Genitourinary imaging

Barbosa, Felipe de Galiza; Galgano, Samuel J.; Botwin, Ariel; Gongora, Aline Bobato Lara; Sawaya, Giovanna; Baroni, Ronaldo Hueb; Queiroz, Marcelo A.

doi:10.1016/b978-0-323-88537-9.00012-x

Cited by 4 publications

(4 citation statements)

References 108 publications

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“…The F-measure is a metric for evaluating the performance of classifiers using confusion matrices. F-Measure is the opposite correlation between accuracy and recall, defined as the harmonic mean of precision and recall as in (5). It is essential to determine if a model's accuracy and recall are pretty well balanced [52].…”

Section: Research Design and Evaluation Metricsmentioning

confidence: 99%

“…Prediction, which encompasses the subcategories of classification, regression, and density estimation, is a paradigm in EDM [4]. Relation mining, association mining, correlation mining, sequential pattern mining, and causative data mining are all types of clustering [5]. In addition, prediction also incorporates data distillation to aid in human logic and model finding.…”

mentioning

confidence: 99%

“…[1]: N/R,[2]: A/R,[3]: G/R,[4]: C/R,[5]: U/W,[6]: U/E,[7]: S/R,[8]: NE/R,[9]: W/R,[10]: E/R,[11]: V/R,[12]: O/R,[13]: Ahafo/ R, {14]: Bono East,[15]: Bono,[16]: WN/R Nominal 15 Integrated Science [1]: A1, [2]: B2, [3]: B3, [4]: C4, [5]: C5, [6]: C6, [7]: D7, [8]: E8,[9]: F9 Nominal 16 English Language [1]: A1, [2]: B2, [3]: B3, [4]: C4, [5]: C5, [6]: C6, [7]:…”

mentioning

confidence: 99%

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Exploring the Impact of Students Demographic Attributes on Performance Prediction through Binary Classification in the KDP Model

Iddrisu,

Appiahene,

Appiah

et al. 2023

Knowl. Eng. Data Sci.

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During the course of this research, binary classification and the Knowledge Discovery Process (KDP) were used. The experimental and analytical capabilities of Rapid Miner's 9.10.010 instructional environment are supported by five different classifiers. Included in the analysis were 2334 entries, 17 characteristics, and one class variable containing the students' average score for the semester. There were twenty experiments carried out. During the studies, 10-fold cross-validation and ratio split validation, together with bootstrap sampling, were used. It was determined whether or not to use the Random Forest (RF), Rule Induction (RI), Naive Bayes (NB), Logistic Regression (LR), or Deep Learning (DL) methods. RF outperformed the other four methods in all six selection measures, with an accuracy of 93.96%. According to the RF classifier model, the level of education that a child's parents have is a major factor in that child's academic performance before entering higher education.

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Section: Research Design and Evaluation Metricsmentioning

confidence: 99%

mentioning

confidence: 99%

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Exploring the Impact of Students Demographic Attributes on Performance Prediction through Binary Classification in the KDP Model

Iddrisu,

Appiahene,

Appiah

et al. 2023

Knowl. Eng. Data Sci.

View full text Add to dashboard Cite

show abstract

“…On the part of (Ahmed, Abdulazeez, Zeebaree, & Ahmed, 2021), Prediction, which encompasses the subcategories of classi cation, regression, and density estimation, is a paradigm in educational data mining. Per the view of Barbosa et al(2022) relation mining, association mining, correlation mining, sequential pattern mining, and causative data mining, are all types of clustering. In addition, prediction also incorporates data distillation to aid in human logic and model nding.…”

Section: Introductionmentioning

confidence: 99%

Determining Student Demographic Attributes Influencing Performance Using Binary Classification in KDP Model

Issah

Appiahene

Appiah

et al. 2023

Preprint

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Machine learning (ML) is one way that can help decipher the intricate relationship between students' data and their performance. When implemented correctly in learning environments, machine learning will improve knowledge of fundamental processes by simplifying the identification, extraction, and evaluation of underlying factors that affect student learning and levels of achievement. This study employed the experimental research approach using binary classification techniques based on the six-step Knowledge Discovery Process (KDP) model. Five classifiers were used within the Rapid Miner's 9.10.010 educational environment as both experimental and analytical tool. The dataset comprised of 2334 records, 17 attributes with one class variable (students’ semester average score) inclusive. Twenty different tests were conducted. The experiments' results were evaluated using 10-fold cross-validation and ratio split validation with bootstrap sampling. The Random Forest algorithm (RF), Rule Induction methods (RI), Naive Bayes (NB), Logistic Regression (LR) and Deep Learning (DL) algorithms were used in the experiment. The experimental results demonstrated that the RF method outperforms the other four techniques in all six-evaluation metrics that were employed for the selection process with the accuracy being 93.96%. According to the RF classifier model, the mother's and father's education levels of students are two recognized demographic factors per this study that significantly influence pre-tertiary students’ academic achievement. This study has significantly reduced the gap in practical knowledge observed in the literature by introducing an intervention scheme for respective student's requiring intensive or minimal academic interventions in its prediction procedure.

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Beyond City Limits: Exploring the Untapped Potential of Social Media in Rural Karnataka: A Structural Equation Model Analysis

Amudhan,

Chavadi,

Poojara

et al. 2024

Studies in Systems, Decision and Control

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Genitourinary imaging

Cited by 4 publications

References 108 publications

Exploring the Impact of Students Demographic Attributes on Performance Prediction through Binary Classification in the KDP Model

Exploring the Impact of Students Demographic Attributes on Performance Prediction through Binary Classification in the KDP Model

Determining Student Demographic Attributes Influencing Performance Using Binary Classification in KDP Model

Beyond City Limits: Exploring the Untapped Potential of Social Media in Rural Karnataka: A Structural Equation Model Analysis

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