A comprehensive review on machine learning in healthcare industry: classification, restrictions, opportunities and challenges
Recently, various sophisticated methods, including machine learning and artificial
intelligence, have been employed to examine health-related data. Medical professionals are …
intelligence, have been employed to examine health-related data. Medical professionals are …
Artificial intelligence in radiotherapy
S Siddique, JCL Chow - Reports of Practical Oncology and …, 2020 - journals.viamedica.pl
Artificial intelligence (AI) has already been implemented widely in the medical field in the
recent years. This paper first reviews the background of AI and radiotherapy. Then it …
recent years. This paper first reviews the background of AI and radiotherapy. Then it …
An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem
W Deng, J Xu, H Zhao - IEEE access, 2019 - ieeexplore.ieee.org
In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-
population strategy, co-evolution mechanism, pheromone updating strategy, and …
population strategy, co-evolution mechanism, pheromone updating strategy, and …
Deep facial diagnosis: deep transfer learning from face recognition to facial diagnosis
The relationship between face and disease has been discussed from thousands years ago,
which leads to the occurrence of facial diagnosis. The objective here is to explore the …
which leads to the occurrence of facial diagnosis. The objective here is to explore the …
Generalized incomplete multiview clustering with flexible locality structure diffusion
An important underlying assumption that guides the success of the existing multiview
learning algorithms is the full observation of the multiview data. However, such rigorous …
learning algorithms is the full observation of the multiview data. However, such rigorous …
Incomplete multiview spectral clustering with adaptive graph learning
In this paper, we propose a general framework for incomplete multiview clustering. The
proposed method is the first work that exploits the graph learning and spectral clustering …
proposed method is the first work that exploits the graph learning and spectral clustering …
Self-weighted robust LDA for multiclass classification with edge classes
Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative
features for multi-class classification. A vast majority of existing LDA algorithms are prone to …
features for multi-class classification. A vast majority of existing LDA algorithms are prone to …
Learning Robust Discriminant Subspace Based on Joint L₂,ₚ- and L₂,ₛ-Norm Distance Metrics
Recently, there are many works on discriminant analysis, which promote the robustness of
models against outliers by using L 1-or L 2, 1-norm as the distance metric. However, both of …
models against outliers by using L 1-or L 2, 1-norm as the distance metric. However, both of …
A sustainable deep learning framework for object recognition using multi-layers deep features fusion and selection
With an overwhelming increase in the demand of autonomous systems, especially in the
applications related to intelligent robotics and visual surveillance, come stringent accuracy …
applications related to intelligent robotics and visual surveillance, come stringent accuracy …
Battery aging mode identification across NMC compositions and designs using machine learning
A comprehensive understanding of lithium-ion battery (LiB) lifespan is the key to designing
durable batteries and optimizing use protocols. Although battery lifetime prediction methods …
durable batteries and optimizing use protocols. Although battery lifetime prediction methods …