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The prognostic impact of molecular classification in endometrial cancer that undergoes fertility-sparing treatment.
Int J Gynecol Cancer
January 2025
Department of Gynecology, European Institute of Oncology, IEO, IRCCS, Milan, Italy. Electronic address:
Objective: No biomarkers are available to predict treatment response in patients with endometrial cancers who undergo fertility-sparing treatment. Therefore, we aimed to evaluate the prognostic role of molecular classification.
Methods: Patients with endometrial cancer who underwent fertility-sparing treatment with progestins between 2005 and 2021 were retrospectively identified.
Development of a handheld GPU-assisted DSC-TransNet model for the real-time classification of plant leaf disease using deep learning approach.
Sci Rep
January 2025
Amal Jyothi College of Engineering (Autonomous), Kanjirappally, Kerala, India.
In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and précised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants.
View Article and Find Full Text PDFThe Status of Wild Grapevine ( L. subsp. (C.C. Gmel.) Hegi) Populations in Georgia (South Caucasus).
Plants (Basel)
January 2025
National Wine Agency of Georgia, Tbilisi 0159, Georgia.
Repeated expeditions across various regions of Georgia in the early 2000s led to the identification of 434 wild grapevine individuals ( L. subsp. (C.
View Article and Find Full Text PDFCombining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines.
Sensors (Basel)
January 2025
Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany.
Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features).
View Article and Find Full Text PDFSparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection.
Micromachines (Basel)
December 2024
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China.
Reconfigurable processor-based acceleration of deep convolutional neural network (DCNN) algorithms has emerged as a widely adopted technique, with particular attention on sparse neural network acceleration as an active research area. However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. Consequently, there remains significant potential for further exploration into improving the efficiency, latency, and power consumption of neural network accelerators across diverse computational scenarios.
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