Comparing Models and Performance Metrics for Lung Cancer Prediction using Machine Learning Approaches. by Ruqiya, Noman Khan, Saira Khan

“…It optimizes the performance of models for predicting lung cancer. …”
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Generalizable Solar Irradiance Prediction for Battery Operation Optimization in IoT-Based Microgrid Environments by Ray Colucci, Imad Mahgoub

“…Using satellite data from weather sensors, we trained machine learning models to enhance solar irradiance predictions. We evaluated five popular machine learning algorithms and applied ensemble methods, achieving a substantial improvement in predictive accuracy. …”
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Using machine learning techniques for DSP software performance prediction at source code level by Weihua Liu, Erh-Wen Hu, Bogong Su, Jian Wang

“…Therefore, we propose a new algorithm called MAX/MIN algorithm to select the best-predicted execution time. …”
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The good, the better and the challenging: Insights into predicting high-growth firms using machine learning by Sermet Pekin, Aykut Şengül

“…This research contributes valuable insights to financial analysts and investors in identifying high-growth firms and underscores the potential of machine learning in economic prediction.…”
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Preeclampsia pathogenesis and prediction - where are we now: the focus on the role of galectins and miRNAs by Natasa Karadzov Orlic, Ivana Joksić

“…Preeclampsia is a complex, progressive multisystem hypertensive disorder during pregnancy that significantly contributes to increased maternal and perinatal morbidity and mortality. Two screening algorithms are in clinical use for detecting preeclampsia: first-trimester screening, which has been developed and validated for predicting early-onset preeclampsia but is less effective for late-onset disease; and the sFlt-1:PlGF biomarker ratio (soluble tyrosine kinase and placental growth factor) used in suspected cases of preeclampsia. …”
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Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval by Syed Furqan Rafique, Zhang Jianhua, Rizwan Rafique, Jing Guo, Irfan Jamil

“…In this paper, an improved fuzzy prediction horizon forecasting method is developed to address the issue of intermittence and uncertainty problem related to renewable generation and load forecast. …”
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Stock Price Prediction Using Machine Learning: Evidence from Pakistan Stock Exchange by Zafar Akhter, Dr. Hassan Raza

“…The application of a Random Forest classifier is utilized on a dataset including historical stock prices (namely, the KSE-100 Index) to generate predictions regarding the future movement of stocks, specifically whether they would experience an increase or decrease. …”
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An Augmented AutoEncoder With Multi-Head Attention for Tool Wear Prediction in Smart Manufacturing by Chunping Dong, Jiaqiang Zhao

“…The result validates the superior performance of the proposed model compared to other deep learning algorithms in predicting tool wear.…”
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Dimensions management of traffic big data for short-term traffic prediction on suburban roadways by Arash Rasaizadi, Fateme Hafizi, Seyedehsan Seyedabrishami

“…In this study, big traffic data were used to predict traffic state on a section of suburban road from Karaj to Chalous located in the north of Iran. …”
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Dielectric tensor prediction for inorganic materials using latent information from preferred potential by Zetian Mao, WenWen Li, Jethro Tan

“…This study leverages multi-rank equivariant structural embeddings from a universal neural network potential to enhance predictions of dielectric tensors. We develop an equivariant readout decoder to predict total, electronic, and ionic dielectric tensors while preserving O(3) equivariance, and benchmark its performance against state-of-the-art algorithms. …”
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Ecotoxicity prediction of chemical compounds using machine learning and different molecular structure representations by Michał Marek, Rafał Kurczab

“…This paper presents the development of models for predicting chemical ecotoxicity (HC50) based on machine learning algorithms and different molecular representations. …”
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Leveraging AHP and transfer learning in machine learning for improved prediction of infectious disease outbreaks by Reham Abdallah, Sayed Abdelgaber, Hanan Ali Sayed

“…The researchers adopt the Analytic Hierarchy Process (AHP) for feature selection and integrated transfer learning to boost the accuracy of the study’s predictions. The researchers’ approach involves the deployment of several machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, and an ensemble of these methods. …”
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Predicting the Aquatic Toxicity of Pharmaceutical and Personal Care Products: A Multitasking Modeling Approach by Amit Kumar Halder, Tanushree Pradhan, M. Natália D. S. Cordeiro

“…Multitasking Quantitative Structure–Toxicity Relationship (mt-QSTR) models were then developed employing the Box–Jenkins moving average approach, incorporating both linear and non-linear frameworks based on diverse feature selection algorithms and machine learning techniques. To further improve the external predictivity, a consensus modeling approach was also implemented. …”
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Research on Improved Deadbeat Control Strategy Based on Interpolation Prediction and Online Inductance Identification by Zhihe Fu, Huangsheng Xie, Jiaxiang Xue, Haisong Luo, Zhuangbin Lin

“…An improved Newton interpolation prediction algorithm was proposed to compensate the delay problem of deadbeat control, and an on-line inductance identification algorithm based on double frequency sampling was proposed to correct the inductance deviation. …”
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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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