Modeling Deep Learning Neural Networks With Denotational Mathematics in UbiHealth Environment

Sarivougioukas, John; Vagelatos, Aristides

doi:10.4018/ijssci.2020070102

Cited by 30 publications

(8 citation statements)

References 15 publications

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“…How to generate the initial beliefs for the situation assessment information is an open issue related to the topic of generating mass function automatically. In the following research, the algorithms such as the deep learning neural networks 45 , the Markov decision process 46 , 47 and hidden Markov model 48 , 49 can can be adopted to determine or generate the initial mass functions.…”

Section: Methodsmentioning

confidence: 99%

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Zhou

Tang

Zhao

2022

Sci Rep

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For situation assessment in air combat, there may be incomplete information because of new technologies and unknown or uncertain targets and threats. In this paper, an improved method of situation assessment for air combat environment considering incomplete frame of discernment in the evidence theory is proposed to get a more accurate fusion result for decision making in the battlefield environment. First, the situation in air combat is assessed with knowledge. Then, the incomplete frame of discernment in the generalized evidence theory, which is an extension of Dempster–Shafer evidence theory, is adopted to model the incomplete and unknown situation assessment. After that, the generalized combination rule in the generalized evidence theory is adopted for fusion of situations in intelligent air combat. Finally, real-time decision-making in situation assessment can be reached for actions to take. Experiments in situation assessment of air combat with incomplete and uncertain situations show the rationality and effectiveness of the proposed method.

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Section: Methodsmentioning

confidence: 99%

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Zhou

Tang

Zhao

2022

Sci Rep

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“…Further, deep learning networks may be used to make suitable decisions by fusing huge and complicated sets of data, and Denotational Mathematics can serve as a formal foundation for modeling and regulating deep learning networks, therefore improving decision-making quality. 44 The deep learning modalities based on convolutional neural networks and convolutional long short-term memory were used in the coronavirus disease 2019 (COVID-19) detection system. 45 Internet of Things (IoT) .…”

Section: Paradata In Improving Data Qualitymentioning

confidence: 99%

Digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection

et al. 2023

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Dealing with the threats of vector-borne diseases necessitates robust disease surveillance systems. The gathered information from surveillance studies is used to evaluate the effectiveness of control measures. It also guides the allocation of resources within the healthcare system. The disease surveillance data also identify high-risk populations or geographic areas to target interventions. Because of the importance of surveillance in decision-making and its timely requirement, real-time data collection is vital. A few advantages of real-time data collection apps are building powerful digital forms, exporting data for quick analysis in various formats, and being open-source. These apps automate data collection and transfer to an online server even without an internet connection. While collecting disease surveillance data digitally one crucial aspect lacking is data quality. This paper aims to present the importance of dashboards that includes paradata in improving data quality using real-time data collection tools in disease surveillance. Various types of paradata such as timestamps, geo-referencing, audio recording and so on help enhance the quality of data and can help monitor and evaluate surveillance staff. The outcomes of the paradata analysis may lead to the retraining of the surveillance team and even re-planning of surveillance. Undoubtedly, real-time data collection is the way of the future in any field-based study, and studies should be planned in conjunction with paradata to ensure that high-quality data are recorded.

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“…In the medical healthcare environment, such as in UbiHealth, a distributed application that supports healthcare demands in a ubiquitous computing environment is implemented. In (Sarivougioukas & Vagelatos, 2020), it is stated that deep learning networks can be utilized for carrying out the aggregation of huge information which is of complex nature to make efficient medical diagnoses in case of distributed environment. On the other hand, IoT is making smart devices accessible in the lives of common people, and attracting lots of work (Tewari & Gupta, 2017, (Sejdiu et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Handling Data Scarcity Through Data Augmentation in Training of Deep Neural Networks for 3D Data Processing

Srivastava

Rotte

Jain

et al. 2022

International Journal on Semantic Web and Information Systems

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Due to the availability of cheap 3D sensors such as Kinect and LiDAR, the use of 3D data in various domains such as manufacturing, healthcare, and retail to achieve operational safety, improved outcomes, and enhanced customer experience has gained momentum in recent years. In many of these domains, object recognition is being performed using 3D data against the difficulties posed by illumination, pose variation, scaling, etc present in 2D data. In this work, we propose three data augmentation techniques for 3D data in point cloud representation that use sub-sampling. We then verify that the 3D samples created through data augmentation carry the same information by comparing the Iterative Closest Point Registration Error within the sub-samples, between the sub-samples and their parent sample, between the sub-samples with different parents and the same subject, and finally, between the sub-samples of different subjects. We also verify that the augmented sub-samples have the same characteristics and features as those of the original 3D point cloud by applying the Central Limit Theorem.

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Modeling Deep Learning Neural Networks With Denotational Mathematics in UbiHealth Environment

Cited by 30 publications

References 15 publications

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Situation assessment in air combat considering incomplete frame of discernment in the generalized evidence theory

Digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection

Handling Data Scarcity Through Data Augmentation in Training of Deep Neural Networks for 3D Data Processing

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