr M. Alshammari, Amer AlGhadhban, Abderrahim Allal

“…The methodology also incorporates machine learning model tuning through Tree-Structured Parzen Estimators (TPE) for optimal hyperparameter adjustment, ensuring high-performance classification. …”
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Constructing a prediction model for acute pancreatitis severity based on liquid neural network by Jie Cao, Shike Long, Huan Liu, Fu’an Chen, Shiwei Liang, Haicheng Fang, Ying Liu

“…A new feature selection method was designed to optimize model performance. Logistic regression (LR), decision tree (DCT), random forest (RF), Extreme Gradient Boosting (XGBoost), and LNN models were built. …”
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Evaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modeling by Hyginus Obinna Ozioko, Emmanuel Ebube Eze

“…Linear regression (R2 = 0.801, MAE = 0.663, RMSE = 0.757, SI = 0.060) outperformed polynomial regression and ANN models. The findings underscore DA’s potential as a sustainable aggregate alternative, emphasizing the importance of optimizing replacement levels to maintain concrete performance.…”
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The relationship between the annual catch of bigeye tuna and climate factors and its prediction by Peng Ding, Hui Xu, Xiaorong Zou, Xiaorong Zou, Xiaorong Zou, Shuyi Ding, Siqi Bai

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Cockpit-Llama: Driver Intent Prediction in Intelligent Cockpit via Large Language Model by Yi Chen, Chengzhe Li, Qirui Yuan, Jinyu Li, Yuze Fan, Xiaojun Ge, Yun Li, Fei Gao, Rui Zhao

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Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation by Zhiguo Wang, Shuai Ma, Yongguang Zhai, Pingping Huang, Xiangli Yang, Jianhao Cui, Qimuge Eridun

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Spatiotemporal Bayesian Machine Learning for Estimation of an Empirical Lower Bound for Probability of Detection with Applications to Stationary Wildlife Photography by Mohamed Jaber, Robert D. Breininger, Farag Hamad, Nezamoddin N. Kachouie

“…The goal of this research is implementing a spatiotemporal Bayesian machine learning model to estimate a lower bound for probability of detection of a monitoring system. …”
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Intelligent Decision Framework for Booster Fan Optimization in Underground Coal Mines: Hybrid Spherical Fuzzy-Cloud Model Approach Enhancing Ventilation Safety and Operational Effi... by Shibin Yao, Jian Zhou, Manoj Khandelwal, Abiodun Ismail Lawal, Chuanqi Li, Moshood Onifade, Sangki Kwon

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FedDBO: A Novel Federated Learning Approach for Communication Cost and Data Heterogeneity Using Dung Beetle Optimizer by Dongyan Wang, Limin Chen, Xiaotong Lu, Yidi Wang, Yue Shen, Jingjing Xu

“…This paper proposes a federated learning approach based on the dung beetle optimizer, named FedDBO. In this method, the model parameters uploaded from clients to the server are transformed into model scores. …”
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Comparative analysis of machine learning models for malaria detection using validated synthetic data: a cost-sensitive approach with clinical domain knowledge integration by Gudi V. Chandra Sekhar, Chekol Alemu

“…We systematically compared five machine learning models—Naive Bayes, Logistic Regression, Random Forest, XGBoost, and Enhanced Bayesian Logistic Regression—for malaria detection using a rigorously validated synthetic dataset ( $$N=10,100$$ ) representing Sub-Saharan African epidemiological conditions. …”
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Interactive cardiovascular disease prediction system using learning techniques: Insights from extensive experiments by Purnima Pal, Harsh Vikram Singh, Veena Grover, R. Manikandan, Rasoul Karimi, Mohammad Khishe

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Physics instrument design with reinforcement learning by Shah Rukh Qasim, Patrick Owen, Nicola Serra

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Email Spam Detection Using a Hybrid Approach of Feedforward Neural Network and Penguin Optimization Algorithm by Layth Al-busultan

“…We therefore used the POA to optimize these two crucial components of our model and achieved a surprising level of accuracy—98.7%. …”
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Evaluating Ecological Vulnerability and Its Driving Mechanisms in the Dongting Lake Region from a Multi-Method Integrated Perspective: Based on Geodetector and Explainable Machine... by Fuchao Li, Tian Nan, Huang Zhang, Kun Luo, Kui Xiang, Yi Peng

“…Furthermore, the LightGBM algorithm was used for feature optimization, followed by the construction of six machine learning models—Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), LightGBM, and K-Nearest Neighbors (KNN)—to conduct multi-class classification of ecological vulnerability. …”
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Optimizing Human-Centric Warehouse Operations: A Digital Twin Approach Using Dynamic Algorithms and AI/ML by Erhan Arslan

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Models of user experience quality in computer information systems by П. Р. Пелех, В. М. Юзевич

“…The model incorporates three synergistic components: a Fuzzy Inference System (FIS) to interpret imprecise or ambiguous user feedback; Multi-Criteria Decision Analysis (MCDA), used to normalize and prioritize heterogeneous parameters in real time; a regression-based machine learning model for dynamic adjustment of input weights based on contextual variations. …”
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Research on Optimal Design of Ultra-High-Speed Motors Based on Multi-Physical Field Coupling Under Mechanical Boundary Constraints by Jianguo Bu, Xudong Lan, Weifeng Zhang, Yan Yu, Hailong Pang, Wei Lei

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Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM) by Lei Wang, Jinghang Li, Wenbo Zhang, Yu Li

“…In order to further accurately predict gas emission of working face, this paper proposes a prediction model of gas emission of working face based on the combination of improved artificial bee colony algorithm and weighted least squares support vector machine (IABC-WLSSAVM). …”
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