A Principal Component Analysis-Based Feature Optimization Network for Few-Shot Fine-Grained Image Classification by Meijia Wang, Boyuan Zheng, Guochao Wang, Junpo Yang, Jin Lu, Weichuan Zhang

“…First, FRN exhibits high sensitivity to noise, particularly ambient noise, which can lead to substantial reconstruction errors and hinder the network’s ability to extract meaningful features. Second, FRN is particularly vulnerable to changes in data distribution. …”
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Analysis of Development Patterns of Built Land and Spatial Structure in Bandung City Using Landsat 8 Image Data by Mukhoriyah Mukhoriyah, Samsul Arifin, Esthi Kurnia Dewi, Silvia Silvia

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Convolutional neural networks for specific and merged data sets of optical array probe images: compatibility of retrieved morphology-dependent size distributions by L. Jaffeux, J. Breiner, P. Coutris, A. Schwarzenböck

“…<p>This study addresses the challenges of ice particle morphology classification from images of optical array probes (OAPs) and proposes a more refined processing to enable better interpretation of observational data. …”
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Deep learning for COVID-19 by X-ray images Analysis and Designing Diagnostic Application by Thamer Khaleel, Ali Kalakech

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An Enhanced Approach Using AGS Network for Skin Cancer Classification by Hwanyoung Lee, Seeun Cho, Jiyoon Song, Hoyoung Kim, Youjin Shin

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Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification by V. Hnatushenko, V. Hnatushenko, V. Hnatushenko, D. Soldatenko

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Body image flexibility and embodiment in eating disorders: a mixed-methods approach combining network analysis and pilot exposure protocol by Paolo Meneguzzo, Chiara Cazzola, Francesca Buscaglia, Anna Pillan, Filippo Pettenuzzo, Patrizia Todisco

“…Results Findings suggest that significant differences in embodiment-related features emerged (self-regulation and body trust), while both groups reported similar levels of interoceptive awareness. Network analysis revealed distinct patterns of partial correlations between variables within each group, with higher centrality for body image concerns and weight phobia in EDs. …”
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Application value of different imaging methods in the early diagnosis of small hepatocellular carcinoma: a network meta-analysis by Jian Dong, Zhen Wang, Si-Rui Wang, Huan Zhao, Jun Li, Ting Ma

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A graph convolutional network approach for hyperspectral image analysis of blueberries physiological traits under drought stress by Md. Hasibur Rahman, Savannah Busby, Sajid Hanif, Md Mesbahul Maruf, Faraz Ahmad, Sushan Ru, Alvaro Sanz-Saez, Jingyi Zheng, Tanzeel U. Rehman

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Image-based machine learning and cluster analysis for urban road network: employing Orange for codeless visual programming by Zuo Zhang, Mengwei Zhang, Xiangxiang Song, Zhi Li

“…In this study, we constructed an analytical framework that leveraged road network image recognition, multi-scale influence factors analysis, and predictions using codeless visual programming. …”
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Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data by Cesilia Mambile, Shubi Kaijage, Judith Leo

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Comparative Analysis of Deep Learning Methods for Classification of Ablated Regions in Hyperspectral Images of Atrial Tissue by Yeva Gabrielyan, Arpi Hunanyan, Sona Bezirganyan, Lusine Davtyan, Ani Avetisyan, Narine Sarvazyan, Aram Butavyan, Varduhi Yeghiazaryan

“…For both porcine and bovine data, models incorporating graph neural networks demonstrate superior performance. …”
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Sentiment Analysis from Face Expressions Based on Image Processing Using Deep Learning Methods by Orhan Emre Aksoy, Selda Güney

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Effective Algorithm for Biomedical Image Segmentation by Zhao Di, Tang Yi, A. B. Gourinovitch

“…Biomedical image segmentation plays an important role in quantitative analysis, clinical diagnosis, and  medical manipulation. …”
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Survey of Quantum Generative Adversarial Networks (QGAN) to Generate Images by Mohammadsaleh Pajuhanfard, Rasoul Kiani, Victor S. Sheng

“…Quantum Generative Adversarial Networks (QGANs) represent a useful development in quantum machine learning, using the particular properties of quantum mechanics to solve the challenges of data analysis and modeling. …”
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CDBA-GAN: A Conditional Dual-Branch Attention Generative Adversarial Network for Robust Sonar Image Generation by Wanzeng Kong, Han Yang, Mingyang Jia, Zhe Chen

“…Consequently, sonar image simulation technology has become increasingly vital in the field of sonar data analysis. …”
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Application of a generative adversarial network algorithm to filling blank strips of fractures in formation microresistivity imaging images by Zhengming KANG, Chensheng WU, Guodong YANG, Disheng WU, Ruifei WANG, Xiangyu YANG, Wei GAN

“…Using a dataset constructed utilizing both simulated and actual data, this study proposed a generative adversarial network (GAN)-based method for filling the blank strips of fractures in FMI images. …”
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Active Contours Connected Component Analysis Segmentation Method of Cancerous Lesions in Unsupervised Breast Histology Images by Vincent Majanga, Ernest Mnkandla, Zenghui Wang, Donatien Koulla Moulla

“…Therefore, we present a hybrid active contour (connected component + active contours) method to segment cancerous lesions in unsupervised human breast histology images. Initially, this approach prepares and pre-processes data through various augmentation methods to increase the dataset size. …”
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Enhancing Ground Penetrating Radar (GPR) Data Analysis Utilizing Machine Learning by Mohanad Shehab, Musab T.S. Al-Kaltakchi, Ammar Dukhan, Wai Lok Woo

“…Finally, various machine learning techniques are employed to classify the collected images using models like Decision Trees,agged trees, Naive Bayes, Artificial Neural Networks, Quadratic Discriminant Analysis, Support Vector Machines, and K-nearest neighbors. …”
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Algorithm of Encrypting Digital Image Using Chaos Neural Network by Ying Mao

“…With the rapid development of digital communication technology, data and image encryption methods will get more and more in-depth research in these aspects. …”
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