Fusion of BIM and SAR for Innovative Monitoring of Urban Movement – Towards 4D Digital Twin by C.-H. Yang, C. Stemmler, N. Wolf, M. Koppe, T. Rudolph, A. Müterthies

“…This fusion step is implemented by machine learning, employing a novel distance metric adapted through dimensionality reduction. …”
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Impact of the COVID-19 pandemic on dengue in Brazil: Interrupted time series analysis of changes in surveillance and transmission. by Kirstin Oliveira Roster, Tiago Martinelli, Colm Connaughton, Mauricio Santillana, Francisco A Rodrigues

“…In this study, we estimated the gap between expected and observed dengue cases in each Brazilian state from March to April 2020 using an interrupted time series design with forecasts from machine learning models. We then decomposed the gap into the contributions of pandemic-induced changes in disease surveillance and transmission dynamics, using proxies for care availability and care seeking behavior. …”
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Bibliometric analysis of financial management research issues in the information technologies field by O.Yu. Polchanov

“…Modern approaches to financial management are increasingly based on advanced computing techniques, such as machine learning and blockchain. The transformation of management approaches challenges traditional risk reduction systems, investment analysis and corporate governance. …”
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Assessing Predictive Ability of Dynamic Time Warping Functional Connectivity for ASD Classification by Christopher Liu, Juanjuan Fan, Barbara Bailey, Ralph-Axel Müller, Annika Linke

“…We used PC fcMRI data and DTW fcMRI data as predictors in machine learning models for classifying autism spectrum disorder (ASD). …”
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AI in Agriculture: Advanced Smart Irrigation for Enhanced Crop Yields by Rahman F.

“…The proposed smart irrigation system will leverage machine learning algorithms, predictive analytics, as well as other AI technologies and real time data in the soil moisture, weather conditions, crop health and water usage. …”
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A Correlation-Based Feature Selection Algorithm for Operating Data of Nuclear Power Plants by Yuxuan He, Hongxing Yu, Ren Yu, Jian Song, Haibo Lian, Jiangyang He, Jiangtao Yuan

“…Nuclear power plant operating data are characterized by a large variety, strong coupling, and low data value density. When using machine learning techniques for fault diagnosis and other related research, feature selection enables dimensionality reduction while maintaining the physical meaning of the original features, thus improving the computational efficiency and generalization ability of the learning model. …”
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Beyond the numbers: App-enabled stroke prediction system for high-risk individuals in imbalanced datasets by Abrar Faiaz Eram, Aliva Sadnim Mahmud, Marwan Mostafa Khadem, Md Amimul Ihsan

“…Background:: Brain stroke, characterized by interrupted blood flow to the brain, poses significant mortality risks and quality-of-life impacts. While machine learning approaches show promise in stroke prediction, current research often relies on synthetic data to address dataset imbalance, potentially compromising real-world model performance in clinical settings. …”
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GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Extensive comparison across 50 diverse single cell datasets against 18 existing methods demonstrates that GNODEVAE consistently outperforms three major categories of benchmark methods: 8 machine learning dimensionality reduction techniques, 7 deep generative VAE variants, and 3 graph-based and contrastive learning deep predictive models. …”
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Disentangling High-Paced Alternating I/O in Gaze-Based Interaction by Yulia G. Shevtsova, Artem S. Yashin, Sergei L. Shishkin, Anatoly N. Vasilyev

“…When playing the game, 15 volunteers selected screen objects using a 500 ms dwell time without additional actions for intention confirmation. By applying machine learning algorithms to gaze features and action context information, we achieved a threefold reduction in false positives, improved the quality of in-game decisions, and increased participant satisfaction with system ergonomics. …”
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Domain- and Language-Adaptable Natural Language Interface for Property Graphs by Ioannis Tsampos, Emmanouil Marakakis

“…However, existing solutions are typically limited to high-resource languages; are difficult to adapt to evolving domains with limited annotated data; and often depend on Machine Learning (ML) approaches, including Large Language Models (LLMs), that demand substantial computational resources and advanced expertise for training and maintenance. …”
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Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration by Pin Chen, Xiyue Wang, Zexia Yang, Changfeng Shi

“…Urban agglomerations with a high economic activity and population density are key areas for carbon emissions and pioneers in achieving carbon peaking and the Sustainable Development Goals (SDGs). This study combines machine learning with an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to uncover the mechanisms driving carbon peaking disparities within these regions. …”
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Positron emission tomography imaging biomarker and artificial intelligence for the characterization of solitary pulmonary nodule by Ashish Kumar Jha, Ashish Kumar Jha, Umeshkumar Baburao Sherkhane, Umeshkumar Baburao Sherkhane, Nilendu C. Purandare, Nilendu C. Purandare, Leonard Wee, Andre Dekker, Venkatesh Rangarajan, Venkatesh Rangarajan

“…The literature suggests using combined Positron Emission Tomography (PET) and Computed Tomography (CT) to characterise a SPN. Radiomics and machine learning are other promising technologies which can be utilised to characterise the SPN.PurposeThis study explores the potential of PET radiomics signatures and machine learning algorithms to characterise the SPN.MethodsThis retrospective study aimed to characterize solitary pulmonary nodules (SPNs) using PET radiomics. …”
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Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell by E. Acuña Espinoza, F. Kratzert, D. Klotz, D. Klotz, M. Gauch, M. Álvarez Chaves, R. Loritz, U. Ehret

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Exploiting Data Distribution: A Multi-Ranking Approach by Beata Zielosko, Kamil Jabloński, Anton Dmytrenko

“…So-called local rankings were constructed for local data sources using an approach based on machine learning models, i.e., the greedy algorithm for the induction of decision rules. …”
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Personalized prediction of esophageal cancer risk based on virtually generated alcohol data by Oswald Ndi Nfor, Pei-Ming Huang, Ming-Fang Wu, Ke-Cheng Chen, Ying-Hsiang Chou, Mong-Wei Lin, Ji-Han Zhong, Shuenn-Wen Kuo, Yu-Kwang Lee, Chih-Hung Hsu, Jang-Ming Lee, Yung-Po Liaw

“…We analyzed data from 86,845 individuals, including 763 diagnosed EC patients, sourced from the Taiwan Biobank. Eight machine learning models were employed: Bayesian Network, Decision Tree, Ensemble, Gradient Boosting, Logistic Regression, LASSO, Random Forest, and Support Vector Machines (SVM). …”
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Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches by Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin

“…By the 100th epoch, training and validation losses reached 0.0023 and 0.0029, respectively, indicating a substantial improvement over baseline models. These methodologies extend beyond mRNA vaccine research, demonstrating versatility across diverse machine learning domains. …”
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Growth Differentiation Factor 15 Predicts Cardiovascular Events in Peripheral Artery Disease by Ben Li, Farah Shaikh, Houssam Younes, Batool Abuhalimeh, Abdelrahman Zamzam, Rawand Abdin, Mohammad Qadura

“…When combined with clinical variables in an interpretable machine learning model, GDF15 supports the early identification of patients at high risk for systemic cardiovascular events, facilitating personalized treatment strategies including multidisciplinary specialist referrals and aggressive cardiovascular risk reduction therapy. …”
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A Methodology for Turbine-Level Possible Power Prediction and Uncertainty Estimations Using Farm-Wide Autoregressive Information on High-Frequency Data by Francisco Javier Jara Ávila, Timothy Verstraeten, Pieter Jan Daems, Ann Nowé, Jan Helsen

“…Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. …”
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Generating context-specific sports training plans by combining generative adversarial networks. by Juquan Tan, Jingwen Chen

“…Statistical significance is analyzed using ANOVA testing. The proposed GAN model outperforms traditional ML and rule-based methods, achieving a 22% reduction in MSE and a 45% improvement in generation time. …”
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Multidimensional analysis reveals gene expression, cell interactions, and signaling networks in glioma and Alzheimer’s disease by Meiyi Wang, Jie Yu, Dongfang Ge, Zhiwen Geng, Chengqun Wei, Dan Xu

“…This research also involved building machine learning models to classify glioma and assessing their performances, as well as a model that can best classify each type.. …”
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