Convolutional neural networks in medical image understanding: a survey
DR Sarvamangala, RV Kulkarni - Evolutionary intelligence, 2022 - Springer
Imaging techniques are used to capture anomalies of the human body. The captured images
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
A survey on deep learning for human activity recognition
Human activity recognition is a key to a lot of applications such as healthcare and smart
home. In this study, we provide a comprehensive survey on recent advances and challenges …
home. In this study, we provide a comprehensive survey on recent advances and challenges …
Linevul: A transformer-based line-level vulnerability prediction
M Fu, C Tantithamthavorn - … of the 19th International Conference on …, 2022 - dl.acm.org
Software vulnerabilities are prevalent in software systems, causing a variety of problems
including deadlock, information loss, or system failures. Thus, early predictions of software …
including deadlock, information loss, or system failures. Thus, early predictions of software …
Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences
Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
Pytorch: An imperative style, high-performance deep learning library
Deep learning frameworks have often focused on either usability or speed, but not both.
PyTorch is a machine learning library that shows that these two goals are in fact compatible …
PyTorch is a machine learning library that shows that these two goals are in fact compatible …
Literature review: Efficient deep neural networks techniques for medical image analysis
MA Abdou - Neural Computing and Applications, 2022 - Springer
Significant evolution in deep learning took place in 2010, when software developers started
using graphical processing units for general-purpose applications. From that date, the deep …
using graphical processing units for general-purpose applications. From that date, the deep …
Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions
The rapid growth of urban populations worldwide imposes new challenges on citizens' daily
lives, including environmental pollution, public security, road congestion, etc. New …
lives, including environmental pollution, public security, road congestion, etc. New …
Unmasking Clever Hans predictors and assessing what machines really learn
Current learning machines have successfully solved hard application problems, reaching
high accuracy and displaying seemingly intelligent behavior. Here we apply recent …
high accuracy and displaying seemingly intelligent behavior. Here we apply recent …
Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …
impact on every industry and research discipline. At the core of this revolution lies the tools …
VulRepair: a T5-based automated software vulnerability repair
As software vulnerabilities grow in volume and complexity, researchers proposed various
Artificial Intelligence (AI)-based approaches to help under-resourced security analysts to …
Artificial Intelligence (AI)-based approaches to help under-resourced security analysts to …