Soft Computing (SC) is an Artificial Intelligence (AI) approach that is more effective at solving real-life problems than traditional computing models. Soft Computing models are tolerant of partial ...
To evaluate the diagnostic performance of semi-supervised learning models for aggressive prostate cancer detection on MRI compared to fully supervised models trained with additional expert annotations ...
Recently, federated learning has been successfully applied in fields related to cyber-physical-social systems (CPSSs), owing to its ability to harness decentralized clients for training a global model ...
Graph Neural Networks (GNNs) have emerged as a dominant framework for semi-supervised learning on graph-structured data, achieving remarkable performance in tasks such as node classification through ...
TraPO is a semi-supervised reinforcement learning framework that bridges unlabeled and labeled samples for training large reasoning models (LRMs). Built upon GRPO, TraPO leverages a small set of ...
Abstract: As a compromise between supervised and unsupervised learning, semi-supervised learning (SSL) harnesses both labeled and unlabeled data to enhance learning performance. Graph-based ...
ABSTRACT: Government procurement contracts can be complicated, with extensive risk analysis and compliance reviews. The traditional methods of contract analytics are time-consuming and often inexact, ...
Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...
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