A Keyframe Extraction Method for Assembly Line Operation Videos Based on Optical Flow Estimation and ORB Features by Xiaoyu Gao, Hua Xiang, Tongxi Wang, Wei Zhan, Mengxue Xie, Lingxuan Zhang, Muyu Lin

“…Each video frame is first encoded into a feature vector using the ORB algorithm and a bag-of-visual-words model. …”
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Sarcopenia prediction model based on machine learning and SHAP values for community-based older adults with cardiovascular disease in China by Peil Yu, Xinxin Zhang, Guoxuan Sun, Ping Zeng, Ping Zeng, Ping Zeng, Chu Zheng, Chu Zheng, Chu Zheng, Ke Wang, Ke Wang, Ke Wang

“…Subsequently, we built four machine learning (ML) models to predict SP. After 100 iterations, we selected the best performing model for risk stratification by comparing model discrimination and calibration. …”
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Predictive Modeling for Fetal Health: A Comparative Study of PCA, LDA, and KPCA for Dimensionality Reduction by Ariana Deyaneira Jimenez-Narvaez, Victor David Casa Vaca, Jonathan Javier Loor-Duque, Isidro Rafael Amaro Martin, Ivan Galo Reyes-Chacon, Paulina Vizcaino, Manuel Eugenio Morocho-Cayamcela

“…These findings highlight the importance of dimensionality reduction and feature selection in developing robust ML models for fetal health assessment, emphasizing their potential impact on improving clinical diagnostics and medical decision-making.…”
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Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis by Xiaobing Li, Xiaobing Li, Xiaobing Li, Xiaobing Li, Xiaobing Li, Qian Li, Qian Li, Qian Li, Qian Li, Xinyi Xie, Wei Wang, Xuemei Li, Tingqiang Zhang, Tingqiang Zhang, Tingqiang Zhang, Tingqiang Zhang, Li Zhang, Li Zhang, Li Zhang, Li Zhang, Yongsheng Liu, Yongsheng Liu, Yongsheng Liu, Yongsheng Liu, Li Wang, Li Wang, Li Wang, Li Wang, Wutao Xie

“…The joint model demonstrated the highest predictive performance and clinical benefit in both the training and test cohorts.ConclusionThe multimodal model, combining CT radiomics and clinical features, offers an effective and accurate tool for predicting the progression of pulmonary fibrosis in CWP.…”
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A Classification-Based Blood–Brain Barrier Model: A Comparative Approach by Ralph Saber, Sandy Rihana

“…Furthermore, the GA approach, utilizing a fitness function based on classifier performance, consistently improved prediction accuracy across all tested models, whereas SFS showed lower effectiveness. …”
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The development of CC-TF-BiGRU model for enhancing accuracy in photovoltaic power forecasting by Guomin Xie, Zijian Zhang, Zhongbao Lin, Sen Xie

“…Moreover, teacher forcing is seamlessly integrated into the model to bolster forecasting accuracy and stability. …”
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An appraisal of backscatter removal and refraction calibration models for improving the performance of vision-based mapping and navigation in shallow underwater environments by Fickrie Muhammad, Poerbandono, Harald Sternberg, Eka Djunarsjah, Hasanuddin Z Abidin

“…It is argued that the proposed VbM-dedicated models can significantly improve the feature detection method and conformity of object positions underwater around the camera's motion. …”
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Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry by Ari Primantara, Udisubakti Ciptomulyono, Berlian Al Kindhi

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Validation of a deep learning model for cattle lameness detection: Comparison of human scorer performance and automated gait analysis by Sandra Reitmaier, Serhan Narli

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Machine vision-based detection of key traits in shiitake mushroom caps by Jiuxiao Zhao, Jiuxiao Zhao, Wengang Zheng, Wengang Zheng, Yibo Wei, Yibo Wei, Qian Zhao, Qian Zhao, Jing Dong, Jing Dong, Xin Zhang, Xin Zhang, Mingfei Wang, Mingfei Wang

“…Finally,M3 group using GWO_SVM algorithm achieved optimal performance among six mainstream machine learning models tested with an R²value of 0.97 and RMSE only at 0.038 when comparing predicted values with true values. …”
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A robust and statistical analyzed predictive model for drug toxicity using machine learning by Deepak Rawat, Rohit Bajaj, Rachit Manchanda, Ankush Mehta, Prabhu Paramasivam, Suraj Kumar Bhagat, Abinet Gosaye Ayanie

“…The principal component analysis is performed for feature selection. An optimized ensembled model performs well in comparison to other models in all three scenarios. …”
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AI-based prediction of traffic crash severity for improving road safety and transportation efficiency by Ayman Mohamed Mostafa, Bader Aldughayfiq, Mayada Tarek, Alaa S. Alaerjan, Hisham Allahem, Murtada K. Elbashir, Mohamed Ezz, Eslam Hamouda

“…Among the evaluated classifiers, the Extra Trees (ET Classifier) ensemble model demonstrated superior performance, achieving 96.19% accuracy and an F1-score (macro) of 95.28%, ensuring a well-balanced prediction system. …”
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Research on credit risk of listed companies: a hybrid model based on TCN and DilateFormer by Chuanhe Shen, Junzhe Wu

“…Consequently, academics have begun to explore the potential of models based on deep learning. In this paper, we apply the concept of combining Transformer and CNN to the financial field, building on the traditional CNN-Transformer model’s capacity to effectively process local features, perform parallel processing, and handle long-distance dependencies. …”
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A novel method for optimizing epilepsy detection features through multi-domain feature fusion and selection by Guanqing Kong, Guanqing Kong, Shuang Ma, Shuang Ma, Wei Zhao, Wei Zhao, Haifeng Wang, Haifeng Wang, Qingxi Fu, Qingxi Fu, Jiuru Wang

“…Finally, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forest (RF) and XGBoost classifiers are used to construct epileptic seizure detection models based on the optimized detection features.ResultAccording to experimental results, the proposed method achieves 99.32% accuracy, 99.64% specificity, 99.29% sensitivity, and 99.32% score, respectively.ConclusionThe detection performance of the three classifiers is compared using 10-fold cross-validation. …”
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Optimised hybrid deep learning classification model for kidney stone diagnosis by Y Jini Jacob, Bethanney Janney J, Hemalatha RJ, Preethi S

“…These models are integrated to deliver optimal training parameter performance. …”
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Support Vector Machine – Recursive Feature Elimination for Feature Selection on Multi-omics Lung Cancer Data by Nuraina Syaza Azman, Azurah A Samah, Ji Tong Lin, Hairudin Abdul Majid, Zuraini Ali Shah, Nies Hui Wen, Chan Weng Howe

“…Feature selection was performed on the LUSC multi-omics data using SVM-RFE to select several optimal feature subsets. …”
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Robot Visual Tracking Model Based on Improved GOTURN-LD Algorithm by Lijuan Xu, Dalong Liu, Huanjian Ma

“…Compared with the other four models, its comprehensive performance was significantly better. …”
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Evaluation of crop water status using UAV-based images data with a model updating strategy by Ning Yang, Zhitao Zhang, Xiaofei Yang, Ning Dong, Qi Xu, Junying Chen, Shikun Sun, Ningbo Cui, Jifeng Ning

“…This study aims to evaluate crop water status by fusing multiple features from the unmanned aerial vehicle (UAV)-based canopy images with model updating strategy. …”
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Pain Level Classification Using Eye-Tracking Metrics and Machine Learning Models by Oussama El Othmani, Sami Naouali

“…Multiple machine learning models, including Random Forest, SVM, MLP, XGBoost, and NGBoost, are trained on the extracted features. …”
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Deep-m6Am: a deep learning model for identifying N6, 2′-O-Dimethyladenosine (m6Am) sites using hybrid features by Islam Uddin, Salman A. AlQahtani, Sumaiya Noor, Salman Khan

“…Finally, a multilayer deep neural network (DNN) is used as a classification algorithm for identifying m6Am sites. The performance of the proposed model was evaluated in comparison with traditional machine learning (ML) algorithms and existing models. …”
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