As such, although a deep learning model may extract information that is already there, the raw information may not be sufficient. Hence, a model that is capable of extracting information from dynamic ...

Abstract: A novel method for accurate speed estimation of a vehicle using a deep learning convolutional neural network (CNN), with accelerometer and gyroscope measurements as input, is presented. It ...

This research paper introduces an innovative study that centers on the creation and assessment of a novel deep learning (DL) model, which combines a Convolutional Neural Network (CNN) and Support ...

Drowning is the top cause of death in young kids. As pools and beaches reopen for the summer, emergency physician Dr. Leana ...

Using clinical images, this study investigates different CNN algorithms for the bracket of six current skin conditions on the face rosacea, actinic keratosis, seborrheic keratosis, lupus erythematosus ...

A mixed-methods study design was used which included analysis of four deep learning models for predicting student performance ... convolutional neural networks and long short-term memory (CNN-LSTM) ...

Abstract: Traffic Classification (TC) is experiencing a renewed interest, fostered by the growing popularity of Deep Learning (DL) approaches. In exchange for their proved effectiveness, DL models are ...

This research introduces an innovative hybrid deep learning architecture that seamlessly integrates Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) layers. While maintaining ...

We also propose a Multi-stream Deep Fusion Network (MDFN) for combining high-level semantics with CNN features. Our experimental results demonstrate that the proposed approach significantly improves ...

This study explores the efficacy of DANN in boosting CNN performance across varied environments ... and potential for advancing deep learning-based image classification tasks.

Abstract: Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. However, the quality of data labels is a concern because of the lack of ...

Deep CNNs are powerful machine learning algorithms that can learn complex features ... Though many state-of-the-art deep CNN architectures have been suggested for automatically detecting malignant ...