Sum of Poisson-Distributed Random Variables: A Convolution Method Approach by A. A. Ayenigba, O. M. Ajao, F. A. Okolie

“…The proposed model is constructed as the sum of two independent Poisson random variables, using a convolution method. Some properties of the distribution, including the probability mass function (PMF), moment-generating function (MGF), mean, variance, higher-order moments, Skewness, and kurtosis, are derived. …”
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Hierarchical Classification of Variable Stars Using Deep Convolutional Neural Networks by Mahdi Abdollahi, Nooshin Torabi, Sadegh Raeisi, Sohrab Rahvar

Subjects: “…variable stars…”
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Classification of maize seed hyperspectral images based on variable-depth convolutional kernels by Yating Hu, Hongchen Zhang, Hongchen Zhang, Changming Li, Qianfu Su, Wei Wang

Subjects: “…variable-depth convolutional kernels…”
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A Spatiotemporal Data Cube Approach to Classification of Variable Stars: A Catalog of Candidate Variable Stars from the TESS Full-frame Image Raw Data by Harry Qiang, Marina Kounkel, Sally Bass, Ryan Lingg, Logan Sizemore, Dylan Carroll, Brian Hutchinson, Keivan G. Stassun

Subjects: “…Variable stars…”
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Bearing fault diagnosis for variable operating conditions based on KAN convolution and dual branch fusion attention by Qibing Wang, Chuanjie Yin, Kun She, Qinfeng Tong, Guoxiong Lu, Hongbing Zhang, Jiawei Lu

“…Firstly, a new structure of KANs is applied to Convolutional Neural Networks (CNN) for replacing traditional linear convolutional kernels. …”
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Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect by Maria Rugenstein, Senne VanLoon, Elizabeth A. Barnes

“…Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. …”
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Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions by Yunfeng Ni, Shuang Li, Ping Guo

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Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks by Yue Zhang, Yang Yu, Zheng Yang, Qiang Liu

Subjects: “…Convolutional neural network…”
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More Efficient and Reliable: Identifying RRab Stars with Blazhko Effect by Deep Convolutional Neural Network by Nan Jiang, Tianrui Sun, Siyuan Pan, Lingzhi Wang, Xue Li, Bin Sheng, Xiaofeng Wang

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A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability by Xitailang Cao, Shan Lin, Miao Dong, Quanke Hu, Hong Zheng

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DTCformer: A Temporal Convolution-Enhanced Autoformer with DILATE Loss for Photovoltaic Power Forecasting by Quanhui Qiu, Dejun Ning, Qiang Guo, Jiang Wei, Huichang Chen, Lihui Sui, Yi Liu, Zibing Du, Shipeng Liu

“…The proposed model integrates a Temporal Convolution Feedforward Network module and a Variable Selection Embedding module, effectively capturing inter-variable dependencies and temporal periodicity. …”
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Fault Diagnosis of Rotating Machines Based on Combination of One-Dimensional Convolutional Neural Network and Long Short-Term Memory in Variable Working Conditions by Fasikaw Kibrete, Dereje Engida Woldemichael, Hailu Shimels Gebremedhen

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Reducing the Stochastic Coal Output Model Using the Convolution of Probability Density Functions by Ryszard Snopkowski, Marta Sukiennik, Aneta Napieraj

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Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism by Xiaolan Wang

Subjects: “…Variable resolution…”
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Machine Learning-Enhanced Model-Based Optical Proximity Correction Framework With Convolutional Neural Network-Based Variable Threshold Method Near the Diffraction Limit by Jinhao Zhu, Liwan Yue, Ying Li, Xianhe Liu, Qiang Wu, Qi Wang, Yanli Li

“…Compared to the conventional polynomial-based variable threshold method, the CNN-based variable threshold strategy significantly improves threshold prediction accuracy and critical dimension (CD) simulation consistency by integrating geometric features of local patterns and slice’s optical intensity distribution parameters. …”
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Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD by Gabriel Cubillos, Javier Perez-Valenzuela, Herman Aguirre, Luz Martínez, Lorena Castro, Gabriel Mezzano, Claudio A. Perez

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Improved CNN-GRU algorithm application in enterprise legal consultation system by Xuejie Zhao

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The Time–Fractional Wave Equation with Variable Coefficients by Chenkuan Li

“…In this paper, we primarily use the inverse operator method to find a unique series solution to a time–fractional wave equation with variable coefficients based on the Mittag–Leffler function. …”
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CNN-Based Optimization for Fish Species Classification: Tackling Environmental Variability, Class Imbalance, and Real-Time Constraints by Amirhosein Mohammadisabet, Raza Hasan, Vishal Dattana, Salman Mahmood, Saqib Hussain

“…However, challenges such as environmental variability, class imbalance, and computational demands hinder the development of robust classification models. …”
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Comparative Analysis of CNN-Based Semantic Segmentation for Apple Tree Canopy Size Recognition in Automated Variable-Rate Spraying by Tantan Jin, Su Min Kang, Na Rin Kim, Hye Ryeong Kim, Xiongzhe Han

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