The Manila Times

WiMi Studies Quantum Hybrid Neural Network Model to Empower Intelligent Image Classification

The core idea of LCQHNN is to center on quantum feature amplification (Quantum Feature Amplification) while combining a classical stability optimization strategy, establishing an efficient information ...
IEEE

Dual-pronged deep learning preprocessing on heterogeneous platforms with CPU, Accelerator and CSD

Abstract: For image-related deep learning tasks, the first step often involves reading data from external storage and performing preprocessing on the CPU. As accelerator speed increases and the number ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
Scientific Research Publishing

Intelligent Agents in Cybersecurity: Deep Learning to Analyze User Behavior Applying ()

The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior using deep learning methods and ensuring interpretability of decisions. A four ...
www.cs.utexas.edu

Hyperspherical Normalization for Scalable Deep Reinforcement Learning

@InProceedings{pstone_simba, author = {Hojoon Lee and Youngdo Lee and Takuma Seno and Donghu Kim and Peter Stone and Jaegul Choo}, title = {Hyperspherical Normalization for Scalable Deep Reinforcement ...
C&EN

Leveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In recent AI-driven disease diagnosis, the success of models has depended mainly on ...
C&EN

Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications

You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters ...
Frontiers

Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations

The rapid evolution of railway systems, driven by digitization and the proliferation of Internet-of-Things (IoT) devices, has resulted in an unprecedented volume of diverse and complex data. This ...
Frontiers

Deep learning super-resolution for temperature data downscaling: a comprehensive study using residual networks

Extreme weather events such as heatwaves, cyclones, floods, wildfires, and droughts are becoming more frequent due to climate change. Climate change causes shifts in biodiversity and impacts ...
IEEE

A Brief Survey of Data Preprocessing in Machine Learning and Deep Learning Techniques

Abstract: Internet of Things (IoT) generates vast amounts of sensor data across various scientific and engineering domains. This raw data is often flawed and unsuitable for analysis due to noise, ...