Comparative modeling approaches for predicting Olea and Quercus pollen seasons in Thessaloniki, Greece by S. Papadogiannaki, K. Karatzas, S. Kontos, A. Poupkou, D. Melas

“…Abstract In the Mediterranean region, Olive (Olea europaea L.) is a primary source of airborne allergenic pollen, while Quercus contribute substantial quantities of pollen grains to the atmosphere, posing significant challenges in predicting their Main Pollen Seasons (MPS). This study addresses these challenges through the application of various predictive methodologies, including Thermal Time (TT) models, which integrate chilling and heat requirements, along with Partial Least Squares Regression (PLS), and Temperature-Photoperiod (TP) models. …”
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COMPARISON OF LEAST SQUARE SPLINE AND ARIMA MODELS FOR PREDICTING INDONESIA COMPOSITE INDEX by Any Tsalasatul Fitriyah, Nur Chamidah, Toha Saifudin

“…The parametric approach in this study uses the ARIMA model. ARIMA is widely used to predict time series data. …”
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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…This study aimed to determine the critical risk factors associated with suicidal behavior mortality and identify an effective classification model for predicting suicidal behavior outcomes. …”
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Preoperative MR - based model for predicting prognosis in patients with intracranial extraventricular ependymoma by Liyan Li, Xueying Wang, Zeming Tan, Yipu Mao, Deyou Huang, Xiaoping Yi, Muliang Jiang, Bihong T. Chen

“…Objectives: To develop and validate a prediction model based on brain MRI features to predict disease-free survival (DFS) and overall survival (OS) for patients with intracranial extraventricular ependymoma (IEE). …”
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IGWO-MSVR model for predicting stress in coal seam during drilling process by Jian Tan, Yanfeng Geng, Liangke Xu

“…Furthermore, Back Propagation Neural network model (BP), Spatial Autoregressive model (SAR) and MSVR model were adopted to perform the stress prediction, and the stress prediction accuracy from these models was analyzed. …”
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Improving deceased donor kidney utilization: predicting risk of nonuse with interpretable models by Ruoting Li, Sait Tunç, Osman Y. Özaltın, Matthew J. Ellis

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Application of the RFA-XGBoost model in predicting potential complaint users in mobile network by ZHANG Peng, GAO Yuan

“…At the same time, the recursive feature augmented XGBoost (RFA-XGBoost) prediction model was proposed for the prediction of potential complaint users. …”
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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…After evaluating each model, the prediction model based on XGBoost was determined to be the optimal model, with AUC of 0.7856, 0.8484, and 0.796 at 1, 3, and 5 years. …”
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An efficient method for predicting the morphology of proppant packs based on a surrogate model by Tao ZHANG, Hangyu ZHOU, Yifan ZHANG, Jianchun GUO, Haoran GOU, Tang TANG

“…Through correlation analysis, the primary factors influencing these characteristic parameters were identified. Intelligent proxy models for the prediction of proppant placement patterns were established on the basis of the cascade neural network, including a time-concentration model for predicting particle volume fraction and a displacement-height model for predicting particle placement height. …”
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Adaptive machine learning framework: Predicting UHPC performance from data to modelling by Yinzhang He, Shaojie Gao, Yan Li, Yongsheng Guan, Jiupeng Zhang, Dongliang Hu

“…Ultra-High Performance Concrete (UHPC) is vital for next-generation infrastructure, necessitating complex interaction modeling beyond empirical methods. This study proposes an interpretable machine learning (ML) framework to predict the compressive strength (CS) of UHPC and analyze input variable influences. …”
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Evaluation of different erosion models for predicting guide vane wear in Francis turbine by Rakish Shrestha, Rakish Shrestha, Kushal Shrestha, Kushal Shrestha, Sailesh Chitrakar, Sailesh Chitrakar, Bhola Thapa, Hari Prasad Neopane, Zhongdong Qian, Zhiwei Guo

“…By dividing the CG into sections and comparing the erosion predictions by different erosion models with the actual erosion, Finnie erosion model is found to be the most suitable model for this application. …”
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Predicting oxytocin binding dynamics in receptor genetic variants through computational modeling by Preeti Dubey, Yingye Fang, K. Lionel Tukei, Shobhan Kuila, Xinming Liu, Annika Sahota, Antonina I. Frolova, Erin L. Reinl, Manasi Malik, Sarah K. England, Princess I. Imoukhuede

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Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy by Saswati Rakshit, Anal Ranjan Sengupta

“…This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. …”
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Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models by Filipe Augusto Felix de Queiroz, Igor Barbosa Negreiros, Giovana de Souza, Débora Cordeiro de Sousa, Sílvio Fernando Alves Xavier Júnior

“…We compare different statistical modeling and Machine Learning techniques, identifying the ones with the greatest accuracy in predicting sentiments. …”
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Predicting in-hospital mortality in ICU patients with lymphoma using machine learning models. by Ling Xu, Guang Tu, Zhonglan Cai, Tianbi Lan

“…Machine learning (ML) models offer a more accurate alternative for predicting outcomes by analyzing large datasets. …”
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Enhancing mental well-being: An artificial intelligence model for predicting mental disorders by Jahanur Biswas, Md. Nahid Hasan, Md. Shakil Rahman Gazi, Md. Mahbubur Rahman

“…This imbalanced dataset is balanced by the Random Oversampling model. In our study, we introduced a state-of-the-art approach to predicting mental conditions such as depression, anxiety, and stress. …”
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A Nomogram Model for Predicting Recurrent Coronary Thrombosis in Kawasaki Disease Patients by Zhou X, Peng Y, Yi Q

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Factors predicting the mathematics anxiety of adolescents: a structural equation modeling approach by Suman Ahmmed, Jashodhan Saha, Maruf Ahmed Tamal, Khondaker Abdullah Al Mamun, Sajani Islam

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Predicting transient response using data-driven models for ball-impact simulations by Ross Pivovar, Fei Chen, Raghunath Katragadda, Vidyasagar Ananthan

“…This study investigates the application of machine learning (ML) models for predicting transient responses in ball-impact elastodynamics simulations. …”
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Establishment of a nomogram model for predicting the risk of diabetic nephropathy in diabetic patients by HOU Xin-yue, HU Song, FEI Chun-xiao, LIU Shu-hao, SHAO Li-yan, XING Ang

“…Bootstrap was used to verify the model, to plot the ROC curve, and to calculate the predictive performance of the C-index evaluation model. …”
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