Distillation and Supplementation of Features for Referring Image Segmentation by Zeyu Tan, Dahong Xu, Xi Li, Hong Liu

“…Existing methods typically obtain multi-modal features by fusing linguistic features with visual features, which are fed into a mask decoder to generate segmentation masks. …”
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Enhancing Sentiment-Driven Recommender Systems With LLM-Based Feature Engineering: A Case Study in Drug Review Analysis by Samuel Matia Kangoni, Obed Tshimanga Tshipata, Pierre Sedi Nzakuna, Vincenzo Paciello, Jean-Gilbert Mbula Mboma, Jean-Robert Makulo, Kyandoghere Kyamakya

“…This study evaluates the effectiveness of word-level and sentence-level embeddings for feature extraction in sentiment analysis. These embeddings are used in sequential models (Bi-LSTM, CNN) and non-sequential models (Random Forest, DNN, ExtraTreesClassifier). …”
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Signal Processing and Feature Extraction in Markerless Telerehabilitation by Silvana G. Dellepiane, Federica Ferraro, Camilla Baffigo, Marina Simonini

“…By utilizing a diverse healthy subject group, a reference model is established, providing optimal features for accurate exercise execution. …”
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Exploring feature sparsity for out-of-distribution detection by Qichao Chen, Kuan Li, Zhiyuan Chen, Tomas Maul, Jianping Yin

“…Abstract Out-of-distribution (OOD) detection is a crucial problem in practice, especially, for the safe deployment of machine learning models in industrial settings. Previous work has used free energy as a score function and proposed a fine-tuning method that utilized OOD data in the training phase of the classification model, which achieves a higher performance on the OOD detection task compared with traditional methods. …”
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Advanced long-term actual evapotranspiration estimation in humid climates for 1958–2021 based on machine learning models enhanced by the RReliefF algorithm by Ahmed Elbeltagi, Salim Heddam, Okan Mert Katipoğlu, Abdullah A. Alsumaiei, Mustafa Al-Mukhtar

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LIM: Lightweight Image Local Feature Matching by Shanquan Ying, Jianfeng Zhao, Guannan Li, Junjie Dai

“…LIM integrates efficient feature extraction and matching modules that significantly reduce model complexity while maintaining competitive performance. …”
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A combined model integrating deep learning, radiomics, and clinical ultrasound features for predicting BRAF V600E mutation in papillary thyroid carcinoma with Hashimoto’s thyroidit... by Peng-Fei Zhu, Xiao-Feng Zhang, Pu Zhou, Jiang-Yuan Ben, Hao Wang, Shu-E Zeng, Xin-Wu Cui, Ying He

“…Model performance was assessed using AUC, calibration curves, and decision curve analysis (DCA), while SHAP analysis was used to interpret the contribution of each feature to the combined model’s output.ResultsThe combined model achieved superior diagnostic performance, with AUC values of 0.895, 0.864, and 0.815 in the training, validation, and external test sets, respectively, outperforming the RAD model, DL model, and RAD_DL model. …”
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A framework for online social networking features by Mohsen Shafiei Nikabadi, Fatemeh Maleki

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Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

“…These results validate the effectiveness of our optimization strategy in improving classification performance. The study highlights the effectiveness of feature engineering and model optimization in enhancing DDoS detection accuracy, making machine learning a viable solution for real-time cybersecurity applications.…”
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CA-STIM: an interpolation model with spatio-temporal evolution characteristics and cross-attention mechanism for 2D island morphology sequences by Peng Zhang, Wenzhou Wu, Shaochen Shi, Fengyu Li, Fenzhen Su

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Self-Supervised Feature Disentanglement for Deepfake Detection by Bo Yan, Pan Liu, Yumin Yang, Yanming Guo

“…To tackle these issues, we propose a self-supervised learning framework based on feature disentanglement, which enhances the generalization ability of detection models by uncovering the intrinsic features of forged content. …”
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A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection by Tathagat Banerjee, Davinder Paul Singh, Pawandeep Kour, Debabrata Swain, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…This new deep learning model merges U-Net and Inception networks, incorporating advanced feature selection and optimization strategies. …”
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Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological featu... by Xiaoyan Wu, Yiman Li, Jilong Chen, Jie Chen, Wenchuan Zhang, Xunxi Lu, Xiaorong Zhong, Min Zhu, Yuhao Yi, Hong Bu

“…Model performance was evaluated using a five-fold cross-validation approach and externally validated on HR+/HER2- EBC patients from the TCGA cohort. …”
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SEM-Net: A Social–Emotional Music Classification Model for Emotion Regulation and Music Literacy in Individuals with Special Needs by Yu-Chi Chou, Shan-Ken Chien, Pen-Chiang Chao, Yuan-Jin Lin, Chih-Yun Chen, Kuang-Kai Yeh, Yen-Chia Peng, Chen-Hao Tsao, Shih-Lun Chen, Kuo-Chen Li

“…SEM-Net employs a convolutional neural network (CNN) architecture composed of 17 meticulously structured layers to capture complex emotional and musical features effectively. To further enhance the precision and robustness of the classification system, advanced social–emotional music feature preprocessing and sophisticated feature extraction techniques were developed, significantly improving the model’s predictive performance. …”
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A hybrid object detection approach for visually impaired persons using pigeon-inspired optimization and deep learning models by Abdullah M. Alashjaee, Hussah Nasser AlEisa, Abdulbasit A. Darem, Radwa Marzouk

“…In addition, the backbone fusion of feature extraction models such as CapsNet and InceptionV3 is implemented to capture diverse spatial and contextual information. …”
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Palmprint recognition based on principal line features by Hongxia Wang, Teng Lv

“…The output results of these different blocking strategies are fused by “sum fusion” and “maximum fusion”, and the local and global features are effectively utilized by combining complementary information to improve the recognition performance and get state-of-the-art results on multiple databases. …”
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Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adeno... by Song Lin, Song Lin, Yanli Niu, Yanli Niu, Lina Song, Yingjian Ye, Jinfang Yang, Junjie Liu, Xin Zhou, Xin Zhou, Peng An, Peng An

“…Among machine learning models, CatBoost achieved superior performance (AUC=0.883, 95% CI:0.811–0.955) compared to logistic regression (AUC=0.877, 95% CI:0.804–0.949) in test set. …”
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Border Feature Enhancement in Ultrasound Image Segmentation by Xianghao Cui, Qingjing Fei, Ruixuan Li, Wen Xiong, Fang Hou, Zhi Chen, Qi He

“…This dual-decoder scheme simultaneously predicts both the target area and its border in ultrasonic images, with feature fusion between these two decoders to boost overall performance. …”
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Pressure-driven polymeric membrane performance prediction, new membrane dimensionless number, and considerations for effective membrane design, selection, testing, and operation by Alexander R. Anim-Mensah, Alexander R. Anim-Mensah

“…Lower Cpi values indicate better performance. The model integrates solvent densities (ρi), solubility parameters of the membrane (δM), solute (δSo), solvent (δSv), and the extent of membrane constraint (ϕ). …”
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Development and validation of a machine learning model for central compartmental lymph node metastasis in solitary papillary thyroid microcarcinoma via ultrasound imaging features... by Haiyang Han, Heng Sun, Chang Zhou, Li Wei, Liang Xu, Dian Shen, Wenshu Hu

“…The performance of each model was then evaluated. Results The combined model, which incorporates age, the presence of Hashimoto’s thyroiditis (HT), and radiomics features selected via an optimal feature selection approach (percentage-based), exhibited superior predictive efficacy, with AUC values of 0.767 (95% CI: 0.716–0.818) in the training set and 0.729 (95% CI: 0.648–0.810) in the validation set. …”
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