Viral particle prediction in wastewater treatment plants using nonlinear lifelong learning models by Jianxu Chen, Ibrahima N’Doye, Yevhen Myshkevych, Fahad Aljehani, Mohammad Khalil Monjed, Taous-Meriem Laleg-Kirati, Pei-Ying Hong

“…Tests on total viral prediction across four municipal WWTPs in Saudi Arabia showed the lifelong learning model’s value for adaptive viral particle prediction and performance enhancement.…”
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Optimizing battery health monitoring in electric vehicles using interpretable CART–GX model by Mohnish Karthikeyan B, Anirudh N, Navaneetha Krishnan S, Christopher Columbus C, Aravind C. K

“…The proposed model combines Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), attention mechanisms, residual connections, and transformers to extract spatial and temporal features, prioritize critical information, and model long-range dependencies. …”
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Explainable ML modeling of saltwater intrusion control with underground barriers in coastal sloping aquifers by Asaad M. Armanuos, Martina Zeleňáková, Mohamed Kamel Elshaarawy

“…The dataset was divided into 70% for training and 30% for testing. Model performance was assessed using both visual and quantitative metrics. …”
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Modelling and Simulation of Perturb and Observe MPPT Algorithm Based on The PI Controller for Photovoltaic System by Ahlam Mohamad Shakoor, Hersh Hasan Taha

“…All elements of the PV system were simulated using MATLAB/Simulink. The proposed model is used to simulate and optimize the photovoltaic array output current, voltage and power features under various environmental conditions including solar temperature and radiation. …”
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An ensemble time-embedded transformer model for traffic conflict prediction at RRFB pedestrian crossings by Md Jamil Ahsan, Mohamed Abdel-Aty, B M Tazbiul Hassan Anik, Zubayer Islam

“…The thresholds of ≤1.5 seconds as conflict for MTTC and ≥4 m/s² as conflict for DRAC produced the best results, achieving recall 82% and 83%, and False Alarm Rate (FAR) 27% and 38%, respectively. For improved performance, these models were then combined using a voting classifier technique. …”
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EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model by Weikang He, Xizhe Li, Yujin Wan, Honming Zhan, Nan Wan, Sijie He, Yaoqiang Lin, Longyi Wang, Wenxuan Yu, Liqing Chen

“…The performance of four machine learning models was compared, and the optimal model was selected. …”
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A flat-joint contact model and meso analysis on mechanical characteristics of brittle rock by Yu Zhou, Nengbin Chen, Li Wang, Tianhua Wu

“…Based upon particle flow theory and particle flow code (PFC), the flat-joint contact model is used to construct the granite meso-analysis model, which can reflect the mineral structure features. …”
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Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models by Kashvi Ankitbhai Sheth, Rujuta Prajakt Kulkarni, G. K. Revathi

“…The research concludes that the ensemble model’s superior performance makes it a valuable tool for the multi-class classification of natural disaster images.…”
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Predicting sensory evaluation of spinach freshness using machine learning model and digital images. by Kento Koyama, Marin Tanaka, Byeong-Hyo Cho, Yusaku Yoshikawa, Shige Koseki

“…The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. …”
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Cell‐free epigenomes enhanced fragmentomics‐based model for early detection of lung cancer by Yadong Wang, Qiang Guo, Zhicheng Huang, Liyang Song, Fei Zhao, Tiantian Gu, Zhe Feng, Haibo Wang, Bowen Li, Daoyun Wang, Bin Zhou, Chao Guo, Yuan Xu, Yang Song, Zhibo Zheng, Zhongxing Bing, Haochen Li, Xiaoqing Yu, Ka Luk Fung, Heqing Xu, Jianhong Shi, Meng Chen, Shuai Hong, Haoxuan Jin, Shiyuan Tong, Sibo Zhu, Chen Zhu, Jinlei Song, Jing Liu, Shanqing Li, Hefei Li, Xueguang Sun, Naixin Liang

“…Conclusions With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics‐based machine learning model demonstrated outstanding performance in the independent validation cohort. …”
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MF‐UFNO: A Fourier Neural Operators‐Based Model for Radar Echo Extrapolation by Tingting Ye, Weihua Ai, Dachao Jin, Zhonghui Tan, Li Wang, Fenghua Ling, Xi Liu, Nan Chen, Senshen Hu

“…The model integrates Fast Fourier Transform to extract frequency‐domain features, enhancing the representation of the spatiotemporal evolution of radar echoes. …”
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Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

“…The least absolute shrinkage and selection operator (LASSO) regression cross-validation method was utilized to identify key features for model construction. Using these features, four machine learning (ML) algorithms were developed. …”
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Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions by Darren Green, Mark Holland, Pinkie Chambers, Matthew Watson, Noura Al Moubayed, Stelios Boulitsakis Logothetis

“…These large data stores lend themselves to use in modern machine learning (ML) models. This paper investigates the use of transformer-based models to identify critical deterioration in unplanned ED admissions, using free-text fields, such as triage notes, and tabular data, including early warning scores (EWS).Design A retrospective ML study.Setting A large ED in a UK university teaching hospital.Methods We extracted rich feature sets of routine clinical data from the EHR and systematically measured the performance of tree- and transformer-based models for predicting patient mortality or admission to critical care within 24 hours of presentation to ED. …”
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Generation driven understanding of localized 3D scenes with 3D diffusion model by Hao Sun, Junping Qin, Zheng Liu, Xinglong Jia, Kai Yan, Lei Wang, Zhiqiang Liu, Shaofei Gong

“…When combined with the spatial convolutional properties of the 3D U-Net architecture, the modelling accuracy and generation quality of complex 3D shapes are further enhanced, ensuring excellent performance in complex environments. …”
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Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters by Zhaojia Tang, Yu Han

“…We analyzed the causes of common secondary disaster events, established the correlation between common everyday items and the types of secondary disasters, and constructed six secondary disaster factor datasets, namely, fire, flammable objects, explosive objects, toxic substances, trapped personnel, and dangerous buildings. We proposed a multi-model cluster decision method to extract the secondary disaster factors’ visual features, and we created a ResNet-CDMV image classification algorithm with higher accuracy recognition performance than the traditional single model. …”
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A Multi-granularity Heterogeneous Ensemble Model for Point and Interval Forecasting of Carbon Prices by Di Sha, Xianyi Zeng, Kim-Phuc Tran, Lin Xia, Ruolin Wang

“…Second, the distinct features identified through feature selection methods are input into six point forecasting models. …”
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A Novel Wood Surface Defect Detection Model Based on Improved YOLOv8 by Wenmiao Dou, Jun You

“…The model integrates a multi-head mixed self-attention mechanism into the backbone to improve the representation of fine-grained defect features. …”
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Swin-Diff: a single defocus image deblurring network based on diffusion model by Hanyan Liang, Shuyao Chai, Xixuan Zhao, Jiangming Kan

“…Our approach employs a two-stage training strategy where a diffusion model generates prior information in a compact latent space, which is then hierarchically fused with intermediate features to guide the regression model. …”
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General retrieval network model for multi-class plant leaf diseases based on hashing by Zhanpeng Yang, Jun Wu, Xianju Yuan, Yaxiong Chen, Yanxin Guo

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SAR Image Target Segmentation Guided by the Scattering Mechanism-Based Visual Foundation Model by Chaochen Zhang, Jie Chen, Zhongling Huang, Hongcheng Zeng, Zhixiang Huang, Yingsong Li, Hui Xu, Xiangkai Pu, Long Sun

“…Directly transferring a pretrained SAM from optical scenes to SAR image instance segmentation tasks can lead to a substantial decline in performance. Therefore, this paper fully integrates the SAR scattering mechanism, and proposes a SAR image target segmentation method guided by the SAR scattering mechanism-based visual foundation model. …”
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