Advanced Computational Methods for Mitigating Shock and Vibration Hazards in Deep Mines Gas Outburst Prediction Using SVM Optimized by Grey Relational Analysis and APSO Algorithm by Xiang Wu, Zhen Yang, Dongdong Wu

“…Moreover, adaptive particle swarm optimization (APSO) was used to optimize the penalty factor and kernel parameters of the support vector machine to improve the global search ability and avoid the occurrence of the local optimal solutions. …”
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The Hybrid Modern Network Model: A Multi-Technique Framework for Comprehensive Network Analysis by Theodoros Kyriazos, Mary Poga

“…Motivated by the need for a more comprehensive and flexible framework, the study introduces the Hybrid Modern Network Model (HMNM). The HMNM integrates foundational models like the Stochastic Block Model (SBM) and Preferential Attachment with advanced machine learning techniques, including Graph Neural Networks (GNNs), Reinforcement Learning (RL), Hierarchical Random Graphs (HRGs), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). …”
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A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems by Achin Jain, Arun Kumar Dubey, Shakir Khan, Arvind Panwar, Mohammad Alkhatib, Abdulaziz M Alshahrani

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Determination Model of Epidermal Wettability for Apple Rootstock Cutting Based on the Improved U-Net by Xu Wang, Lixing Liu, Jinxuan Zou, Hongjie Liu, Jianping Li, Pengfei Wang, Xin Yang

“…This model optimizes the structure of the semantic segmentation model U-Net. …”
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Predictive modeling of mechanical properties in cold recycled asphalt mixtures enhanced with industrial byproducts by Mohsen Amouzadeh Omrani, Rezvan Babagoli, Mehraveh Hasirchian

“…Mechanical tests, including Marshall stability, indirect tensile strength, moisture susceptibility were conducted at 25°C, 5°C, and −5°C, with fatigue performance assessed at 25°C after 7 and 42 days. Machine learning models specifically Random Forest and XGBoost, were employed to predicte tensile strength and fatigue life, with XGBoost achieved an R² of approximately 0.95 and RMSE of approximately 0.02 for the tensile strength ratio, and R² of approximately 0.95 with MSE of approximately 15,000 for fatigue life. …”
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Assessing training needs and influencing factors among personnel at centers for disease control and prevention in northeast China: a cross-sectional study framed by SDT and TPB usi... by Kexin Wang, Peng Wang, Min Wei, Yanping Wang, Huan Liu, Ruiqian Zhuge, Qunkai Wang, Nan Meng, Yiran Gao, Yuxuan Wang, Lijun Gao, Jingjing Liu, Xin Zhang, Mingli Jiao, Qunhong Wu

“…SHapley Additive exPlanations (SHAP) were used to explain the output of the optimal machine learning model. Results This study identified the four subgroups of competency patterns, including novice (25.3%), public health experts (15.1%), potential expansion talents (24.7%), and versatile talents (34.9%). …”
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Construction of gas content model based on KPCA-SVR for Southern Sichuan shale gas by Zhong-yuan Liu, Di-Quan Li, Jing Jia, Yun-Qi Zhu, Zhong-Le Wang, Xue-Song Xie

“…This research contributes a high-precision, machine learning-based model for predicting shale gas content.…”
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Using large language models to extract plant functional traits from unstructured text by Viktor Domazetoski, Holger Kreft, Helena Bestova, Philipp Wieder, Radoslav Koynov, Alireza Zarei, Patrick Weigelt

“…The pipeline's performance is evaluated on two large databases with over 50,000 species descriptions, utilizing approaches ranging from a keyword search to large language models. Results Our final optimized pipeline used a transformer architecture and obtained a mean precision of 90.8% (range 81.6–97%) and a mean recall of 88.6% (77.4–97%) across five categorical traits, representing a 9.83% increase in precision and 42.35% increase in recall over a regular expression‐based approach. …”
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Big Data and AI Algorithms for Sustainable Development Goals: A Topic Modeling Analysis by Prema Nedungadi, Simi Surendran, Kai-Yu Tang, Raghu Raman

“…This study makes significant contributions to the field by examining the transformative role of big data and artificial intelligence (AI) in advancing Sustainable Development Goals (SDGs), particularly healthcare (SDG3), sustainable energy (SDG7), and industry and infrastructure (SDG9). Using BERTopic modeling, a machine learning technique, this research systematically analyzes literature from 2013 to 2024, providing an overview of AI and big data applications mapped to SDGs which is a first. …”
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Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis by Tengfei Zhou, Tengfei Zhou, Xinming Tian, Wei Wang, Zhe Chu

“…A total of 29 predictive models were developed, comprising 10 optimal models, primarily utilizing logistic regression or machine learning algorithms. …”
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Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture by Khosro Rezaee, Safoura Farsi Khavari, Mojtaba Ansari, Fatemeh Zare, Mohammad Hossein Alizadeh Roknabadi

“…Bayesian optimization is employed as the metaheuristic approach to optimize the BiLSTM model’s architecture. …”
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Method to Use Transport Microsimulation Models to Create Synthetic Distributed Acoustic Sensing Datasets by Ignacio Robles-Urquijo, Juan Benavente, Javier Blanco García, Pelayo Diego Gonzalez, Alayn Loayssa, Mikel Sagues, Luis Rodriguez-Cobo, Adolfo Cobo

“…These synthetic DAS signals serve as large-scale, scenario-controlled, labeled datasets on training machine learning models for various transport applications. …”
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Construction and Application of Carbon Emissions Estimation Model for China Based on Gradient Boosting Algorithm by Dongjie Guan, Yitong Shi, Lilei Zhou, Xusen Zhu, Demei Zhao, Guochuan Peng, Xiujuan He

“…Key variables were selected through correlation analysis, missing values were interpolated using K-means clustering, and model parameters were systematically optimized via grid search and cross-validation. …”
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Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers by Hongqi Zhou, Weiyun Jin, Lindi Li, Xiangwen Nie, Weiwei Wu, Ran Chen, Qizhen Xie, Haixia Wu, Weiwei Jiang, Min Tang, Jinhai Wang, Maoyuan Wang

“…Abstract This study aims to develop a multidimensional risk prediction model, identify characteristic inflammation-nutrition biomarkers, and optimize clinical decision-making. …”
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Biologically inert ceramics thermodynamic mechanisms and models in cutting and grinding: a comparative assessment by Xianggang Kong, Xiaotong Chen, Min Yang, Fuhe Hao, Xin Cui, Mingzheng Liu, Benkai Li, Yanbin Zhang, Xiao Ma, Changhe Li

“…Therefore, establishing predictive models for cutting and grinding forces, as well as temperature, during the machining process are important. …”
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Assessment of textile sludge pyrolysis behaviour through advance predictive models for bioenergy production by Arslan Khan, Asif Hussain Khoja, Salman Raza Naqvi, Waheed Miran, Imtiaz Ali

“…Machine learning models like Artificial Neural Networks (ANN), Classification and Regression Trees (C&RT), Boosted regression trees (BRT), and K Nearest Neighbors (KNN) employed to predict the activation energy (Ea) with ANN emerging as superior predicting capabilities (R2 = 0.999) over other models. …”
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UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR Model by Jiaxin Gao, Feng Tan, Zhaolong Hou, Xiaohui Li, Ailin Feng, Jiaxin Li, Feiyu Bi

“…Due to the limitations of the sowing machine performance and rice seed germination rates, missing seedlings inevitably occur after rice is sown in large fields. …”
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A Hybrid Large Language Model for Context-Aware Document Ranking in Telecommunication Data by Abhay Bindle, Preeti Singla, Sachin Sharma, Abdukodir Khakimov, Reem Ibrahim Alkanhel, Ammar Muthanna

“…The future scope includes dynamic embedding techniques to handle domain adaptation and attention-based ranking optimizations for long-form information retrieval. This research enhances information retrieval by combining machine learning-based ranking with traditional methods, improving knowledge discovery and decision-making in telecommunications and technical document processing.…”
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Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

“…The optimal model was visualized and clarified using SHapley Additive exPlanations (SHAP) and presented as a nomogram. …”
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Weighted Hybrid Random Forest Model for Significant Feature prediction in Alzheimer’s Disease Stages by M. Rohini, D. Surendran

“…Abstract In recent studies, several machine learning and deep learning prediction models have been proposed for the early detection and classification of various stages of Alzheimer’s Disease (AD). …”
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