Comparative Analysis of Facial Expression Recognition Methods by Denys - Florin COT

“…The research compares the performance of classical machine learning algorithms (such as K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree, and Random Forest) with the modern deep learning methods (such as Convolutional Neural Networks, Deep Neural Networks, and Recursive Neural Networks) using standardized datasets. …”
Get full text

A Comparative Study between Traditional Learning and Virtual Learning (Design a Virtual Learning System) by Maisireem Al-Sayeg

Get full text

Machine Learning Methods for Predicting Cardiovascular Diseases: A Comparative Analysis by Aiym B. Temirbayeva, Arshyn Altybay

“…The study aims to accurately predict the presence of heart disease using machine learning models. The research evaluates and compares the performance of five algorithms - Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting - on a dataset containing clinical features of patients. …”
Get full text

COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU SHALLOTS by Desy Lusiyanti, Selvy Musdalifah, Agusman Sahari, Iman Al Fajri

“…This study conducts a comparative analysis of various machine learning methods for classifying the quality of Palu shallots based on the Indonesian National Standard (SNI). …”
Get full text

Comparative performance analysis of ensemble learning methods for fetal health classification by Tasnim Bill Zannah, Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Afjal Hossan Sarower, Mahbuba Begum

Get full text

Comparative Study on Performance Evaluation of Eager versus Lazy Learning Methods by Selvi Lukman, Jimmy Loekito, Pin Panji Yapinus

Get full text

Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods by Kalina Kitova, Ivan Ivanov, Vincent Hooper

“…Their Random Forest (RF) classifier, combined with Feature Importance (FI) selection, achieved an accuracy of 97.17%, illustrating the positive impact of RF and relevant feature selection on model performance. A comparative analysis indicated that Ivanov et al.’s method achieved the highest accuracy rate. …”
Get full text

A Comparative Study of Machine Learning Methods for Pyrolysis Yield Prediction by Seyed Mohammad Razavi, Rahmat Sotudeh Gharebagh, Navid Mostoufi, Jamal Chaouki, K.D.P. Nigam

“…This innovative approach offers a broader range and higher accuracy of feedstock compared to traditional kinetics-based methods. The KNN model demonstrated superior performance, achieving a correlation coefficient greater than 0.998 and an RMSE of 0.64. …”
Get full text

Unlocking Potential: Comparing collaborative and Traditional Learning Methods for Students with Learning Disabilities in Special Education Classrooms by Carmit Gal, Chen Hanna Ryder

“…Learning disabilities present significant challenges in educational settings, with traditional teaching methods often failing to fully engage affected students. …”
Get full text

Comparing statistical learning methods for complex trait prediction from gene expression. by Noah Klimkowski Arango, Fabio Morgante

“…Here, we used data from the Drosophila Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance and startle response from gene expression in the two sexes separately. …”
Get full text

Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification by Md Takbir Alam, Md Ashibul Islam Khan, Nahian Nakiba Dola, Tahia Tazin, Mohammad Monirujjaman Khan, Amani Abdulrahman Albraikan, Faris A. Almalki

“…This research covers the findings and analyses of multiple machine learning models for fetal health classification. The method was developed using the open-access cardiotocography dataset. …”
Get full text

Modern vs Traditional: Comparative Study of Efficacious Arabic Language Learning Methods by Budi Pratama, Haerani Kadar, Busyro Husaini, Muslih Abdul Aziz, Dimas Adi Saputra

“…This article described and analyzed the comparative of efficacious methods, modern and traditional learning methods, in Arabic learning. …”
Get full text

A Comparative Study of Loan Approval Prediction Using Machine Learning Methods by Vahid Sinap

“…In this context, the main objective of this research is to develop models for loan approval prediction using machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest and to compare their performances. …”
Get full text

Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors by Nazanin Mola, Hrafn Weishaupt, Valentin Krasontovitsch, Erlend Hodneland, Sabine Leh

“…Digital image analysis could streamline this process, yet few comprehensive comparisons exist between different tools. We compared a non-machine learning (non-ML) tool (ImageScope, Leica Biosystems) with a machine learning (ML) tool (Aiforia Create, Aiforia Technologies) on Ki67-stained slides from 10 low proliferative neuroendocrine tumor cases (Ki67 score < 5%, eight regions per slide). …”
Get full text

A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification by Ahmad Mustafid, Muhammad Murah Pamuji, Siti Helmiyah

Get full text

A Comparative Evaluation of Machine Learning Methods for Predicting Student Outcomes in Coding Courses by Zakaria Soufiane Hafdi, Said El Kafhali

Get full text

Comparative Assessment of Several Effective Machine Learning Classification Methods for Maternal Health Risk by Md Nurul Raihen, Sultana Akter

Get full text

Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper by Kiranmayee Janardhan, Vinay Martin D’Sa Prabhu, T. Christy Bobby

Get full text

A Comparative Study of Various Transfer Learning Models on Skin Cancer Confirmation Methods by Mehmet Ali Altuncu, Kaplan Kaplan, Melih Kuncan

“…The dataset includes four different confirmation methods: confocal, consensus, follow-up, and histopathology. …”
Get full text

Quantifying uncertainty in foraminifera classification: How deep learning methods compare to human experts by Iver Martinsen, Steffen Aagaard Sørensen, Samuel Ortega, Fred Godtliebsen, Miguel Tejedor, Eirik Myrvoll-Nilsen

“…The results of the deep learning uncertainty quantification methods were then analyzed and compared with the human benchmark, to see how the methods performed individually and how the methods aligned with humans. …”
Get full text