Integrating testing and modeling methods to examine the feasibility of blended waste materials for the compressive strength of rubberized mortar by Amin Muhammad Nasir, Nassar Roz-Ud-Din, Khan Kaffayatullah, Ul Arifeen Siyab, Khan Mubasher, Qadir Muhammad Tahir

“…Similarly, partial dependence plot analysis suggests that SF, MP, and GP have a comparable effect on the fc′{f}_{\text{c}}^{^{\prime} } of rubberized mortar. The machine learning models demonstrated a significant resemblance to test results. …”
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Modeling Canopy Height of Forest–Savanna Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data by Arifou Kombate, Guy Armel Fotso Kamga, Kalifa Goïta

“…Variables from remote sensing data and machine learning models are tools that are being increasingly used for this purpose. …”
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Deep Learning Unravels Differences Between Kinematic and Kinetic Gait Cycle Time Series from Two Control Samples of Healthy Children Assessed in Two Different Gait Laboratories by Alfonso de Gorostegui, Damien Kiernan, Juan-Andrés Martín-Gonzalo, Javier López-López, Irene Pulido-Valdeolivas, Estrella Rausell, Massimiliano Zanin, David Gómez-Andrés

“…Our study emphasizes the importance of standardized protocols and robust data pre-processing to enhance the transferability of machine learning models across clinical settings, particularly for deep learning approaches.…”
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R-CNN Based Vehicle Object Detection via Segmentation Capabilities in Road Scenes by Bisma Riaz Chughtai, Haifa F. Alhasson, Mohammed Alnusayri, Mohammed Alatiyyah, Hanan Aljuaid, Ahmad Jalal, Jeongmin Park

“…We propose a novel approach that leverages state-of-the-art machine learning and deep learning algorithms to enhance accuracy and efficiency. …”
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Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data by Jessada Sresakoolchai, Chayut Ngamkhanong, Sakdirat Kaewunruen

“…The supervised learning model, developed from validated simulations, predicts wear progression, while reinforcement learning improves maintenance decision-making using basic operational data without regular measurements. Various machine-learning techniques are explored and fine-tuned to identify the best models for preventing faulty wheels without the need for frequent inspections. …”
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AppAuth: Authorship Attribution for Android App Clones by Guoai Xu, Chengpeng Zhang, Bowen Sun, Xinyu Yang, Yanhui Guo, Chengze Li, Haoyu Wang

“…AppAuth first extracts a number of coding-style-related features from the executable <italic>.apk</italic> files, and then relies on machine learning techniques to train a classification model. …”
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Predicting Patients&#x2019; Revisit Intention Based on Satisfaction Scores: Combination of Penalized Regression and Neural Networks by Farshid Abdi, Shaghayegh Abolmakarem, Amir Karbassi Yazdi, Paul Leger, Yong Tan, Giuliani Coluccio

“…This study aims to forecast patient satisfaction in the healthcare industry and develop a strategic roadmap for hospitals, thereby expanding the knowledge of machine learning methods for predicting customer satisfaction.…”
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A patient-based iPSC-derived hepatocyte model of alcohol-associated cirrhosis reveals bioenergetic insights into disease pathogenesis by Bani Mukhopadhyay, Cheryl Marietta, Pei-Hong Shen, Abdul Oiseni, Faridoddin Mirshahi, Maria Mazzu, Colin Hodgkinson, Eli Winkler, Qiaoping Yuan, Daniel Miranda, George Kunos, Arun J. Sanyal, David Goldman

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Remodeling of the brain angioarchitecture in experimental chronic neurodegeneration by Maj Schneider Thomsen, Serhii Kostrikov, Lisa Greve Routhe, Kasper Bendix Johnsen, Steinunn Sara Helgudóttir, Johann Mar Gudbergsson, Thomas Lars Andresen, Torben Moos

“…Brains were examined at 28 days (short-term neurodegeneration) and 91 days (long-term neurodegeneration) and analyzed for vascular remodeling taking both 2D and 3D approaches, the latter involving confocal microscopy of optically cleared samples combined with machine learning-based image analysis. Crysectioned and microdissected samples were analyzed for protein and gene expression respectively. …”
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Recent Developments in Heavy Metals Detection: Modified Electrodes, Pretreatment Methods, Prediction Models and Algorithms by Yujie Shi, Shijie Zhang, Hang Zhou, Yue Dong, Gang Liu, Wenshuai Ye, Renjie He, Guo Zhao

“…To address these issues, two potential solutions have been proposed: the development of advanced algorithms (such as machine learning (ML), back-propagation neural network (BPNN), support vector machines (SVM), random forests (RF), etc.) for signal processing and the use of pretreatment methods (such as Fenton oxidation (FO), ozone oxidation, and photochemical oxidation) to suppress such interferences. …”
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Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities by Munya A. Arasi, Hussah Nasser AlEisa, Amani A. Alneil, Radwa Marzouk

“…They are efficient in certifying functions of detection of actions, observing crucial functions, and tracking. Conventional machine learning and deep learning approaches effectively detect human activity. …”
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Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping by Giacomo Quattrini, Simone Pesaresi, Nicole Hofmann, Adriano Mancini, Simona Casavecchia

“…Unlike traditional ground truthing activities, drone truthing enabled the generation of large, spatially balanced reference datasets, which are critical for machine learning classification systems. These datasets improved classification accuracy by ensuring a comprehensive representation of vegetation spectral variability, enabling the classifier to identify the key phenological patterns that best characterize and distinguish different vegetation types across the landscape. …”
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Data from Emergency Medical Service Activities: A Novel Approach to Monitoring COVID-19 and Other Infectious Diseases by Daniele del Re, Luigi Palla, Paolo Meridiani, Livia Soffi, Michele Tancredi Loiudice, Martina Antinozzi, Maria Sofia Cattaruzza

“…<b>Conclusions</b>: This novel approach, combined with a machine learning predictive approach, could be a powerful public health tool to signal the start of disease outbreaks and monitor the spread of infectious diseases.…”
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Climate change impact assessment on groundwater level changes: A study of hybrid model techniques by Stephen Afrifa, Tao Zhang, Xin Zhao, Peter Appiahene, Mensah Samuel Yaw

“…The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. …”
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Multi-label text classification via secondary use of large clinical real-world data sets by Sai Pavan Kumar Veeranki, Akhila Abdulnazar, Diether Kramer, Markus Kreuzthaler, David Benjamin Lumenta

“…Support vector machines as a classical machine learning method outperformed the non-contextual fastText approach. …”
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Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT by Emanuele Trucco, Joanna M Wardlaw, Wenwen Li, Grant Mair, Amos Storkey, Alessandro Fontanella, Antreas Antoniou, Eleanor Platt, Paul Armitage

“…Background CT is commonly used to image patients with ischaemic stroke but radiologist interpretation may be delayed. Machine learning techniques can provide rapid automated CT assessment but are usually developed from annotated images which necessarily limits the size and representation of development data sets. …”
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Time-series forecasting of microbial fuel cell energy generation using deep learning by Adam Hess-Dunlop, Harshitha Kakani, Stephen Taylor, Dylan Louie, Jason Eshraghian, Colleen Josephson

“…Very little work currently exists attempting to model and predict the relationship between soil conditions and SMFC energy generation, and we are the first to use machine learning to do so. In this paper, we train Long Short Term Memory (LSTM) models to predict the future energy generation of SMFCs across timescales ranging from 3 min to 1 h, with results ranging from 2.33 to 5.71% Mean Average Percent Error (MAPE) for median voltage prediction. …”
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Deep Learning and Radiomics in Triple-Negative Breast Cancer: Predicting Long-Term Prognosis and Clinical Outcomes by Cheng C, Wang Y, Zhao J, Wu D, Li H, Zhao H

“…Deep learning and radiomics techniques represent advanced machine learning methodologies and are also emerging outcomes in the medical-engineering field in recent years. …”
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Identification of Bacterial Lipopolysaccharide-Associated Genes and Molecular Subtypes in Autism Spectrum Disorder by He Y, He Y, Cheng B

“…This research aims to explore bacterial lipopolysaccharide (LPS)- and immune-related (BLI) molecular subgroups in ASD to enhance understanding of the disorder.Methods: We analyzed 89 control samples and 157 ASD samples from the GEO database, identifying BLI signatures using least absolute shrinkage and selection operator regression (LASSO) and logistic regression machine learning algorithms. A nomogram prediction model was developed based on these signatures, and we performed Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and immune cell infiltration analysis to assess the impact of BLI subtypes and their underlying mechanisms.Results: Our findings revealed 17 differentially expressed BLI genes in children with ASD, with BLNK, MAPK8, PRKCQ, and TNFSF12 identified as potential biomarkers. …”
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Precision livestock farming applied to the dairy sector: 50 years of history with a text mining and topic analysis approach by Lucia Trapanese, Giovanna Bifulco, Alfio Calanni Macchio, Francesca Aragona, Sissy Purrone, Giuseppe Campanile, Angela Salzano

“…A comprehensive search on the Scopus® bibliometric database was carried out using various related keywords such as: “precision livestock farming, sensors, machine learning and dairy”. The research identified 5362 papers published from January 1976 to April 2024 that, after filtering, became 1794 eligible records. …”
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