Identification of key programmed cell death genes for predicting prognosis and treatment sensitivity in colorectal cancer by Jian-ying Ma, Jian-ying Ma, Yi-xian Wang, Zhen-yu Zhao, Zhen-yu Xiong, Zi-long Zhang, Jun Cai, Jia-wei Guo

“…However, a comprehensive predictive model for CRC prognosis, immunotherapy response, and drug sensitivity is still lacking. …”
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An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining by Charan Kumar Ala, Zefree Lazarus Mayaluri, Aman Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra Muduli

“…This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with conventional machine learning (ML) algorithms for the accurate prediction and optimization of BIGV. …”
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PuTMO3 systems crystal structures prediction and stability analysis based on first principles by Zexin Jiang, Jintao Wang, Yilin Fang, Tao Wang, Rui Zhou, Wenli Yu, Rusong Li, Ning Gao

“…Abstract Based on particle swarm optimization algorithm and first-principle calculations, the most stable structures of 10 different PuTMO3 systems are predicted according to their thermodynamical, mechanical and dynamical properties. …”
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Anomaly-Aware Tropical Cyclone Track Prediction Using Multi-Scale Generative Adversarial Networks by He Huang, Difei Deng, Liang Hu, Nan Sun

“…Our model demonstrates robustness during testing, accurately predicting TC paths over both ocean and land while also identifying weak TC remnants. …”
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A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks by Jose Abreu, Roberto L. Oliveira, Joao V. Fonseca Neto

“…This study proposes a comprehensive approach to determine the impact point prediction of ballistic rocket payloads. This approach combines tracking algorithms that are based on stochastic estimators with artificial neural network (ANN) models. …”
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Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning by Sarah Dandou, Kriti Amin, Véronique D’Hondt, Jérôme Solassol, Olivier Dereure, Peter J. Coopman, Ovidiu Radulescu, Holger Fröhlich, Romain M. Larive

“…We used machine learning algorithms and pre-processed genomic data to test whether they could contain useful information to improve the progression-free survival (PFS) prediction. …”
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Multi-strategy enhanced artificial rabbits optimization for prediction of grades in tourism service communication courses by Xiaodan Qu, Zhuyin Jia

“…Abstract Predicting students’ grades through their classroom behavior has been a longstanding concern in education. …”
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Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach by Rafael Travincas, Maria Paula Mendes, Isabel Torres, Inês Flores-Colen

“…This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. …”
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Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning by Po-Ting Pan, Yamine Bouzembrak, Miguel Quemada, Bedir Tekinerdogan

“…Therefore, this study integrated PlanetScope satellite images with weather data while adopting three ML algorithms, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to predict NNI in Spain from 2018 to 2019. …”
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Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review by Farkhondeh Asadi, Milad Rahimi, Nahid Ramezanghorbani, Sohrab Almasi

“…ABSTRACT Background This systematic review investigates the use of machine learning (ML) algorithms in predicting survival outcomes for ovarian cancer (OC) patients. …”
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Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding Process by Oluwole Abiodun Raimi, Bong-Kee Lee

“…To evaluate the prediction accuracy and capability of the proposed method, three different algorithms, namely Levenberg–Marquardt (lm), BGFS quasi-Newton (bfg), and scale conjugate gradient (scg), were included in the proposed model. …”
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Comparison of Machine Learning Methods (Linear Regression, Random Forest, and XGBoost) for Predicting Poverty in Central Java in 2024 by Zahwa Bunga Putri Pratama, Yani Parti Astuti

“…To respond to and address this challenge more effectively, a predictive, data-driven approach is essential. This study applies machine learning techniques to forecast the number of people living in poverty in 2024 at the district/city level, utilizing socio-economic data from 2019 to 2023 provided by the Central Bureau of Statistics (BPS). …”
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Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model by D. V. Gavrilov, T. Yu. Kuznetsova, M. A. Druzhilov, I. N. Korsakov, A. V. Gusev

“…The introduction of such risk stratification algorithms into practice will increase the accuracy and quality of CVR prediction and optimize the system of preventive measures.…”
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A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments by Jae Kwon Bae, Jinhwa Kim

“…This paper builds several personal credit rating prediction models based on the UDM and benchmarks their performance against other models which employ logistic regression (LR), Bayesian style frequency matrix (BFM), multilayer perceptron (MLP), classification tree methods (C5.0), and neural network rule extraction (NR) algorithms. …”
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Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations by Ayoub Jadouli, Chaker El El Amrani

“…We compile essential environmental indicators and employ state-of-the-art machine learning (ML) and deep learning (DL) algorithms to predict next-day wildfire occurrences. …”
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Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing. by Sven B Gould, Jonas Magiera, Carolina García García, Parth K Raval

“…Hence, hundreds of studies make use of algorithms that predict a localisation based on a protein's sequence. …”
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Link prediction accuracy on real-world networks under non-uniform missing-edge patterns. by Xie He, Amir Ghasemian, Eun Lee, Alice C Schwarze, Aaron Clauset, Peter J Mucha

“…To investigate the impact of different missing-edge patterns on link prediction accuracy, we employ 9 link prediction algorithms from 4 different families to analyze 20 different missing-edge patterns that we categorize into 5 groups. …”
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Setting the standard for machine learning in phase field prediction: a benchmark dataset and baseline metrics by Laura Hannemose Rieger, Klemen Zelič, Igor Mele, Tomaž Katrašnik, Arghya Bhowmik

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Machine Learning-Based Prediction of Ecosystem-Scale CO₂ Flux Measurements by Jeffrey Uyekawa, John Leland, Darby Bergl, Yujie Liu, Andrew D. Richardson, Benjamin Lucas

“…In this study, we use machine learning algorithms to predict CO₂ flux measurements at NEON sites (a subset of Ameriflux sites), creating a model to gap-fill measurements when sites are down or replace measurements when they are incorrect. …”
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Development and application of an early prediction model for risk of bloodstream infection based on real-world study by Xiefei Hu, Shenshen Zhi, Yang Li, Yuming Cheng, Haiping Fan, Haorong Li, Zihao Meng, Jiaxin Xie, Shu Tang, Wei Li

“…Based on the optimal combination, six machine learning algorithms were used to construct an early BSI risk prediction model. …”
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