Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability

Haldorai, Anandakumar; Ramu, Arulmurugan

doi:10.1007/s11063-020-10327-3

Cited by 186 publications

(28 citation statements)

References 13 publications

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“…In [9], authors argue that a greater percentage of younger physicians were CDSS users among our respondents than senior clinicians. A successful healthcare involvement approach with experienced physicians is needed to overcome obstacles to CDSS implementation.…”

Section: Literature Reviewmentioning

confidence: 91%

An Analysis of Artificial Intelligence Based Clinical Decision Support Systems

2021

JBSHA

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The growing availability of medical data has sparked fresh interests in Computerized Clinical Decision Support Systems (CDSS), thanks to recent breakthroughs in machine and deep learning. CDSS has showed a lot of promise in terms of improving healthcare, enhancing the safety of patients and minimizing treatment costs. The application of CDSS, nonetheless, is unsafe since an insufficient or defective CDSS may possibly degrade healthcare quality and place patients at potential threat. Furthermore, the deployment of a CDSS may fail when the CDSS's output is ignored by its intended users owing to a lack of confidence, relevance, or actionability. We offer literature-based advice for the various elements of CDSS adoption, with a particular emphasis on Artificial Intelligence (AI) and Machine Learning (ML) systems: quality assurance, deployment, commissioning, acceptability tests, and selection, in this research. A critical selection process will assist in the process of identifying CDSS, which effectively suits the localized sites’ needs and preferences. Acceptance testing ensures that the chosen CDSS meets the specified standards and meets the safety criteria. The CDSS will be ready for safe clinical usage at the local site once the commissioning procedure is completed. An efficient system implementation must result in a smooth rollout of the CDSS to well-trained end-users with reasonable expectations. Furthermore, quality assurance will ensure that the CDSS's levels are maintained and that any problems are discovered and resolved quickly. We conclude this research by discussing the methodical adoption process for CDSS to assist in avoiding issues, enhance the safety of patients and increasing quality of service.

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Section: Literature Reviewmentioning

confidence: 91%

An Analysis of Artificial Intelligence Based Clinical Decision Support Systems

2021

JBSHA

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“…Feature Extraction: After data augmentation deep feature extraction is done from the CNN models and a pre-trained model. The extracted features are given to the SVM, the SVM then constructs hyperplanes to maximize the distance between distinct classes provides precise output [7].…”

Section: Feature Extraction With Classificationmentioning

confidence: 99%

Occlusion Detection for COVID-19 Identification: A Review

Udawat

Santani

Agrawal

2021

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)

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Coronavirus has significantly entered the globe and has affected everyone's life in some form or another. The identification of the coronavirus is as important as prevention. An early and precise diagnosis will have significant-good benefits for us. The Convolutional Neural Network can be implemented for virus detection, and content-adaptive progressive occlusion analysis could be used to handle occlusion. To improve precision, it is also possible to add many other classification methods accordingly, which are mentioned in this paper.

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“…H. Tiirmaa-Klaar in [9] proposes and synthesizes the definition of CI to be data intensive, large-scale, computationally powerful, interoperable, hierarchical and integrated with the second-order development over a considerable time-frame. It integrates a general and specialized hardware, high-performing computing applications, data and communication initiatives, both human and non-human agents, connective and interacting via the interdimensional network.…”

Section: Literature Reviewmentioning

confidence: 99%

Strategic Analysis of the Advanced Computing Infrastructure and Future Directions

2021

JCNS

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This paper provides a critical evaluation of the Advanced Computing Infrastructure (ACI), which is a fundamental element of the Cyber Infrastructure (CI) 21st of the Century. Whereas the infrastructure focuses widely on the cyber infrastructure required by education, engineering, and science communities to handle more complex issues and problems, the ACI particularly concentrates on ensuring these societies have ready accessibility to the required advanced computing capacities. The CI of the 21st Century and its framework integrate other key complementariness; however, it integrates overlapping elements: scientific instruments, Computational Data-enhanced Engineering & Science (CDE&S), computational science, grand challenge societies, workforce development, Cyber Security (CS), campus bridging, software and data. Various elements were addressed in the CI of the 21st Century in the past decade and processes are in progress to design strategic plans focusing on the different elements.

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Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability

Cited by 186 publications

References 13 publications

An Analysis of Artificial Intelligence Based Clinical Decision Support Systems

An Analysis of Artificial Intelligence Based Clinical Decision Support Systems

Occlusion Detection for COVID-19 Identification: A Review

Strategic Analysis of the Advanced Computing Infrastructure and Future Directions

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