Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images by Lubna Farhi, Razia Zia, Zain Anwar Ali

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A Review of Simulations and Machine Learning Approaches for Flow Separation Analysis by Xueru Hao, Xiaodong He, Zhan Zhang, Juan Li

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Machine learning empowered coherent Raman imaging and analysis for biomedical applications by Yihui Zhou, Xiaobin Tang, Delong Zhang, Hyeon Jeong Lee

“…Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. …”
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ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay by Sebastian Duno-Miranda, David M. Warshaw, Shane R. Nelson

“…To address this shortfall, we introduce ATLAS, an open-source, platform independent software package that utilizes state-of-the-art machine learning algorithms to identify fluorescently labeled actin filaments and then track and analyze their motion in the IVMA. …”
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Leveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages: A Preliminary Application by Andrea Giuseppe di Stefano, Matteo Ruta, Gabriele Masera, Simi Hoque

“…This study identifies three key phases in a design process framework where machine learning can be applied to optimize energy consumption in early design stages. …”
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Sentiment analysis of emoji fused reviews using machine learning and Bert by Amit Khan, Dipankar Majumdar, Bikromadittya Mondal

“…In Spite of the fact that these elements are a significant part of the review comment provided by the customer, it is a common practice among the contemporary researchers to eliminate them right at the data-cleaning or the preprocessing stage. With an objective to provide a solution to the above drawback, we present a novel approach that performs sentiment analysis, with effective utilization of emojis and emoticons, upon the US Airline tweet dataset using various Machine Learning classifiers and the BERT model. …”
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Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare by Keyi Tang, Shuyuan Ma, Xiaohui Sun, Dongfang Guo

“…By optimizing classification models with metaheuristic algorithms, such as JADE, the study achieves significant performance improvements, highlighting the effectiveness of integrating advanced optimization techniques into ECG analysis processes. Ultimately, the findings underscore the potential of machine learning and deep learning algorithms in advancing automated ECG analysis for improved cardiovascular healthcare.…”
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Stroke Prediction Analysis using Machine Learning Classifiers and Feature Technique by Md. Monirul Islam, Sharmin Akter, Md. Rokunojjaman, Jahid Hasan Rony, Al Amin, Susmita Kar

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Analysis of the Effectiveness of Traditional and Ensemble Machine Learning Models for Mushroom Classification by Neny Sulistianingsih, Galih Hendro Martono

“…Overall, this study highlights the importance of algorithm selection tailored to data characteristics and supports the use of ensemble learning to boost predictive reliability. …”
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Comparative analysis of machine learning models for the detection of fraudulent banking transactions by Pedro María Preciado Martínez, Ricardo Francisco Reier Forradellas, Luis Miguel Garay Gallastegui, Sergio Luis Náñez Alonso

“…This research presents a comparative analysis of machine learning models for detecting fraudulent banking transactions, a growing problem in the digital financial sector. …”
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Machine Learning Methods for Predicting Cardiovascular Diseases: A Comparative Analysis by Aiym B. Temirbayeva, Arshyn Altybay

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SENTIMENT ANALYSIS OF JAKLINGKO APP REVIEWS USING MACHINE LEARNING AND LSTM by Maghfiroh Maulani, Windu Gata

“…The machine learning models used include Naïve Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Long Short-Term Memory (LSTM), categorizing sentiment into positive, negative, and neutral. …”
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Genomic selection in pig breeding: comparative analysis of machine learning algorithms by Ruilin Su, Jingbo Lv, Yahui Xue, Sheng Jiang, Lei Zhou, Li Jiang, Junyan Tan, Zhencai Shen, Ping Zhong, Jianfeng Liu

“…Abstract Background The effectiveness of genomic prediction (GP) significantly influences breeding progress, and employing SNP markers to predict phenotypic values is a pivotal aspect of pig breeding. Machine learning (ML) methods are usually used to predict phenotypic values since their advantages in processing high dimensional data. …”
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Machine Learning Analysis of Lipid and Metabolic Profiles in Adults with Adenoid Hyperplasia by Mansur Doğan, Merve Çiftçi, Yusuf Yeşil

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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…Machine learning algorithms were trained to classify hearing impairment thresholds and predict pure tone average values. …”
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Machine learning for grading prediction and survival analysis in high grade glioma by Xiangzhi Li, Xueqi Huang, Yi Shen, Sihui Yu, Lin Zheng, Yunxiang Cai, Yang Yang, Renyuan Zhang, Lingying Zhu, Enyu Wang

“…Abstract We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classification of high-grade glioma (HGG) and determined the optimal machine learning (ML) approach. This retrospective analysis included 184 patients (59 grade III lesions and 125 grade IV lesions). …”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Through ensemble learning and feature selection, the models cope well with big data sets with enhanced fault classification accuracy and robustness against noise. …”
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Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis by Marcos Uceta, Alberto del Cerro‐León, Danylyna Shpakivska‐Bilán, Luis M. García‐Moreno, Fernando Maestú, Luis Fernando Antón‐Toro

“…Methods In this article, using unsupervised machine learning (UML) algorithms, we analyze the relationship between electrophysiological activity of healthy teenagers and the levels of consumption they had 2 years later. …”
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Machine learning applications in the analysis of sedentary behavior and associated health risks by Ayat S Hammad, Ayat S Hammad, Ali Tajammul, Ismail Dergaa, Ismail Dergaa, Ismail Dergaa, Maha Al-Asmakh, Maha Al-Asmakh

“…As prolonged inactivity becomes a growing public health concern, researchers are increasingly utilizing machine learning (ML) techniques to examine and understand these patterns. …”
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The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release by Hongrui Gu, Zhou Fan, Gang Zhao, Huang Yang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu

“…Our analysis employs data primarily sourced from the Stellar Abundances and Galactic Evolution Survey (SAGES), which aims to observe much of the Northern Hemisphere. …”
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