Exploring the advancements and future research directions of artificial neural networks: a text mining approach
Artificial Neural Networks (ANNs) are machine learning algorithms inspired by the structure
and function of the human brain. Their popularity has increased in recent years due to their …
and function of the human brain. Their popularity has increased in recent years due to their …
A systematic literature review on AutoML for multi-target learning tasks
AM Del Valle, RG Mantovani, R Cerri - Artificial Intelligence Review, 2023 - Springer
Automated machine learning (AutoML) aims to automate machine learning (ML) tasks,
eliminating human intervention from the learning process as much as possible. However …
eliminating human intervention from the learning process as much as possible. However …
Price forecasting for real estate using machine learning: A case study on Riyadh city
Real estate is potentially contributing to the economic growth. It has a strong correlation
between property owners and beneficiaries. The accurate forecast of future property prices …
between property owners and beneficiaries. The accurate forecast of future property prices …
Joint design and compression of convolutional neural networks as a bi-level optimization problem
Over the last decade, deep neural networks have shown great success in the fields of
machine learning and computer vision. Currently, the CNN (convolutional neural network) is …
machine learning and computer vision. Currently, the CNN (convolutional neural network) is …
Mixed integer linear programming models to solve a real-life vehicle routing problem with pickup and delivery
This paper presents multiple readings to solve a vehicle routing problem with pickup and
delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real …
delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real …
Advancing Sustainable COVID-19 Diagnosis: Integrating Artificial Intelligence with Bioinformatics in Chest X-ray Analysis
Responding to the critical health crisis triggered by respiratory illnesses, notably COVID-19,
this study introduces an innovative and resource-conscious methodology for analyzing chest …
this study introduces an innovative and resource-conscious methodology for analyzing chest …
Evolutionary optimization for CNN compression using thoracic X-ray image classification
Computer Vision, as an area of Artificial Intelligence, has recently achieved success in
tackling numerous difficult challenges in health care and has the potential to contribute to …
tackling numerous difficult challenges in health care and has the potential to contribute to …
Topology optimization search of deep convolution neural networks for CT and X-ray image classification
Covid-19 is a disease that can lead to pneumonia, respiratory syndrome, septic shock,
multiple organ failure, and death. This pandemic is viewed as a critical component of the …
multiple organ failure, and death. This pandemic is viewed as a critical component of the …
Traffic disturbance mining and feedforward neural network to enhance the immune network control performance
Traffic disturbance in urban cities challenges the most advanced traffic signal control
systems (TSCS). The challenge is mainly related to the capability of TSCS to ensure a quick …
systems (TSCS). The challenge is mainly related to the capability of TSCS to ensure a quick …
Design and compression study for convolutional neural networks based on evolutionary optimization for thoracic X-Ray image classification
Computer Vision has lately shown progress in addressing a variety of complex health care
difficulties and has the potential to aid in the battle against certain lung illnesses, including …
difficulties and has the potential to aid in the battle against certain lung illnesses, including …