MACHINE LEARNING TECHNIQUES FOR RETINOPATHY DETECTION IN DIABETIC PATIENTS by Ajay Kushwaha, Ahankari Sachin Suresh, Chennoju Phanindra, Anil Kumar Sahu, Devanand Bhonsle, Yamini Chouhan

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Data Augmentation Techniques for fMRI Data: A Technical Survey by Valentina Sanchez, Cicek Guven, Gonzalo Napoles, Marie Safar Postma

“…The application of machine learning to fMRI data classification, prediction, and analysis tasks has experienced rapid growth in recent years. …”
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A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors by Ahmad Aminzadeh, Sasan Sattarpanah Karganroudi, Soheil Majidi, Colin Dabompre, Khalil Azaiez, Christopher Mitride, Eric Sénéchal

“…This research combines updated concepts from the Internet of Things, machine learning, multi-sensor data collection, structured data mining, and cloud-based data analysis. …”
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Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System by Xiongwei Zhang, Hager Saleh, Eman M. G. Younis, Radhya Sahal, Abdelmgeid A. Ali

“…It is considered the most significant streaming data source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. …”
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Utilizing machine learning and digital twin technology for rock parameter estimation from drilling data by Abdullah Khan, Yiming Li, Muhammad Shoaib, Umair Sajjad, Fuxin Rui

“…This paper offers a comprehensive review of the current methodologies for estimating rock parameters derived from drilling tests, encompassing experimental, analytical, numerical, and machine learning (ML) approaches, as well as digital twin (DT) technologies. …”
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Second-generation downscaled earth system model data using generative machine learningOEDI by Grant Buster, Brandon N. Benton, Deeksha Rastogi, Shih-Chieh Kao, Guilherme Castelao, Jordan Eisenman

“…This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). …”
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Multimodal data integration with machine learning for predicting PARP inhibitor efficacy and prognosis in ovarian cancer by Xi’an Xiong, Li Cai, Li Cai, Zhen Yang, Zhongping Cao, Nayiyuan Wu, Nayiyuan Wu, Qianxi Ni

“…The integration of multimodal data with machine learning holds significant potential for enhancing prognosis prediction in PARPi treatment for ovarian cancer.…”
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Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data. by Dominik Schaack, Markus A Weigand, Florian Uhle

“…Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). …”
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Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique by Shafi Muhammad Pathan, Abdul Ghani Pathan, Muhammad Saad Memon

“…This study investigates shovel excavation performance across various rockmass conditions by integrating real-time performance assessments, rockmass property analysis, and machine learning techniques. Correlation analysis revealed significant positive relationships between Total Loading Time (TLT) and selected rock properties, specifically uniaxial compressive strength (UCS), tensile strength (TS) cohesion (C), and moisture content (M), while a negative correlation was observed with wet bulk density (WBD). …”
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Adaptive Weighted Diversity Ensemble Learning Approach for Fetal Health Classification on Cardiotocography Data by K. Aditya Shastry, Mohan Sellappa Gounder, T. G. Mohan Kumar, D. U. Karthik, V. Sushma, D. Subashree

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Data science and automation in the process of theorizing: Machine learning's power of induction in the co-duction cycle. by Daan Kolkman, Gwendolyn K Lee, Arjen van Witteloostuijn

“…Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. …”
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Analyzing risk factors and handling imbalanced data for predicting stroke risk using machine learning by Adiwijaya Adiwijaya, Nur Ghaniaviyanto Ramadhan

“…Effective preventive measures are crucial to minimize stroke risk, and using predictive methods based on data analysis from the clinical examination dataset over the last three years (2019-2021), known as the general checkup (GCU) dataset, presents an innovative approach. …”
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Data Elements Marketization and Corporate Investment Efficiency: Causal Inference via Double Machine Learning by Yeteng Ma, Zhuo Li, Li He

“…Based on data from Chinese A-share listed companies spanning 2007 to 2023, this study systematically evaluates the impact of data element marketization on corporate investment efficiency using a Double Machine Learning approach. …”
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Discovering action insights from large-scale assessment log data using machine learning by Minyoung Yun, Minjeong Jeon, Heyoung Yang

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A machine learning framework for predicting healthcare utilization and risk factors by Yead Rahman, Prerna Dua

“…Medicaid data, with its vast scale and heterogeneity, presents significant challenges in predictive modeling and healthcare analytics. …”
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Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy by Tamás Ungvári, Döme Szabó, András Győrfi, Zsófia Dankovics, Balázs Kiss, Judit Olajos, Károly Tőkési

“…The aim of this study was to determine whether planning CT scans can predict which patients are more likely to develop lung lesions after treatment. A retrospective analysis of 242 patient records was performed using different machine learning models. …”
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A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data by Shahid Ahmad Wani, SMK Quadri, Mohammad Shuaib Mir, Yonis Gulzar

“…Most methods demonstrated robust capabilities in annotating major cell types and identifying rare cell populations, though naive Bayes was the least effective due to its inherent limitations in handling high-dimensional and interdependent data. This study provides valuable insights into the relative strengths and weaknesses of machine learning methods for single-cell annotation, offering guidance for selecting appropriate techniques in scRNA-seq analyses.…”
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Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning by Sarah Dandou, Kriti Amin, Véronique D’Hondt, Jérôme Solassol, Olivier Dereure, Peter J. Coopman, Ovidiu Radulescu, Holger Fröhlich, Romain M. Larive

“…We used machine learning algorithms and pre-processed genomic data to test whether they could contain useful information to improve the progression-free survival (PFS) prediction. …”
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Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data by Silvia Elaine Cardozo Macedo, Marina de Borba Oliveira Freire, Oscar Schmitt Kremer, Ricardo Bica Noal, Fabiano Sandrini Moraes, Mauro André Barbosa Cunha

“…Epidemiological, clinical, and laboratory data were processed by machine learning algorithms in order to identify patterns. …”
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Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data by Gabriel Ferreira dos Santos Silva, Roberta Moreira Wichmann, Francisco Costa da Silva Junior, Alexandre Dias Porto Chiavegatto Filho

“…Leveraging advancements in technology, such as machine learning (ML) algorithms, offers the potential to improve neonatal care by enabling precise prediction and prevention of mortality risks. …”
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