Differentiable Deep Learning Surrogate Models Applied to the Optimization of the IFMIF-DONES Facility by Galo Gallardo Romero, Guillermo Rodríguez-Llorente, Lucas Magariños Rodríguez, Rodrigo Morant Navascués, Nikita Khvatkin Petrovsky, Rubén Lorenzo Ortega, Roberto Gómez-Espinosa Martín

“…Overall, these results demonstrate the synergy between deep learning models and differentiable programming, offering a promising collaboration among physicists and computer scientists to further improve the design and optimization of IFMIF-DONES and other accelerator facilities. …”
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A novel hybrid deep learning approach for super-resolution and objects detection in remote sensing by Muhammad Asif, Mohammad Abrar, Faizan Ullah, Abdu Salam, Farhan Amin, Isabel de la Torre, Mónica Gracia Villar, Helena Garay, Gyu Sang Choi

“…Preprocessing techniques, including data augmentation, are incorporated to improve the diversity and accuracy of the training dataset. …”
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A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles by Jie Chen, Ruochen Wang, Wei Liu, Dong Sun, Yu Jiang, Renkai Ding

“…Future research should explore multi-system collaborative control, such as integrating active suspension with intelligent terrain perception, to improve adaptability and robustness across both vehicle categories. …”
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Cybersecurity and Major Cyber Threats of Smart Meters: A Systematic Mapping Review by Jones Márcio Nambundo, Otávio de Souza Martins Gomes, Adler Diniz de Souza, Raphael Carlos Santos Machado

“…These gaps include design requirements, software and firmware updates, physical security, the use of big data to detect vulnerabilities, user data privacy, and inconsistencies in machine learning algorithms. Future research should focus on these aspects to improve the stability and reliability of smart meters.…”
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DoubleNet: A Method for Generating Navigation Lines of Unstructured Soil Roads in a Vineyard Based on CNN and Transformer by Xuezhi Cui, Licheng Zhu, Bo Zhao, Ruixue Wang, Zhenhao Han, Kunlei Lu, Xuguang Feng, Jipeng Ni, Xiaoyi Cui

“…This research introduces DoubleNet, an innovative deep-learning model designed to generate navigation lines for such conditions. To improve the model’s ability to extract image features, DoubleNet incorporates several key innovations, such as a unique multi-head self-attention mechanism (Fused-MHSA), a modified activation function (SA-GELU), and a specialized operation block (DNBLK). …”
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Establishment and validation of an immune-related nomogram for the prognosis of pancreatic adenocarcinoma by Kan Wang, Yunkun Lu, Yanfei Cao, Ping Feng, Qiu Wu, Peng Xiao, Yimin Ding

“…Abstract Pancreatic adenocarcinoma (PDAC) is a highly aggressive neoplasm characterized by limited therapeutic options, particularly in the realm of immunotherapy. This study aims to improve prognosis prediction to guide therapeutic decision-making, and to identify novel targets for immunotherapy of PDAC. …”
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Development experience of information system for ranking of academic and pedagogical staff by A. A. Chernousov, E. V. Vavilova

“…The aim of this work is research of algorithms for quantitative assessment of intellectual potential (rating) of academic and pedagogical staff in higher educational institutions, as well as the development of technology for the application of these algorithms in practice.Materials and methods. …”
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Human face localization and detection in highly occluded unconstrained environments by Abdulaziz Alashbi, Abdul Hakim H.M. Mohamed, Ayman A. El-Saleh, Ibraheem Shayea, Mohd Shahrizal Sunar, Zieb Rabie Alqahtani, Faisal Saeed, Bilal Saoud

“…Unconstrained face identification has been significantly improved by the advancements in Deep Learning algorithms (DL). …”
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Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome by Yaxuan He, Zekai Chen, Zhaohui Tang, Yuexiang Qin, Fang Wang

“…Ten machine learning algorithms were evaluated using 10-fold cross-validation. …”
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Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

“…The results showed that: (1) Machine learning algorithms based on Sentinel-2 and UAV imagery effectively improved the accuracy of FVC estimation in alpine meadows. …”
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Global soil moisture mapping at 5 km by combining GNSS reflectometry and machine learning in view of HydroGNSS by Emanuele Santi, Davide Comite, Laura Dente, Leila Guerriero, Nazzareno Pierdicca, Maria Paola Clarizia, Nicolas Floury

“…Regardless of the ML technique applied, this study confirmed the promising potential of GNSS-R for the global monitoring of SM at improved resolution with respect to SM products available from microwave satellite radiometers.…”
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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. …”
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Accelerometers can correctly count orthopaedic patients' early post‐operative steps while using walking aids by Spiros Tsamassiotis, Michael Schwarze, Philipp Gehring, Roman F. Karkosch, Lars‐René Tücking, Ann‐Kathrin Einfeldt, Eike Jakubowitz

“…Increased gait speed generally improved accuracy, reducing RE in most devices, except for the AX6, which showed the opposite trend. …”
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Intelligent collaborative management and control platform for continuous mining equipment in open-pit mines by Zhiyong LEI, Xiaolong MA, Shujun ZHAO, Shiming ZHANG, Bin YAN

“…The platform delivers multiple functionalities, including comprehensive multi-machine synchronous monitoring, online fault self-diagnosis, and early warning systems, alongside improved multi-machine cooperative control efficiency. …”
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Synthesis of a reversible quantum Vedic multiplier on IBM quantum computers by Mojtaba Noorallahzadeh, Mohammad Mosleh

“…Abstract Quantum computers provide considerable potential to enhance computing technology, anticipated to surpass conventional computers by resolving intricate challenges that existing systems cannot tackle. They use quantum algorithms for improved performance and depend on reversible computations based on quantum physics and linear algebra. …”
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Machine learning's model-agnostic interpretability on the prediction of students' academic performance in video-conference-assisted online learning during the covid-19 pandemic by Eka Miranda, Mediana Aryuni, Mia Ika Rahmawati, Siti Elda Hiererra, Albert Verasius Dian Sano

“…The research variables included students' academic performance as the dependent variable, and the video conference application (VC), learning material (LM), internet connection (IC), students' ability to learn (SL), and student knowledge (SK) as independent variables, which were mapped into 28 attributes. Result: The SMOTE improved the performance of three algorithms, with RF outperforming SVM and GNB in almost all tests, achieving an accuracy of 79.45%, precision of 75.71%, and recall of 79.45%. …”
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Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression by Kun Guo, Kun Guo, Bo Zhu, Lei Zha, Yuan Shao, Zhiqin Liu, Naibing Gu, Kongbo Chen

“…Despite therapeutic advancements, many patients still lack effective interventions, underscoring the need for improved prognostic assessment tools. Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.ObjectiveThe aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.MethodsA retrospective observational cohort study was conducted involving 398 patients diagnosed with Acute Cerebral Infarction from January 2023 to February 2024. …”
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Named Entity Recognition in Aviation Products Domain Based on BERT by Mingye Yang, Bernadin Namoano, Maryam Farsi, John Ahmet Erkoyuncu

“…Through experiments on the constructed aviation product dataset, the model achieved a Precision value of 91.74%, a Recall value of 92.46%, and an F1 score of 92.1%, Compared with other baseline models, the F1-score is improved by 0.9% to 1.5%. At the same time, the model also performs well on standard datasets such as CoNLLpp, with a Precision value of 92.87%, a Recall value of 92.54%, and an F1-Score of 92.70%. …”
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Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning by Daniel Paluba, Bertrand Le Saux, Francesco Sarti, Přemysl Štych

“…The inclusion of DEM-based auxiliary features and additional meteorological information improved the results. In the comparison of ML models, the traditional ML algorithms, Random Forest Regressor and Extreme Gradient Boosting (XGB) slightly outperformed the Automatic Machine Learning (AutoML) approach, auto-sklearn, for all forest parameters, achieving high accuracies (R2 between 70% and 86%) and low errors (0.055–0.29 of mean absolute error). …”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…Being consensus-driven solution, it improved reliability of breast cancer prediction results. …”
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