Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images by Yuanjun Guan, Yang Liu, Jiayi Wang, Tao Wang, Qianchuan Yi, Wenxin Jiang, Xiaopu Gu, Yichen Zhang, Li Zhang, Tianyan Han, Binbing Huang, Lilei Hu

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A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework by Mengji Yang, Haiqing Zhang, Xi Yu, Aicha Sekhari Seklouli, Abdelaziz Bouras, Yacine Ouzrout

“…This framework enhances the ability to capture long-term dependencies through the combined effects of efficient convolution parameter optimization and variable-oriented multivariate modeling. …”
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Enhanced Offline Writer Recognition System Employing Blended Multi-Input CNN and Bi-LSTM Model on Diverse Handwritten Texts by Naresh Purohit, Subhash Panwar

“…This synergy makes it particularly effective in handling the variability and complexity of offline writer recognition. …”
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SAR Small Ship Detection Based on Enhanced YOLO Network by Tianyue Guan, Sheng Chang, Chunle Wang, Xiaoxue Jia

“…Since the rise of deep learning, ship detection in synthetic aperture radar (SAR) images has achieved significant progress. However, the variability in ship size and resolution, especially the widespread presence of numerous small-sized ships, continues to pose challenges for effective ship detection in SAR images. …”
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Detecting Anomalies in Attributed Networks Through Sparse Canonical Correlation Analysis Combined With Random Masking and Padding by Wasim Khan, Mohammad Ishrat, Ahmad Neyaz Khan, Mohammad Arif, Anwar Ahamed Shaikh, Mousa Mohammed Khubrani, Shadab Alam, Mohammed Shuaib, Rajan John

“…SCCA incorporates sparsity by making the model choose fewer variables, which adds another level of complexity. …”
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Indirect Measurement of Tensile Strength of Materials by Grey Prediction Models GMC(1,n) and GM(1,n) by Tzu-Li Tien

“…However, for the first-order grey prediction model with n variables, specifically the traditional GM(1,n) model, modelling values are derived using a rough approximation method. …”
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AdaptiveSwin-CNN: Adaptive Swin-CNN Framework with Self-Attention Fusion for Robust Multi-Class Retinal Disease Diagnosis by Imran Qureshi

“…Utilizing the adaptive baseline augmentation and dataset-driven preprocessing of input images, the AdaptiveSwin-CNN model resolves the problem of the variability of fundus images in the dataset. AdaptiveSwin-CNN achieved a mean accuracy of 98.89%, sensitivity of 95.2%, specificity of 96.7%, and F1-score of 97.2% on RFMiD and ODIR benchmarks, outperforming other solutions. …”
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YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation by Chaojie Sun, Junguo Hu, Qingyue Wang, Chao Zhu, Lei Chen, Chunmei Shi

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Predicting renal function using fundus photography: role of confounders by Hyun-Woong Park, Hae Ri Kim, Ki Yup Nam, Bum Jun Kim, Taeseen Kang

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Real-Time Defect Detection for Fast-Moving Fabrics on Circular Knitting Machine Under Various Illumination Conditions by Yan-Qin Ni, Pei-Kai Huang, Ching-Han Yang, Chin-Chun Chang, Wei-Jen Wang, Deron Liang

“…First, to tackle the challenges of real-time detection, limited training data, and varying illumination conditions, we develop a lightweight semantic segmentation model, LBUnet, which leverages local binary (LB) convolution to effectively handle variable lighting conditions. …”
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Environmental Sensitivity in AI Tree Bark Detection: Identifying Key Factors for Improving Classification Accuracy by Charles Warner, Fanyou Wu, Rado Gazo, Bedrich Benes, Songlin Fei

“…We investigated three environmental variables—time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation—to identify sources of classification inaccuracies. …”
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Deep learning method for cucumber disease detection in complex environments for new agricultural productivity by Jun Liu, Xuewei Wang, Qian Chen, Peng Yan, Xin Liu

“…The model effectively handles symptom variability and complex detection scenarios, outperforming mainstream detection algorithms in accuracy, speed, and compactness, making it ideal for embedded agricultural applications.…”
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LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification by Yao Lu

“…Remote sensing landscape image classification is essential for environmental monitoring, land management, and ecological assessment, but presents critical challenges due to complex spatial distributions and high intra-class variability inherent in landscape scenes. Traditional deep convolutional neural networks, such as VGG16, though effective, are computationally intensive and unsuitable for deployment on resource-constrained platforms commonly used in landscape monitoring applications. …”
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Handwritten Words Image Character Extraction Adaptive Algorithm Based on the Multi-branch Structure by GUO Xiaojing, ZHAO Xiaoyuan, ZOU Songlin

“…First, the enhanced re-parameterized structure across multiple stages and branches achieves an effect equivalent to variable convolution. Second, the refined classifier with fully convolutional layers combines features from specific intermediate layers with the output layer, resulting in improved precision for complex and similar words. …”
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Design and modeling of a nanocomposite system for demineralization of sweet whey by Mina Rezapour, Mohsen Esmaiili, Mehdi Mahmoudian, Alireza Behrooz Sarand

“…The effects of various process variables, including, transmembrane pressure (TMP), Reynolds number, feed pH, and temperature, on the rejection of the minerals were surveyed. …”
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Deep Time Series Intelligent Framework for Power Data Asset Evaluation by Lihong Ge, Xin Li, Li Wang, Jian Wei, Bo Huang

“…It can simultaneously capture short-term local features and long-term global trends in power data, help to deeply mine spatial correlations and local patterns in data, effectively extract fine relationships between variables and optimize information flow. In the evaluation of the complex and rich Solar-Power dataset and Electricity dataset, TSENet achieved significant performance improvements over other state-of-the-art baseline methods.Through the synergistic design of deep convolutional structures and an efficient memory mechanism, it effectively addresses issues such as inadequate modeling of long-term dependencies, insufficient extraction of short-term features, and high prediction volatility, thereby significantly enhancing both the accuracy and robustness of forecasting in power asset evaluation tasks.…”
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A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting by Zain Ahmed, Mohsin Jamil, Ashraf Ali Khan

“…These optimal inputs are fed to D-TCNet (Deep – Temporal Convolution Network). This network uses multi-layer perceptrons (MLP) to encode the spatial relationship among exogenous variables which is fed to a Temporal Convolutional Network (TCN). …”
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Traffic Accident’s Severity Prediction: A Deep-Learning Approach-Based CNN Network by Ming Zheng, Tong Li, Rui Zhu, Jing Chen, Zifei Ma, Mingjing Tang, Zhongqiang Cui, Zhan Wang

“…Based on the weights of traffic accident’s features, the feature matrix to gray image (FM2GI) algorithm is proposed to convert a single feature relationship of traffic accident’s data into gray images containing combination relationships in parallel as the input variables for the model. Moreover, experiments demonstrated that the proposed model for traffic accident’s severity prediction has a better performance.…”
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Predictive and Explainable Artificial Intelligence for Weight Loss After Sleeve Gastrectomy: Insights from Wide and Deep Learning with Medical Image and Non-Image Data by Jaechan Park, Sungsoo Park, Kwang-Sig Lee, Yeongkeun Kwon

“…They were followed for one year after surgery. The dependent variable consisted of three categories: minimal, moderate, and significant change groups, classified based on postoperative percentage total weight loss (%TWL) in body mass index. …”
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Machine vision-based recognition of safety signs in work environments by Jesús-Ángel Román-Gallego, María-Luisa Pérez-Delgado, Miguel A. Conde, Marcos Luengo Viñuela

“…However, to improve classification capabilities, especially for highly degraded or complex images, a larger and more diverse data set might be needed, including real-world images that introduce greater entropy and variability. Implementing such a system would provide workers and companies with a proactive measure against workplace accidents, thereby enhancing overall safety in occupational environments.…”
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