Transferable Deep Learning Models for Accurate Ankle Joint Moment Estimation during Gait Using Electromyography by Amged Elsheikh Abdelgadir Ali, Dai Owaki, Mitsuhiro Hayashibe

“…The best-performing intrasubject models were recurrent convolutional neural networks trained using signal energy features. …”
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Enhancing Model Accuracy of UAV-Based Biomass Estimation by Evaluating Effects of Image Resolution and Texture Feature Extraction Strategy by Yaxiao Niu, Xiaoying Song, Liyuan Zhang, Lizhang Xu, Aichen Wang, Qingzhen Zhu

“…We explored the impacts of spatial resolution and TF_CP on the performance of AGB models and analyzed the potentials of combination of SIs and TFs for improving maize AGB estimation accuracy. …”
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Forecasting and Feature Analysis of Ship Fuel Consumption by Explainable Machine Learning Approaches by Pham Nguyen Dang Khoa, Dinh Gia Huy, Nguyen Canh Lam, Dang Hai Quoc, Pham Hoang Thai, Nguyen Quyen Tat, Tran Minh Cong

“…Hence, explainable machine learning methods like Shapley additive explanations, the DT structure, and local interpretable model-agnostic explanations (LIME) were employed to comprehend the models and perform feature analysis. …”
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Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning by Rab Nawaz, Hani A. Albalawi, Syed Basit Ali Bukhari, Khawaja Khalid Mehmood, Muhammad Sajid

“…Feature selection through dimensionality reduction techniques is used to improve model performance and more efficient use of computational resources. …”
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Multi-Layer Modeling and Visualization of Functional Network Connectivity Shows High Performance for the Classification of Schizophrenia and Cognitive Performance via Resting fMRI by Duc My Vo, Anees Abrol, Zening Fu, Vince D. Calhoun

“…We also separated individuals into three cognitive performance groups based on cognitive scores and showed that the model can accurately predict the cognitive level using the FNC data. …”
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Small sample smart contract vulnerability detection method based on multi-layer feature fusion by Jinlin Fan, Yaqiong He, Huaiguang Wu

“…The experimental results demonstrate that the MULF model significantly enhances the performance of smart contract vulnerability identification compared to other models. …”
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Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys by Yan Zhang, Cheng Wen, Pengfei Dang, Xue Jiang, Dezhen Xue, Yanjing Su

“…By incorporating these descriptions derived from a simple logistic regression model, the performance of various classifiers improved by at least 15%. …”
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Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning by M. Vimala, SatheeshKumar Palanisamy, Sghaier Guizani, Habib Hamam

“…The problem of brain tumor classification (BTC) has been approached with several methods and uses different features obtained from MRI brain scans. However, they suffer from achieving higher performance in BTC and produce poor performance with a higher false ratio. …”
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EPI-DynFusion: enhancer-promoter interaction prediction model based on sequence features and dynamic fusion mechanisms by Ao Zhang, Jianhua Jia, Mingwei Sun, Xin Wei

“…As a result, the development of efficient computational models has become essential. However, many current deep learning and machine learning approaches utilize simplistic feature fusion strategies, such as direct averaging or concatenation, which fail to effectively model complex relationships and dynamic importance across features. …”
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Application Value of an AI-based Imaging Feature Parameter Model 
for Predicting the Malignancy of Part-solid Pulmonary Nodule by Mingzhi LIN, Yiming HUI, Bin LI, Peilin ZHAO, Zhizhong ZHENG, Zhuowen YANG, Zhipeng SU, Yuqi MENG, Tieniu SONG

“…This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). …”
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Hybrid pre trained model based feature extraction for enhanced indoor scene classification in federated learning environments by Monica Dutta, Deepali Gupta, Vikas Khullar, Sapna Juneja, Roobaea Alroobaea, Pooja Sapra

“…Deep Learning (DL) models, especially Convolutional Neural Networks (CNNs), have improved classification accuracy significantly by extracting the image features. …”
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SCM-DL: Split-Combine-Merge Deep Learning Model Integrated With Feature Selection in Sports for Talent Identification by Didem Abidin, Muhammed G. Erdem

“…The SCM-DL integrated with the RFE_DTC feature selection method achieved the highest performance for six features, yielding an accuracy rate of 97.40% and a Matthews Correlation Coefficient performance rate of 96.6%. …”
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Enhancing IDS for the IoMT based on advanced features selection and deep learning methods to increase the model trustworthiness. by Ahmed Muqdad Alnasrallah, Maheyzah Md Siraj, Hanan Ali Alrikabi

“…This study proposes an IDS model for the IoMT that integrates advanced feature selection techniques and deep learning to enhance detection performance. …”
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Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification by Abdulaziz AlMohimeed

“…Hybrid models are developed based on Transformer models: Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), and Swin Transformer are used to extract deep features from images, Principal Component Analysis (PCA) is used to reduce the complexity of extracted features, and Machine Learning (ML) models are used as classifiers to enhance performance. …”
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A methodological systematic review of validation and performance of sepsis real-time prediction models by Zichen Wang, Wen Wang, Che Sun, Jili Li, Shuangyi Xie, Jiayue Xu, Kang Zou, Yinghui Jin, Siyu Yan, Xuelian Liao, Yan Kang, Craig M. Coopersmith, Xin Sun

“…Combining AUROC and Utility Score identified top-performing SRPMs in 18.7% of studies. Hand-crafted features significantly improved performance. …”
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Performance evaluation of photovoltaic scenario generation by Siyu Ren, Tongxin Yang, Jun Luo, Gang Wu, Gang Wu, Kai Mao, Bowen Liu

“…Experimental results demonstrate that, compared to conventional probability-based metrics, the proposed model more effectively reveals the performance characteristics of photovoltaic scenario generation technologies. …”
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Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques by Atefeh Rostami, Mostafa Robatjazi, Amir Dareyni, Ali Ramezan Ghorbani, Omid Ganji, Mahdiye Siyami, Amir Reza Raoofi

“…Models’ performances in test data set were evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score. …”
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Improving appendix cancer prediction with SHAP-based feature engineering for machine learning models: a prediction study by Ji Yoon Kim

“…Purpose This study aimed to leverage Shapley additive explanation (SHAP)-based feature engineering to predict appendix cancer. Traditional models often lack transparency, hindering clinical adoption. …”
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A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python by Li Taobei

“…Performance metrics were used to evaluate the models' predictive capacity. …”
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Enhancement in Bearing Fault Classification Parameters Using Gaussian Mixture Models and Mel Frequency Cepstral Coefficients Features by Youcef ATMANI, Said RECHAK, Ammar MESLOUB, Larbi HEMMOUCHE

“…This paper investigates bearing faults diagnosis based on classification approach using Gaussian Mixture Model (GMM) and the Mel Frequency Cepstral Coefficients (MFCC) features. …”
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