Humans and artificial neural networks exhibit some similar patterns during learning

Past psychology and behavioral science studies have identified various ways in which people's acquisition of new knowledge ...
IEEE

Enhancing Lithium-Ion Battery Reliability Through Machine Learning: A CNN-Based Approach for RUL Estimation

Abstract: With their high energy efficiency, extended lifespan, and minimal self-discharge, lithium-ion batteries have become the backbone of modern energy storage systems, particularly in renewable ...
IEEE

A Novel Transformer-CNN Hybrid Deep Learning Architecture for Robust Broad-Coverage Diagnosis of Eye Diseases on Color Fundus Images

Abstract: We present a novel and robust deep-learning architecture that takes into account the pathological characteristics of eye diseases on color fundus images. The proposed hybrid architecture is ...
IEEE

The Application of Intelligent Recommendation Systems in Personalized Learning Path Planning: Combining CNN and Genetic Algorithms

Abstract: Aiming at the problem that traditional personalized learning path planning methods rely on expert experience and are difficult to accurately capture the deep relationship between learners' ...
Frontiers

Advances and Challenges in AI-Driven Visual Intelligence: Bridging Theory and Practice

AI-Driven Visual Intelligence forms a critical cornerstone of both computer vision and artificial intelligence, serving ...
NDTV

IISc Bangalore Invites Applications For Free Deep Learning, AI Course, Details Here

IISc Bangalore Deep Learning Course 2025: The Indian Institute of Science (IISc), Bengaluru, in collaboration with SWAYAM, is ...

Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem

Inspired by how the human brain consolidates memory, the 'Nested Learning' framework allows different parts of a model to ...
IEEE

Accurate and Efficient Solid Waste Recognition: A Novel Approach Using Google Teachable Machine Based on Convolutional Neural Network (CNN)

Abstract: This paper explores the use of Convolutional Neural Networks (CNNs) for solid waste recognition, using Google Teachable Machine. A CNN model was trained on 13,745 images from Kaggle datasets ...
IEEE

SPOT: An Active Learning Algorithm for Efficient Deep Neural Network Training

Abstract: Recent advancements in deep neural networks heavily rely on large-scale labeled datasets. However, acquiring annotations for large datasets can be challenging due to annotation constraints.
eLife

CNN-based learning of single-cell transcriptomes reveals a blood-detectable multi-cancer signature of brain metastasis

This important study describes a deep learning framework that analyzes single-cell RNA data to identify a tumor-agnostic gene signature associated with brain metastases. The identified signature ...

Yixian Jiang Integrates Bayesian Networks and Metadata Standards to Advance Intelligent Digital Object Systems

A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN ...
EurekAlert!

Smart tea agriculture: Machine learning opens new pathways for quality and sustainability

A research team has mapped how machine learning is transforming the global tea industry, revealing that data-driven technologies now enhance tea cultivation, harvesting, processing, and quality ...