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

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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. …”
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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. …”
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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). …”
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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

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

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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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A comparative study of methods for dynamic survival analysis by Wieske K. de Swart, Marco Loog, Jesse H. Krijthe, Jesse H. Krijthe

“…With advancements in machine learning, several new methods have been introduced, often using a two-stage approach: first extracting features from longitudinal trajectories and then using these to predict survival probabilities.MethodsThis work compares several combinations of longitudinal and survival models, assessing their predictive performance across different training strategies. …”
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Comparing the effect of lecture method and cooperative teaching method on the learning, communication skills, and attitudes of students: a quasi-experimental study by Amin Beigzadeh, Hadi Bazyar, Hadi Bazyar, Mozhdeh Delzendeh, Mohammad Hassan Razmi, Nafiseh Sharifi

“…Additionally, following the intervention, the mean scores for communication skills and attitudes were significantly higher in the cooperative group compared to the lecture group, with statistical significance indicated by p < 0.001 and p = 0.03, respectively.ConclusionThis study highlights the effectiveness of cooperative teaching over traditional lecture methods in enhancing students’ learning outcomes, communication skills, and attitudes. …”
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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. …”
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Comparative evaluation of CAM methods for enhancing explainability in veterinary radiography by Piotr Dusza, Tommaso Banzato, Silvia Burti, Margherita Bendazzoli, Henning Müller, Marek Wodzinski

“…Abstract Explainable Artificial Intelligence (XAI) encompasses a broad spectrum of methods that aim to enhance the transparency of deep learning models, with Class Activation Mapping (CAM) methods widely used for visual interpretability. …”
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A Comparative Evaluation of Machine Learning Methods for Predicting Student Outcomes in Coding Courses by Zakaria Soufiane Hafdi, Said El Kafhali

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

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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

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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. …”
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