Comparative Analysis of Single-Channel and Multi-Channel Classification of Sleep Stages Across Four Different Data Sets by Xingjian Zhang, Gewen He, Tingyu Shang, Fangfang Fan

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Cyclical hybrid imputation technique for missing values in data sets by Kurban Kotan, Serdar Kırışoğlu

“…In cases where there is missing data, the performance of machine learning models may differ depending on the amount of data contained in the data set. …”
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Reconstructing missing data of damaged buildings from post-hurricane reconnaissance data using XGBoost by Hyunje Yang, Jun-Whan Lee, Steven Klepac, Armando Ulises Santos Cruz, Arthriya Subgranon, Junfeng Jiao

“…This study introduces a machine learning approach based on extreme gradient boosting (XGBoost) to reconstruct missing structural features of the damaged buildings from four types of data (known structural, geospatial, hazard, and damage level information). …”
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BiLSTM-Based Parallel CNN Models With Attention and Ensemble Mechanism for Twitter Sentiment Analysis by Anas W. Abulfaraj

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Relationship Between the Results of Arm Swing Data From the OpenPose-Based Gait Analysis System and MDS-UPDRS Scores by Kenta Abe, Ken-Ichi Tabei, Keita Matsuura, Kazuyuki Kobayashi, Tomoyuki Ohkubo

“…In this study, we calculated the thresholds to distinguish between normal and abnormal gaits from the data of healthy subjects. We compared the peak-to-peak (P-P) data of the left and right arm swing and arm swing asymmetry (ASA) using an OpenPose-based gait analysis system developed in our previous study with the MDS-UPDRS scores. …”
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PTK6 mediated immune signatures revealed by single cell transcriptomic and multi omics big data analysis in cervical cancer by Fen Zhao, Huanxin Zhong, Lifang You, Yi Du, Changchang Huang

“…Methods We analyzed TCGA and GEO transcriptomic data with single-cell RNA sequencing datasets. Fifteen machine learning algorithms constructed prognostic models using immune infiltration-related genes. …”
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Strengthening nutrition routine data using institutionalized health management information systems for decision making: analysis of best practices and lessons learned from implementation in Burkina Faso by Ousmane Ouedraogo, Mahamadi Tassembedo, Assane Ouangare, Estelle Bambara, Paton Guillaume Paré, Boro Gosso, Fulbert Ilboudo, Céline Zongo, Rodrigue Kouamé, Mediatrice Kiburente, Saidou Diallo, Barbara Baille, Justine Marie Francoise Briaux, John Ntambi, Norah Stoops, Simeon Nanama

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Data analysis in the healthcare context: a smart cities perspective by Fabiane Florencio de Souza, Alana Corsi, Clayton Pereira de Sá, Regina Negri Pagani, João Luiz Kovaleski

“…For this, a bibliographic review was carried out, using the Methodi Ordinatio methodology, resulting in a portfolio of articles with scientific relevance, which was the source of data collection and analysis. Thus, the results obtained demonstrate that the most studied technologies in this context seek to analyze data with Big Data Analytics techniques, encompassing Artificial Intelligence and Machine Learning, which analyze data generated by "devices" in which Electronic Health Records are collected, and "sensors" often associated with the Internet of Things. …”
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A multi-source data-driven framework for probabilistic flood risk assessment using cascade machine learning models: case study in the Sichuan Basin by Yan Lu, Ying Huang, Xiaoling Liu

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Using Machine Learning to Unveil the Dynamics of Insider Trading in Financial Markets by Ilona Vladimirovna Tregub, Alexander Sebastian Wagner

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Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study by Vladimir Koković, Kosta Pavlović, Andjela Mijanović, Slavko Kovačević, Ivan Mačužić, Vladimir Božović

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Infilling of missing rainfall radar data with a memory-assisted deep learning approach by J. Meuer, L. M. Bouwer, L. M. Bouwer, F. Kaspar, R. Lehmann, W. Karl, T. Ludwig, C. Kadow

“…This novel approach represents a step forward in hydrological applications, potentially improving the way we predict and manage water-related events by increasing the accuracy and reliability of precipitation data analysis.</p>…”
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Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms by Manfred Wiessner, Ernst Gamsjäger

“…The clusters obtained by this procedure agree well with the labeled data. By supervised learning via a support vector machine, hyperplanes are constructed that allow separating the clusters from each other based on the X-ray measurements. …”
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Analyzing the vulnerabilities in Split Federated Learning: assessing the robustness against data poisoning attacks by Aysha-Thahsin Zahir-Ismail, Raj Shukla

“…This research provides a comprehensive study, analysis, and presentation of the impact of data poisoning attacks on Split Federated Learning (SFL). …”
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Towards Improved Turbomachinery Measurements: A Comprehensive Analysis of Gaussian Process Modeling for a Data-Driven Bayesian Hybrid Measurement Technique by Gonçalo G. Cruz, Xavier Ottavy, Fabrizio Fontaneto

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Machine Learning for Accurate Office Room Occupancy Detection Using Multi-Sensor Data by Yusuf Ibrahim, Umar Yusuf Bagaye, Abubakar Ibrahim Muhammad

“…In this paper, we present a comparative study of several machine learning (ML) approaches for accurate office room occupancy detection through the analysis of multi-sensor data. …”
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Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with neural networks by Stefan Hildebrand, Sandra Klinge

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A Comparative Study of Unsupervised Deep Learning Methods for Anomaly Detection in Flight Data by Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri

“…The paper puts forth a compelling case for shifting from the existing method, which relies on examining events through threshold exceedances, to a deep learning-based approach that offers a more proactive style of data analysis. …”
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Activity recognition in motor-manual cross-cutting operations by machine learning on multimodal data by Stelian Alexandru Borz, Tomi Kaakkurivaara, Gabriel Osei Forkuo, Nopparat Kaakkurivaara

“…Advancements in machine learning and innovative data loggers present opportunities to reconsider and enhance these methods. …”
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Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

“…Consequently, there is no unified approach for predicting wildfire occurrences using remote sensing data and AI techniques. The goal of this study is to explore the potential of predicting wildfire occurrences using various available environmental parameters - meteorological, geo-spatial, and anthropogenic - and machine learning (ML) algorithms. …”
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