AI-based digital histopathology for perihilar cholangiocarcinoma: A step, not a jump by Dieter P. Hoyer, Saskia Ting, Nina Rogacka, Sven Koitka, René Hosch, Nils Flaschel, Johannes Haubold, Eugen Malamutmann, Björn-Ole Stüben, Jürgen Treckmann, Felix Nensa, Giulia Baldini

“…Convolutional neural networks (CNN) analyzed whole-slide images. Survival models incorporated clinical and histological features. …”
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AI-driven dynamic orthodontic treatment management: personalized progress tracking and adjustments—a narrative review by Xuanchi Guo, Yuhan Shao

“…This review will focus on the fusion of AI-driven multimodal data analysis (e.g., cone-beam CT, intraoral scanning, and 3D facial images) and deep learning algorithms (e.g., convolutional neural networks) to elucidate the technological breakthroughs in key aspects such as tooth movement trajectory prediction and early detection of root resorption. …”
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Geographical analyses and artificial intelligence for the study of late medieval settlements in Southern Tuscany (Italy) by Luca Deravignone

“…Through the use of different data such as satellite images, historical cartography and archaeological evidences, it is possible to individuate, with the help of ANN methodologies, some of the links between human settlements and the territory. …”
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Domain Adaptation and Fine-Tuning of a Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using High-Resolution Sentinel-2 Observations: A Case Study of Pun... by Anamika Anand, Ryoichi Imasu, Surendra K. Dhaka, Prabir K. Patra

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Constructing an artificial intelligence-assisted system for the assessment of gastroesophageal valve function based on the hill classification (with video) by Jian Chen, Ganhong Wang, Kaijian Xia, Zhenni Wang, Luojie Liu, Xiaodan Xu

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ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano by Felipe Grijalva, Washington Ramos, Noel Perez, Diego Benitez, Roman Lara, Mario Ruiz

“…However, the direct application of such algorithms is defiant, given the high complexity of waveforms and the scarce and often imbalanced amount of labeled data. In light of this and motivated by the wide success that generative adversarial networks (GANs) have seen at generating images, we present ESeismic-GAN, a GAN model to generate the magnitude frequency response of volcanic events. …”
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Мethods of Machine Learning in Ophthalmology: Review by D. D. Garri, S. V. Saakyan, I. P. Khoroshilova-Maslova, A. Yu. Tsygankov, O. I. Nikitin, G. Yu. Tarasov

“…By using of machine learning methods, it’s possible to find out, identify and count almost any pathological signs of diseases by analyzing medical images, clinical and laboratory data. Machine learning includes models and algorithms that mimic the architecture of biological neural networks. …”
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Geometric Deep Learning for Computer-Aided Design: A Survey by Negar Heidari, Alexandros Iosifidis

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Numerical Simulation and Holdup Prediction of Oil-Water Two-Phase in Low Yield Shale Oil Near Horizontal Wells by QIN Honglong, LIU Junfeng, SHI Shoubo, DAI Yuexiang

“…The built-in volume of fluid (VOF) model is employed to simulate oil-water two-phase flow in wells with different inclinations, yielding flow patterns, water holdup data, and holdup imaging under varying well deviations, flow rates, and water-cut conditions. …”
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The Emerging Role of Artificial Intelligence in Dermatology: A Systematic Review of Its Clinical Applications by Ernesto Martínez-Vargas, Jeaustin Mora-Jiménez, Sebastian Arguedas-Chacón, Josephine Hernández-López, Esteban Zavaleta-Monestel

“…Through machine learning and deep learning techniques, AI enables accurate analysis of clinical and dermoscopic images, improving early detection and clinical outcomes. …”
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Long-term algal blooms mapping and crucial driving factors analysis based on Landsat series imagery in Lake Victoria (2001–2021) by D. Zhong, D. Zhong, Y. Lin, Y. Lin, J. Yu, J. Yu, C. Gao, C. Gao, L. He, L. He, Y. Song, Y. Song, Y. Yang, Y. Yang, X. Chen, X. Chen

“…In this study, we used more than 20 years of Landsat series images to extract and map algal bloom occurrences based on a neural network model in the Transform framework and to revel the long-term changes law and trends of cyanobacterial. …”
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IR thermography & NN models for damaged component thickness detection by Chunming Ai, Haichuan Lin, Pingping Sun

“…Abstract To achieve rapid detection of damage thickness in metal components using infrared thermography, a combination of heat transfer theory and image theory was employed. This involved theoretical analysis, finite element numerical simulation, a BP neural network prediction model, and infrared thermography experiments. …”
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Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods by Panagiota-Kyriaki Revelou, Efstathia Tsakali, Anthimia Batrinou, Irini F. Strati

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Explainability of Subfield Level Crop Yield Prediction Using Remote Sensing by Hiba Najjar, Miro Miranda, Marlon Nuske, Ribana Roscher, Andreas Dengel

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SBAS-InSAR Analysis of tectonic derived ground deformation and subsidence susceptibility mapping via machine learning in Quetta City, Pakistan by Sajid Hussain, Bin Pan, Zeeshan Afzal, Meer Muhammad Sajjad, Najeebullah Kakar, Nisar Ahmed, Wajid Hussain, Muhammad Ali

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Short-term prediction of regional energy consumption by metaheuristic optimized deep learning models by Ngoc-Quang Nguyen, Phuong-Thao-Nguyen Nguyen, Quynh-Chau Truong

“…First, numerical data were encoded into grayscale images for input of the proposed model, showcasing the novelty of using image data for analysis. …”
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Predictors of fairness assessment for social media screening in employee selection by Alicja Balcerak, Jacek Woźniak, Alexandra Zbuchea

“…It expands the knowledge about the applicability of social networking site content analysis of Polish users, especially of innovative candidates. …”
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Research on Pork Cut and Freshness Determination Method Based on Computer Vision by Shihao Song, Qiqi Guo, Xiaosa Duan, Xiaojing Shi, Zhenyu Liu

“…First, high-resolution cameras were used to capture image data of Jinfen white pigs, covering three pork cuts—hind leg, loin, and belly—across three different collection times. …”
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Pan-cancer profiling of FZD2 as a prognostic biomarker: integrative multi-omics analysis with experimental validation and functional characterization in gastric cancer by Sijiang Zhou, Sijiang Zhou, Sijiang Zhou, Sijiang Zhou, Da Li, Da Li, Da Li, Da Li, Chao Quan, Chao Quan, Zhu Yu, Zhu Yu, Zhu Yu, Yue Feng, Yue Feng, Yue Feng, Yue Feng, Shengyu Wang, Shengyu Wang, Shengyu Wang, Shengyu Wang, Yong Li, Yong Li, Yong Li, Yong Li, Tongtong Qi, Tongtong Qi, Tongtong Qi, Tongtong Qi, Junqiang Chen, Junqiang Chen, Junqiang Chen, Junqiang Chen

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Machine learning-based prediction model for post-stroke cerebral-cardiac syndrome: a risk stratification study by Tingyu Zhang, Zelin Hao, Qunlian Jiang, Linhui Zhu, Lifang Ye

“…Data on demographics, laboratory results, imaging findings, and medications were collected. …”
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