A Real-Time Road Scene Semantic Segmentation Model Based on Spatial Context Learning by Xiaomei Xiao, Jialiang Tang, Xiaoyan Lu, Zhengyong Feng, Yi Li

“…The proposed Spatial Context Guided Feature Alignment module (SC-FA) learns aligned features from image-level to local regions, exploring and integrating contextual information. …”
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The Fermentation Degree Prediction Model for Tieguanyin Oolong Tea Based on Visual and Sensing Technologies by Yuyan Huang, Jian Zhao, Chengxu Zheng, Chuanhui Li, Tao Wang, Liangde Xiao, Yongkuai Chen

“…In contrast, the data fusion models demonstrated superior performance, with the MAE reduced to 2.232–2.783, the RMSE reduced to 2.693–3.969, and R² increased to 0.982–0.991, confirming that feature fusion enhanced characterization accuracy. …”
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NATIONAL AND CULTURAL SPECIFICITIES OF MANAGEMENT IN FRANCE by L. Efanova, V. Polyakova

“…National management models in each country have their own characteristics. …”
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Depression recognition using voice-based pre-training model by Xiangsheng Huang, Fang Wang, Yuan Gao, Yilong Liao, Wenjing Zhang, Li Zhang, Zhenrong Xu

“…The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. …”
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Development of a flexible feature selection framework in radiomics-based prediction modeling: Assessment with four real-world datasets by Sungsoo Hong, Sungjun Hong, Eunsun Oh, Won Jae Lee, Woo Kyoung Jeong, Kyunga Kim

“…Through this framework, we can explore the optimal combination of radiomics features and evaluate the predictive performance using only selected features. …”
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Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images by Bangxin Xiao, Yang Lv, Canjie Peng, Zongjie Wei, Qiao Xv, Fajin Lv, Qing Jiang, Huayun Liu, Feng Li, Yingjie Xv, Quanhao He, Mingzhao Xiao

“…Principal Component Analysis reduced features to 64. Using the extracted features, Decision Tree, XGBoost, and LightGBM models were trained with 5-fold cross-validation and ensembled in a stacking model. …”
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Enhancing E-Nose Performance via Metal-Oxide Based MEMS Sensor Arrays Optimization and Feature Alignment for Drug Classification by Ruiwen Kong, Wenfeng Shen, Yang Gao, Dawu Lv, Ling Ai, Weijie Song, Ruiqin Tan

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Comparative Analysis of AlexNet, ResNet-50, and VGG-19 Performance for Automated Feature Recognition in Pedestrian Crash Diagrams by Baraah Qawasmeh, Jun-Seok Oh, Valerian Kwigizile

“…In addition to evaluating model performance, computational efficiency was also considered. …”
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Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms by M. S. Aswathanrayan, N. Santhosh, Srikanth Holalu Venkataramana, Kurugundla Sunil Kumar, Sarfaraz Kamangar, Amir Ibrahim Ali Arabi, Sameer Algburi, Osamah J. Al-sareji, A. Bhowmik

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Improving the Performance of Deep Learning Model-Based Classification by the Analysis of Local Probability by Guanghao Jin, Yixin Hu, Yuming Jiao, Junfang Wen, Qingzeng Song

“…Generally, the performance of deep learning-based classification models is highly related to the captured features of training samples. …”
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Adaptive machine learning framework: Predicting UHPC performance from data to modelling by Yinzhang He, Shaojie Gao, Yan Li, Yongsheng Guan, Jiupeng Zhang, Dongliang Hu

“…LightGBM demonstrated the most stable performance among all models. As the number of features decreased, model performance initially increased then decreased, peaking at FS_14 (test set: R2 = 0.9677, MAE = 4.4621 MPa, RMSE = 6.9226 MPa), showing the mutual information scoring method can improve the model performance by reducing redundant information. …”
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Leveraging Topic Features in Prediction of Social Network Community Evolutions by Rahman Nahi Abid, Hassan Naderi

“…Our study aims to fill this research gap by proposing topical features to enhance the accuracy of tracking and efficiency of predictive models. …”
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Comparing Cross-Subject Performance on Human Activities Recognition Using Learning Models by Zhe Yang, Mengjie Qu, Yun Pan, Ruohong Huan

“…Extra analysis indicates that hand-crafted features are one major reason for the better performance of traditional machine learning on cross-subject HAR, while deep learning is more prone to learning subject-dependent features under an end-to-end training process. …”
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Tailoring Super‐Performed Chemo‐Sensor via Simulation‐Modeling and MEMS‐Screening by Wei Xu, Wukun Zhang, Zhengqi Shen, Wenxing Xu, Jianhao Zhao, Huizi Li, Qingguo He, Yanyan Fu, Jiangong Cheng

“…Due to strong path dependence on reported groups/mechanisms, conventional chemo‐sensors often fail to meet critical application demands, especially in achieving high reversibility without compromising other features. Here, a three‐step screen and design strategy is developed for gaining customized chemo‐sensors, through Structure modeling; MEMS (Micro Electro Mechanical Systems) analysis, and Performance verification. …”
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Optimization and prediction of corporate credit rating through advanced feature selection based on AI and deep learning by Jumanah Ahmed Darwish

“…A key contribution of this work is the introduction of a feature selection method based on RF, which significantly enhances model performance. …”
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PREDICT LEARNERS’ PERFORMANCE USING AN ONTOLOGICAL-BASED MODEL ON AN E-LEARNING PLATFORM by Safa Ridha Albo Abdullah

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Performance of two selectively bred strains of eastern oyster, Crassostrea virginica, in Delaware Bay, USA with implications for living shoreline features by Rileigh E. Hudock, Brendan Campbell, Noah Motz, Aaron Carlisle, Edward Hale, Edward Hale

“…The effect of tidal position, temperature, salinity, and dissolved oxygen on oyster performance (average oyster length and weekly oyster mortality) was examined using Generalized Additive Models (GAMs) to determine covariate importance towards the growth and survival of both oyster strains. …”
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Evaluating Super-Resolution Models in Biomedical Imaging: Applications and Performance in Segmentation and Classification by Mario Amoros, Manuel Curado, Jose F. Vicent

“…Using U-Net and ResNet architectures, we quantify how SR influences diagnostic tasks across different datasets, and we evaluate model generalization in cross-domain settings. Our findings show that advanced SR models such as SwinIR preserve diagnostic features effectively and, when appropriately applied, can enhance or maintain clinical performance even in low-resolution contexts. …”
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Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition by Omar Ibrahim Obaid, Mazin Mohammed, Akbal Omran Salman, Salama A. Mostafa, Ahmed Elngar

“…The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. …”
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Driver Takeover Performance Prediction Based on LSTM-BiLSTM-ATTENTION Model by Lijie Chen, Daofei Li, Tao Wang, Jun Chen, Quan Yuan

“…However, current takeover prediction models suffer from poor prediction accuracy and do not consider the time dependence of input features. …”
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