EmbryoNet-VGG16 framework for deep learning-based embryo classification with Otsu segmentation by M. Saraniya, J. Anitha Ruth

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Multi-CNN Deep Feature Fusion and Stacking Ensemble Classifier for Breast Ultrasound Lesion Classification by Kemal PANÇ, Sümeyye SEKMEN

“…A stacking ensemble classifier, integrating LightGBM, XGBoost, CatBoost, and random forest with a logistic regression meta-learner, was trained using 5-fold cross-validation on a 75% training set (balanced with synthetic minority oversampling technique), and evaluated on a 25% test set. …”
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Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography. by MinJae Woo, Linglin Zhang, Beatrice Brown-Mulry, InChan Hwang, Judy Wawira Gichoya, Aimilia Gastounioti, Imon Banerjee, Laleh Seyyed-Kalantari, Hari Trivedi

“…The classification model achieved an AUC of 0.975 and a recall of 0.927. …”
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Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression by Xuelin Wang, Qinqin Huang, Dong Wu, Jinhua Xie, Ming Cao, Jun Liu

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Comparison among grazing animal behavior classification algorithms for use with open-source wearable sensors by B.R. dos Reis, S. Sujani, D.R. Fuka, Z.M. Easton, R.R. White

“…Behavior classification analyses leveraged simple approaches (analysis of variance and logistic regression), as well as more complex machine learning algorithms (support vector machine (SVM) and random forest (RF)) to better understand the trade-offs between classification approach complexity and accuracy. …”
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AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening by Lichuan Liu, Wei Li, Beth Moxley

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Financial Sentiment Analysis and Classification: A Comparative Study of Fine-Tuned Deep Learning Models by Dimitrios K. Nasiopoulos, Konstantinos I. Roumeliotis, Damianos P. Sakas, Kanellos Toudas, Panagiotis Reklitis

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Application of the modified computed tomography severity index and retroperitoneal extension classification for evaluation of acute pancreatitis by Cam Nhung Dang, Anh Tuan Nguyen, Thanh Thao Nguyen, Trong Binh Le

“…Necrotizing AP was observed in 24.3% of cases and extrapancreatic complications in 35.7%. …”
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Model-Based Electroencephalogram Instantaneous Frequency Tracking: Application in Automated Sleep–Wake Stage Classification by Masoud Nateghi, Mahdi Rahbar Alam, Hossein Amiri, Samaneh Nasiri, Reza Sameni

“…This study presents a novel EEG feature extraction pipeline for the accurate classification of various wake and sleep stages. We propose a noise-robust model-based Kalman filtering (KF) approach to track changes in a time-varying auto-regressive model (TVAR) applied to EEG data during different wake and sleep stages. …”
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Modeling coronary heart disease risk based on age, fatty food consumption and anxiety factors using penalized spline nonparametric logistic regression by Nur Chamidah, Budi Lestari, Hendri Susilo, Triana Kesuma Dewi, Toha Saifudin, Naufal Ramadhan Al Akhwal Siregar, Dursun Aydin

“…The proposed method gave results a percentage of model classification accuracy of 94.31 % and the value of area under the receiver operating characteristic curve (AUC) of 0.96. …”
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Estimating GPP in China using different site-level datasets, vegetation classification and vegetation indices by Jiahui Xu, Tiexi Chen, Xin Chen, Shengjie Zhou, Zhe Gu, Wenhui Li, Yingying Cui, Shengzhen Wang, Shuci Liu

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Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Hassi R’Mel Field, Algeria by Baouche Rafik, Baddari Kamel

“…In comparing the relative predictive performance of the three regression methods, the alternating conditional expectations with ACE method appears to outperform the other two methods.…”
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Impacts of Land Cover Changes on Land Surface Temperature Using Landsat Imagery with the Supervised Classification Method by Farisya Isnaayu Khairunisa, Astrid Damayanti, Kintan Maulidina

“…This study used Landsat 8 Surface Reflectance Tier 1 satellite imagery to extract information on the Normalized Difference Vegetation Index and process ground surface temperatures for three periods, 2014, 2017, and 2020, and the guided classification method. …”
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Deep learning based localisation and classification of gamma photon interactions in thick nanocomposite and ceramic monolithic scintillators by Mushen Shen, Ragy Abraham, Elise Cribbin, Harrison Gregor, Mitra Safavi-Naeini, Daniel Franklin

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Dual-Language Sentiment Analysis: A Comprehensive Evaluating SVM, Logistic Regression, XGBoost, and Decision Tree Using TF-IDF On Arabic and English Dataset by Hawraa Ali Taher

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Deep learning application to roughness classification of road surface conditions through an e-scooter’s ride quality by Asher Virin, Lalitphat Khongsomchit, Sakdirat Kaewunruen

“…Future research could expand on these findings by examining a wider variety of surfaces and speeds and incorporating regression analysis to advance the models from classification to predictive analytics.…”
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Performance Analysis and Improvement of Machine Learning with Various Feature Selection Methods for EEG-Based Emotion Classification by Sherzod Abdumalikov, Jingeun Kim, Yourim Yoon

“…Emotion classification is a challenge in affective computing, with applications ranging from human–computer interaction to mental health monitoring. …”
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From Data to Knowledge: A Knowledge Graph-Guided Framework to Deep Learning for Hyperspectral Image Classification by Runmin Lei, Yuchuan Zhou, Zixuan Wang, Xiang Zhang

“…Furthermore, we develop two KG integration approaches: a regression-based method and a classification-based method, demonstrating the complementary role of structured symbolic knowledge in enhancing connectionist learning. …”
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Fusion of UAV-Acquired Visible Images and Multispectral Data by Applying Machine-Learning Methods in Crop Classification by Zuojun Zheng, Jianghao Yuan, Wei Yao, Paul Kwan, Hongxun Yao, Qingzhi Liu, Leifeng Guo

“…The study focused on five crops: rice, soybean, red bean, wheat, and corn. To improve classification accuracy, the researchers extracted three key feature sets: band values and vegetation indices, texture features extracted from a grey-scale co-occurrence matrix, and shape features. …”
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Deterministic and Stochastic Machine Learning Classification Models: A Comparative Study Applied to Companies’ Capital Structures by Joseph F. Hair, Luiz Paulo Fávero, Wilson Tarantin Junior, Alexandre Duarte

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