Model-Based Data Generation for Support Vector Machine Stroke Classification

Mariano, Valeria; Vasquez, J. A. Tobon; Casu, Mario R.; Vipiana, F.

doi:10.1109/aps/ursi47566.2021.9704125

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…To address this limitation, researchers have developed the concept of using multiple kernels in SVMs, known as the multiple kernel SVM. This approach’s ability to handle nonlinearity can lead to enhanced process understanding, early fault detection (FD), and improved process control, ultimately contributing to safer and more efficient industrial operation …”

Section: Introductionmentioning

confidence: 99%

“…This approach's ability to handle nonlinearity can lead to enhanced process understanding, early fault detection (FD), and improved process control, ultimately contributing to safer and more efficient industrial operation. 3 The data collected from the BR is nonlinear in nature. In closed-loop experiments, such as BR setups, where events are tightly controlled and interconnected, the applicability of the methods used in ref 4 may be limited.…”

Section: Introductionmentioning

confidence: 99%

“…This approach’s ability to handle nonlinearity can lead to enhanced process understanding, early fault detection (FD), and improved process control, ultimately contributing to safer and more efficient industrial operation. 3 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine

Simiyon,

Sachidanand,

Halmakki Krishnamurthy

et al. 2024

ACS Omega

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine

Simiyon,

Sachidanand,

Halmakki Krishnamurthy

et al. 2024

ACS Omega

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“…Neuroimaging is crucial in defining the diagnosis, other tests being necessary depending on the clinical picture. Thus, PIS is better recognized among practitioners as a clinically significant entity [22]. It is known that the diagnosis of stroke in children can often be delayed, and sometimes missed due to mild and nonspecific clinical symptoms and since they are initially related to other causes than stroke.…”

mentioning

confidence: 99%

Pediatric Ischemic Stroke: Clinical and Paraclinical Manifestations—Algorithms for Diagnosis and Treatment

Wessel,

Sprincean,

Sidorenko

et al. 2024

Algorithms

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Childhood stroke can lead to lifelong disability. Developing algorithms for timely recognition of clinical and paraclinical signs is crucial to ensure prompt stroke diagnosis and minimize decision-making time. This study aimed to characterize clinical and paraclinical symptoms of childhood and neonatal stroke as relevant diagnostic criteria encountered in clinical practice, in order to develop algorithms for prompt stroke diagnosis. The analysis included data from 402 pediatric case histories from 2010 to 2016 and 108 prospective stroke cases from 2017 to 2020. Stroke cases were predominantly diagnosed in newborns, with 362 (71%, 95% CI 68.99–73.01) cases occurring within the first 28 days of birth, and 148 (29%, 95% CI 26.99–31.01) cases occurring after 28 days. The findings of the study enable the development of algorithms for timely stroke recognition, facilitating the selection of optimal treatment options for newborns and children of various age groups. Logistic regression serves as the basis for deriving these algorithms, aiming to initiate early treatment and reduce lifelong morbidity and mortality in children. The study outcomes include the formulation of algorithms for timely recognition of newborn stroke, with plans to adopt these algorithms and train a fuzzy classifier-based diagnostic model using machine learning techniques for efficient stroke recognition.

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Hardware Acceleration of Microwave Imaging Algorithms

Mansoori

Casu

2023

Lecture Notes in Bioengineering

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Model-Based Data Generation for Support Vector Machine Stroke Classification

Cited by 4 publications

References 9 publications

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine

Pediatric Ischemic Stroke: Clinical and Paraclinical Manifestations—Algorithms for Diagnosis and Treatment

Hardware Acceleration of Microwave Imaging Algorithms

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