Social Factors Influencing Healthcare Expenditures: A Machine Learning Perspective on Australia’s Fiscal Challenges by Wei Gu, Zhantian Zhang, Ou Liu

“…This study employs machine learning techniques to investigate the key determinants of healthcare expenditures in Australia from 2011 to 2021. …”
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Challenges in Imputation of ICU Time-Series Data: A Comparison of Classical and Machine Learning Approaches by Favio Salinas, Marvin Agristean, Sobhan Moazemi, Steven Kessler, Bastian Dewitz, Hug Aubin, Artur Lichtenberg, Falko Schmid

“… Handling missing data is a major challenge in machine learning, particularly in clinical settings using electronic health records like ICU time-series data. …”
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Methods and reliability study of moral education assessment in universities: A machine learning-based approach by Ting Jin

“…The study demonstrates how machine learning efficiently assesses student moral education performance by leveraging PCA to identify patterns and using ML models to make accurate predictions. …”
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The Financial Language of Gender: A Consumer Study Using Machine Learning, Statistical, and Linguistic Analyses by Andrzej Cwynar, Kamil Filipek, Paweł Nowak, Robert Porzak, Dorota Weziak-Bialowolska

“…These differences were observed in word frequency, metaphor usage, professionalization of language, and conversational strategies, confirming patterns known from previous research in other subject domains. …”
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Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review by ShiYing Shen, Wenhao Qi, Jianwen Zeng, Sixie Li, Xin Liu, Xiaohong Zhu, Chaoqun Dong, Bin Wang, Yankai Shi, Jiani Yao, Bingsheng Wang, Xiajing Lou, Simin Gu, Pan Li, Jinghua Wang, Guowei Jiang, Shihua Cao

“…We summarized the technical approaches, revealed the association patterns between behavioral features and mental disorders, and explored potential directions for future advancements. …”
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Internet of Things Enabled Machine Learning-Based Smart Systems: A Bird’s Eye View by Ashish Kumar Rastogi, Swapnesh Taterh, Billakurthi Suresh Kumar

“…Machine learning (ML) helps the Internet of Things (IoT) become widely used by automatically identifying data patterns and extracting important insights from the vast pool of observed data. …”
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A Machine Learning-Reconstructed Dataset of River Discharge, Temperature, and Heat Flux into the Arctic Ocean by Zihan Wang, Fengming Hui, Xiao Cheng

“…Our understanding of these patterns is limited by substantial data gaps. To address this, we present the Reconstructed Arctic-draining river DIscharge and Temperature (RADIT) dataset, a comprehensive record of reconstructed daily discharge, temperature, and heat flux for 25 major Arctic rivers from 1950 to 2023. …”
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A Comparative Analysis of Machine Learning Algorithms for Classification of Diabetes Utilizing Confusion Matrix Analysis by Maad M. Mijwil, Mohammad Aljanabi

“…Machine learning algorithms can scrutinize vast quantities of data from electronic health records, medical images, and other sources to identify patterns and make predictions, which can support healthcare professionals and experts in making better-informed decisions, enhancing patient care, and determining a patient's health status. …”
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Machine Learning Algorithm for Assessing Photovoltaic Panels Partial Shading Losses based on Inverter Data by Armando Luís Sousa Araujo, Tiago Francisco Pires

“…In this paper, we document and describe two distinct Machine Learning models that aim to identify and assess the impact of partial shading in a real case study. …”
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Shoreline dynamics prediction using machine learning models: from process learning to probabilistic forecasting by Afshar Adeli, Afshar Adeli, Ali Dastgheib, Ali Dastgheib, Dano Roelvink, Dano Roelvink, Dano Roelvink

“…Through comprehensive testing across one complex shoreline evolution scenario, this research identifies the ConvLSTM model—trained on 2D gridded data— as the optimal machine learning approach suited for addressing specific shoreline complexities and evolution patterns. …”
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Robust fault detection and classification in power transmission lines via ensemble machine learning models by Tahir Anwar, Chaoxu Mu, Muhammad Zain Yousaf, Wajid Khan, Saqib Khalid, Ahmad O. Hourani, Ievgen Zaitsev

“…This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms—including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks—are evaluated. …”
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HAML-IRL: Overcoming the Imbalanced Record Linkage Problem Using Hybrid Active Machine Learning by Mourad Jabrane, Mouad JBEL, Imad HAFIDI, Yassir ROCHD

“…By combining and balancing informativeness, which selects record pairs to reduce model uncertainty, and representativeness, which ensures the chosen pairs reflect the overall dataset patterns, our hybrid approach, called Hybrid Active Machine Learning for Imbalanced Record Linkage (HAML-IRL), demonstrates significant advancements.HAML-IRL achieves an average 12% improvement in F1-scores across eleven real-world datasets, including structured, textual, and dirty data, when compared to state-of-the-art AML methods. …”
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Innovative Techniques for Enhancing the Reliability of Machine Learning Classifiers in Protein-Protein Interaction Hotspot Prediction by M. O. Otun

“…Accurate prediction of these interaction hotspots is essential for understanding molecular mechanisms and facilitating drug discovery. Machine learning (ML) classifiers have emerged as powerful tools for PPI hotspot prediction due to their ability to identify complex patterns in large biological datasets. …”
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Enhancing decision-making on detractor-causing failures: an approach combining data mining and machine learning by Yuri A. V. da Silva, Geraldo Cardoso de Oliveira Neto, Gustavo Lima, Sidnei A. de Araújo, Rodrigo Neri Bueno da Silva, Francisco Elanio Bezerra, Marlene Amorim

“…The proposed approach employs Decision Tree (DT) algorithms to uncover patterns linked to service failures. The results highlight two primary issues: delivery discrepancies and product returns due to dissatisfaction, both of which directly affect customer loyalty. …”
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The influence of digital transformation on the total factor productivity of enterprises: the intermediate role of human-machine cooperation by Qi Xiong, Jingyi Yang, Xiuwu Zhang, Yarui Deng, Yao Gui, Xiaoyang Guo

“…The findings reveal that (1) digital transformation significantly enhances TFP, a conclusion that remains valid after considering endogeneity issues and conducting a series of robustness checks, thereby refuting the productivity paradox associated with digital transformation; (2) furthermore, the enabling effect of digital transformation on TFP varies significantly across enterprises due to differences in ownership, factor intensity, asset size, degree of marketization, tax preference, and geographical location; (3) in terms of the impact mechanism, digital transformation promotes TFP by enabling efficient human–machine collaboration patterns. This study not only complements research on the influencing factors of microenterprise TFP, providing empirical evidence for improving enterprise production efficiency, but also offers insights for local governments to formulate differentiated digital policies.…”
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Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach by Carlos Alejandro Perez Garcia, Patrizia Tassinari, Daniele Torreggiani, Marco Bovo

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Structuring a textile knitting dataset for machine learning and data mining applicationsMendeley Data by Toufique Ahmed, Abu Saleh Muhammad Junayed

“…Knitting is a vital sector of the fabric manufacturing industry. Concurrently, machine learning is emerging as a highly regarded technique for predicting patterns and classifying various parameters derived from datasets. …”
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Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning by Aggrey Muhebwa, Colin J. Gleason, Dongmei Feng, Jay Taneja

“…Abstract Current machine learning methods for discharge prediction often employ aggregated basin‐wide hydrometeorological data (lumped modeling) for parametric and non‐parametric training. …”
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Short-Term Electric Load Forecasting for an Industrial Plant Using Machine Learning-Based Algorithms by Oğuzhan Timur, Halil Yaşar Üstünel

“…Their capacity to analyze intricate patterns and enhance prediction accuracy renders them a favored option for enhancing energy management and operational efficiency. …”
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Enhancing DDoS Attack Classification through SDN and Machine Learning: A Feature Ranking Analysis by Aymen AlAwadi, Kawthar Rasoul ALesawi

“…Due to the growing dependence of digital services on the Internet, Distributed Denial of Service (DDoS) attacks are a common threat that can cause significant disruptions to online operations and financial losses. Machine learning (ML) offers a promising way for early DDoS attack detection due to its ability to analyze large datasets and identify patterns. …”
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