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Optimizing VGG16 deep learning model with enhanced hunger games search for logo classification.
Sci Rep
December 2024
Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine.
Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.
View Article and Find Full Text PDFGADIFF: a transferable graph attention diffusion model for generating molecular conformations.
Brief Bioinform
November 2024
School of Information Science and Technology, Northeast Normal University, 130117 Changchun, China.
The diffusion generative model has achieved remarkable performance across various research fields. In this study, we propose a transferable graph attention diffusion model, GADIFF, for a molecular conformation generation task. With adopting multiple equivariant networks in the Markov chain, GADIFF adds GIN (Graph Isomorphism Network) to acquire local information of subgraphs with different edge types (atomic bonds, bond angle interactions, torsion angle interactions, long-range interactions) and applies MSA (Multi-head Self-attention) as noise attention mechanism to capture global molecular information, which improves the representative of features.
View Article and Find Full Text PDFMandarin-speaking children with different types of cochlear implant exhibit variations in the activation patterns of their central auditory processing.
Front Neurosci
December 2024
Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China.
Background: Cochlear implants (CIs) have the potential to facilitate auditory restoration in deaf children and contribute to the maturation of the auditory cortex. The type of CI may impact hearing rehabilitation in children with CI. We aimed to study central auditory processing activation patterns during speech perception in Mandarin-speaking pediatric CI recipients with different device characteristics.
View Article and Find Full Text PDFPredicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features.
Radiat Oncol
December 2024
Department of Cognitive Neuropsychology, Tilburg University, Tilburg, The Netherlands.
Background And Purpose: Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learning (DL) features to clinical features increased Local Control (LC) prediction accuracy, their combined potential to predict LC remains unexplored. We examined whether a model using a combination of radiomics, DL and clinical features achieves better accuracy than models using only a subset of these features.
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