Results 61 to 70 of about 60,104 (158)

Fed-DPSDG-WGAN: Differentially Private Synthetic Data Generation for Loan Default Prediction via Federated Wasserstein GAN

IEEE Access
Lenders typically conduct thorough credit checks to mitigate credit default risk before lending money. Proactively predicting loan defaulters has become increasingly important.
Padmaja Ramachandra, Santhi Vaithiyanathan   +1 more
doaj   +1 more source

Comparative analysis of machine learning algorithms for personalising educational content in distance learning

Ìнформаційні технології та компʼютерна інженерія
The aim of this research was to conduct a comprehensive evaluation of the effectiveness of machine learning algorithms for the task of personalised educational content recommendation in distance education systems.
V. Yanishevskyi
doaj   +1 more source

Developing Synthetic Data Tools for Trusted Research Environments to Enable Researcher Training

International Journal of Population Data Science
We aimed to develop an open-source Python package for the Trusted Research Environment (TRE) community to facilitate the generation of varying levels of synthetic data.
Lewis Hotchkiss, Kafayat Adeoye, Emma Squires, Simon Thompson   +3 more
doaj   +1 more source

Enriched Pedestrian Crossing Prediction Using Carla Synthetic Data

IET Intelligent Transport Systems
Pedestrian crossing prediction, which involves anticipating whether a pedestrian will cross the street or not, is a crucial function in autonomous driving systems.
Mohsen Azarmi, Mahdi Rezaei, He Wang
doaj   +1 more source

Digital twin framework for personalized behavioral health optimization with Q-learning and graph-based planning

Frontiers in Artificial Intelligence
Promoting healthy lifestyle behaviors, including physical activity, sleep, diet, stress management, and healthy habits, requires adaptive systems capable of responding to dynamic changes in human behavior.
Ayan Chatterjee, Nurilla Avazov
doaj   +1 more source

Procedural Generation of Synthetic Forest Environments to Train Machine Learning Algorithms

, 2021
O campo de Machine Learning está a evoluir a um ritmo frenético e novas soluções e conceitos estão a ser desenvolvidos todos os dias. Estas descobertas estão principalmente a ser impulsionadas pelo Deep Learning. Os modelos de Deep Learning são inspirados no cérebro humano e apresentam uma estrutura em camadas de neurónios.
openaire   +1 more source

A digital twin-based comparative reinforcement learning framework for personalized behavioral recommendation

Frontiers in Artificial Intelligence
Promoting healthy lifestyle behaviors such as physical activity, sleep, diet quality, stress management, hydration, and healthy habits requires adaptive systems capable of responding dynamically to changing behavioral and environmental conditions ...
Ayan Chatterjee, Nurilla Avazov
doaj   +1 more source

Linking repeated subjective judgments and ConvNets for multimodal assessment of the immediate living environment

MethodsX
The integration of alternative data extraction approaches for multimodal data, can significantly reduce modeling difficulties for the automatic location assessment.
Miroslav Despotovic, David Koch, Simon Thaler, Eric Stumpe, Wolfgang Brunauer, Matthias Zeppelzauer   +5 more
doaj   +1 more source

Sim-Grasp: Learning 6-DOF Grasp Policies for Cluttered Environments Using a Synthetic Benchmark

IEEE Robotics and Automation Letters
In this paper, we present Sim-Grasp, a robust 6-DOF two-finger grasping system that integrates advanced language models for enhanced object manipulation in cluttered environments. We introduce the Sim-Grasp-Dataset, which includes 1,550 objects across 500 scenarios with 7.9 million annotated labels, and develop Sim-GraspNet to generate grasp poses from
Juncheng Li 0010, David J. Cappelleri
openaire   +2 more sources

Learning, locomotion, and navigation of soft synthetic snakes in three-dimensional, heterogeneous environments

Limbless terrestrial animals exhibit exceptional locomotor versatility and control, currently unmatched by engineered counterparts. Here, we introduce a computational framework that enables soft synthetic snakes to navigate unstructured, heterogeneous 3D terrains.
Zhang, Xiaotian, Albazroun, Ali, Wang, Tixian, Cui, Songyuan, Mehta, Prashant G., Gazzola, Mattia   +5 more
openaire   +2 more sources