Exhaustive Search Applied to Time Series Forecasting Methods Using Parallel Processing

Bucio, Jesus De la Torre; Paz, Antonio Ramos; Rios, J. Aurelio Medina

doi:10.1109/ropec55836.2022.10018762

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) 2022

DOI: 10.1109/ropec55836.2022.10018762

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Exhaustive Search Applied to Time Series Forecasting Methods Using Parallel Processing

Jesus De la Torre Bucio

Antonio Ramos Paz

J. Aurelio Medina Rios

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2024

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Analysis of Handwriting for Recognition of Parkinson’s Disease: Current State and New Study

Białek,

Potulska-Chromik,

Jakubowski

et al. 2024

Electronics

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One of the symptoms of Parkinson’s disease (PD) is abnormal handwriting caused by motor dysfunction. The development of tablet technology opens up opportunities for an effective analysis of the writing process of people suffering from Parkinson’s disease, aimed at supporting medical diagnosis using machine learning methods. Several approaches have been used and presented in the literature that discuss the analysis and understanding of images created during the writing of single words or sentences. In this study, we propose an analysis based on a sequence of sentences, which allows us to assess the evolution of writing over time. The study material consisted of handwriting image samples acquired in a group of 24 patients with PD and 24 healthy controls. The parameterization of the handwriting image samples was carried out using domain knowledge. Using the exhaustive search method, we selected the relevant features for the SVM algorithm performing binary classification. The results obtained were assessed using quality measures, including overall accuracy, which was 91.67%. The results were compared with competitive works on the same subject and seem to be better (a higher level of accuracy with a much smaller number of features than those presented by others).

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Analysis of Handwriting for Recognition of Parkinson’s Disease: Current State and New Study

Białek,

Potulska-Chromik,

Jakubowski

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Exhaustive Search Applied to Time Series Forecasting Methods Using Parallel Processing

Cited by 1 publication

References 14 publications

Analysis of Handwriting for Recognition of Parkinson’s Disease: Current State and New Study

Analysis of Handwriting for Recognition of Parkinson’s Disease: Current State and New Study

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