Deep learning for monocular depth estimation: A review
Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …
applications such as augmented reality, target tracking and autonomous driving. Traditional …
Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service
S Aminizadeh, A Heidari, M Dehghan, S Toumaj… - Artificial Intelligence in …, 2024 - Elsevier
The healthcare sector, characterized by vast datasets and many diseases, is pivotal in
shaping community health and overall quality of life. Traditional healthcare methods, often …
shaping community health and overall quality of life. Traditional healthcare methods, often …
An enhanced MSIQDE algorithm with novel multiple strategies for global optimization problems
Quantum-inspired differential evolution (QDE) is an evolutionary algorithm, which can
effectively solve complex optimization problems. However, sometimes, it easily leads to …
effectively solve complex optimization problems. However, sometimes, it easily leads to …
The role of artificial intelligence and machine learning in wireless networks security: Principle, practice and challenges
Security is one of the biggest challenges concerning networks and communications. The
problem becomes aggravated with the proliferation of wireless devices. Artificial Intelligence …
problem becomes aggravated with the proliferation of wireless devices. Artificial Intelligence …
Brain tumor classification using fine-tuned GoogLeNet features and machine learning algorithms: IoMT enabled CAD system
In the healthcare research community, Internet of Medical Things (IoMT) is transforming the
healthcare system into the world of the future internet. In IoMT enabled Computer aided …
healthcare system into the world of the future internet. In IoMT enabled Computer aided …
A hybrid deep CNN model for abnormal arrhythmia detection based on cardiac ECG signal
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients
suffering from various cardiovascular diseases (CVDs). This research aims to develop a …
suffering from various cardiovascular diseases (CVDs). This research aims to develop a …
Sparsely connected CNN for efficient automatic modulation recognition
This paper proposes a convolutional neural network (CNN), called SCGNet, for low-
complexity and robust modulation recognition in intelligent communication receivers …
complexity and robust modulation recognition in intelligent communication receivers …
Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model
Software defect prediction aims to find a reliable method for predicting defects in a particular
software project and assisting software engineers in allocating limited resources to release …
software project and assisting software engineers in allocating limited resources to release …
Application of convolutional neural network-based detection methods in fresh fruit production: a comprehensive review
C Wang, S Liu, Y Wang, J Xiong, Z Zhang… - Frontiers in plant …, 2022 - frontiersin.org
As one of the representative algorithms of deep learning, a convolutional neural network
(CNN) with the advantage of local perception and parameter sharing has been rapidly …
(CNN) with the advantage of local perception and parameter sharing has been rapidly …
Distribution network reconfiguration for short-term voltage stability enhancement: An efficient deep learning approach
The rapid growth of renewables and dynamic loads has highlighted the short-term voltage
stability (STVS) issue in distribution network operation. With the advances in metering …
stability (STVS) issue in distribution network operation. With the advances in metering …