Through the Citizen Scientists’ Eyes: Insights into Using Citizen Science with Machine Learning for Effective Identification of Unknown-Unknowns in Big Data by Kameswara Bharadwaj Mantha, Hayley Roberts, Lucy Fortson, Chris Lintott, Hugh Dickinson, William Keel, Ramanakumar Sankar, Coleman Krawczyk, Brooke Simmons, Mike Walmsley, Izzy Garland, Jason Shingirai Makechemu, Laura Trouille, Clifford Johnson

“…In the era of rapidly growing astronomical data, the gap between data collection and analysis is a significant barrier, especially for teams searching for rare scientific objects. …”
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Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy by Wu Tenghui, Liu Xinyi, Si Ziyi, Zhang Yanting, Ma Ziqian, Zhu Yiwen, Gan Ling

“…Finally, clinical data, radiomics, and deep learning features were integrated to form a fusion model, which was evaluated using receiver operating characteristic (ROC) analysis.ResultsThe combined model achieved the highest predictive performance for pathological complete response (pCR) across both cohorts. …”
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Hybrid deep learning and isogeometric analysis for bearing capacity assessment of sand over clay by Toan Nguyen-Minh, Tram Bui-Ngoc, Jim Shiau, Tan Nguyen, Trung Nguyen-Thoi

“…In this paper, Isogeometric analysis (IGA) is effectively integrated with machine learning (ML) to investigate the bearing capacity of strip footings in layered soil profiles, with a focus on a sand-over-clay configuration. …”
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PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data by Mousa Moradi, Saber Kazeminasab Hashemabad, Daniel M. Vu, Allison R. Soneru, Asahi Fujita, Mengyu Wang, Tobias Elze, Mohammad Eslami, Nazlee Zebardast

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Trajectory-Ordered Objectives for Self-Supervised Representation Learning of Temporal Healthcare Data Using Transformers: Model Development and Evaluation Study by Ali Amirahmadi, Farzaneh Etminani, Jonas Björk, Olle Melander, Mattias Ohlsson

“… BackgroundThe growing availability of electronic health records (EHRs) presents an opportunity to enhance patient care by uncovering hidden health risks and improving informed decisions through advanced deep learning methods. However, modeling EHR sequential data, that is, patient trajectories, is challenging due to the evolving relationships between diagnoses and treatments over time. …”
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Analysing Bibliometrics Trends: The Flipped Classroom Approach in Foreign Language Learning by Nuraeni Nuraeni, Nurul Fachrunnisa

“…Data analysis, for research trend mapping comparatively, used Vosviewer. …”
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Leveraging moisture elimination and hybrid deep learning models for soil organic carbon mapping with multi-modal remote sensing data by Yilin Bao, Xiangtian Meng, Weimin Ruan, Huanjun Liu, Mingchang Wang, Abdul Mounem Mouazen

“…Currently, SOC prediction remains challenging due to unresolved moisture disturbances, underutilized multimodal remote sensing data, and uncertain model transferability. To address these challenges, a new paradigm integrating moisture elimination with advanced hybrid deep learning has been developed. …”
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A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections by Yemeng Yang, Kun Han, Jiatao Li, Tao Zhang, Zhijing Zhu, Ling Su, Zhaoyong Han, Chunyan Xu, Yi Lu, Likun Pan, Tao Yang

“…A total of 26 relevant clinical features were extracted from this cohort. Following data preprocessing, we trained four models: Logistic Regression, Random Forest, Support Vector Machine, and Gaussian Naive Bayes. …”
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Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines by E León, A Li, M A Bahena Schott, B Bos, M Busch, J R Chapman, G L Duran, J Gruszko, R Henning, E L Martin, J F Wilkerson

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Investigation of Freeway Incident Duration Using Classification and Regression Trees Based on Multisource Data by Xun Xie, Gen Li, Lan Wu, Shuxin Du

“…This study has important practical implications for advancing the engineering application of machine learning methods and the analysis of sensor data.…”
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From counting stations to city-wide estimates: data-driven bicycle volume extrapolation by Silke K. Kaiser, Nadja Klein, Lynn H. Kaack

“…Our analysis reveals that crowdsourced cycling flow data from Strava in the area around the point of interest are most important for the prediction. …”
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What is in a food store name? Leveraging large language models to enhance food environment data by Analee J. Etheredge, Samuel Hosmer, Aldo Crossa, Rachel Suss, Mark Torrey

“…IntroductionIt is not uncommon to repurpose administrative food data to create food environment datasets in the health department and research settings; however, the available administrative data are rarely categorized in a way that supports meaningful insight or action, and ground-truthing or manually reviewing an entire city or neighborhood is rate-limiting to essential operations and analysis. …”
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Real-Time Algal Monitoring Using Novel Machine Learning Approaches by Seyit Uguz, Yavuz Selim Sahin, Pradeep Kumar, Xufei Yang, Gary Anderson

“…To overcome these limitations, this study proposes an automated, real-time, and cost-effective solution by integrating machine learning with image-based analysis. We evaluated the performance of Decision Trees (DTS), Random Forests (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (k-NN) algorithms using RGB color histograms extracted from images of <i>Scenedesmus dimorphus</i> cultures. …”
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A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques by Manuel Mendoza-Hurtado, Gonzalo Cerruela-García, Domingo Ortiz-Boyer

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MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems by Abdul Jabbar, Marco Cocconcelli, Gianluca D’Elia, Davide Borghi, Luca Capelli, Jacopo Cavalaglio Camargo Molano, Matteo Strozzi, Riccardo Rubini

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E-Scrapbook: The Needs of HOTS Oriented Digital Learning Media in Elementary Schools by I Gede Wahyu Suwela Antara, Kadek Andre Karisma Dewantara

“…Data analysis was carried out qualitatively. The results show that digital-based learning media containing HOTS questions used in post-covid-19 learning are still limited and not varied. …”
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Peer Learning for Inclusive Growth: A Cross-National Study of Thailand and Indonesia by Hery Rahmat, Ika Rama Suhandra, Eliya Eliya, Siwapoorn Yuenchon

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Unlocking autism’s complexity: the Move Initiative’s path to comprehensive motor function analysis by Ashley Priscilla Good, Elizabeth Horn

“…Advanced technologies, including current wearable devices, integrate both active and passive (sensor-based) data collection, providing a more comprehensive view of human health. …”
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Multispectral Sensors and Machine Learning as Modern Tools for Nutrient Content Prediction in Soil by Rafael Felippe Ratke, Paulo Roberto Nunes Viana, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro, Dthenifer Cordeiro Santana, Carlos Eduardo da Silva Santos, Alan Mario Zuffo, Jorge González Aguilera

“…The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. …”
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New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models by Amir Hossein Sheikhshoaei, Ali Sanati

“…Outlier detection using the Leverage method indicated that 95.11% of pure IL viscosity data and 94.92% of mixed ILs viscosity data are statistically valid.…”
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