Crash severity prediction using a virtual geometry-group-based deep learning approach with images-based feature representation by Nanon Sonnatthanon, Kasem Choocharukul

“…The model's performance is evaluated using two key indicators: the F1-score and the Area Under the Curve. …”
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Development and Validation of Predictive Models for Non-Adherence to Antihypertensive Medication by Cristian Daniel Marineci, Andrei Valeanu, Cornel Chiriță, Simona Negreș, Claudiu Stoicescu, Valentin Chioncel

“…Notably, to our knowledge, this study represents the first application of permutation and SHapley Additive exPlanations feature importance in combination with probability-based adherence stratification, offering a novel framework for predictive adherence modelling.…”
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LULC-SegNet: Enhancing Land Use and Land Cover Semantic Segmentation with Denoising Diffusion Feature Fusion by Zongwen Shi, Junfu Fan, Yujie Du, Yuke Zhou, Yi Zhang

“…We developed LULC-SegNet, a semantic segmentation network for land use and land cover (LULC), which integrates features from the denoising diffusion probabilistic model (DDPM). …”
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Development of a Model of the System of Organizational Development of Treatment and Diagnostic Processes of a Multidisciplinary Hospital by A. Yu. Perminov, N. S. Fomenko, S. S. Petrikov, A. R. Samarin, N. P. Dragunova, A. I. Maksimov, I. K. Melnikova, V. A. Molodov

“…The most important factor in improving the quality of medical services at present is the continuous improvement of all aspects of the activities of a medical organization, which determines the appropriateness of using modern approaches to organizational development in medical institutions with their appropriate adaptation.The paper presents the results of the analysis of 16 known models of organizational development, as well as the performance indicators of the Kaidzen proposal system at 19 Russian and foreign enterprises in various industries. …”
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Developing a hybrid machine learning model for employee turnover prediction: Integrating LightGBM and genetic algorithms by Hojat Talebi, Amid Khatibi Bardsiri, Vahid Khatibi Bardsiri

“…In this study, we propose a novel hybrid model for employee turnover prediction that integrates Genetic Algorithms (GA) for feature selection with LightGBM for classification. …”
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A machine learning model integrating clinical-radiomics-deep learning features accurately predicts postoperative recurrence and metastasis of primary gastrointestinal stromal tumor... by WenJie Xie, Zhen Zhang, Zhao Sun, XiaoChen Wan, JieHan Li, JianWu Jiang, Qi Liu, Ge Yang, Yang Fu

“…A total of nine machine learning models were established based on the selected features. …”
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Proposing a framework for body mass prediction with point clouds: A study applied in typical swine pen environments by Gabriel Pagin, Luciane Silva Martello, Rubens André Tabile, Rafael Vieira de Sousa

“…Feature selection was performed using a Wrapper method and additional statistical testing. …”
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A strategy for the automatic diagnostic pipeline towards feature-based models: a primer with pleural invasion prediction from preoperative PET/CT images by Xiangxing Kong, Annan Zhang, Xin Zhou, Meixin Zhao, Jiayue Liu, Xinliang Zhang, Weifang Zhang, Xiangxi Meng, Nan Li, Zhi Yang

“…The performance of the feature-based diagnostic model outperformed both the radiomics model and individual machine learning models. …”
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iHofman: a predictive model integrating high-order and low-order features with weighted attention mechanisms for circRNA-miRNA interactions by Chang-Qing Yu, Chen Jiang, Lei Wang, Zhu-Hong You, Xin-Fei Wang, Meng-Meng Wei, Tai-Long Shi, Si-Zhe Liang

“…Results To tackle this challenge, we present a novel model, iHofman, designed to predict CMIs by integrating high-order and low-order features with weighted attention mechanisms. …”
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CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer by Xiaoyan Lu, Fan Liu, Jiahui E, Xiaoting Cai, Jingyi Yang, Xueqi Wang, Yuwei Zhang, Bingsheng Sun, Ying Liu

“…The radiomics signature of PTV9 showed superior diagnostic performance compared to PTV3 and PTV6 models. Integrating GPTV radiomics signature (incorporating Rad-score of GTV and PTV9) with clinical risk factor of serum CEA levels and CT imaging features of lobulation sign and tumor–pleura relationship demonstrated favorable accuracy in predicting OLNM in the training cohort (AUC, 0.819; 95% CI: 0.780–0.857) and validation cohort (AUC, 0.801; 95% CI: 0.741–0.860). …”
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Predictive model integrating deep learning and clinical features based on ultrasound imaging data for surgical intervention in intussusception in children younger than 8 months by Wan-liang Guo, Yu-feng Qian, Jin-jin Zhou, San-li Shi

“…In the prospective validation cohort, the combined model also demonstrated impressive performance with an AUC of 0.890.Conclusion The combined model, integrating DL and clinical features, demonstrated stable predictive accuracy, suggesting its potential for improving clinical therapeutic strategies for intussusception.…”
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Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support ve... by Yunfei Li, Dongni Zhang, Yiren Wang, Yiren Wang, Yiheng Hu, Zhongjian Wen, Zhongjian Wen, Cheng Yang, Ping Zhou, Wen-Hui Cheng

“…Model training and hyperparameter tuning were conducted on the training set (n=369), followed by evaluation on a validation set (n=93).Results6 pathomics features were screened as important features. …”
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Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus by Tianze Ding, Peijie Liu, Jie Jia, Hui Wu, Jie Zhu, Kefeng Yang

“…We applied random forest mean decrease impurity for feature selection and the models are built using logistic regression, XGBoost, and LightGBM algorithms. …”
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A high-resolution remote sensing land use/land cover classification method based on multi-level features adaptation of segment anything model by Hui Yang, Zhipeng Jiang, Yaobo Zhang, Yanlan Wu, Heng Luo, Peng Zhang, Biao Wang

“…To address this problem, we propose an innovative network model named multi-level feature adaptation-segment anything Model (MLFA-SAM). …”
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Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features by Hongwei Zhang, Kexin Wang, Shurong Liu, Guowei Chen, Yong Jiang, Yingchao Wu, Xiaocong Pang, Xiaoying Wang, Junling Zhang, Xin Wang

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A Study on Partial Discharge Fault Identification in GIS Based on Swin Transformer-AFPN-LSTM Architecture by Jiawei Li, Shangang Ma, Fubao Jin, Ruiting Zhao, Qiang Zhang, Jiawen Xie

“…Aiming at the problem of manual feature extraction and insufficient mining of feature information for partial discharge pattern recognition under different insulation faults in GIS, a deep learning model based on phase and timing features with Swin Transformer-AFPN-LSTM architecture is proposed. …”
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A two-stage forecasting model using random forest subset-based feature selection and BiGRU with attention mechanism: Application to stock indices. by Shafiqah Azman, Dharini Pathmanathan, Vimala Balakrishnan

“…These features are then used as input in a bidirectional gated recurrent unit with an attention mechanism (BiGRU-AM) model to forecast daily opening prices of ten stock indices. …”
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AI-powered smart emergency services support for 9-1-1 call handlers using textual features and SVM model for digital health optimization by Afraa Attiah, Manal Kalkatawi

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Data-Driven Model for Sliced 5G Network Dimensioning and Planning, Featured With Forecast and “what-if” Analysis by Dominik Dulas, Justyna Witulska, Agnieszka Wylomanska, Ireneusz Jablonski, Krzysztof Walkowiak

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Grid Search Based Hyperparameter-Tuned Deep Learning Model for Osteoporosis Diagnosis with Bi-Cubic Interpolation of X-Ray Images by Ruhul Amin, Md.Shamim Reza, Dewan Ahmed Muhtasim, Jungpil Shin, Md. Maniruzzaman, Md.Mahfujul Hasan

“…To enhance osteoporosis classification, we developed a hybrid stacked CNN model, which demonstrated excellent performance on interpolated images. …”
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