Evaluating ensemble models for fair and interpretable prediction in higher education using multimodal data by Felipe Emiliano Arévalo-Cordovilla, Marta Peña

“…Abstract Early prediction of academic performance is vital for reducing attrition in online higher education. …”
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Quality prediction of semi-solid die casting of aluminum alloy in terms of machine learning by Zhiyuan Wang, Xiaogang Hu, Gan Li, Zhen Xu, Hongxing Lu, Qiang Zhu

“…In this study, a machine learning (ML) model has been developed to identify defective products through the detection of injection pressure, thereby providing a foundation for monitoring and further optimizing the manufacturing process. Among various ML algorithms, the Multilayer Perceptron (MLP) is the most effective for overall quality prediction. …”
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An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate by Milad Jajarmizadeh, Sobri Harun, Mohsen Salarpour

“…In this study, a hybrid network presented as a feedforward modular neural network (FF-MNN) has been developed to predict the daily rainfall-runoff of the Roodan watershed at the southern part of Iran. …”
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Obesity Status Prediction Through Artificial Intelligence and Balanced Label Distribution Using SMOTE by Arif Riyandi, Mahazam Afrad, M Yoka Fathoni, Yogo Dwi Prasetyo

“…The findings underscore the critical role of SMOTE in improving AI model accuracy for obesity prediction and highlight Random Forest as the most reliable algorithm for clinical decision-making. …”
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Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction by P. Nancy, Prasad Raghunath Mutkule, Kalpana Sunil Thakre, Ajay S. Ladkat, S.B.G. Tilak Babu, Sunil L. Bangare, Mohd Naved

“…The aim of this work is to provide a method for the prediction and classification of cardiac disease based on machine learning and feature selection. …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…Predicting innovation outcomes at the firm level continues to be an important but challenging goal for researchers and practitioners alike. …”
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Closed-Loop Clustering-Based Global Bandwidth Prediction in Real-Time Video Streaming by Sepideh Afshar, Reza Razavi, Mohammad Moshirpour

“…Unlike local models, GFMs apply the same function to all traces enabling cross-learning, and leveraging relationships among traces to address the performance issues seen in current SBP algorithms. To address potential heterogeneity within the data and improve prediction quality, a clustered-wise GFM is utilized to group similar traces based on prediction accuracy. …”
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Advancements in Machine Learning (ML): Transforming the Future of Blood Cancer Detection and Outcome Prediction by Wiebke Rösler, Michael Roiss, Corinne Widmer

“…Recent studies demonstrate that ML algorithms can rapidly predict hematologic malignancies and patient outcomes, matching or exceeding the accuracy of experienced hematologists. …”
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Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction by Xiheng Wang, Jie Ji

“…Sixteen blood-based biomarkers were used to predict BA and frailty. Four tree-based ML algorithms were employed in the training and validation, and performance metrics were compared to select the best models. …”
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A method to manage the energy consumption of cloud centers for predictability in neuro-fuzzy networks by Ying Zhang

“…The results underlined the potential of predictive models combined with optimization algorithms for significant energy savings and operational efficiency in cloud data centers.…”
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A prediction method for radiation proctitis based on SAM-Med2D model by Ning Zhang, Haifeng Ling, Wenyu Zhang, Mei Zhang

“…We apply T-tests and Lasso regression to identify features most correlated with radiation proctitis and build predictive models using logistic regression, random forest, and naive Gaussian Bayesian algorithms. …”
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Classical machine learning and artificial neural network (ANN) to predict rejection in weaving industry by Toufique Ahmed

“…This study found that fabric allowance can be predicted from required gray fabrics by using logarithmic function. …”
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Subphenotypic Classification of Immune Response in Sepsis: Predicting Mortality and Guiding Future Personalized Immunotherapy by Velma Herwanto, Khie Chen Lie, Robert Sinto, Leonard Nainggolan

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STATE PREDICTION OF WIND TURBINE GENERATOR BASED ON K-CNN AND N-GRU (MT) by CHAI Tong, YUAN YiPing, MA JunYan, FAN PanPan

“…The feature extraction results after dimensionality reduction were input into N-GRU for prediction and reconstruction error was obtained, then the state evaluation was realized by setting the alarm threshold. …”
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Prediction for Tunnelling-Induced Ground Settlement in Multilayered Soils: An Improved Gradient Boosting Approach by Hongbin An, Yangyang Chen, Jingjie Lei, Hanbin Luo, Elton J. Chen

“…The research is based on the machine learning algorithm to establish a prediction model of stratum settlement caused by shield tunneling, which provides a new idea for real time prediction of the ground response caused by shield tunneling and risk reduction. …”
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Optimized deep learning models for stress-based stroke prediction from EEG signals by Sivasankaran Pichandi, Gomathy Balasubramanian, Venkatesh Chakrapani, J. Samuel Manoharan

“…The proposed research aims to classify stress-induced emotions and predict stroke risk using advanced deep learning algorithms. …”
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Evaluation of machine learning techniques for real-time prediction of implanted lower limb mechanics by Chase Maag, Clare K. Fitzpatrick, Paul J. Rullkoetter

“…The models were trained on joint alignment data, ligament information, and external boundary conditions. Several predictive algorithms were explored, including linear regression (LRM), multilayer perceptron (MLP), bi-directional long short-term memory (biLSTM), convolutional neural network (CNN), and transformer-based approaches. …”
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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…This study aims to develop machine learning (ML) models for CRC risk prediction using clinical laboratory data. Methods This retrospective, single-center study analyzed laboratory examination data from healthy controls (HC), polyp patients (Polyp), and CRC patients between 2013 and 2023. …”
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Specificity and Areas of Usage of Cardiovascular Prediction Models Among Athletes—State-of-the-art Review by Tomasz Chomiuk, Przemysław Kasiak, Artur Mamcarz, Daniel Śliż

“…Athletes with confirmed or suspected cardiovascular disease should be guided to perform training in carefully adjusted safe zones. Indirect prediction algorithms are feasible and easy-to-apply methods of individual cardiovascular disease risk estimation. …”
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Tool wear prediction based on XGBoost feature selection combined with PSO-BP network by Zhangwen Lin, Yankun Fan, Jinling Tan, Zhen Li, Peng Yang, Hua Wang, Weiwei Duan

“…Experimental results show that PSO outperforms other algorithms in training the tool wear prediction model, with XGBoost feature selection reducing model construction time by 57.4% and increasing accuracy by 63.57%, demonstrating superior feature selection capabilities over Decision Tree, Random Fores, Adaboost and Extra Trees. …”
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