Conceptual understanding of convolutional neural network-a deep learning approach
Deep learning has become an area of interest to the researchers in the past few years.
Convolutional Neural Network (CNN) is a deep learning approach that is widely used for …
Convolutional Neural Network (CNN) is a deep learning approach that is widely used for …
The relationship between air pollution and COVID-19-related deaths: an application to three French cities
Being heavily dependent to oil products (mainly gasoline and diesel), the French transport
sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful …
sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful …
[HTML][HTML] Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks
The relevance of the tasks of detecting and recognizing objects in images and their
sequences has only increased over the years. Over the past few decades, a huge number of …
sequences has only increased over the years. Over the past few decades, a huge number of …
A survey on deep learning based channel estimation in doubly dispersive environments
Wireless communications systems are impacted by multi-path fading and Doppler shift in
dynamic environments, where the channel becomes doubly-dispersive and its estimation …
dynamic environments, where the channel becomes doubly-dispersive and its estimation …
Image-based failure detection for material extrusion process using a convolutional neural network
The material extrusion (ME) process is one of the most widely used 3D printing processes,
especially considering its use of inexpensive materials. However, the error known as the …
especially considering its use of inexpensive materials. However, the error known as the …
Fault detection and diagnosis for Air Handling Unit based on multiscale convolutional neural networks
F Cheng, W Cai, X Zhang, H Liao, C Cui - Energy and Buildings, 2021 - Elsevier
This paper proposes a novel fault detection and diagnosis (FDD) method using multiscale
convolutional neural networks (MCNNs) for Air Handling Unit (AHU) in Heating, Ventilation …
convolutional neural networks (MCNNs) for Air Handling Unit (AHU) in Heating, Ventilation …
An explainable AI for green hydrogen production: A deep learning regression model
Currently, hydrogen generation is considered a crucial aspect of sustainable energy
production. This paper disscusses the hypothesis that hydrogen generation occurs during …
production. This paper disscusses the hypothesis that hydrogen generation occurs during …
A new hybrid convolutional neural network and eXtreme gradient boosting classifier for recognizing handwritten Ethiopian characters
Handwritten character recognition has been profoundly studied for many years in the field of
pattern recognition. Due to its vast practical applications and financial implications, the …
pattern recognition. Due to its vast practical applications and financial implications, the …
A deep learning approach for subject-dependent & subject-independent emotion recognition using brain signals with dimensional emotion model
This paper aims to design a deep-learning based approach in combination with machine
learning classifiers for two different perspectives. In first perspective, the performance is …
learning classifiers for two different perspectives. In first perspective, the performance is …
Sensitivity examination of YOLOv4 regarding test image distortion and training dataset attribute for apple flower bud classification
Applications of convolutional neural network (CNN)-based object detectors in agriculture
have been a popular research topic in recent years. However, complicated agricultural …
have been a popular research topic in recent years. However, complicated agricultural …