Random Forest Algoritmasının FPGA Üzerinde Gerçekleştirilerek Performans Analizinin Yapılması

Kumral, Cem Deniz; Topal, Ali; Ersoy, Mevlüt; Çolak, Recep; Yi̇ği̇t, Tuncay

doi:10.31202/ecjse.1134799

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…In a random forest, training sets are initially generated through bootstrapping, each leading to the creation of an individual decision tree. The outcome of the algorithm materializes as a collective result of these operations [18]. In Figure 1, there is a recursive partitioning illustrated within a two-dimensional input space, employing boundaries aligned with the axes.…”

Section: Random Forestmentioning

confidence: 99%

Adaptive learning-based content management tool for online education platforms

PİŞİRİR

2023

Uluslararası Teknolojik Bilimler Dergisi

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The ways of accessing information are highly developed today. Online education platforms are widely used directly or indirectly in the education of students. Instructors create their course content on these platforms and teach their courses to their students. With the developing internet technologies, the variety of visual, audio and textual course content is also increasing. However, students' learning tendencies differ while studying these course contents. While some of the students can learn more easily from course content consisting only of texts, others can learn more easily from course content supported by audio-visual materials. Identifying these learning differences among students has become important today. In order to enrich learning activities, it would be useful to create content in accordance with the learning tendencies of each student. In this study, we developed an adaptive learning-based help tool for instructors to create course content. This tool analyzes the learning styles of the students and provides recommendations to the instructor for creating the course content. Thus, all the course content prepared by the instructor will be selected and created according to the learning tendencies of the students. It will contribute to the improvement of learning activities.

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Section: Random Forestmentioning

confidence: 99%

Adaptive learning-based content management tool for online education platforms

PİŞİRİR

2023

Uluslararası Teknolojik Bilimler Dergisi

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Critical thinking in higher education: a bibliometric analysis

Nuryana,

Sugeng,

Soesilowati

et al. 2024

JARHE

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PurposeCritical thinking (CT) in higher education institutions (HEIs) is rarely examined using bibliometric methods to provide a better reference path for future research. This study aims to provide a broad survey of the bibliometric literature on CT in HEIs.Design/methodology/approachCompiled from the Scopus database, there were 670 articles published from 2018 to 2022 analyzed in the study. VOSviewer software was also used to analyze co-authorship, co-occurrence and citation.FindingsThe results showed that the CT research literature was mainly published in the US However, the highest number of citations was from Australian authors. The most frequent keywords were CT, skills and higher education.Research limitations/implicationsDue to the high variability of assessment strategies for each study, the current study suggests that further research focuses on the global assessment model of CT in HEIs.Originality/valueTo the best of the research’s knowledge, the study on CT in higher education with bibliometric analysis is rarely explored.

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Feature Selection in the Diabetes Dataset with the Marine Predator Algorithm and Classification using Machine Learning Methods

Türk,

Metin,

Lüy

2024

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Diabetes, which is classified as one of the leading causes of mortality, is a chronic and intricate metabolic disorder defined by disruptions in the metabolism of carbohydrates, fats, and proteins. Type 1 diabetes is categorized alongside Type 2 diabetes, as well as other distinct kinds of diabetes, including gestational diabetes. Complications, both acute and chronic, manifest in individuals with diabetes due to diminished insulin secretion and disruptions in the metabolism of carbohydrates, fats, and proteins. Following the completion of the data preparation step, the diabetes dataset that was collected from Kaggle is then sent to the feature extraction module for analysis. After the optimization process has been completed, the feature selection block will determine which characteristics stand out the most. The selected traits discussed before are sorted into several categories using the categorization module. The findings are compared to those that would have been obtained if the marine predator optimization algorithm (MPOA) technique had not been carried out, specifically regarding metrics like the F1 score, Recall, Accuracy, and Precision. The findings indicate that the LR classification approach achieves an accuracy rate of 77.63% without property selection. However, when the characteristics are selected using the MPOA, the accuracy rate increases to 79.39%.

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Random Forest Algoritmasının FPGA Üzerinde Gerçekleştirilerek Performans Analizinin Yapılması

Cited by 3 publications

References 21 publications

Adaptive learning-based content management tool for online education platforms

Adaptive learning-based content management tool for online education platforms

Critical thinking in higher education: a bibliometric analysis

Feature Selection in the Diabetes Dataset with the Marine Predator Algorithm and Classification using Machine Learning Methods

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