Optimizing Fire Scene Analysis: Hybrid Convolutional Neural Network Model Leveraging Multiscale Feature and Attention Mechanisms by Shakhnoza Muksimova, Sabina Umirzakova, Mirjamol Abdullaev, Young-Im Cho

“…The proposed model integrates advanced convolutional neural networks with multiscale feature extraction, attention mechanisms, and ensemble learning to achieve superior performance in real-time fire detection. …”
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Snow depth estimation in Northeast China based on space-borne scatterometer data and ML model with optimal features by Wenfei Chen, Lingjia Gu, Xiaofeng Li, Xintong Fan

“…In comparison to the public SD product and the ground-based SD measurements, the experimental results using the RF model with optimal features demonstrate superior SD estimation performance, yielding a root mean square error (RMSE) of 3.91 cm, mean absolute error (MAE) of 2.27 cm, and an R2 of 0.80. …”
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AI-Driven Detection of Alkali-Silica Reaction in Concrete Structures Using Feature-Enhanced Deep Learning Models by Yujie Wu, Mengze Wu, Tianyi Cui, Jiani Lin, Qingke Liao, Jinqiu Shu

“…Among the tested models, InceptionV3 demonstrated superior performance with high accuracy and robustness. …”
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An Improved Software Source Code Vulnerability Detection Method: Combination of Multi-Feature Screening and Integrated Sampling Model by Xin He, Asiya, Daoqi Han, Shuncheng Zhou, Xueliang Fu, Honghui Li

“…To address these issues, this paper introduces a multi-feature screening and integrated sampling model (MFISM) to enhance vulnerability detection efficiency and accuracy. …”
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An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments by Mimouna Abdullah Alkhonaini

“…Furthermore, the FOFSDL-SCD model utilizes the Fox optimizer algorithm (FOA) method for the feature selection process to select the most significant features from the dataset. …”
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Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening. by Yaoyang Yi

“…This paper evaluates the performance of the proposed model using the carbon markets of Guangdong, Hubei, and Shanghai in China as examples. …”
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A Novel Stacking Heterogeneous Ensemble Model with Hybrid Wrapper-Based Feature Selection for Reservoir Productivity Predictions by Changlin Zhou, Lang Zhou, Fei Liu, Weihua Chen, Qian Wang, Keliang Liang, Wenqiu Guo, Liying Zhou

“…The results proved that the hybrid wrapper-based feature selection strategy introduced in this study reduced data acquisition costs and improved model comprehensibility without reducing model performance.…”
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Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation by Enyu Yuan, Yuntian Chen, Lei Ye, Ben He, ChunLei He, Junchao Ma, Ting Yang, Hao Zeng, Ling Yang, Jin Yao, Bin Song

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Diagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography features by Shen Su, Shun-Yao Wu, Yun-He Tang, Lin-Lin Ren, Fan Yin, Yu-Shuang Xu, Xiao-Yu Li, Hua Liu, Shao-Hua Zhang, Xing-Lin Zhang, Zi-Bin Tian, Tao Mao

“…The predictive performance of the model was obtained through a five-fold cross-validation, and each decision tree model was evaluated by the area under the curve (AUC) and F1. …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…Also, challenges such as data imbalance and high-dimensional feature sets often hinder model performance. <b>Objective:</b> This paper aims to propose a computationally efficient, reliable, and transparent machine learning-based framework for the classification of Alzheimer’s disease patients. …”
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Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features by Xinhua Li, Minping Hong, Zhendong Lu, Zilin Liu, Lifu Lin, Hongfa Xu

“…The performance of the nomogram combined model (AUC: 0.818; 95% CI:0.702-0.916) surpassed those of both the radiomics and clinical models (AUC: 0.753; 95% CI: 0.630-0.869). …”
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Cross-Scale Feature Blending Model for Surface Defect Identification in Machine Tool Elements Resilient to Contaminant Interference by Dong Wu, Chunhua Guo, Renpu Li, Zhigang Ma

“…Furthermore, the CSFB framework incorporates assigned weights to guide the network during training, prioritizing features with a significant impact on the final decisions, thereby enhancing model performance and flexibility in handling complex image processing tasks. …”
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Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways by Kittisak Taoma, Marasri Ruengjitchatchawalya, Kanthida Kusonmano, Teerasit Termsaithong, Thana Sutthibutpong, Monrudee Liangruksa, Teeraphan Laomettachit

“…In this study, we developed a random forest model using simulated protein activities derived from Boolean modeling of breast cancer signaling pathways as input features. …”
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A novel lightweight model for tea disease classification based on feature reuse and channel focus attention mechanism by Junjie Liang, Renjie Liang, Dongxia Wang

“…On the other hand, the popular vision transformer(ViT) model has a higher recognition accuracy as it has better global feature expression ability than CNN model. …”
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Improved UAV Target Detection Model for RT-DETR by Yong He, Yufan Pang, Guolin Ou, Renfeng Xiao, Yifan Tang

“…The efficacy of these enhancements is substantiated by the model&#x2019;s superior performance in comparison to other target detection models at equivalent levels.…”
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Hybrid model for predicting microsatellite instability in colorectal cancer using hematoxylin & eosin-stained images and clinical features by Hangping Wei, Xiaowei Zhang, Zhen Zhou, Jianbin Xie, Weidong Han, Xiaofang Dong

“…Furthermore, the hybrid model, which combines pathological and clinical features, demonstrated strong predictive ability.…”
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Random Undersampled Digital Elevation Model Super-Resolution Based on Terrain Feature-Aware Deep Learning Network by Ziqiang Huo, Meng Xi, Jingyi He, Zhengjian Li, Jiabao Wen

“…Experimental results show that compared with traditional spatial interpolation and classical super-resolution networks (SRCNN, SRResNet, SRGAN, and ESRGAN), our D-ResDCN model is comparable to the best performing SRCNN method in terms of peak signal-to-noise ratio and structural similarity index, while the performance in terms of mean absolute error and root-mean-square error is 10.5% and 10.1% lower than that of the SRResNet method, respectively.…”
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A scoring model based on MRI features for predicting early recurrence after surgical resection of hepatocellular carcinoma by Yi-Jing Wang, Jian-Xia Xu, Tian-Yu Ke, Bao-Na Li, Xiao-Zhong Zheng, Jun-Yi Xiang, Shu-Feng Fan, Xiao-Shan Huang

“…The independent predictive factors for early recurrence of liver cancer were weighted using regression coefficient-based scores and construct a score model integrating preoperative variables. Subsequently, receiver operating characteristic (ROC) curves and calibration curves were created to evaluate the performance of the scoring model. …”
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Interpretable XGBoost model identifies idiopathic central precocious puberty in girls using four clinical and imaging features by Lu Tian, Yan Zeng, Helin Zheng, Jinhua Cai

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Simulation-Based Electrothermal Feature Extraction and FCN–GBM Hybrid Model for Lithium-ion Battery Temperature Prediction by Luyan WANG, Hongliang HAO, Zhongkang ZHOU, Huimin MA, Jin ZHAO, Zeyang LIU, Qiangqiang LIAO

“…The combination of voltage and resistance as input features significantly enhances prediction performance. …”
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