Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces by Zhaojun Sun, Xin Li, Yiming Wu, Qilin Gu, Shiyou Zheng

“…By employing ML algorithms and machine vision, simulations of lithium dendrite growth, SEI formation, and interfacial dynamics can be performed. …”
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Prediction and evaluation of residual life of casing with corrosion defects by JIA Aoyin, QIAN Liqin, WANG Jie, YANG Jingwei, WU Yaokun, WEI Mingji

“…The particle swarm optimization-gaussian process regression （PSO-GPR) algorithm was employed to construct a predictive model for casing corrosion rates.ResultsIntegration of the two models, the evaluation of the residual life of casings with corrosion defects was completed, it has theoretical guidance significance for the prevention and management of corroded casings in oil field operations.…”
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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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Dynamic Interference Control in OFDM-Based Cognitive Radio Network Using Genetic Algorithm by Hamza Khan, Sang-Jo Yoo

“…In this paper, we propose a dynamic interference control method using the additive signal side lobe reduction technique and genetic algorithm (GA) in CR-OFDM systems. …”
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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

“…Different machine learning algorithms were applied to build regression models, and the goodness-of-fit of each model was assessed using the statistical parameters of R squared (R²), the Root-Mean-Squared Error, and the Mean Absolute Error. …”
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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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Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model by Changzhen XIONG, Hui ZHI

“…In order to improve the accuracy of weakly-supervised semantic segmentation method,a segmentation and optimization algorithm that combines multi-scale feature was proposed.The new algorithm firstly constructs a multi-scale feature model based on transfer learning algorithm.In addition,a new classifier was introduced for category prediction to reduce the failure of segmentation due to the prediction of target class information errors.Then the designed multi-scale model was fused with the original transfer learning model by different weights to enhance the generalization performance of the model.Finally,the predictions class credibility was added to adjust the credibility of the corresponding class of pixels in the segmentation map,avoiding false positive segmentation regions.The proposed algorithm was tested on the challenging VOC 2012 dataset,the mean intersection-over-union is 58.8% on validation dataset and 57.5% on test dataset.It outperforms the original transfer-learning algorithm by 12.9% and 12.3%.And it performs favorably against other segmentation methods using weakly-supervised information based on category labels as well.…”
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Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study by Tanzila Tamanna, Shohel Mahmud, Nahid Salma, Md. Musharraf Hossain, Md. Rezaul Karim

“…This study highlights the effectiveness of Random Forest (RF) in predicting malnutrition outcomes—stunting, wasting, and underweight—using key features identified by the Boruta algorithm. …”
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Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction by Lise M. Bjerre, Cayden Peixoto, Rawan Alkurd, Robert Talarico, Rami Abielmona

“…Objectives: This retrospective cohort study aimed to compare the predictive performance of AI/ML algorithms against conventional multivariate logistic regression models using linked health administrative data. …”
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Data-driven predictive models for sustainable smart buildings by Prabhu Rajaram, Gnana Swathika O․V․

“…It highlights the critical role of energy efficiency and the importance of lowering carbon footprints through the implementation of advanced algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest, XGBOOST, AdaBoost, and Naive Bayes classifiers. …”
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MobNas ensembled model for breast cancer prediction by Tariq Shahzad, Sheikh Muhammad Saqib, Tehseen Mazhar, Muhammad Iqbal, Ahmad Almogren, Yazeed Yasin Ghadi, Mamoon M. Saeed, Habib Hamam

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An Accurate Phase Interface Locating Algorithm for Pore-Scale Two-Phase Interfacial Flows by Qiliang Mei, Tianjiang Wu, Chao Li, Xiaoying Zhou

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Cellular-free RAN slicing resource allocation algorithm based on multi-timescale collaboration by XIA Weiwei, WANG Boye, XIA Yaxing, MIAO Weiwei, WANG Dayang, JING Dongsheng, YAN Feng, SHEN Lianfeng

“…Simulation results demonstrate that the proposed algorithm can accurately predict slice resource, improve the system transmission rates while reducing the average user latency and service blocking probability.…”
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Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm. by Nanqi Li, ZuEn Shang, Yang Zhao, Hui Wang, Qiyuan Min

“…Subsequently, the snake algorithm with multi-strategy fusion is introduced. …”
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Online stability assessment for isolated microgrid via LASSO based neural network algorithm by Ahmed Lasheen, Hatem F. Sindi, Hatem H. Zeineldin, Mohammed Y. Morgan

“…Online prediction of the dominant modes is very important for microgrid operation. …”
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