Optimizing agricultural yield: a predictive model for profitable crop harvesting based on market dynamics by Nilesh P. Sable, Nilesh P. Sable, Vinod Kumar Shukla, Parikshit N. Mahalle, Vijayshri Khedkar

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Compositional transformations can reasonably introduce phenotype-associated values into sparse features by George I. Austin, Tal Korem

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Analysis of soil suitability for agricultural needs using machine learning methods by Kurashkin Sergei, Kravtsov Kirill, Kukartsev Anatoly, Boyko Andrey, Volneikina Ekaterina

“…This research highlights the value of machine learning in optimizing agricultural practices by enabling data-driven soil assessment. …”
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IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning by U. A. Vishniakou, Xia YiWei

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Student dropout prediction through machine learning optimization: insights from moodle log data by Markson Rebelo Marcolino, Thiago Reis Porto, Tiago Thompsen Primo, Rafael Targino, Vinicius Ramos, Emanuel Marques Queiroga, Roberto Munoz, Cristian Cechinel

“…These findings underscore the potential of targeted machine learning approaches to facilitate early identification of at-risk students, thereby enabling timely interventions and improving educational outcomes.…”
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Interpreting microbiologically influenced stress corrosion with machine learning and theoretical analysis by Bo Liu, Boxin Wei, Cuiwei Du, Xiaogang Li

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Application of machine learning in acute upper gastrointestinal bleeding: bibliometric analysis by Qun Li, Guolin Chen, Qiongjie Li, Dongna Guo

“…The study found that these papers mainly focus on three core research hotspots: deepening clinical consensus, precise analysis of medical images, and optimization of data integration and decision support systems.ConclusionsThis study summarizes the latest advancements in the application of machine learning to AUGIB research. …”
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A Machine Learning Approach for User Behavior Analysis in Developing Websites by Aeesha Shaheen

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Machine learning classification meets migraine: recommendations for study evaluation by Igor Petrušić, Andrej Savić, Katarina Mitrović, Nebojša Bačanin, Gabriele Sebastianelli, Daniele Secci, Gianluca Coppola

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A Social Media Sentiment Analysis Using Machine Learning Approaches by Noor Salah Irzooqi Al-Agele, Didem KIVANÇ TÜRELİ

“…With consideration for the most suitable models for precisely classifying sentiments within social media data, this study aims to evaluate the efficacy of machine learning algorithms in analyzing sentiment text that people post or comment on Twitter, thus bridging the research gap in the analysis of sentiment in an understudied domain. a set of machine learning algorithms was applied along with feature extraction methods, including TF-IDF and Natural Language Processing (NLP). …”
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Machine Learning-Based Approaches and Comparisons for Estimating Missing Meteorological Data and Determining the Optimum Data Set in Nuclear Energy Applications by Fatih Topaloglu

“…The first motivation of the study was to define the estimation of missing data in the meteorological data set and its usability in the nuclear energy industry by using Machine Learning (ML)-based Linear Regression (LR), Decision Trees (DT) and Random Forest (RF) algorithms. …”
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Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model. by Arezoo Bozorgmehr, Anika Thielmann, Birgitta Weltermann

“…In our cross-sectional study with 136 general practices, 26.4% of 550 practice assistants showed high chronic stress. As machine learning strategies offer the opportunity to improve understanding of chronic stress by exploiting complex interactions between variables, we used data from our previous study to derive the best analytic model for chronic stress: four common machine learning (ML) approaches are compared to a classical statistical procedure.…”
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Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data by Nandhini Santhanam, Hee E. Kim, David Rügamer, Andreas Bender, Stefan Muthers, Chang Gyu Cho, Angelika Alonso, Kristina Szabo, Franz-Simon Centner, Holger Wenz, Thomas Ganslandt, Michael Platten, Christoph Groden, Michael Neumaier, Fabian Siegel, Máté E. Maros

“…Therefore, we developed machine learning models using locoregional weather data to forecast daily acute ischemic stroke (AIS) admissions. …”
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Utilising machine learning classification models for meteorological drought monitoring and analysis by Iqra Mumtaz, Rizwan Niaz, Zamama Sajid, Abdu Qaid Alameri, Zulfiqar Ali, Khaled A. Gepreel

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Multiple Machine Learning as a Powerful Tool for Star Cluster Analysis by Denilso Camargo

“…This work proposes a multiple machine learning method (MMLM) aiming to improve the accuracy and robustness of the analysis of star clusters. …”
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Survival analysis using machine learning in transplantation: a practical introduction by Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque

“…The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making. …”
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Predictive analysis of heart disease using quantum-assisted machine learning by Mehroush Banday, Sherin Zafar, Parul Agarwal, M. Afshar Alam, Siddhartha Sankar Biswas, Imran Hussain, K. M. Abubeker

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Analysis and prediction of infectious diseases based on spatial visualization and machine learning by Yunyun Cheng, Yanping Bai, Jing Yang, Xiuhui Tan, Ting Xu, Rong Cheng

“…Then, autoregressive integrated moving average model (ARIMA), extreme learning machine (ELM), support vector regression (SVR), wavelet neural network (Wavelet), recurrent neural network (RNN) and long short-term memory (LSTM) were used to predict COVID-19 epidemic data in Guangdong Province, China; And the prediction performance of each model was compared through prediction accuracy indicators. …”
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Integrating Sentiment Analysis With Machine Learning for Cyberbullying Detection on Social Media by Maram Fahaad Almufareh, Noor Zaman Jhanjhi, Mamoona Humayun, Ghadah Naif Alwakid, Danish Javed, Saleh Naif Almuayqil

“…These steps ensure high-quality input data for machine learning models which significantly enhances their performance. …”
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Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis by Jun Zhao, Huayu Zhong, Congfeng Wang

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