SevPredict: Exploring the Potential of Large Language Models in Software Maintenance by Muhammad Ali Arshad, Adnan Riaz, Rubia Fatima, Affan Yasin

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Prognostic predictions in psychosis: exploring the complementary role of machine learning models by Metten Somers, Hugo G Schnack, Rene S Kahn, Diane F van Rappard, Frank L Gerritse, Edwin van Dellen, Violet van Dee, Seyed M Kia, Caterina Fregosi, Wilma E Swildens, Anne Alkema, Albert Batalla, Coen van den Berg, Danko Coric, Lotte G Dijkstra, Arthur van den Doel, Livia S Dominicus, John Enterman, Marte Z van der Horst, Fedor van Houwelingen, Charlotte S Koch, Lisanne E M Koomen, Marjan Kromkamp, Michelle Lancee, Brian E Mouthaan, Eline J Regeer, Raymond W J Salet, Jorgen Straalman, Marjolein H T de Vette, Judith Voogt, Inge Winter-van Rossum, Wiepke Cahn

“…However, psychiatrists struggled to recognise when to rely on the model’s output, and we were unable to determine a clear pattern in these cases based on their characteristics.Conclusions MLMs may have the potential to support psychiatric decision-making, particularly in difficult-to-predict cases, but at present, their effectiveness remains limited due to constraints in predictive accuracy and the ability to identify when to rely on the model’s output. …”
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The reporting quality and methodological quality of dynamic prediction models for cancer prognosis by Peijing Yan, Zhengxing Xu, Xu Hui, Xiajing Chu, Yizhuo Chen, Chao Yang, Shixi Xu, Huijie Cui, Li Zhang, Wenqiang Zhang, Liqun Wang, Yanqiu Zou, Yan Ren, Jiaqiang Liao, Qin Zhang, Kehu Yang, Ling Zhang, Yunjie Liu, Jiayuan Li, Chunxia Yang, Yuqin Yao, Zhenmi Liu, Xia Jiang, Ben Zhang

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Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability by Kai Ma, Junzhi Zhang, Xinhang Huang, Mengyang Wang

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Assessment of Machine Learning Models for Predicting Aboveground Biomass in the Indian Subcontinent by S. Mamgain, B. Ghale, H. C. Karnatak, A. Roy

“…This study evaluates three machine learning models—Random Forest (RF), Gradient Tree Boosting (GTB), & Classification and Regression Trees (CART)—for predicting AGB across the subcontinent. …”
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Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects by Tariq Shahzad, Sunawar Khan, Tehseen Mazhar, Wasim Ahmad, Khmaies Ouahada, Habib Hamam

“…In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. …”
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Development of Ensemble Tree Models for Generalized Blood Glucose Level Prediction by Aashima, Shashank Bhargav, Shruti Kaushik, Varun Dutt

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Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models by Anderson Targino da Silva Ferreira, Regina Célia de Oliveira, Maria Carolina Hernandez Ribeiro, Pedro Silva de Freitas Sousa, Lucas de Paula Miranda, Saulo de Oliveira Folharini, Eduardo Siegle

“…Ultimately, incorporating geomorphological variables into predictive models enhances understanding of MPs deposition, providing a foundation for environmental policies focused on marine pollution mitigation and coastal ecosystem conservation while also contributing to achieve SDG 14.…”
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Artificial intelligence and numerical weather prediction models: A technical survey by Muhammad Waqas, Usa Wannasingha Humphries, Bunthid Chueasa, Angkool Wangwongchai

“…Can artificial intelligence (AI) models beat traditional numerical weather prediction (NWP) models based on physical principles? …”
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Number of Publications on New Clinical Prediction Models: A Bibliometric Review by Banafsheh Arshi, Laure Wynants, Eline Rijnhart, Kelly Reeve, Laura Elizabeth Cowley, Luc J Smits

“… Abstract BackgroundConcerns have been expressed about the abundance of new clinical prediction models (CPMs) proposed in the literature. …”
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Comparative Analysis of Advanced Models for Predicting Housing Prices: A Review by Inmaculada Moreno-Foronda, María-Teresa Sánchez-Martínez, Montserrat Pareja-Eastaway

“…ML models (neural networks, decision trees, random forests, among others) provide high predictive capacity and greater explanatory power due to the better fit of their statistical measures. …”
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Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels by Chengce Yuan, Yimin Shi, Zhichen Ba, Daxin Liang, Jing Wang, Xiaorui Liu, Yabei Xu, Junreng Liu, Hongbo Xu

“…This study presents a machine-learning-based model for predicting the performance of radiative cooling aerogels (RCAs). …”
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Dissolved Oxygen Prediction Based on SOA-SVM and SOA-BP Models by ZHANG Xuekun

“…To improve the accuracy of dissolved oxygen prediction,this paper researches and proposes a prediction method that combines seagull optimization algorithm (SOA) with support vector machine (SVM) and BP neural network,prepares four prediction schemes based on the monthly dissolved oxygen monitoring data of the Jinghong Power Station in Xishuangbanna,a national important water supply source in Yunnan Province,from January 2009 to September 2020,optimizes the key parameters of SVM and weight threshold of BP neural network by SOA to construct SOA-SVM and SOA-BP models,predicts the dissolved oxygen of Jinghong Power Station based on the models,and compares the prediction results with those of SVM and BP models.The results show that:The absolute values of the average relative errors of the SOA-SVM and SOA-BP models for the 4 schemes of dissolved oxygen prediction are between 4.07%~4.98% and 3.85%~4.83%,and that of the average absolute errors are 0.309~0.374 mg/L and 0.294~0.371 mg/L,respectively.With better prediction accuracy than SVM and BP models,they have good prediction accuracy and generalization ability.SOA can effectively optimize the key parameters of SVM and weight threshold of BP neural network.SOA-SVM and SOA-BP models are feasible for dissolved oxygen prediction,which can provide references for related prediction research.…”
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Using machine learning models to predict post-revascularization thrombosis in PAD by Samir Ghandour, Adriana A. Rodriguez Alvarez, Isabella F. Cieri, Shiv Patel, Mounika Boya, Rahul Chaudhary, Rahul Chaudhary, Rahul Chaudhary, Anna Poucey, Anahita Dua

“…The Synthetic Minority Oversampling Technique (SMOTE) was employed to address the class imbalance between the primary outcomes (ATE vs. no ATE). Model performance was assessed by area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value, and positive predictive value.ResultsOf the 308 patients analyzed, 66% were male, 84% were White, and 18.3% experienced an ATE during the one-year post-revascularization follow-up period. …”
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Analysis of the Influence of Different Turbulence Models on the Prediction of Vehicle Aerodynamic Performance by Luwei Wang, Xingjun Hu, Peng Guo, Zirui Wang, Jingyu Wang, Yuqi Wang, Yan Ma, Ying Li, Jing Zhao, Xu Yang, Ruixing Ma, Yinan Zhu, Jianjiao Deng

“…The k-ε model best predicts the steady-state drag coefficient (Cd) (error 0.0009). …”
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Advanced predictive modeling for enhanced mortality prediction in ICU stroke patients using clinical data. by Armin Abdollahi, Negin Ashrafi, Xinghong Ma, Jiahao Zhang, Daijia Wu, Tongshou Wu, Zizheng Ye, Maryam Pishgar

“…We developed a deep learning model to assess mortality risk and implemented several baseline machine learning models for comparison. …”
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Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System by Diana Car-Pusic, Silvana Petruseva, Valentina Zileska Pancovska, Zlatko Zafirovski

“…A model for early construction cost prediction is useful for all construction project participants. …”
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Predictive Model to Analyse Real and Synthetic Data for Learners' Performance Prediction Using Regression Techniques by SHABNAM ARA S.J, Tanuja R, Manjula S.H

“…The models are evaluated using precision metrics to assess their predictive accuracy. …”
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Seasonal Prediction of Atmospheric Rivers in the Western North Pacific Using a Seasonal Prediction Model by Yuya Baba

“…ABSTRACT Seasonal prediction of atmospheric rivers (ARs) in the western north Pacific (WNP) is examined using a seasonal prediction model with and without atmospheric initialisation. …”
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A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models by J. C. Vogeler, W. B. Cohen

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