Machine Learning and Data Visualization to Evaluate a Robotics and Programming Project Targeted for Women

Lima, Danielli A.; Ferreira, Maria Eugênia A.; Silva, Aline Fernanda F.

doi:10.1007/s10846-021-01443-w

Cited by 14 publications

(12 citation statements)

References 25 publications

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“…Then the Euclidean numerical distances are calculated in order to serve as input to the SS. The SS algorithm implemented in KNIME performs a search based on row's comparisons [Lima et al 2021b, Lima et al 2017a]. The similarity criteria must be established and the selected attributes were the same as in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

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Guidelines for Google Classroom usage as an e-learning tool during Covid-19 pandemic based on similarity search

Lima

Isotani²

2022

Anais Do XXXIII Simpósio Brasileiro De Informática Na Educação (SBIE 2022)

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The coronavirus pandemic has changed the routine of almost every school around the world. To solve the problems caused by this disease, many schools adapted and computerized their activities so that the teaching and learning process would not be harmed. Thus, learning management systems were essential, and an example of this type of tool is the Google Classroom, which was widely used in several countries around the world. This article aims to make a similarity search of scientific studies mined by a systematic review to discover guidelines for the implementation of Google Classroom during the pandemic. This study can contribute to the e-learnings implementation not only in the pandemic, but as a support for hybrid and distance classes.

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Section: Methodsmentioning

confidence: 99%

“…In addition, it has the advantages of being a tool integrated with several Google services [Lima et al 2017b], and also has a good usability, simplified navigation by hyperlinks, security and privacy, portable file posting and is also considered a m-learning [Lima et al 2021a, Lima et al 2021b].…”

Section: Introductionmentioning

confidence: 99%

Guidelines for Google Classroom usage as an e-learning tool during Covid-19 pandemic based on similarity search

Lima

Isotani²

2022

Anais Do XXXIII Simpósio Brasileiro De Informática Na Educação (SBIE 2022)

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“…A correlation filter is a technique used to select the most relevant features in a dataset. This technique evaluates the relationship between variables in the dataset by calculating the correlation coefficient between them [Liu et al, 2021;Yuan et al, 2020;Lima et al, 2021]. The correlation coefficient measures the strength and direction of the linear relationship between two variables, ranging from 1 to 1, where 1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation.…”

Section: Correlation F Iltersmentioning

confidence: 99%

Cardiac Disease Diagnosis Using K-Nearest Neighbor Algorithm: A Study on Heart Failure Clinical Records Dataset

Stefane Souza,

Araújo Lima

2024

AIA

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This article introduces an approach to diagnose heart diseases utilizing the K-Nearest Neighbor algorithm and diverse correlation filters for selecting the most pertinent attributes. Results high- light that meticulous filter selection enhances survival predictions in patients with heart diseases. Employing K = 5 and correlation filter CF = 0.1, key attributes for classification were identified as anemia, high blood pressure, serum creatinine, and sex. Omitting the 'time' attribute led to information loss but was crucial to prevent biases and generalize predictions across various clinical scenarios. Utilizing these classification parameters, we designed an Android mobile application called “Heart Info System”, functioning as an artificial intelligence service. It employs the K-Nearest Neighbor algorithm with optimal parameters to evaluate the probability of survival in the progression of heart disease. The main activity of the application retrieves data from a Firebase database. While the study results show promise, the accuracy of the application may be influenced by inaccurate or incomplete input data. Nevertheless, this application has the potential to improve the early detection of heart diseases, paving the way for life-saving interventions.

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“…Data are facts collected and normally stored; however, only when some analysis technique is applied, and they are able to produce information (de Carvalho et al, 2017;de Morais et al, 2020;Lima et al, 2021). Data Science is the term used to define the extraction of this information and is also composed of specialized skill sets, such as programming, techniques such as predictive analysis, data mining, and visualization, among others.…”

Section: Data Science In Classificationmentioning

confidence: 99%

Correlation Filters in Machine Learning Algorithms to Select Demographic and Individual Features for Autism Spectrum Disorder Diagnosis

Dornelas¹,

Lima²

2023

JDSIS

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Autism spectrum disorder is currently considered one of the main neurodevelopmental disorders with predominant characteristics of difficulty in social communication and cognitive skills, and limited and repetitive patterns. This disorder has no cure and has different levels of severity that vary according to the appearance of symptoms in each patient. Generally, the waiting time for the diagnosis of autism spectrum disorder is slow, having as one of the reasons for this situation the lack of development of simple screening procedures to be implemented and which have efficient results. The objective of this work is to analyze a public database in order to find patterns of the autism spectrum. That is, to isolate the attributes that together with behavioral characteristics can bring greater reliability to the precursor model. The preliminary results showed that the probabilistic neural network algorithm performed well in this classification. In addition, the application of correlation filters demonstrated greater efficiency in accuracy. By applying 8 data mining algorithms and aggregating the demographic, individual and behavioral attributes, and excluding some attributes, we obtained an accuracy of 100% through the Support Vector Machine. Finally, the results with machine learning have shown that the patient’s ethnicity, continent and the presence of jaundice, tend to reveal more likely that the patient will be diagnosed with autism spectrum disorder.

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Machine Learning and Data Visualization to Evaluate a Robotics and Programming Project Targeted for Women

Cited by 14 publications

References 25 publications

Guidelines for Google Classroom usage as an e-learning tool during Covid-19 pandemic based on similarity search

Guidelines for Google Classroom usage as an e-learning tool during Covid-19 pandemic based on similarity search

Cardiac Disease Diagnosis Using K-Nearest Neighbor Algorithm: A Study on Heart Failure Clinical Records Dataset

Correlation Filters in Machine Learning Algorithms to Select Demographic and Individual Features for Autism Spectrum Disorder Diagnosis

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