Breast Cancer Classification with Various Optimized Deep Learning Methods by Mustafa Güler, Gamze Sart, Ömer Algorabi, Ayse Nur Adıguzel Tuylu, Yusuf Sait Türkan

“…<b>Methods:</b> In this study, 11 different deep learning algorithms (Vanilla, ResNet50, ResNet152, VGG16, DenseNet152, MobileNetv2, EfficientB1, NasNet, DenseNet201, ensemble, and Tuned Model) were used. …”
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Combination of Feature Selection and Learning Methods for IoT Data Fusion by V. Sattari-Naeini, Zahra Parizi-Nejad

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Research on the Application of Deep Learning Methods in the Field of Image Classification by Peng Yuhui

“…The application of deep learning methods such as Convolutional Neural Network, Recurrent Neural Networks and Long Short-Term Memory in image classification is also discussed. …”
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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

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Aortic Aneurysm Inflammatory Cell Detection with Deep Learning methods by Kristóf-Gergö Nagy, Csaba Szferle, Attila Fintha

“…OBJECTIVE: Our objective was to determine the number of inflammatory cells in the aortic wall using MLP and ResNet50 methods and to compare these results with data obtained from traditional immunohistochemical methods. …”
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A Survey of Machine Learning Methods for Time Series Prediction by Timothy Hall, Khaled Rasheed

“…This study provides a comprehensive survey of the top-performing research papers in the field of time series prediction, offering insights into the most effective machine learning techniques, including tree-based, deep learning, and hybrid methods. …”
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A review of deep learning methods in aquatic animal husbandry by Marzuraikah Mohd Stofa, Fatimah Az Zahra Azizan, Mohd Asyraf Zulkifley

“…Yet, for an automation system to be effectively deployed, it needs an intelligent decision-making system, which is where deep learning techniques come into play. In this article, an extensive methodological review of machine learning methods, primarily the deep learning methods used in aquatic animal husbandry are concisely summarized. …”
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Comprehensive analysis of supervised learning methods for electrical source imaging by Sarah Reynaud, Adrien Merlini, Douraied Ben Salem, François Rousseau

“…The choice of this constraint, or prior, remains a challenge for most ESI methods. This work explores the application of supervised learning methods for spatio-temporal ESI, where the relationship between measurements and sources is learned directly from the data. …”
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Genomic prediction of plant traits by popular machine learning methods by K. N. Kozlov, M. P. Bankin, E. A. Semenova, M. G. Samsonova

“…Attention is  paid to Data Augmentation, Transfer Learning, Dimensionality Reduction methods and hybrid methods. …”
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Reinforcement Learning Methods for Emulating Personality in a Game Environment by Georgios Liapis, Anna Vordou, Stavros Nikolaidis, Ioannis Vlahavas

“…Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. …”
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A mixed-methods study of online learning in the EFL classroom by Carolina Girón-García, Sorina Boghiu-Balaur

“…The purpose of this mixed-methods study, which involves a combination of both qualitative and quantitative data analyses, is to survey the perceptions and engagement of higher education students as regards learning theoretical content and vocabulary through the implementation of two online called ‘Cybertasks’. …”
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A comprehensive survey of imbalanced learning methods for bankruptcy prediction by Tuong Le

“…To give an overview of imbalanced learning methods for bankruptcy prediction, this study first reviews several state‐of‐the‐art approaches for handling this problem in bankruptcy prediction, including an oversampling‐based framework, a cost‐sensitive method (the CBoost algorithm), a combination of resampling techniques and a cost‐sensitive framework, and an ensemble‐based model (the XGBS algorithm). …”
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Parents’ learning in family literacy: a mixed-methods evaluation by Carol Clymer, Jungeun Lee, Elisabeth L. McLean, Esther Prins

“…This study reports findings from a multi-year, mixed-methods evaluation of five family literacy programs in Philadelphia, Pennsylvania. …”
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Examining Individual Tax Morale in Europe with Machine-Learning Methods by Lejla Lazović Pita, Almasa Odžak, Lamija Šćeta

“…It focuses on the use and potential of machine-learning (ML) methods and models to predict individual tax morale across Europe, and it identifies the factors that influence predic­tive accuracy. …”
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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…(Research purpose) The study aims to review and systemize scientific literature on the application of unmanned aerial vehicles, remote sensing technologies and deep learning 24 methods for the early detection and prediction of crop diseases. …”
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Dynamical systems-inspired machine learning methods for drought prediction by Andrew Watford, Chris T. Bauch, Madhur Anand

“…In this study, we compare the performance of a mechanistic model and two physics-informed machine learning methods (Sparse Identification of Nonlinear Dynamics [SINDy] and reservoir computing) on the prediction of NDVI time-series data in drought-prone regions of Kenya. …”
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Pretreatment Methods for Enhancing Machine Learning Performance on Metabolomics Data by Rustam

“…Pretreatment methods are critical for metabolomics data analysis, yet their impact on machine learning performance remains insufficiently explored. …”
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Methodological Critique of Teaching and Learning Methods from the Perspective of Embodiment by Bijan Babaei, Bakhtiar Shabani Varaki

“…Conclusions: Based on the holistic and embodied approach to teaching and learning methods; the pillars of teaching, learning, teacher-learner relationships, and the design of the learning environment need to be revised. …”
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CLASSIFICATION OF COVID-19 DATASET WITH SOME MACHINE LEARNING METHODS by Muhammed Nuri Dudak, Yavuz Ünal

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MODEL METHODS OF UPBRINGING OF PUPILS IN THE LEARNING PROCESS OF BIOLOGY AND ECOLOGY by A. Semenov

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