A text classification method by integrating mobile inverted residual bottleneck convolution networks and capsule networks with adaptive feature channels by Tao Jin, Jiaming Liu

“…It transforms from the original text matrix to a more compact and representative feature representation. A Capsule Network is designed to adaptively adjust the importance of different feature channels, including N-gram convolutional layers, selective kernel network layers, primary capsule layers, convolutional capsule layers, and fully connected capsule layers, aiming to enhance the model’s ability to capture semantic information of text across different feature channels. …”
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Dynamic graph structure and spatio-temporal representations in wind power forecasting by Zang Peng, Dong Wenqi, Wang Jing, Fu Jianglong

“…In conclusion, our model provides a promising approach.…”
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DA-ResNeXt50 method for radio frequency fingerprint identification based on time-frequency and bispectral feature fusion by CHEN Mengdi, ZHANG Wei, SHEN Lei, LEI Fuqiang, ZHANG Jiafei

“…The experimental results show that compared with that of using a single feature extraction method, the proposed feature fusion approach significantly improves performance. …”
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Prediction of EGFR mutation status in non-small cell lung cancer based on CT radiomic features combined with clinical characteristics by YANG Taotao, WANG Xianqi, CHEN Cancan

“…Conclusion‍　Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets, which may be helpful for clinical decision-making for treatment strategies. …”
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A Framework for Predicting Winter Wheat Yield in Northern China with Triple Cross-Attention and Multi-Source Data Fusion by Shuyan Pan, Liqun Liu

“…The multi-source feature fusion module first extracts deeper-level feature information based on the characteristics of different data, and then performs multi-source feature fusion through a triple cross-attention fusion mechanism. …”
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Bearing Fault Diagnosis Based on Time–Frequency Dual Domains and Feature Fusion of ResNet-CACNN-BiGRU-SDPA by Jarula Yasenjiang, Yingjun Zhao, Yang Xiao, Hebo Hao, Zhichao Gong, Shuaihua Han

“…In order to verify the model performance, experiments were carried out on the CWRU and JNU bearing datasets, and the results showed that the method had high accuracy under both small sample size and noise perturbation conditions, which verified the model’s good fault-feature-learning capability and noise immunity performance.…”
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Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control by Ebunle Akupan Rene, Willy Stephen Tounsi Fokui

“…This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints, thereby improving stability and performance under dynamic conditions. …”
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Atypical ductal hyperplasia diagnosed by US-guided core needle biopsy: clinical, pathological and US features associated with upgrading to malignancy by Jun Kang Li, Yong Jie Xu, Rui Lan Niu, Nai Qin Fu, Zhi Ying Jin, Shi Yu Li, Yu Chen Liu, Zhi Li Wang

“…Model was validated both internally and externally by calculating its performance on validation sets. …”
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Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

“…Among the 12 ML models evaluated, the RF model with the RFE-generated feature subset (RFE-50) emerged as the top performer. …”
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Role of Visual Saliency in Video Quality Assessments by Syeda Eesha-Tir-Razia, Qaisar Abbas, Imran Fareed Nizami, Adil Ali Raja, Mutlaq B. Aldajani, Mudasir Wahab, Tallha Akram, Sadiq Ahmad

“…This highlights the effectiveness of visual saliency and feature selection in improving the reliability and efficiency of VQA models, making them more aligned with human perception.…”
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Advancing patient care with AI: a unified framework for medical image segmentation using transfer learning and hybrid feature extraction by Nazife Çevik, Taner Çevik, Onur Osman, Shtwai Alsubai, Jawad Rasheed, Jawad Rasheed, Jawad Rasheed

“…These segmented images and extracted features were subsequently fine-tuned pre-trained transfer learning models. …”
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Crack Detection, Classification, and Segmentation on Road Pavement Material Using Multi-Scale Feature Aggregation and Transformer-Based Attention Mechanisms by Arselan Ashraf, Ali Sophian, Ali Aryo Bawono

“…Trained on a dataset of 10,000 images, the model achieved substantial performance improvements across all tasks after integrating transformer-based attention. …”
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TMS: Ensemble Deep Learning Model for Accurate Classification of Monkeypox Lesions Based on Transformer Models with SVM by Elsaid Md. Abdelrahim, Hasan Hashim, El-Sayed Atlam, Radwa Ahmed Osman, Ibrahim Gad

“…The proposed model is developed by selecting the top four models based on evaluation metrics for performance. …”
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A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques by Rabita Hasan, Sheikh Md. Rabiul Islam

“…A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. …”
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PIONet: A Positional Encoding Integrated Onehot Feature-Based RNA-Binding Protein Classification Using Deep Neural Network by Mahmood A. Rashid, Mayank Chaturvedi, Kuldip K. Paliwal

“…Additionally, we applied transfer learning to enhance the model’s performance, which led to improvements on 23 out of 24 datasets. …”
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Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features by Haoru Wang MD, Ling He MD, Xin Chen MD, Shuang Ding MD, Mingye Xie MD, Jinhua Cai MD

“…Results The radiomics model included 16 features, with a predominant focus on texture features (12/16, 75%). …”
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MSFF-Net: Multi-Sensor Frequency-Domain Feature Fusion Network with Lightweight 1D CNN for Bearing Fault Diagnosis by Miao Dai, Hangyeol Jo, Moonsuk Kim, Sang-Woo Ban

“…The vibration and acoustic signals are initially converted into the frequency domain using the fast Fourier transform (FFT), enabling the extraction of temporally invariant spectral features. These features are processed by a compact one-dimensional convolutional neural network, where modality-specific representations are fused at the feature level to capture complementary fault-related information. …”
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CDSE-UNet: Enhancing COVID-19 CT Image Segmentation With Canny Edge Detection and Dual-Path SENet Feature Fusion by Jiao Ding, Jie Chang, Renrui Han, Li Yang

“…This model enhances the standard UNet architecture by employing the Canny operator for edge detection in sample images, paralleling this with a similar network structure for semantic feature extraction. …”
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Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Algorithm and Fusion of Multiple Health Features by Guoju Liu, Zhihui Deng, Yonghong Xu, Lianfeng Lai, Guoqing Gong, Liang Tong, Hongguang Zhang, Yiyang Li, Minghui Gong, Mengxiang Yan, Zheng Ye

“…This paper proposes a novel SOH predication method by combing the four-vector intelligent metaheuristic (FVIM) with the CNN-LSTM-Attention basic model. The model adopts the collaborative architecture of a convolutional neural network and time series module, strengthens the cross-level feature interaction by introducing a multi-level attention mechanism, then uses the FVIM optimization algorithm to optimize the key parameters to realize the overall model architecture. …”
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Predicting Tropical Cyclone Extreme Rainfall in Guangxi, China: An Interpretable Machine Learning Framework Addressing Class Imbalance and Feature Optimization by Yuexing Cai, Cuiyin Huang, Fengqin Zheng, Guangtao Li, Sheng Lai, Liyun Zhu, Qiuyu Zhu

“…A total of 202 experiments were conducted to comprehensively evaluate the framework's performance. Genetic Algorithm (GA) optimization and Shapley additive explanations (SHAP) were utilized to identify the optimal subsets of features and accurately quantify the contributions of each variable. …”
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