Results 141 to 150 of about 130,387 (281)

Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence

open access: yesJournal of Imaging
Astrocytoma is the most common type of brain glioma and is classified by the World Health Organization into four grades, providing prognostic insights and guiding treatment decisions.
Christos Ch. Andrianos   +6 more
doaj   +1 more source

Multi‐Scale Mapping of Gene Expression from Whole‐slide Images for Identifying Phenotype‐Associated Subpopulations

open access: yesAdvanced Science, EarlyView.
BiSCALE: A pathology‐driven deep learning framework for multi‐scale gene expression prediction from whole‐slide images. It accurately infers bulk and near‐cellular spot‐level expression, links predictions to clinical phenotypes, identifies disease‐associated niches, and enables applications in risk stratification and cell‐identity annotation, providing
Hailong Zheng   +8 more
wiley   +1 more source

Boosting YOLO11: Global Attention & Hyperparameter Tuning for High-Fidelity Military Aircraft Detection

open access: yesJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Military aircraft detection from aerial and satellite imagery is crucial for strategic surveillance and intelligence. This study evaluated the impact of the Global Attention Mechanism (GAM) and hyperparameter optimization on the performance of the YOLO11
Satyo Widijanuarto, Ema Utami
doaj   +1 more source

Rapid Proteome‐Wide Discovery of Protein–Protein Interactions With ppIRIS

open access: yesAdvanced Science, EarlyView.
ppIRIS is a lightweight deep learning framework for proteome‐wide protein–protein interaction prediction directly from sequence. By fusing evolutionary and structural embeddings with a regularized Siamese architecture, ppIRIS achieves state‐of‐the‐art accuracy across species, enables minute‐scale screening, and reveals biologically validated bacterial ...
Luiz Felipe Piochi   +4 more
wiley   +1 more source

A Scalable Framework for Comprehensive Typing of Polymorphic Immune Genes from Long‐Read Data

open access: yesAdvanced Science, EarlyView.
SpecImmune introduces a unified computational framework optimized for long‐read sequencing to resolve over 400 highly polymorphic immune genes. This scalable approach achieves high‐resolution typing, enabling the discovery of cross‐family co‐evolutionary networks and population‐specific diversity.
Shuai Wang   +5 more
wiley   +1 more source

Hyperparameter Optimization in Machine Learning

open access: yesFoundations and Trends® in Machine Learning
Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems based on these technologies.
Franceschi, Luca   +7 more
openaire   +2 more sources

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

open access: yesAdvanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu   +4 more
wiley   +1 more source

Deep Brain Stimulation Induces Antidepressant Effects by Restoring High‐Fidelity Communication in the BNST‐NAc Circuit

open access: yesAdvanced Science, EarlyView.
This cross‐species study reveals that pathological hyperactivity of BNST neurons in depressive states disrupts inhibitory period and isolated spikes in the BNST‐NAc circuit. DBS achieves its antidepressant effects by precisely restoring network inhibitory periods and high‐fidelity signal transmission.
Xin Lv   +12 more
wiley   +1 more source

AOA-guided hyperparameter refinement for precise medical image segmentation

open access: yesAlexandria Engineering Journal
Medical image segmentation faces significant challenges, including the need for extensive annotated data, the impact of hyperparameters, and the limitations of traditional CNN models.
Hossam Magdy Balaha   +6 more
doaj   +1 more source

Neural Network‐Based Permittivity Engineering of Magnetic Absorbers for Customizable Microwave Absorption

open access: yesAdvanced Science, EarlyView.
A neural network‐enabled permittivity engineering paradigm is introduced, transcending traditional trial‐and‐error design. By decoupling electromagnetic parameters and screening a high‐throughput feature space, an ultrathin (1.0 mm) magnetic absorber is inversely designed, experimentally achieving a superior and customizable 5.1 GHz bandwidth and ...
Chenxi Liu   +9 more
wiley   +1 more source

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