Data drift in medical machine learning: implications and potential remedies
Data drift refers to differences between the data used in training a machine learning (ML)
model and that applied to the model in real-world operation. Medical ML systems can be …
model and that applied to the model in real-world operation. Medical ML systems can be …
[HTML][HTML] Internet of Intelligent Things: A convergence of embedded systems, edge computing and machine learning
This article comprehensively reviews the emerging concept of Internet of Intelligent Things
(IoIT), adopting an integrated perspective centred on the areas of embedded systems, edge …
(IoIT), adopting an integrated perspective centred on the areas of embedded systems, edge …
[HTML][HTML] DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks
Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role
in optimizing energy scheduling and enabling more flexible and intelligent power grid …
in optimizing energy scheduling and enabling more flexible and intelligent power grid …
Digital transformation of cancer care in the era of big data, artificial intelligence and data-driven interventions: navigating the field
Objectives To navigate the field of digital cancer care and define and discuss key aspects
and applications of big data analytics, artificial intelligence (AI), and data-driven …
and applications of big data analytics, artificial intelligence (AI), and data-driven …
Zero-touch networks: Towards next-generation network automation
The Zero-touch network and Service Management (ZSM) framework represents an
emerging paradigm in the management of the fifth-generation (5G) and Beyond (5G+) …
emerging paradigm in the management of the fifth-generation (5G) and Beyond (5G+) …
Concept drift handling: A domain adaptation perspective
M Karimian, H Beigy - Expert Systems with Applications, 2023 - Elsevier
Data stream prediction is challenging when concepts drift, processing time, and memory
constraints come into account. Concept drift refers to changes in data distribution over time …
constraints come into account. Concept drift refers to changes in data distribution over time …
Adaptive tree-like neural network: Overcoming catastrophic forgetting to classify streaming data with concept drifts
With the development of deep neural networks (DNNs), classifying streaming data with
concept drifts based on DNNs is becoming more and more effective. However, the …
concept drifts based on DNNs is becoming more and more effective. However, the …
Concept drift adaptation methods under the deep learning framework: A literature review
Q Xiang, L Zi, X Cong, Y Wang - Applied Sciences, 2023 - mdpi.com
With the advent of the fourth industrial revolution, data-driven decision making has also
become an integral part of decision making. At the same time, deep learning is one of the …
become an integral part of decision making. At the same time, deep learning is one of the …
Dc-check: A data-centric ai checklist to guide the development of reliable machine learning systems
While there have been a number of remarkable breakthroughs in machine learning (ML),
much of the focus has been placed on model development. However, to truly realize the …
much of the focus has been placed on model development. However, to truly realize the …
The role of optical transport networks in 6G and beyond: A vision and call to action
As next-generation networks begin to take shape, the necessity of Optical Transport
Networks (OTNs) in helping achieve the performance requirements of future networks is …
Networks (OTNs) in helping achieve the performance requirements of future networks is …