Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …
elderly people. AD identification in early stages is a difficult task in medical practice and …
Image inpainting based on deep learning: A review
X Zhang, D Zhai, T Li, Y Zhou, Y Lin - Information Fusion, 2023 - Elsevier
Image inpainting is an important research direction in the study of computer vision, and is
widely used in image editing and photo inpainting etc. Traditional image inpainting …
widely used in image editing and photo inpainting etc. Traditional image inpainting …
A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus
In recent years, the prevalence of chronic diseases such as type 2 diabetes mellitus (T2DM)
has increased, bringing a heavy burden to healthcare systems. While regular monitoring of …
has increased, bringing a heavy burden to healthcare systems. While regular monitoring of …
Deep sequence modelling for Alzheimer's disease detection using MRI
Background Alzheimer's disease (AD) is one of the deadliest diseases in developed
countries. Treatments following early AD detection can significantly delay institutionalisation …
countries. Treatments following early AD detection can significantly delay institutionalisation …
M3S: Scene graph driven multi-granularity multi-task learning for multi-modal NER
J Wang, Y Yang, K Liu, Z Zhu… - IEEE/ACM Transactions on …, 2022 - ieeexplore.ieee.org
Multi-modal Named Entity Recognition (MNER), which mainly focuses on enhancing text-
only NER with visual information, has recently attracted considerable attention. Most current …
only NER with visual information, has recently attracted considerable attention. Most current …
Multi-omics integration method based on attention deep learning network for biomedical data classification
P Gong, L Cheng, Z Zhang, A Meng, E Li… - Computer Methods and …, 2023 - Elsevier
Background and objective Integrating multi-omics data for the comprehensive analysis of the
biological processes in human diseases has become one of the most challenging tasks of …
biological processes in human diseases has become one of the most challenging tasks of …
High-order multi-view clustering for generic data
Graph-based multi-view clustering has achieved better performance than most non-graph
approaches. However, in many real-world scenarios, the graph structure of data is not given …
approaches. However, in many real-world scenarios, the graph structure of data is not given …
K-centroid link: a novel hierarchical clustering linkage method
A Dogan, D Birant - Applied Intelligence, 2022 - Springer
In hierarchical clustering, the most important factor is the selection of the linkage method
which is the decision of how the distances between clusters will be calculated. It extremely …
which is the decision of how the distances between clusters will be calculated. It extremely …
Unified low-rank tensor learning and spectral embedding for multi-view subspace clustering
Multi-view subspace clustering aims to utilize the comprehensive information of multi-source
features to aggregate data into multiple subspaces. Recently, low-rank tensor learning has …
features to aggregate data into multiple subspaces. Recently, low-rank tensor learning has …
Mapreduce accelerated attribute reduction based on neighborhood entropy with apache spark
C Luo, Q Cao, T Li, H Chen, S Wang - Expert Systems with Applications, 2023 - Elsevier
Attribute reduction is nowadays an extremely important data preprocessing technique in the
field of data mining, which has gained much attention due to its ability to provide better …
field of data mining, which has gained much attention due to its ability to provide better …