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A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope
This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
Advances in machine learning and IoT for water quality monitoring: A comprehensive review
Water holds great significance as a vital resource in our everyday lives, highlighting the
important to continuously monitor its quality to ensure its usability. The advent of the. The …
important to continuously monitor its quality to ensure its usability. The advent of the. The …
[HTML][HTML] Robust machine learning algorithms for predicting coastal water quality index
Coastal water quality assessment is an essential task to keep “good water quality” status for
living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool …
living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool …
Cloud-based intrusion detection approach using machine learning techniques
H Attou, A Guezzaz, S Benkirane… - Big Data Mining and …, 2023 - ieeexplore.ieee.org
Cloud computing (CC) is a novel technology that has made it easier to access network and
computer resources on demand such as storage and data management services. In …
computer resources on demand such as storage and data management services. In …
[HTML][HTML] Marine waters assessment using improved water quality model incorporating machine learning approaches
In marine ecosystems, both living and non-living organisms depend on “good” water quality.
It depends on a number of factors, and one of the most important is the quality of the water …
It depends on a number of factors, and one of the most important is the quality of the water …
An ensemble learning based intrusion detection model for industrial IoT security
Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in
industrial sectors. It is designed to implicate embedded technologies in manufacturing fields …
industrial sectors. It is designed to implicate embedded technologies in manufacturing fields …
[PDF][PDF] A Lightweight Hybrid Intrusion Detection Framework using Machine Learning for Edge-Based IIoT Security.
Due to the development of cloud computing and Internet of Things (IoT) environments, such
as healthcare systems, telecommunications and Industry 4.0 or Industrial IoT (IIoT) many …
as healthcare systems, telecommunications and Industry 4.0 or Industrial IoT (IIoT) many …
Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review
JC Agbasi, JC Egbueri - Environmental Science and Pollution Research, 2024 - Springer
Water resources are constantly threatened by pollution of potentially toxic elements (PTEs).
In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) …
In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) …
[HTML][HTML] Water quality prediction based on machine learning and comprehensive weighting methods
X Wang, Y Li, Q Qiao, A Tavares, Y Liang - Entropy, 2023 - mdpi.com
In the context of escalating global environmental concerns, the importance of preserving
water resources and upholding ecological equilibrium has become increasingly apparent …
water resources and upholding ecological equilibrium has become increasingly apparent …
New generation neurocomputing learning coupled with a hybrid neuro-fuzzy model for quantifying water quality index variable: A case study from Saudi Arabia
Ensuring availability in terms of quality and quantity and sustainable management of safe,
affordable drinking water is one of the integral parts of envisioning the 2030 Sustainable …
affordable drinking water is one of the integral parts of envisioning the 2030 Sustainable …