Weakly supervised veracity classification with LLM-predicted credibility signals by João A. Leite, Olesya Razuvayevskaya, Kalina Bontcheva, Carolina Scarton

“…This paper introduces Pastel (Prompted weAk Supervision wiTh crEdibility signaLs), a weakly supervised approach that leverages large language models (LLMs) to extract credibility signals from web content, and subsequently combines them to predict the veracity of content without relying on human supervision. …”
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Impact of data spatial resolution on barley yield prediction mapping by F. Ksantini, M. Quemada, N. Arencibia-Pérez, A.F. Almeida-Ñauñay, E. Sanz, Ana M. Tarquis

“…Data-driven models and remote sensing techniques offer great crop monitoring and yield prediction potential. …”
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Impact of Parameter Uncertainties on Power Electronic Device Lifetime Predictions by Faezeh Kardan, Aditya Shekhar, Pavol Bauer

“…Properly addressing uncertainties in reliability analysis is essential for realistic lifetime predictions of power devices. This paper investigates parameter uncertainties on the lifetime estimation of power devices using an empirical lifetime model and Monte Carlo simulations. …”
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Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning by Jayasree R, Sheela Selvakumari

“…Initially, there was inadequate study of the various prediction techniques to select the ones that would best predict students’ success in educational environments. …”
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Construction of a Nomogram Prediction Model for Individualized Prediction of the Risk of Pulmonary Fungal Infection in Lung Cancer by Lai Q, Liao K, Kuang G, Liao W, Zhang S

“…Qixun Lai,1 Kaifu Liao,1 Guangzhi Kuang,1 Weijie Liao,2 Shengrui Zhang2 1Department of Thoracic Surgery, Ganzhou Fifth People’s Hospital, Ganzhou City, 341000, People’s Republic of China; 2Department of Critical Care Medicine, Ganzhou People’s Hospital, Ganzhou City, 341000, People’s Republic of ChinaCorrespondence: Shengrui Zhang, Department of Critical Care Medicine, Ganzhou People’s Hospital, No. 16 Meiguan Avenue, Ganzhou City, Jiangxi Province, 341000, People’s Republic of China, Tel +8615279719190, Email a13755016764@126.comObjective: To construct a nomogram model for individualized prediction of pulmonary fungal infection risk in lung cancer patients.Methods: A total of 483 lung cancer patients hospitalized between August 2021 and August 2024 were retrospectively analyzed and randomly divided into a modeling group (n=338) and validation group (n=145). …”
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Development and validation of PRE-FRA (PREdiction of FRAilty risk in community older adults) frailty prediction model by Taiping Lin, Xiaotao Huang, Xiang Wang, Miao Dai, Jirong Yue

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Explainable AI based LightGBM prediction model to predict default borrower in social lending platform by Li-Hua Li, Alok Kumar Sharma, Sheng-Tzong Cheng

“…This paper proposes an explainable AI (XAI)-based prediction model utilizing the LightGBM algorithm to predict the likelihood of borrower default on a social lending platform. …”
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Radiographic signature in apical periodontitis improves prediction of apical lesion healing through survival prediction model. by Yuebo Liu, Ge Kong, Fantai Meng, Chunlan Guo, Kuo Wan

“…In summary, the FA method proved to be an effective tool for quantifying the apical lesion boundary and predicting the healing speed using a survival model.…”
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Radiographic signature in apical periodontitis improves prediction of apical lesion healing through survival prediction model by Yuebo Liu, Ge Kong, Fantai Meng, Chunlan Guo, Kuo Wan

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Predicting technostress: The Big Five model of personality and subjective well-being. by Dámaris Cuadrado, Inmaculada Otero, Alexandra Martínez, Tania París, Silvia Moscoso

“…Finally, the combined predictive validity of the Big Five and SWB on technostress is tested. …”
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Construction and validation of a nomogram model for predicting diabetic peripheral neuropathy by Hanying Liu, Qiao Liu, Mengdie Chen, Chaoyin Lu, Ping Feng

“…The Hosmer–Lemeshow test and calibration curves revealed high consistency between the predicted and actual results of the nomogram. DCA demonstrated that the nomogram was valuable in clinical practice.ConclusionsThe DPN nomogram prediction model, containing 7 significant variables, has exhibited excellent performance. …”
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Construction of ubiquitination-related risk model for predicting prognosis in lung adenocarcinoma by Dawei Sun, Xiaohong Duan, Ning Li, Ou Qiao, Yingjie Hou, Zihuan Ma, Siyao Liu, Yanhua Gong, Zichuan Liu

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A theoretical model for predicting the Peak Cutting Force of conical picks by Gao Kuidong, Du Changlong, Jiang Hongxiang, Liu Songyong

“…In order to predict the PCF (Peak Cutting Force) of conical pick in rock cutting process, a theoretical model is established based on elastic fracture mechanics theory. …”
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Establishment and validation of a model for predicting depression risk in stroke patients by Fangbo Lin, Meiyun Zhou

“…Calibration plots confirmed high predictive accuracy, while DCA revealed substantial clinical utility. …”
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Development and validation of a model for predicting myopia in young children in China by Ting Hu, Rong Wu, Xi He, Zi-Yu Hua, Tian-Li Peng, Xiao-Yan Peng, Shi-Ming Li

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A Multilayer Residual Dendritic Neural Model for Predicting Stroke Prognosis by Yuxi Wang, Haochang Jin, Maocheng Cao, Xiong Xiao, Li Wang

Subjects: “…Dendritic neural model…”
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A Novel Formula Based Model for Predicting Skeletal Maturation in Maharashtra by Prachi Gaonkar, Ajay Kubavat, Shailesh Shenava, Robin Mathew, Sandeep Singh, Truppti P. Sonone, Chinmaya Hawaldar, Abhay Paul

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model by Ahmed Mohamed Shitaya, Mohamed El Syed Wahed, Saied Helemy Abd El Khalek, Amr Ismail, Mahmoud Y. Shams, A. A. Salama

“…To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions.…”
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The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion by Maja Reimann, Korkut Avsar, Andrew DiNardo, Torsten Goldmann, Gunar Günther, Michael Hoelscher, Elmira Ibraim, Barbara Kalsdorf, Stefan Kaufmann, Niklas Köhler, Anna Mandalakas, Florian Maurer, Marius Müller, Dörte Nitschkowski, Ioana Olaru, Cristina Popa, Andrea Rachow, Thierry Rolling, Helmut Salzer, Patricia Sanchez-Carballo, Maren Schuhmann, Dagmar Schaub, Victor Spinu, Elena Terhalle, Markus Unnewehr, Nika Zielinski, Jan Heyckendorf, Christoph Lange

“…Objective: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy. …”
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Predicting hypoglycemia in elderly inpatients with type 2 diabetes: the ADOCHBIU model by Rui-Ting Zhang, Yu Liu, Chao Sun, Quan-Ying Wu, Hong Guo, Gong-Ming Wang, Ke-Ke Lin, Jing Wang, Xiao-Yan Bai

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