Automated Detection of Poor-Quality Scintigraphic Images Using Machine Learning by Anil K. Pandey, Akshima Sharma, Param D. Sharma, Chandra S. Bal, Rakesh Kumar

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Predicting properties of quantum systems by regression on a quantum computer by Andrey Kardashin, Yerassyl Balkybek, Vladimir V. Palyulin, Konstantin Antipin

“…Many quantum machine learning techniques have been developed for solving classification problems, such as distinguishing between phases of matter or quantum processes. …”
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regAL: Python package for active learning of regression problems by Elizaveta Surzhikova, Jonny Proppe

“…Although active learning is widely studied for classification problems (discrete outcomes), comparatively few works handle this method for regression problems (continuous outcomes). …”
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The Effect of Transitive Closure on the Calibration of Logistic Regression for Entity Resolution by Yumeng Ye, John Talburt

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A nomogram for diagnosis of BI-RADS 4 breast nodules based on three-dimensional volume ultrasound by Xianping Jiang, Chen Chen, Jincao Yao, Liping Wang, Chen Yang, Wei Li, Di Ou, Zhiyan Jin, Yuanzhen Liu, Chanjuan Peng, Yifan Wang, Dong Xu

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Decoding healthcare resilience for sustainable development goal 3: A machine learning analysis of global health systems by Ibrahim Alnafrah, Alexander Poroshin

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A HYBRID APPROACH FOR MALARIA CLASSIFICATION USING CNN-BASED FEATURE EXTRACTION AND TRADITIONAL MACHINE LEARNING CLASSIFIERS by omar Mohammed Alzakholi, Walat A. Ahmed, Bafreen N. Mohammed, Asaad Kh. Ibrahim

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Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan by Farnaz, Nuthammachot Narissara, Shabbir Rabia, Iqbal Benazeer

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ENSEMBLE BAGGING WITH ORDINAL LOGISTIC REGRESSION TO CLASSIFY TODDLER NUTRITIONAL STATUS by Luthfia Hanun Yuli Arini, Solimun Solimun, Achmad Efendi, Adji Achmad Rinaldo Fernandes

“…The best classification method obtained was bagging logistic regression with a bootstrap number of 500 and obtained an accuracy value of 85%, sensitivity of 87.2%, specificity of 72.6%, and F1-Score of 79.3%.…”
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Expectation-maximization alternating least squares for tensor network logistic regression by Naoya Yamauchi, Hidekata Hontani, Tatsuya Yokota, Tatsuya Yokota

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Exploiting DBSCAN and Combination Strategy to Prioritize the Test Suite in Regression Testing by Zikang Zhang, Jinfu Chen, Yuechao Gu, Zhehao Li, Rexford Nii Ayitey Sosu

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Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm by Mirko Stumpo, Monica Laurenza, Simone Benella, Maria Federica Marcucci

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Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression by Ezekiel N. N. Nortey, Reuben Pometsey, Louis Asiedu, Samuel Iddi, Felix O. Mettle

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Revolutionizing heart attack prognosis: Introducing an innovative regression model for prediction by Hanaa Albanna, Madhav Raj Theeng Tamang, Chandan Patel, Mhd Saeed Sharif

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Automated classification of online reviews of otolaryngologists by Jake G. Stenzel, Nicholas R. Schultz, Michael J. Marino

“…Abstract Objectives The study aimed to extract online comments of otolaryngologists in the 20 most populated cities in the United States from healthgrades.com, develop and validate a natural language processing (NLP) logistic regression algorithm for automated text classification of reviews into 10 categories, and compare 1‐ and 5‐star reviews in directly‐physician‐related and non‐physician‐related categories. …”
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Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms by Zilong Pu, Miaomiao Yang, Mingzhi Jiao, Duan Zhao, Yu Huo, Zhi Wang

“…In this work, an integrated model that makes the classification stage results one of the feature inputs for the concentration regression stage was employed, successfully reducing the RMSE of the concentration regression results. …”
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ENSEMBLE RESAMPLING SUPPORT VECTOR MACHINE, MULTINOMIAL REGRESSION TO MULTICLASS IMBALANCED DATA by Laila Qadrini, Hikmah Hikmah, Elviani Tande, Ignasius Presda, Aulia Atika Maghfirah, Nilawati Nilawati, Handayani Handayani

“…This comparison is conducted under pre- and post-resampling conditions, with the evaluation metrics being accuracy, sensitivity, and specificity. The analysis of classification outcomes across the three datasets suggests that the ensemble resampling SVM approach and multinomial regression exhibit superior performance compared to the ensemble SVM and multinomial regression approaches when applied to non-resampled data. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…The physicochemical parameters for broccoli shelf life were predicted using three methods: support vector regression (SVR), random forest classification (RF), and 2D convolutional neural network (2D-CNN) models. …”
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Analysis of spatial and temporal distribution of grassland yield under grassland ecological subsidy policy by ZhanMing Qiao, Feng Lai, ZengLian Xiong, QuanMin Dong, Yang Yu, ChunPing Zhang, Quan Cao

“…The results were as follows: (1) The grassland yield in the Three River Headwater Region gradually decreased from east to west; From the classification space of grassland, it can be seen that with the alpine meadow as the boundary, the spatial distribution of grassland yield in the Three River Headwater Region forms an obvious watershed. (2) The overall grassland yield in the Three River Headwater Region increased first and then decreased, but the overall change trend was not large. (3) The positive correlation between the average annual grassland yield and the average annual precipitation in the Three River Headwater Region was nearly 58%, which was distributed in concentrated contiguous areas, and the eastern and northwestern parts of Three River Headwater Region were more concentrated; The area of positive correlation with annual mean temperature is nearly 48%. …”
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Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants by Aikaterini-Artemis Agiomavriti, Maria P. Nikolopoulou, Thomas Bartzanas, Nikos Chorianopoulos, Konstantinos Demestichas, Athanasios I. Gelasakis

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