Predicting p53 Status in IDH‐Mutant Gliomas Using MRI‐Based Radiomic Model by Jiamin Li, Zhihong Lan, Xiao Zhang, Xiaoyun Liang, Hanwei Chen, Xiangrong Yu

“…Results The VOIPE model, which included the eight best‐performing features, demonstrated the highest predictive performance among the three VOI‐based models, with AUCs of 0.811 (95% CI: 0.782–0.840) and 0.810 (95% CI: 0.786–0.834) in the training and validation cohorts, respectively. …”
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Explainable AI for zero-day attack detection in IoT networks using attention fusion model by Deepa Krishnan, Swapnil Singh, Vijayan Sugumaran

“…The model also demonstrates solid performance for the DDoS_HTTP (0.9791), Password (0.9418), and SQL_Injection (0.9461) classes. …”
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Using Graph-Based Maximum Independent Sets with Large Language Models for Extractive Text Summarization by Cengiz Hark

“…Large Language Models (LLMs) have shown a strong performance across various tasks but still face challenges in automatic text summarization. …”
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Air quality prediction based on factor analysis combined with Transformer and CNN-BILSTM-ATTENTION models by Shuyuan Liu, Yang Hu

“…Additionally, we introduced a cutting-edge CNN-BILSTM-ATTENTION model with discrete wavelet transform, which optimizes predictive performance by extracting local features, capturing temporal dependencies, and enhancing key time steps. …”
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Fault Prediction of Hydropower Station Based on CNN-LSTM-GAN with Biased Data by Bei Liu, Xiao Wang, Zhaoxin Zhang, Zhenjie Zhao, Xiaoming Wang, Ting Liu

“…Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.5</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, with at least a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>160</mn><mo>%</mo></mrow></semantics></math></inline-formula> improvement in the fault recall rate.…”
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Tea leaf disease detection using segment anything model and deep convolutional neural networks by Ananthakrishnan Balasundaram, Prem Sundaresan, Aryan Bhavsar, Mishti Mattu, Muthu Subash Kavitha, Ayesha Shaik

“…The performance of each model was analyzed and compared. …”
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An Improved Phase Space Reconstruction Method-Based Hybrid Model for Chaotic Traffic Flow Prediction by Yue Hou, Da Li, Di Zhang, Zhiyuan Deng

“…Secondly, to address the problem of insufficient learning ability of traditional convolutional combinatorial modeling for complex phase space laws of chaotic traffic flow, the high-dimensional phase space features are extracted using the layer-by-layer pretraining mechanism of convolutional deep belief networks (CDBNs), and the temporal features are extracted by combining with long short-term memory (LSTM). …”
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An edge enhancement graph neural network model with node discrimination for knowledge graph representation learning by Tao Wang, Bo Shen

“…It also employs a specially designed information aggregation mechanism for each edge, incorporating relation and adjacent node features. Experimental results across multiple real-world datasets demonstrate that by discriminating node types and enhancing edge representations, NDEE-GNN can accurately capture and represent complex associations between entities and relations, significantly improving link prediction performance and outpacing other baseline models.…”
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Interpretable multitask deep learning model for detecting and analyzing severity of rice bacterial leaf blight by Sudhesh K. M, Aarthi R., Sainamole Kurian. P, Sikha O.K

“…RCAMNet achieved a test accuracy of 96.23%, outperforming conventional raw image-based models (89.58%). Interpretability is enhanced through Grad-CAM visualizations. …”
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Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

“…The model uses the T-GCN model to capture spatiotemporal features while introducing Luong attention to weight the inputs at different time steps to improve the prediction accuracy and further reduce the prediction error by fusing the outputs of the T-GCN-Luong attention and GRU models through the variable weight combination method. …”
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Advanced Default Risk Prediction in Small and Medum-Sized Enterprises Using Large Language Models by Haonan Huang, Jing Li, Chundan Zheng, Sikang Chen, Xuanyin Wang, Xingyan Chen

“…We identified the most influential factors among these 38 features and introduced a novel prompt-based learning framework using large language models for risk assessment, benchmarking against seven mainstream machine learning algorithms. …”
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Machine learning-based prognostic prediction model of pneumonia-associated acute respiratory distress syndrome by Jing Lv, Juan Chen, Meijun Liu, Xue Dai, Wang Deng

“…The AUC value, AP value, accuracy, sensitivity, specificity, Brier score, and F 1 score were used to evaluate the performance of the models and pick the optimal model. …”
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An MRI-based fusion model for preoperative prediction of perineural invasion status in patients with intrahepatic cholangiocarcinoma by Zuochao Qi, Hao Yuan, Qingshan Li, Pengyu Chen, Dongxiao Li, Kunlun Chen, Bo Meng, Peigang Ning, Haibo Yu, Deyu Li

“…Feature selection was performed, and a radiomics model was constructed using machine learning algorithms, followed by SHapley Additive exPlanations (SHAP) visualization. …”
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Enhancing corn industry sustainability through deep learning hybrid models for price volatility forecasting. by Chengjin Yang, Yanzhong Zhai, Zehua Liu

“…These data fully demonstrate the excellent performance of this model.…”
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A machine learning–based risk prediction model for atrial fibrillation in critically ill patients by Laith Alomari, MD, Yaman Jarrar, MD, Zaid Al-Fakhouri, MD, Emmanuel Otabor, MBBS, Justin Lam, MD, Jana Alomari

“…Multiple machine learning models were trained to predict AF, with the top-performing model undergoing hyperparameter tuning. …”
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Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study by Salah Bouktif, Akib Mohi Ud Din Khanday, Ali Ouni

“…According to Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) XAI algorithms, there was a drift in the features during and before COVID-19. …”
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TAL-SRX: an intelligent typing evaluation method for KASP primers based on multi-model fusion by Xiaojing Chen, Xiaojing Chen, Jingchao Fan, Jingchao Fan, Shen Yan, Longyu Huang, Longyu Huang, Longyu Huang, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

“…In this paper, the performance of the model was tested using the KASP test results of 3399 groups of cotton variety resource materials, with an accuracy of 92.83% and an AUC value of 0.9905, indicating that the method has high accuracy, consistency and stability, and the overall performance is better than that of a single model. …”
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Customizing large-scale hydrological models: Harnessing the open data realm for impactful local applications by Ilias G. Pechlivanidis, Jude Lubega Musuuza

“…Study focus: Large-scale hydrological models (LSHMs), though important for both scientific and societal reasons, require the representation of many unknown features that influence river system response. …”
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Carbamoylated Erythropoietin Rescues Autism-Relevant Social Deficits in BALB/cJ Mice by Amaya L. Street, Vedant P. Thakkar, Sean W. Lemke, Liza M. Schoenbeck, Kevin M. Schumacher, Monica Sathyanesan, Samuel S. Newton, Alexander D. Kloth

“…This study marks the first demonstration of the benefits of a non-erythropoietic EPO derivative for social behavior in a mouse model of autism and merits further investigation into the mechanisms by which this action occurs.…”
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Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration by T. A. Rajaperumal, C. Christopher Columbus

“…A Stacking Ensemble model was also constructed from the top-performing models to boost robustness and forecast reliability. …”
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