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

“…Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. …”
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Dynamic demand response strategies for load management using machine learning across consumer segments by Ravi Kumar Goli, Nazeer Shaik, Manju Sree Yalamanchili

“…Demand Response (DR) programs, which provide financial incentives to promote participation, aim to modify customer energy usage patterns, especially during periods of peak demand. …”
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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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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

“…Based on machine learning regression methods and ERA5-Land reanalysis data, we designed distinct reconstruction frameworks for discharge and temperature, considering the different characteristics of the observational data. …”
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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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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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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

“…Recent studies have emphasized the pervasive utilization of machine learning-based algorithms in the field of electric load forecasting for industrial plants. …”
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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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Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano by C. X. Ren, A. Peltier, V. Ferrazzini, B. Rouet‐Leduc, P. A. Johnson, F. Brenguier

“…In this study we leverage machine learning techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion island) to describe specific patterns of seismic signals recorded during eruptions. …”
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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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Unveiling the complementariness of robotic tablet dispensing machines for elderly care: A bibliometric data analysis by Sunday Adewale Olaleye, Olaleye Esther Olubunmi, Berhanemeskel Weldegerima Atsbeha, Mulugeta Negash Wodaje

“…Data analysis was performed using the Biblioshiny App through RStudio, examining publication trends, authorship patterns, citation networks, and other bibliometric indicators. …”
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Mind vs. machine: Comparative analysis of metaphor-related word translation by human and AI systems by Zhengjian Li, Lang Chen

“…The present study presents a comparative analysis of the translation processes and outcomes of human translators, Neural Machine Translation (NMT) systems and Large Language Models (LLMs) focusing on the translation of Metaphor-related Words (MRW). …”
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Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review by Boris I, Kseniia Barashok, Yongjoon Choi, Yeongil Choi, Mohammed Aslam, Jaesun Lee

“…However, conventional ultrasonic approaches face challenges in analyzing complex signals, limiting their accuracy and efficiency in certain applications. The advent of machine learning (ML) has revolutionized ultrasonic data analysis by utilizing advanced data mining and pattern recognition capabilities to decode intricate signal patterns. …”
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Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data by Christoph Herbert, Adriano Camps, Florian Wellmann, Mercedes Vall‐llossera

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