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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An Efficient Aerial Image Detection with Variable Receptive Fields by Wenbin Liu, Liangren Shi, Guocheng An

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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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Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Excitation Attention Mechanism by Xiaoteng Ma, Kejia Zhai, Nana Luo, Yehui Zhao, Guangming Wang

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Improving SOC estimation in low-relief farmlands using time-series crop spectral variables and harmonic component variables based on minimum sample size by Chenjie Lin, Ling Zhang, Nan Zhong

“…The results showed that: (1) time-series NDVI was established as the characteristic crop spectral variables, based on crop spectral variables extracted from eight-day time-series reflectance products. (2) Seventeen harmonic component variables were derived from time-series NDVI via Fourier transformation. (3) Six crop spectral variables and seven harmonic component variables were determined as the optimal SOC estimators. (4) The convolutional neural network model provided higher SOC estimation accuracy (R2 = 0.81, NRMSE = 7.09%) than the random forest model and the back propagation neural network model. …”
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Variable pulse width dexterous jamming based on chaotic sampling fusion by WANG Guoxuan, YE Zijie

“…Aiming at the problem of strong distribution regularity of false targets in traditional repetitive repeater deception jamming, this paper proposes a variable pulse width smart jamming method based on intermittent chaotic sampling from the perspective of changing the amplitude and position distribution of false targets. …”
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An Ultrafast Image Simulation Technique with Spatially Variable Point-spread Functions by Zeyu Bai, Peng Jia, Jiameng Lv, Xiang Zhang, Wennan Xiang, Lin Nie

“…During real observations, images obtained by optical telescopes are affected by spatially variable point-spread functions (PSFs), a crucial effect requiring accurate simulation. …”
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Fitting the Distribution of Linear Combinations of t− Variables with more than 2 Degrees of Freedom by Onel L. Alcaraz López, Evelio M. Garcia Fernández, Matti Latva-aho

“…The linear combination of Student’s t random variables (RVs) appears in many statistical applications. …”
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Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence by Zhao Zhang, Wang Li, Yuyang Zhang

“…In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. …”
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Deep-Learning Techniques Applied for State-Variables Estimation of Two-Mass System by Grzegorz Kaczmarczyk, Radoslaw Stanislawski, Marcin Kaminski

“…The article is focused on the application of neural models for state-variables estimation. The estimators are applied in the control structure (with the state speed controller) of the electric drive with an elastic shaft. …”
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Identifying a Pattern of Predictable Decadal North Pacific SST Variability in Historical Observations by Emily M. Gordon, Noah S. Diffenbaugh

“…Abstract Improving predictions of decadal climate variability is critical for reducing uncertainty in near‐term climate change. …”
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Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates by Lin Meng, Deyu Wang, Jun Ma, Yu Shi, Hongbo Zhao, Yanlin Wang, Qingqing Shi, Xiaodong Zhu, Dong Ming

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Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…Using satellite altimetry data and the Lagrangian single-particle method, we estimate eddy mixing ellipses across the global surface ocean, revealing substantial spatio-temporal variability. Notably, large mixing ellipses predominantly occur in eddy-rich and energetic ocean regions. …”
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Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Appro... by Lanqian Yang, Jinmin Guo, Huili Tian, Min Liu, Chang Huang, Yang Cai

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Algae-Mamba: A Spatially Variable Mamba for Algae Extraction From Remote Sensing Images by Yaoteng Zhang, Shuaipeng Wang, Yanlong Chen, Shiqing Wei, Mingming Xu, Shanwei Liu

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Enhanced estimation of reference evapotranspiration using hybrid deep learning models and remote sensing variables by Tze Ying Fong, Yuk Feng Huang, Ren Jie Chin, Chai Hoon Koo

“…This study aims to develop ETo estimation models using deep learning algorithms with remote sensing variables as the input variables at Pulau Langkawi and Kuantan stations, located in Peninsular Malaysia. …”
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Improving Climate Bias and Variability via CNN‐Based State‐Dependent Model‐Error Corrections by William E. Chapman, Judith Berner

“…Our results show significant root mean squared error improvements across all state variables, with land precipitation biases reduced by 25%–35%, seasonally dependent. …”
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A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources by M. Wajahat Hassan, Thamer Alquthami, Ahmad H. Milyani, Ashfaq Ahmad, Muhammad Babar Rasheed

“…In this paper, a two-stage approach is proposed on a joint dispatch of thermal power generation and variable resources including a storage system. Although, the dispatch of alternate energy along with conventional resources has become increasingly important in the new utility environment. …”
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Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network by Yan Du, Aiming Wang, Shuai Wang, Baomei He, Guoying Meng

“…However, fault diagnosis under variable working conditions has been a significant challenge due to the domain discrepancy problem. …”
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Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

“…Considering the spatiotemporal features of temperature changes, this paper proposes a variable weight combination model based on a temporal graph convolutional network (T-GCN), Luong attention network (LUA) and gated recurrent unit (GRU) network, which fully utilizes spatiotemporal information to predict future temperature changes more accurately. …”
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