Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking by Ziqi Li, Dongyao Jia, Zihao He, Nengkai Wu

“…To tackle these issues, this paper proposes a multi-modal fusion tracking framework that realizes high-precision tracking in complex scenarios by collaboratively optimizing feature enhancement and motion prediction. Firstly, a multi-scale feature adaptive enhancement (MS-FAE) module is designed, integrating multi-level features and introducing a small object adaptive attention mechanism to enhance the representation ability for small objects. …”
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SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition by Chengfei Qi, Yanli Feng, Junling Wan, Xinying Mao, Peisen Yuan

“…Compared with existing methods, such as Informer, Autoformer, ETSformer, LSTM, and Seq2seq, our model has better precision and time performance for short-term power load forecasting for proxy spot trading.…”
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Optimal feature selection for a weighted k-nearest neighbors for compound fault classification in wind turbine gearbox by Samuel M. Gbashi, Paul A. Adedeji, Obafemi O. Olatunji, Nkosinathi Madushele

“…To address this problem, this study advanced a framework for choosing the best features for a k-NN fault diagnostic model while also leveraging the benefits of “weighting” to further improve its performance. …”
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Multi-stream feature fusion of vision transformer and CNN for precise epileptic seizure detection from EEG signals by Qi Li, Wei Cao, Anyuan Zhang

“…However, most existing deep learning-based epilepsy detection methods are deficient in mining the local features and global time series dependence of EEG signals, limiting the performance enhancement of the models in seizure detection. …”
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Optimized feature selection and advanced machine learning for stroke risk prediction in revascularized coronary artery disease patients by Yong Si, Armin Abdollahi, Negin Ashrafi, Greg Placencia, Elham Pishgar, Kamiar Alaei, Maryam Pishgar

“…Notably, compared to the best existing literature, which reported an AUC of 0.760 on the test set, our model exhibited a 9% improvement in predictive performance while utilizing a more parsimonious feature set. …”
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Fault Diagnosis Method of Permanent Magnet Synchronous Motor Demagnetization and Eccentricity Based on Branch Current by Zhiqiang Wang, Shangru Shi, Xin Gu, Zhezhun Xu, Huimin Wang, Zhen Zhang

“…Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. Experimental results show that the method proposed in this paper achieves a diagnostic accuracy of 98.6%, with a misjudgment rate of only about 2% and no overlapping feature results. …”
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Enhanced Lung Cancer Prediction via Integrated Multi‐Space Feature Adaptation, Collaborative Alignment and Disentanglement Learning by Abigail Kawama, Ronald Waweru Mwangi, Lawrence Nderu

“…Initially, the AMSF‐L1ELM model was employed to address the challenges of feature alignment and multi‐domain adaptation, achieving a baseline performance with a test accuracy of 83.20%, precision of 83.43%, recall of 83.74%, and an F1‐score of 83.07%. …”
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Intelligent classification and prediction of students’ mental health in online learning environments using boosting algorithm and LIWC features by Xiaomin Xu, Tianrong Zhang

“…The performance of the model is enhanced with the Antlion Optimization Algorithm. …”
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Radiomic features at contrast-enhanced CT predict proliferative hepatocellular carcinoma and its prognosis after transarterial chemoembolization by Haifeng He, Zhichao Feng, Junhong Duan, Wenzhi Deng, Zuowei Wu, Yizi He, Qi Liang, Yongzhi Xie

“…In our study, 98 of 180 (54%) patients were confirmed to have proliferative HCCs. The radiomics model comprising 9 radiomic features and exhibited good performance for predicting proliferative HCCs. …”
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Photoplethysmogram (PPG)-Based Biometric Identification Using 2D Signal Transformation and Multi-Scale Feature Fusion by Yuanyuan Xu, Zhi Wang, Xiaochang Liu

“…To address these issues, this paper proposes an improved MSF-SE ResNet50 (Multi-Scale Feature Squeeze-and-Excitation ResNet50) model based on 2D PPG signals. …”
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Sum Rate Analysis of MU-MIMO with a 3D MIMO Base Station Exploiting Elevation Features by Xingwang Li, Lihua Li, Fupeng Wen, Junfeng Wang, Chao Deng

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Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification by Edgar Acuña, Roxana Aparicio

“…Additionally, we apply data engineering techniques to each dataset, allowing us to assess classifier performance concerning the available features and channels within the time series. …”
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Functional Monitoring of Patients With Knee Osteoarthritis Based on Multidimensional Wearable Plantar Pressure Features: Cross-Sectional Study by Junan Xie, Shilin Li, Zhen Song, Lin Shu, Qing Zeng, Guozhi Huang, Yihuan Lin

“…Furthermore, model stacking and average ensemble learning methods were adopted to further improve the generalization performance of the model. …”
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Effective Breast Cancer Classification Using Deep MLP, Feature-Fused Autoencoder and Weight-Tuned Decision Tree by Nagham Rasheed Hameed Alsaedi, Mehmet Fatih Akay

“…Our results suggest that this combination model outperforms traditional classification methods, with high accuracy and robust performance across data partitions. …”
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Determining the Influence of Real Estate Features on Prices with Partial Dependence Plots: A Case Study in Szczecin, Poland by Gnat Sebastian

“…The performance of this model will be compared against a traditional multiple linear regression model, providing insights into the advantages of leveraging advanced machine learning techniques in real estate analysis. …”
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Automated Quality Control of Candle Jars via Anomaly Detection Using OCSVM and CNN-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…The end-to-end CNN reached 100% accuracy, precision, recall, F1-score, and AUC. The best-performing hybrid model CNN-based feature extraction followed by OCIF also achieved 100% across all evaluation metrics, confirming the effectiveness and robustness of the proposed approach. …”
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Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data by Madiha Khalid, Ali Raza, Adnan Akhtar, Furqan Rustam, Julien Brito Ballester, Carmen Lili Rodriguez, Isabel de la Torre Díez, Imran Ashraf

“…Results The research gap is addressed using combined features to improve the performance of epileptic seizure detection compared to a single feature set. …”
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Peritumoral Radiomic Features on CT for Differential Diagnosis in Small-Cell Lung Cancer: Potential for Surgical Decision-Making by Jie Lin BS, Hao Zheng MS, Yuan Dong MS, Lanqi Fu MS, Yujie Ding BS, Shucheng Huang MS, Shiwei Wang MS, Junna Wang MS

“…Feature selection was performed using t-tests, LASSO regression, and mRMR analysis. …”
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Decoding target discriminability and time pressure using eye and head movement features in a foraging search task by Anthony J. Ries, Chloe Callahan-Flintoft, Anna Madison, Louis Dankovich, Jonathan Touryan

“…While eye features demonstrated strong predictive power, the inclusion of head features significantly enhanced model performance. …”
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Improved swin transformer-based thorax disease classification with optimal feature selection using chest X-ray. by Nadim Rana, Yahaya Coulibaly, Ayman Noor, Talal H Noor, Md Imran Alam, Zeba Khan, Ali Tahir, Mohammad Zubair Khan

“…The input is pre-processed by resizing, normalizing pixel values, and applying data augmentation to address the issue of imbalanced datasets and improve model generalization. Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. …”
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