Seismic fault identification of deep fault-karst carbonate reservoir using transfer learning by Hanqing Wang, Han Wang, Kunyan Liu, Jin Meng, Yitian Xiao, Yanghua Wang

“…Seismic fault identification is a critical step in structural interpretation, reservoir characterization, and well-drilling planning. …”
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Multistep PV power forecasting using deep learning models and the reptile search algorithm by Sameer Al-Dahidi, Hussein Alahmer, Bilal Rinchi, Abdullah Bani-Abdullah, Mohammad Alrbai, Osama Ayadi, Loiy Al-Ghussain

“…Forecasting Photovoltaic (PV) power output is a key challenge in renewable energy systems, particularly for short- to mid-term operational planning. Accurate multi-step PV forecasting supports efficient energy scheduling, grid stability, and integration of solar resources. …”
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A Deep Learning Approach Based on Interpretable Feature Importance for Predicting Sports Results by Bendiaf Messaoud, Khelifi Hakima, Mohdeb Djamila, Belazzoug Mouhoub, Saifi Abdelhamid

“…As a final step, this study goes through the critical aspect of model interpretability. …”
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Target repositioning using multi-layer networks and machine learning: The case of prostate cancer by Milan Picard, Marie-Pier Scott-Boyer, Antoine Bodein, Mickaël Leclercq, Julien Prunier, Olivier Périn, Arnaud Droit

“…The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. …”
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Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models by Wenshuo Yue, Kai Wu, Zhiyuan Li, Juchen Zhou, Zeyu Wang, Teng Zhang, Yuxiang Yang, Lintao Ye, Yongqin Wu, Weihai Bu, Shaozhi Wang, Xiaodong He, Xiaobing Yan, Yaoyu Tao, Bonan Yan, Ru Huang, Yuchao Yang

“…This work represents a step towards developing safe, robust, and efficient edge neural network accelerators. …”
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A ubiquitous and interoperable deep learning model for automatic detection of pleomorphic gastroesophageal lesions by Miguel Martins, Miguel José Mascarenhas, Maria João Almeida, João Afonso, Tiago Ribeiro, Pedro Cardoso, Francisco Mendes, Joana Mota, Patrícia Andrade, Hélder Cardoso, Miguel Mascarenhas-Saraiva, João Ferreira, Guilherme Macedo

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A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments by Kim Bjerge, Henrik Karstoft, Hjalte M.R. Mann, Toke T. Høye

“…This filtering approach has been demonstrated to significantly decrease the incidence of false positives, since arthropods, occur in less than 3% of the captured images.The final step involves grouping arthropods into 19 taxonomic classes. …”
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Fast-RF-Shimming: Accelerate RF shimming in 7T MRI using deep learning by Zhengyi Lu, Hao Liang, Ming Lu, Xiao Wang, Xinqiang Yan, Yuankai Huo

“…Finally, we design Non-uniformity Field Detector (NFD), an optional post-processing step, to ensure the extreme non-uniform outcomes are identified. …”
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Advancing blood cell detection and classification: performance evaluation of modern deep learning models by Shilpa Choudhary, Sandeep Kumar, Pammi Sri Siddhaarth, Guntu Charitasri, Monali Gulhane, Nitin Rakesh, Feslin Anish Mon, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…While our primary focus is on detection and classification, the output generated by our approach can be useful for disease prediction. This follows a two-step approach, where YOLO-based detection is first performed to locate blood cells, followed by classification using a hybrid CNN model to ensure accurate identification. …”
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NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning by Yang Yi Poh, Ethan Grooby, Kenneth Tan, Lindsay Zhou, Arrabella King, Ashwin Ramanathan, Atul Malhotra, Mehrtash Harandi, Faezeh Marzbanrad

“…Hence, this study introduces a new deep-learning model named NeoSSNet and evaluates its performance in neonatal chest sound separation with previous methods. …”
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Real world federated learning with a knowledge distilled transformer for cardiac CT imaging by Malte Tölle, Philipp Garthe, Clemens Scherer, Jan Moritz Seliger, Andreas Leha, Nina Krüger, Stefan Simm, Simon Martin, Sebastian Eble, Halvar Kelm, Moritz Bednorz, Florian André, Peter Bannas, Gerhard Diller, Norbert Frey, Stefan Groß, Anja Hennemuth, Lars Kaderali, Alexander Meyer, Eike Nagel, Stefan Orwat, Moritz Seiffert, Tim Friede, Tim Seidler, Sandy Engelhardt

“…Abstract Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. …”
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Adulteration detection in cactus seed oil: Integrating analytical chemistry and machine learning approaches by Said El Harkaoui, Cristina Ortiz Cruz, Aaron Roggenland, Micha Schneider, Sascha Rohn, Stephan Drusch, Bertrand Matthäus

“…The MC-simulated data were then used to simulate larger datasets, a critical step for training and testing two classification models: Random Forest (RF) and Neural Networks (NN), as robust training cannot be achieved with small sample sizes. …”
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Identification and Evaluation of Lipocalin-2 in Sepsis-Associated Encephalopathy via Machine Learning Approaches by Hu J, Chen Z, Wang J, Xu A, Sun J, Xiao W, Yang M

“…All these phenomena were reversed by the downregulation of LCN2.Conclusion: The upregulation of LCN2 expression on peripheral neutrophils is a critical step that triggers neuroinflammation in the central nervous system during SAE.Keywords: sepsis-associated encephalopathy, cognitive dysfunction, lipocalin-2, single-cell RNA sequencing, machine learning, neuroinflammation…”
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Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators. by Girmaw Abebe Tadesse, Laura Ferguson, Caleb Robinson, Shiphrah Kuria, Herbert Wanyonyi, Samuel Murage, Samuel Mburu, Rahul Dodhia, Juan M Lavista Ferres, Bistra Dilkina

“…Our results demonstrate the ability of machine learning models to accurately forecast malnutrition in Kenya at a sub-county level from a variety of indicators.…”
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A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba’s Diverse Ecoregions by Mirmajid Mousavi, James Kobina Mensah Biney, Barbara Kishchuk, Ali Youssef, Marcos R. C. Cordeiro, Glenn Friesen, Douglas Cattani, Mustapha Namous, Nasem Badreldin

“…The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). …”
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Enhancing flood prediction through remote sensing, machine learning, and Google Earth Engine by Sonia Hajji, Samira Krimissa, Kamal Abdelrahman, Abdelghani Boudhar, Abdelghani Boudhar, Abdelghani Boudhar, Abdenbi Elaloui, Maryam Ismaili, Meryem El Bouzekraoui, Mohamed Chikh Essbiti, Ali Y. Kahal, Biraj Kanti Mondal, Mustapha Namous, Mustapha Namous

“…The aim of this study was to automate the identification of flood-prone areas within a data-scarce, mountainous watershed using remote sensing (RS) and machine learning (ML) models. In this study, we integrate the Normalized Difference Flood Index (NDFI), using Google Earth Engine to generate flood inventory, which is considered a crucial step in flood susceptibility mapping. …”
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Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction by Maria Vittoria Chiaruttini, Giulia Lorenzoni, Marco Daverio, Luca Marchetto, Francesca Izzo, Giovanna Chidini, Enzo Picconi, Claudio Nettuno, Elisa Zanonato, Raffaella Sagredini, Emanuele Rossetti, Maria Cristina Mondardini, Corrado Cecchetti, Pasquale Vitale, Nicola Alaimo, Denise Colosimo, Francesco Sacco, Giulia Genoni, Daniela Perrotta, Camilla Micalizzi, Silvia Moggia, Giosuè Chisari, Immacolata Rulli, Andrea Wolfler, Angela Amigoni, Dario Gregori

“…<b>Background/Objectives</b>: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. …”
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Machine Learning for Decision Support and Automation in Games: A Study on Vehicle Optimal Path by Gonçalo Penelas, Luís Barbosa, Arsénio Reis, João Barroso, Tiago Pinto

“…In the field of gaming artificial intelligence, selecting the appropriate machine learning approach is essential for improving decision-making and automation. …”
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Penerapan Assessment Of, For Dan As Learning Dalam Perkuliahan Di Perguruan Tinggi Keagamaan Islam Indonesia by Agung Prihantoro, Fattah Setiawan Santoso, Hilman Haroen

“…The research aims to describe (1) how assessment of learning (AoL), assessment for learning (AfL) and assessment as learning (AaL) are applied by lecturers in Islamic universities in Indonesia, (2) what steps of the AaL the lecturers take, (3) how the lecturers give feedback of the AaL to students in the sociocultural-regious context of Indonesia Islamic universities, and (4) what problems and solutions of applying the AaL. …”
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Integrasi Etnopedagogi dalam Mengembangkan Model Pembelajaran Biologi by Iwan Setia Kurniawan, Rifki Survani

“…Model of learning is a syntax or learning steps depicted from the beginning to the end presented in full or typical in the learning process. …”
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