A deep residual computation model for heterogeneous data learning in smart Internet of Things

Yu, Hang; Yang, Laurence T.; Fan, Xiangchao; Zhang, Qingchen

doi:10.1016/j.asoc.2021.107361

Cited by 22 publications

(10 citation statements)

References 23 publications

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“…In contrast, it is not necessary for classical deep learning models like fully connected neural networks (FCNNs) and convolution neural networks (CNNs) to reduce the dimensionality of omics features to very low dimensions, due the models can automatically learn useful information from high-dimensional space (11,(48)(49)(50)(51).…”

Section: Conventional Machine Learning Technologiesmentioning

confidence: 99%

A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer

Wang

2023

Front. Med.

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Gastrointestinal cancer is becoming increasingly common, which leads to over 3 million deaths every year. No typical symptoms appear in the early stage of gastrointestinal cancer, posing a significant challenge in the diagnosis and treatment of patients with gastrointestinal cancer. Many patients are in the middle and late stages of gastrointestinal cancer when they feel uncomfortable, unfortunately, most of them will die of gastrointestinal cancer. Recently, various artificial intelligence techniques like machine learning based on multi-omics have been presented for cancer diagnosis and treatment in the era of precision medicine. This paper provides a survey on multi-omics-based cancer diagnosis using machine learning with potential application in gastrointestinal cancer. Particularly, we make a comprehensive summary and analysis from the perspective of multi-omics datasets, task types, and multi-omics-based integration methods. Furthermore, this paper points out the remaining challenges of multi-omics-based cancer diagnosis using machine learning and discusses future topics.

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Section: Conventional Machine Learning Technologiesmentioning

confidence: 99%

A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer

Wang

2023

Front. Med.

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“…Electronic product codes, networks, cloud computing, and other technologies are becoming more and more mature. We need to make full and reasonable use of mature electronic devices to better connect human society with the physical world and improve the level of information intelligence in the whole society [24]. Various useful information in the park, including temperature, humidity, and luminance, can be obtained by sensor technology.…”

Section: Iot Technology and Its Exploitablementioning

confidence: 99%

Application of Internet of Things and Data Optimization in the Design of Smart Medical Park

Liao

Zhong

2022

Mobile Information Systems

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The ecological development of industrial parks has become a concern in current urban planning. The traditional development model and development ideas of pharmaceutical industrial parks need to be changed urgently, not only to improve their own functions but also to use land more effectively and save resource costs. Aiming at the problems existing in the construction of the park, this paper studies the use of the Internet of Things technology to build a smart park platform to realize the access of intelligent systems and equipment in the park. First of all, starting from the background of the combination of smart parks and the Internet of Things, by analyzing the architecture of the Internet of Things, it studies the requirements for the combination of smart parks and the Internet of Things technology. Second, according to the results of the demand analysis, we conducted a research on the construction plan of the smart park in the Internet of Things era, including the construction of the smart park platform using the Internet of Things technology, the ideas and access methods of the intelligent system access platforms in the smart park, and the research on the construction of the intelligent park platform architecture based on the Internet of Things and decision tree technology. Finally, the specific implementation process of its access to the smart campus platform is studied through simulation tests, and the feasibility of the access method is verified.

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“…Recently, artificial intelligence has achieved exciting achievements in many fields (Zhou et al, 2020 ; Yu et al, 2021a , b ; Wang S. et al, 2022 ). Benefiting from the progress of deep learning technology, computer-aided medical tools for neurological diseases have been developed and applied in a wide range of fields (Wang et al, 2017 ; Li et al, 2019 ; Zhao et al, 2022a ).…”

Section: Introductionmentioning

confidence: 99%

A pairwise functional connectivity similarity measure method based on few-shot learning for early MCI detection

Zhang

Shams

et al. 2022

Front. Neurosci.

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Alzheimer's disease is an irreversible neurological disease, therefore prompt diagnosis during its early stage, i.e., early mild cognitive impairment (MCI), is crucial for effective treatment. In this paper, we propose an automatic diagnosis method, a few-shot learning-based pairwise functional connectivity (FC) similarity measure method, to detect early MCI. We first employ a sliding window strategy to generate a dynamic functional connectivity network (FCN) using each subject's rs-fMRI data. Then, normal controls (NCs) and early MCI patients are distinguished by measuring the similarity between the dynamic FC series of corresponding brain regions of interest (ROIs) pairs in different subjects. However, previous studies have shown that FC patterns in different ROI-pairs contribute differently to disease classification. To enable the FCs of different ROI-pairs to make corresponding contributions to disease classification, we adopt a self-attention mechanism to weight the FC features. We evaluated the suggested strategy using rs-fMRI data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results point to the viability of our approach for detecting MCI at an early stage.

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A deep residual computation model for heterogeneous data learning in smart Internet of Things

Cited by 22 publications

References 23 publications

A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer

A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer

Application of Internet of Things and Data Optimization in the Design of Smart Medical Park

A pairwise functional connectivity similarity measure method based on few-shot learning for early MCI detection

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