Predicting the Acceptance of Informal Learning Technologies: A Case of the TikTok Application by Ahmed Al-Azawei, Ali Alowayr

“…Moreover, previous literature focused on the use of structural equation modeling (SEM) to predict technology acceptance, whereas the application of data mining algorithms is rare in this direction of research. …”
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Comprehensive Machine Learning Model for Cervical Cancer Prediction and Risk Factor Identification by null Mahendra, Mila Desi Anasanti

“…Our findings showed that selecting fewer features, such as half or even a quarter of the variables, still yielded strong results, emphasizing the importance of careful feature selection in cervical cancer prediction. The RF algorithm achieved the highest accuracy, with 99% using the full feature set and 98% with a reduced set of five features. …”
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Ensemble learning for prediction of inorganic scale formation: A case study in Oman by Mohammed Talib Said Al Harrasi, Alireza Kazemi, Reza Yousefzadeh

“…Owing to the intricate nature of scale formation, developing a closed-form mathematical formulation for its prediction is difficult. Thereby, the ability of six machine learning algorithms and a Power Law Ensemble Model (PLEM) to predict inorganic scale formation in carbonate formations is examined in this study. …”
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Development and validation of a modified SOFA score for mortality prediction in candidemia patients by Xiaofei Liu, Ranran Ding, Guangming Yang, Yuling Qiao, Zhen Ma, Yaping Feng, Feng Qu, Qiang Meng

“…The mSOFA_3 model demonstrated superior predictive performance across multiple machine learning algorithms, with the logistic regression-based model achieving the highest AUC of 0.826 in the internal validation cohort and 0.813 in the test cohort. …”
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Machine learning models for predicting the risk of depressive symptoms in Chinese college students by Chengfu Yu, Xiangxuan Kong, Weijie Yu, Xingcan Ni, Jing Chen, Xiaoyan Liao

“…Given the limitations of traditional linear models in managing high-dimensional data, this study employed machine learning techniques to predict depressive symptoms.MethodData were collected from 1,635 Chinese college students and included 38 sociodemographic, psychological, and social variables. …”
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Apple Trajectory Prediction in Orchards: A YOLOv8-EK-IPF Approach by Jinxing Niu, Zhengyi Liu, Shuo Wang, Jiaxi Huang, Junlong Zhao

“…The algorithm first employs spatial partitioning according to the cyclical motion patterns of apples to constrain the prediction results. …”
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Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models by Ibrahim Abdulwahab, Sulaiman Haruna Sulaiman, Umar Musa, Ibrahim Abdullahi Shehu, Abdullahi Kakumi Musa, Ismaila Mahmud, Mohammed Musa, Abdullahi Abubakar, Abdulrahman Olaniyan

“… The research is set to predict solar irradiation using various machine learning algorithms. …”
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Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost by Yingjie Meng, Chengwu Xu, Tingting Li, Tianyong Liu, Lu Tang, Jinyou Zhang

“…In this study, for the shale of the Qingshankou Formation of the Gulong Sag in the Songliao Basin, TOC content prediction models using various machine learning algorithms are established and compared based on measured data, principal component analysis, and the particle swarm optimization algorithm. …”
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Graph-based machine learning model for weight prediction in protein–protein networks by Hajer Akid, Kirsley Chennen, Gabriel Frey, Julie Thompson, Mounir Ben Ayed, Nicolas Lachiche

“…By leveraging data from the yeast Saccharomyces cerevisiae obtained from the STRING database, we introduce a new model that combines similarity-based algorithms and aggregated confidence score weights for accurate link prediction purposes. …”
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Prediction of early breast cancer patient survival using ensembles of hypoxia signatures. by Inna Y Gong, Natalie S Fox, Vincent Huang, Paul C Boutros

“…Future signatures will likely require integration of intrinsic and extrinsic clinico-pathological variables to better predict disease-related outcomes.…”
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Gorilla troops optimization with deep learning based crop recommendation and yield prediction by A. Punitha, V. Geetha

“…The latest breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) technologies pave the way to designing effective crop recommendation and prediction models. Despite the significant advancements of Deep Learning (DL) models in crop recommendation, hyperparameter tuning using metaheuristic algorithms becomes essential for enhanced performance. …”
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AI-Driven Optimization of Breakwater Design: Predicting Wave Reflection and Structural Dimensions by Mohammed Loukili, Soufiane El Moumni, Kamila Kotrasova

“…This study leverages machine learning (ML) to predict the optimal 2D dimensions of rectangular breakwaters in two configurations: submerged at the bottom of a wave tank and positioned at the free surface. …”
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Predicting stroke with machine learning techniques in a sub-Saharan African population by Benjamin Segun Aribisala, Deirdre Edward, Godwin Ogbole, Onoja M. Akpa, Segun Ayilara, Fred Sarfo, Olusola Olabanjo, Adekunle Fakunle, Babafemi Oluropo Macaulay, Joseph Yaria, Joshua Akinyemi, Albert Akpalu, Kolawole Wahab, Reginald Obiako, Morenikeji Komolafe, Lukman Owolabi, Godwin Osaigbovo, Akinkunmi Paul Okekunle, Arti Singh, Philip Ibinaye, Osahon Osawata, Adeniyi Sunday, Ijezie Chukwuonye, Carolyn Jenkins, Hemant K. Tiwari, Okechukwu Ogah, Ruth Y. Laryea, Daniel T. Lackland, Oyedunni Arulogun, Omotolani Ajala, Rufus Akinyemi, Bruce Ovbiagele, Steffen Sammet, Mayowa Owolabi

“…Accurate models are needed in Africa to predict and prevent stroke occurrence. The aim of this study was to identify the best machine learning (ML) algorithm for stroke prediction. …”
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Exploring the VAK model to predict student learning styles based on learning activity by Ahmed Rashad Sayed, Mohamed Helmy Khafagy, Mostafa Ali, Marwa Hussien Mohamed

“…Multiple tests were performed by different machine learning classifiers, mainly in predicting learning style and recommending an assessment methodology. …”
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Elevating Accuracy: Enhanced Feature Selection Methods for Type 2 Diabetes Prediction by Ghazaleh Kakavand Teimoory, MohammadReza Keyvanpour

“…While machine learning and artificial intelligence show promise in predictive modeling for diabetes, challenges in interpreting models hinder widespread adoption among physicians and patients. …”
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Prediction of Monthly Temperature Over China Based on a Machine Learning Method by Ping Mei, Zixin Yin, Haoyu Wang, Changzheng Liu, Yaoming Liao, Qiang Zhang, Liping Yin

“…Machine learning has achieved significant success in many statistical application scenarios, but has yet to be fully successful in monthly and seasonal predictions. We identified three statistical challenges in climate prediction: instability of statistical models, complexity of feature factors, and the nonlinearity of the relationship between predictors and predictands. …”
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Physics-informed deep learning with Kalman filter mixture for traffic state prediction by Niharika Deshpande, Hyoshin (John) Park

“…We propose an innovative approach to modifying both prediction and correction steps of the Kalman filter (KF) algorithm by leveraging established spatiotemporal correlations. …”
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An ensemble learning model to predict lymph node metastasis in early gastric cancer by Kaiqing Song, Jiaming Wu, Muchen Xu, Mengying Li, Yuqi Chen, Yi Zhang, Hong Chen, Caifeng Jiang

“…This work aimed to establish a more dependable predictive model for identify lymph node metastasis in early gastric cancer. …”
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Predicting hospital outpatient volume using XGBoost: a machine learning approach by Lingling Zhou, Qin Zhu, Qian Chen, Ping Wang, Hao Huang

“…This study aims to develop a predictive model for daily hospital outpatient volume using the XGBoost algorithm. …”
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Adaptive gradient scaling: integrating Adam and landscape modification for protein structure prediction by Vitalii Kapitan, Michael Choi

“…Despite their success, machine learning methods face fundamental limitations in optimizing complex high-dimensional energy landscapes, which motivates research into new methods to improve the robustness and performance of optimization algorithms. Results This study presents a novel approach to protein structure prediction by integrating the Landscape Modification (LM) method with the Adam optimizer for OpenFold. …”
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