FD-GRNet: A Dendritic-Driven GRU Framework for Advanced Stock Market Prediction by Tongyan Liu, Jiayi Li, Zihang Zhang, Hang Yu, Shangce Gao

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Two-Dimensional Numerical Method for Predicting the Resistance of Ships in Pack Ice: Development and Validation by Yan Huang, Ce Sun, Jianqiao Sun

“…A collision method was developed based on the Sweep and Prune (SAP) and Gilbert–Johnson–Keerthi (GJK) algorithms. A program for predicting the resistance of ships navigating in pack ice was developed based on MATLAB and the aforementioned theories. …”
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Mitigating Bias Due to Race and Gender in Machine Learning Predictions of Traffic Stop Outcomes by Kevin Saville, Derek Berger, Jacob Levman

“…We repeated our rigorous validation of AI for the creation of models that predict outcomes with and without race and with and without gender informing the model. …”
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Web application using machine learning to predict cardiovascular disease and hypertension in mine workers by Sohrab Effati, Alireza Kamarzardi-Torghabe, Fatemeh Azizi-Froutaghe, Iman Atighi, Somayeh Ghiasi-Hafez

“…After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines. …”
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Prediction of Traction Energy Consumption for Urban Rail Transit Trains in Relative Speed Mode by GUO Tuansheng

“…［Objective］It is aimed to accurately predict the traction energy consumption of urban rail transit trains operating in relative speed mode using support vector machine（SVM）regression and genetic algorithms, ultimately enhancing energy efficiency during train operation. …”
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Application of Deep Learning for Stock Prediction Within the Framework of Portfolio Optimization in Quantitative Trading by Xiaoyu Qin

“… This paper proposes a method for stock prediction and portfolio optimization as a part of quantitative trading based on a combination of Bi-RNN and a modified snake optimization algorithm (MSOA) to build optimal portfolios and outperform conventional models and benchmarks. …”
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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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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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