RAFFLE: active learning accelerated interface structure prediction by Ned Thaddeus Taylor, Joe Pitfield, Francis Huw Davies, Steven Paul Hepplestone

“…Abstract Interfaces between materials are critical to the performance of many devices, yet predicting their structure is computationally demanding due to the vast configuration space. …”
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Machine learning for predicting earthquake magnitudes in the Central Himalaya by Ram Krishna Tiwari, Rudra Prasad Poudel, Harihar Paudyal

“…The findings illustrate that RFR is achieving better performance than the other two algorithms, as the predicted magnitudes are close to the actual magnitudes. …”
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Score-Predictive Scale for Assessing the Risk of Inguinal and Femoral Hernias Incarceration by B. V. Sigua, V. P. Zemlyanoy, A. A. Kozobin, D. S. Semin

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Proactive Edge Caching With Popularity Prediction and Content Replication by Ilhan Demirci, Omer Korcak

“…The algorithm incorporates both global and local content popularity predictions—obtained via exponential moving average (EMA) and long short-term memory (LSTM) models, and dynamically scales content placement decisions based on delay-aware replication benefits. …”
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Wineinformatics: Wine Score Prediction with Wine Price and Reviews by Yuka Nagayoshi, Bernard Chen

“…The goal of this paper is to determine whether incorporating wine price can improve the accuracy of score prediction. To explore the relationship between wine price and wine score, naive Bayes classifier and support vector machine (SVM) classifier are employed to predict the scores as either equal to or above 90 or below 90. …”
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Aberrant gene expression prediction across human tissues by Florian R. Hölzlwimmer, Jonas Lindner, Georgios Tsitsiridis, Nils Wagner, Francesco Paolo Casale, Vicente A. Yépez, Julien Gagneur

“…Abstract Despite the frequent implication of aberrant gene expression in diseases, algorithms predicting aberrantly expressed genes of an individual are lacking. …”
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Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces by Zhaojun Sun, Xin Li, Yiming Wu, Qilin Gu, Shiyou Zheng

“…This review highlights recent progress in ML‐assisted simulations and predictions at battery interfaces, illustrating how ML accelerates the research and development trajectory. …”
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Predicting the performance of ORB-SLAM3 on embedded platforms by Jacques Matthee, Kenneth Uren, George van Schoor, Corne van Daalen

“…Therefore, a need exists to  evaluate the performance of SLAM algorithms in practical embedded environments – this paper addresses this need by creating  prediction models to estimate the performance that ORB-SLAM3 can achieve on embedded platforms. …”
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Explainable Supervised Learning Models for Aviation Predictions in Australia by Aziida Nanyonga, Hassan Wasswa, Keith Joiner, Ugur Turhan, Graham Wild

“…Given the safety-critical nature of aviation, the lack of transparency in AI-generated predictions poses significant challenges for industry stakeholders. …”
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Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification by Shao-Hsuan Chang, Lung-Kun Yeh, Kuo-Hsuan Hung, Yen-Jung Chiu, Chia-Hsun Hsieh, Chung-Pei Ma

“…<b>Methods:</b> We analyzed the GSE77938 (PRJNA312169) dataset for differential gene expression (DGE) and performed gene set enrichment analysis (GSEA) using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify enriched pathways in keratoconus (KTCN) versus controls. Machine learning algorithms were then used to analyze the gene sets, with SHapley Additive exPlanations (SHAP) applied to assess the contribution of key feature genes in the model’s predictions. …”
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Predicting Employee Turnover Using Machine Learning Techniques by Adil Benabou, Fatima Touhami, My Abdelouahed Sabri

“…This study aims to identify the most effective machine learning model for predicting employee attrition, thereby providing organizations with a reliable tool to anticipate turnover and implement proactive retention strategies.Objective: This study aims to address the challenge of employee attrition by applying machine learning techniques to provide predictive insights that can improve retention strategies.Methods: Nine machine learning algorithms are applied to a dataset of 1,470 employee records. …”
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Energy prediction and optimization for robotic stereoscopic statue processing by Xu-Hui Cheng, Fang-Chen Yin, Cong-Wei Wen, Ye Wang, Yi-Hao Li, Ji-Xiang Huang, Shen-Gui Huang

“…Firstly, a prediction model for the robot’s body power is established by analyzing the energy consumption characteristics of the robot system. …”
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QSAR Models for Predicting the Antioxidant Potential of Chemical Substances by Sofia Ghironi, Edoardo Luca Viganò, Gianluca Selvestrel, Emilio Benfenati

“…To enable the rapid screening of large libraries of substances for antioxidant activity and to provide a useful tool for the initial evaluation of substances of interest with unknown activity, we developed Quantitative Structure–Activity Relationship (QSAR) models to predict the antioxidant potential of chemical substances. …”
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Machine Learning‐Enabled Drug‐Induced Toxicity Prediction by Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo

“…In this review, 10 categories of drug‐induced toxicity is examined, summarizing the characteristics and applicable ML models, including both predictive and interpretable algorithms, striking a balance between breadth and depth. …”
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Prediction of Global Ionospheric TEC Based on Deep Learning by Zhou Chen, Wenti Liao, Haimeng Li, Jinsong Wang, Xiaohua Deng, Sheng Hong

“…In this study, a prediction model of global IGS‐TEC maps are established based on testing several different long short‐term memory (LSTM) network (LSTM)‐based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. …”
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Unsupervised Action Anticipation Through Action Cluster Prediction by Jiuxu Chen, Nupur Thakur, Sachin Chhabra, Baoxin Li

“…Predicting near-future human actions in videos has become a focal point of research, driven by applications such as human-helping robotics, collaborative AI services, and surveillance video analysis. …”
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Adaptive Event-Triggered Predictive Control for Agile Motion of Underwater Vehicles by Bo Wang, Junchao Peng, Jing Zhou, Liming Zhao

“…A novel adaptive event-triggered nonlinear model predictive control (AET-NMPC) algorithm is proposed and compared with traditional Cascaded Proportional–Integral–Derivative (PID) control and event-triggered cascaded PID control algorithms. …”
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Interpreting Predictive Models through Causality: A Query-Driven Methodology by Mahdi Hadj Ali, Yann Le Biannic, Pierre-Henri Wuillemin

“…However, the complexity of predictive models has led to a lack of interpretability in automatic decision-making. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…However, machine learning models offer a time-efficient solution for predicting pavement performance. This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. …”
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A predictive model for damp risk in english housing with explainable AI by Gulala Aziz, Adam Hardy

“…This study develops a predictive model for damp risk, using 2,073 inspection records from a housing association across 125 local authorities. …”
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