Characterizing Storm-Induced Coastal Flooding Using SAR Imagery and Deep Learning by Deanna Edwing, Lingsheng Meng, Suna Lv, Xiao-Hai Yan

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Static Polycode Text Modeling Using Network Analysis (Demotivator Dedicated to Problems of Self-Isolation) by M. N. Latu, A. А. Levit, M. B. Gavrilova

“…The semantic relations between the selected semantic components are revealed, the types of these links, revealing the different nature of their correlation are determined. On the basis of the data obtained, a network model of the considered static polycode text in the form of a semantic network was built. …”
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Intelligent health model for medical imaging to guide laymen using neural cellular automata by Sandeep Kumar Sharma, Chiranji Lal Chowdhary, Vijay Shankar Sharma, Adil Rasool, Arfat Ahmad Khan

“…The motivation behind the proposed invention is to help laymen to make medical images understandable. The health model is trained using a neural network approach that analyses user health examination data; predicts the type and level of the disease and advises precaution to the user. …”
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Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review. by Muhammad Ibtsaam Qadir, Jackson A Baril, Michele T Yip-Schneider, Duane Schonlau, Thi Thanh Thoa Tran, C Max Schmidt, Fiona R Kolbinger

“…Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy. …”
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In Vivo Assessment of Deep Vascular Patterns in Murine Colitis Using Optoacoustic Mesoscopic Imaging by Adrian Buehler, Emma L. Brown, Emmanuel Nedoschill, Markus Eckstein, Petra Ludwig, Felix Wachter, Henriette Mandelbaum, Roman Raming, Mariam‐Eleni Oraiopoulou, Lars‐Philip Paulus, Ulrich Rother, Oliver Friedrich, Markus F. Neurath, Joachim Woelfle, Maximilian J. Waldner, Ferdinand Knieling, Sarah E. Bohndiek, Adrian P. Regensburger

“…Using optoacoustic data from a murine model of chemically‐induced colitis, different image segmentation methods are compared for visualization and quantification of deep vascular patterns in terms of vascular network length and complexity, blood volume, and vessel diameter. …”
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Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning by Ping Zhao, Xiaojian Wang, Qing Zhao, Qingbing Xu, Yiru Sun, Xiaofeng Ning

“…Finally, the qualitative models were established to detect the external defects of potatoes using the machine learning algorithms of backpropagation neural network (BPNN), k-nearest neighbors (KNN), classification and regression tree (CART), and linear discriminant analysis (LDA). …”
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Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis by Barbara Szymanik, Maja Kocoń, Sam Ang Keo, Franck Brachelet, Didier Defer

“…These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. …”
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Evaluation on formation rate of Pleurotus eryngii primordium under different humidity conditions by computer vision by ZHOU Jun, DING Wenjie, ZHU Xuejun, CAO Junyi, NIU Xueming

“…Then, the large data analysis was carried out on the morphological database based on genetic idea, and 12 seeds were acquired. …”
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Spatio-temporal evolution of pulsating aurora observed using a ground-based imagers by Megha Pandya, Megha Pandya, Ankush Bhaskar, Marilia Samara, Robert Michell, Emma Mirizio, Emma Mirizio, Suk-Bin Kang, Suk-Bin Kang, Lauren W. Blum

“…We find that (i) the entire pulsating auroral patch was observed to drift northward at a velocity of approximately 76 m/s, which aligns closely with the local convection velocities obtained from Super Dual Auroral Radar Network (SuperDARN) data, consistent with the idea that the patch motion is primarily due to E×B convection. …”
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COMPARATIVE ANALYSIS OF STANDARD AND ENHANCED SWARM INTELLIGENT DEEP BELIEF NETWORK ON MAIZE DISEASE DETECTION by Kehinde Sotonwa, Adebisi Oluwatosin, Adesesan Adeyemo, Micheal Adenibuyan

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Modeling the Ecological Network in Mountainous Resource-Based Cities: Morphological Spatial Pattern Analysis Approach by Liyun Zeng, Rita Yi Man Li, Hongzhou Du

“…It employs multiple data sets, including land use data, remote sensing images, Shuttle Radar Topography Mission (SRTM) digital elevation, vegetation coverage data, etc., to conduct the quantitative analysis. …”
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Prediction of Key Quality Parameters in Hot Air-Dried Jujubes Based on Hyperspectral Imaging by Quancheng Liu, Chunzhan Yu, Yuxuan Ma, Hongwei Zhang, Lei Yan, Shuxiang Fan

“…In recent years, hyperspectral imaging (HSI) technology has been widely applied as a non-destructive detection method for fruit and vegetable quality assessment. …”
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Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders by Chang Seok Bang

“…Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection and optimization of the deep learning model enabled accurate image analysis. …”
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Shape-based disease grading via functional maps and graph convolutional networks with application to Alzheimer’s disease by Julius Mayer, Daniel Baum, Felix Ambellan, Christoph von Tycowicz, for the Alzheimer’s Disease Neuroimaging Initiative

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Research and Implementation of Image Super Resolution Reconstruction Technology in Python Deep Learning Framework by Qiu Bohan

“…Firstly, the article outlines the basic concept of image super-resolution and points out that in practical applications, the main challenges in current technological development include the demand for computing resources, data problems, model complexity, authenticity of image reconstruction, and robustness of models. …”
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Enhancing cardiac disease detection via a fusion of machine learning and medical imaging by Tao Yu, KeYue Chen

“…This work presents a complete hybrid methodology that integrates machine learning techniques with medical image analysis to improve the identification of cardiovascular diseases. …”
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Image-Based Breast Cancer Histopathology Classification and Diagnosis Using Deep Learning Approaches by Lama A. Aldakhil, Haifa F. Alhasson, Shuaa S. Alharbi, Rehan Ullah Khan, Ali Mustafa Qamar

“…In our study, we focus on using histopathology slide images and assess the current state of breast cancer classification, particularly with artificial intelligence, specifically deep learning and convolutional neural networks. …”
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Efficient Method of Road Outlier Recognition Using Deep Learning Coupled with Data Augmentation Approach by Sarfaraz Natha, Fareed Ahmed Jokhio, Muhammad Shafique, Naeem Ahmed, Danish Munir Arain

“…The proposed Real-time Road Outlier Recognition methodology is formulated as a classification task, involving the analysis and processing of real-time CCTV videos. The proposed study investigated different types of Convolutional Neural Network (CNN) pre-trained models with the Data Augmentation (DA) approach to address the frame variance problem in real-time videos. …”
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Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)... by 周贯旭(ZHOU Guanxu), 姜红(JIANG Hong), 徐雪芳(XU Xuefang)

“…The performance of the model using Precision, Recall, and F1-score metrics are evaluated. Through the analysis of the 1D-CNN, 2D-CNN, and feature fusion neural network classification models established on one-dimensional spectral data and hyperspectral images, it is show that the accuracy of the three models was 89.0%, 71.6%, and 93.3%, respectively. …”
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Deep Learning Segmentation Techniques for Atherosclerotic Plaque on Ultrasound Imaging: A Systematic Review by Laura De Rosa, Serena L’Abbate, Eduarda Mota da Silva, Mauro Andretta, Elisabetta Bianchini, Vincenzo Gemignani, Claudia Kusmic, Francesco Faita

“…Conclusions: DL-based approaches show considerable promise for advancing atherosclerotic plaque analysis in US imaging. To facilitate broader clinical adoption, future research should prioritize methodological standardization, external validation, data and code sharing, and integrating 3D US technologies.…”
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