Computational Molecular Modeling of Pin1 Inhibition Activity of Quinazoline, Benzophenone, and Pyrimidine Derivatives by Nicolás Cabrera, Jose R. Mora, Edgar A. Marquez

“…In order to improve the prediction of the pIC50 values, the aggregation of the individual models was performed through the construction of an ensemble, and the most robust one was constructed by two individual models (LR3 and RF1) by applying the IBK algorithm, and a substantial improvement in predictive performance is reflected in the values of R2ADJ = 0.982, Q2CV = 0.962, and Q2EXT = 0.918. …”
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Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

“…Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to analyze and screen variables. Predictive models were constructed by integrating key clinical features, and eight machine learning algorithms were evaluated to identify the most effective model. …”
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Token Mixing for Breast Cancer Diagnosis: Pre-Trained MLP-Mixer Models on Mammograms by Hosameldin O. A. Ahmed, Asoke K. Nandi

“…Their performance underscores the importance of fine-grained feature extraction in mammographic analysis. …”
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Multimodal fusion radiomic-immunologic scoring model: accurate identification of prostate cancer progression by Zhonglin Zhang, Huan Liu, Xiling Gu, Yang Qiu, Jiangqing Ma, Guangyong Ai, Xiaojing He

“…Logistic regression models were constructed for radiomic, immunologic, and combined RDIS models, with predictive performance rigorously evaluated using Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessments, and Decision Curve Analysis (DCA). …”
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Bridging assessment and treatment for repeat suicidality in prisons: development and validation of a risk model by Thomas R Fanshawe, Jenny Shaw, Seena Fazel, Amanda Perry, Jane Senior, Leen Farouki, Leanne Heathcote

“…The final risk model consisted of nine factors, including sex, calendar age and features of recent suicidal behaviour. …”
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Improving the Reliability of Biometric Authentication Processes Using a Model for Reducing Data Drift by Vladimir Zh. Kuklin, Naur Z. Ivanov, Alexander N. Muranov, Islam A. Alexandrov, Elena Yu. Linskaya

“…The proposed algorithm demonstrates a high degree of accuracy, as evidenced by its equal error rate (EER), and is particularly well-suited to feature recognition tasks due to its adaptive model. …”
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Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation by Wanli Xiong, Qiqi Cao, Lu Jia, Min Chen, Tao Liu, Qingyan Zhao, Yanhong Tang, Bo Yang, Li Li, Shaobo Shi, He Huang, Congxin Huang, China Atrial Fibrillation Center Project Team

“…The final model, refined to include nine essential features, achieved an AUC of 0.787 (95%CI 0.670–0.883). …”
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Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models by Shahzad Hussain, Hafeez Ur Rehman Siddiqui, Adil Ali Saleem, Muhammad Amjad Raza, Josep Alemany Iturriaga, Álvaro Velarde-Sotres, Isabel De la Torre Díez, Sandra Dudley

“…This study seeks to enhance telephysiotherapy by developing a system capable of accurately classifying physiotherapeutic exercises using PoseNet, a state-of-the-art pose estimation model. A dataset was collected from 49 participants (35 males, 14 females) performing seven distinct exercises, with twelve anatomical landmarks then extracted using the Google MediaPipe library. …”
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DC-tCNN: A Deep Model for EEG-Based Detection of Dim Targets by Liangwei Fan, Hui Shen, Fengyu Xie, Jianpo Su, Yang Yu, Dewen Hu

“…Visualization analyses of spatiotemporal features also illustrate the effectiveness and interpretability of our proposed paradigm and learning model. …”
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Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…The hybrid model combines the outlier robustness and heterogeneous data handling capability of GBDTs with the hierarchical feature extraction capability of CNNs, resulting in a significant improvement in performance, with an F1-score of 0.95 on a validation set. …”
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Modeling Time Series with SARIMAX and Skew-Normal and Zero-Inflated Skew-Normal Errors by M. Alejandro Dinamarca, Fernando Rojas, Claudia Ibacache-Quiroga, Karoll González-Pizarro

“…The inclusion of zero-inflation probabilities further enhances model performance in sparse datasets, yielding superior values in goodness-of-fit criteria such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). …”
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Prediction models after hepatectomy for hepatocellular carcinoma-based ultrasonic radiomics: an observational study by Dong Jiang, Jialun Ren, Yi Qian, Yijun Gu, Ru Wang, Hua Yu, Hui Dong, Dongyu Chen, Yan Chen, Haozheng Jiang, Yiran Li

“…Model performance was assessed using calibration plots and AUCs. …”
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Predictive modeling of oil rate for wells under gas lift using machine learning by Famin Ma, Farag M. A. Altalbawy, Pinank Patel, R. Manjunatha, Rishiv Kalia, Shoira Formanova, P. Raja Naveen, Kamal Kant Joshi, Aashna Sinha, Abdolali Yarahmadi Kandahari, Taqi Mohammed Khattab Al-Rubaye, Mohammad Mahtab Alam

“…The Random Forest model demonstrated superior performance, achieving a test R² of 0.867 and the lowest prediction errors (MSE: 18502 and AARE: 8.76%) for the testing phase, while other models were prone to overfitting or underfitting. …”
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A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model by Changhong Hou, Junchuan Yu, Daqing Ge, Liu Yang, Laidian Xi, Yunxuan Pang, Yi Wen

“…Landslides are one of the most destructive natural disasters in the world, threatening human life and safety. With excellent performance as a foundation model for image segmentation, the segment anything model (SAM) has provided a novel paradigm for semantic segmentation research. …”
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An improved U-net and attention mechanism-based model for sugar beet and weed segmentation by Yadong Li, Ruinan Guo, Rujia Li, Rongbiao Ji, Mengyao Wu, Dinghao Chen, Cong Han, Ruilin Han, Yongxiu Liu, Yuwen Ruan, Jianping Yang, Jianping Yang

“…Its performance on a sunflower dataset further verifies the model’s generalizability and robustness. …”
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E-CLIP: An Enhanced CLIP-Based Visual Language Model for Fruit Detection and Recognition by Yi Zhang, Yang Shao, Chen Tang, Zhenqing Liu, Zhengda Li, Ruifang Zhai, Hui Peng, Peng Song

“…Experimental results demonstrate the excellent performance of the model, achieving an F1 score of 0.752, and an mAP@0.5 of 0.791. …”
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Operator-agnostic and real-time usable psychophysiological models of trust, workload, and situation awareness by Erin E. Richardson, Jacob R. Kintz, Savannah L. Buchner, Torin K. Clark, Allison P. Hayman

“…Regression models using LASSO-based feature selection were fit with an emphasis on model stability and generalizability. …”
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Improving risk stratification of PI-RADS 3 + 1 lesions of the peripheral zone: expert lexicon of terms, multi-reader performance and contribution of artificial intelligence by Philip A. Glemser, Nils Netzer, Christian H. Ziener, Markus Wilhelm, Thomas Hielscher, Kevin Sun Zhang, Magdalena Görtz, Viktoria Schütz, Albrecht Stenzinger, Markus Hohenfellner, Heinz-Peter Schlemmer, David Bonekamp

“…While DCE-derived consensus features did not enhance model performance (bootstrapped AUC), there was a trend for increased performance by including multiparametric AI, but not biparametric AI into models, both for combined and AI-only models. …”
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YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

“…Secondly, a Diverse Branch Block (DBB) is embedded in the feature extraction–fusion module, which enriches the feature space by enhancing the representation capability of single-convolution operations, thereby improving model feature extraction performance without increasing inference time costs. …”
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The VMD-Informer-BiLSTM-EAA Hybrid Model for Predicting Zenith Tropospheric Delay by Zhengdao Yuan, Xu Lin, Yashi Xu, Ruiting Dai, Cong Yang, Lunwei Zhao, Yakun Han

“…Furthermore, our model reduces the training time by 8.2% compared to the Transformer model, demonstrating superior prediction performance and robustness in long-term ZTD forecasting. …”
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