Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning by Neng Wang, Shuai Tao, Liang Chen

“…Among them, 786 patients were assigned to the training set, and 338 patients were assigned to the test set. The XGBoost model showed the best overall prediction performance. …”
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Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer Models by Rolando Antonio Gilbert Zequera, Anton Rassolkin, Toomas Vaimann, Ants Kallaste

“…The results show remarkable performance metrics of the Transformer models, achieving over 94% accuracy in Model evaluation compared to traditional Deep Learning algorithms.…”
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A Follow-Up Risk Identification Model Based on Multi-Source Information Fusion by Shuwei Guo, Yunyu Bo, Jie Chen, Yanan Liu, Jiajia Chen, Huimin Ge

“…The high-dimensional latent space feature representation is used as input for Stage 2, enhancing the basic model’s performance. …”
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Efficient detection of highway pavement cracks using computer vision-based MSFNet model by Zhengfa Jiang, Danlan Li, Ting Zhao, Mingxing Gao

“…In addition, a knowledge distillation strategy based on feature-based and response-based is introduced to improve the performance and stability of the model through feature transfer and soft label constraints. …”
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The Estimating Parameter and Number of Knots for Nonparametric Regression Methods in Modelling Time Series Data by Autcha Araveeporn

“…For the real data, monthly coal import data from Thailand is used to estimate the parameters of the nonparametric regression model, with the MSE as the evaluation metric. The performance of these techniques is also assessed in forecasting future values, where the mean absolute percentage error (MAPE) is calculated. …”
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Evaluating Coding Proficiency of Large Language Models: An Investigation Through Machine Learning Problems by Eunbi Ko, Pilsung Kang

“…However, GPT’s hyperparameter tuning improved model performance by up to 6.3% in Titanic and 3.33% in Steel Defect, surpassing human-tuned models in some cases. …”
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IMRMB-Net: A lightweight student behavior recognition model for complex classroom scenarios. by Caihong Feng, Zheng Luo, Deyao Kong, Yunhong Ding, Jingyu Liu

“…However, student classroom behavior recognition methods still face challenges such as occlusion, small objects, and environmental interference, resulting in low recognition accuracy and lightweight performance. To address the above problems, this study proposes a lightweight student behavior recognition model based on Inverted Residual Mobile Block (IMRMB-Net). …”
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Microglial activation contributes to cognitive impairments in rotenone-induced mouse Parkinson’s disease model by Dongdong Zhang, Sheng Li, Liyan Hou, Lu Jing, Zhengzheng Ruan, Bingjie Peng, Xiaomeng Zhang, Jau-Shyong Hong, Jie Zhao, Qingshan Wang

“…Neuroinflammation mediated by overactivated microglia is a common pathological feature in multiple neurological disorders, including PD. …”
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Research on short-term traffic flow prediction based on the PCC-IGA-LSTM model by Junxi Zhang, Shiru Qu, Yang Bi, Lijing Ma

“…Finally, the performance of the model was evaluated with Seattle traffic flow data, and experiments of 5-min short-term traffic flow forecasting on both weekdays’ and weekends’ data sets verified the performance of the model respectively. …”
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Semantic SLAM system for mobile robots based on large visual model in complex environments by Chao Zheng, Peng Zhang, Yanan Li

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Damage and failure modeling of lotus-type porous material subjected to low-cycle fatigue by J. Kramberger, K. Sterkuš, S. Glodež

“…This paper evaluates a numerical approach for determining of damage initiation and evolution of lotus-type porous material with computational simulations, where the considered computational models have different pore topology patterns. The low-cycle fatigue analysis was performed by using a damage evolution law. …”
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Real-Time Hand Gesture Monitoring Model Based on MediaPipe’s Registerable System by Yuting Meng, Haibo Jiang, Nengquan Duan, Haijun Wen

“…This paper constructs a registerable gesture dataset (RGDS) for training registerable hand gesture recognition models. Additionally, it proposes a normalization method for transforming hand gesture data and a FingerComb block for combining and extracting hand gesture data to enhance features and accelerate model convergence. …”
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An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection by Yexin Tian, Shuo Xu, Yuchen Cao, Zhongyan Wang, Zijing Wei

“…We analyze each model’s optimization framework, decision boundaries, and feature importance mechanisms, highlighting the empirical tradeoffs between representational capacity, generalization, and interpretability. …”
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A Robust Ensemble Model for Plant Disease Detection Using Deep Learning Architectures by Fida Zubair, Moutaz Saleh, Younes Akbari, Somaya Al Maadeed

“…The ensemble model achieved state-of-the-art performance, with classification accuracies of 99.69% on PlantVillage, 60% on PlantDoc, and 83% on FieldPlant. …”
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Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model by Lijun Gao, Tiantian Ran, Hua Zou, Huanhuan Wu

“…Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. …”
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Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model by Kunping Li, Jianhua Liu, Cunbo Zhuang

“…TSDM leverages prompt learning with pre-trained large language models to extract detailed expressions of talent ability and demand from unstructured documents while utilizing the powerful text comprehension capabilities of pre-trained models for feature embedding. …”
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Evaluation of the Decision Tree Model for Air Condition Classification on the Global Air Pollution Dataset by Cindy Dinda Sabella, Yoga Pristyanto

“…Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms are applied to perform classification, with a focus on hyperparameter tuning to increase model accuracy. …”
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Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate by Felipe Kenji Nakano, Anna Åkesson, Jasper de Boer, Klest Dedja, Robbe D’hondt, Fateme Nateghi Haredasht, Jonas Björk, Marie Courbebaisse, Lionel Couzi, Natalie Ebert, Björn O. Eriksen, R. Neil Dalton, Laurence Derain-Dubourg, Francois Gaillard, Cyril Garrouste, Anders Grubb, Lola Jacquemont, Magnus Hansson, Nassim Kamar, Christophe Legendre, Karin Littmann, Christophe Mariat, Toralf Melsom, Lionel Rostaing, Andrew D. Rule, Elke Schaeffner, Per-Ola Sundin, Arend Bökenkamp, Ulla Berg, Kajsa Åsling-Monemi, Luciano Selistre, Anders Larsson, Ulf Nyman, Antoine Lanot, Hans Pottel, Pierre Delanaye, Celine Vens

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Intercomparison of Machine Learning Models for Spatial Downscaling of Daily Mean Temperature in Complex Terrain by Sudheer Bhakare, Sara Dal Gesso, Marco Venturini, Dino Zardi, Laura Trentini, Michael Matiu, Marcello Petitta

“…RF performs similar to CNN, particularly in spring and summer, but its performance is reduced in winter and autumn. …”
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Modeling of Bayesian machine learning with sparrow search algorithm for cyberattack detection in IIoT environment by Faten Khalid Karim, José Varela-Aldás, Mohamad Khairi Ishak, Ayman Aljarbouh, Samih M. Mostafa

“…The BMLSSA-CAD technique uses the Bayesian Belief Network (BBN) model for cyberattack detection. The hyperparameter tuning process employs the sparrow search algorithm (SSA) model to enhance the BBN model performance. …”
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