A review on deep learning methods for heart sound signal analysis by Elaheh Partovi, Ankica Babic, Ankica Babic, Arash Gharehbaghi

“…IntroductionApplication of Deep Learning (DL) methods is being increasingly appreciated by researchers from the biomedical engineering domain in which heart sound analysis is an important topic of study. …”
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How Student-centric Methods Improve the Level of Teaching and Learning Process? by Deepali Desai, Sahjid Mukhida, Nikunja Kumar Das, Radhika Paranjpe

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Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods by Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali

“… Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. …”
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A Study on Staging Cystic Echinococcosis Using Machine Learning Methods by Tuvshinsaikhan Tegshee, Temuulen Dorjsuren, Sungju Lee, Dolgorsuren Batjargal

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The Effectiveness Projec Based Learning Methods in Teaching Structure of Indonesian Morpheme by Roni Amrulloh, Muh Taufiq

“… This research was aimed to know the result of learning process by project based learning in the matter of Indonesian language form and structure of its morpheme. …”
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Advanced Machine Learning Methods for the Prediction of the Optical Parameters of Tellurite Glasses by Fahimeh Ahmadi, Mohsen Hajihassani, Tryfon Sivenas, Stefanos Papanikolaou, Panagiotis G. Asteris

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Impact of transfer learning methods and dataset characteristics on generalization in birdsong classification by Burooj Ghani, Vincent J. Kalkman, Bob Planqué, Willem-Pier Vellinga, Lisa Gill, Dan Stowell

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Comparative performance analysis of ensemble learning methods for fetal health classification by Tasnim Bill Zannah, Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Afjal Hossan Sarower, Mahbuba Begum

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Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects by Ma Yuhan

“…Although these factors limit the widespread application of FL in medical settings, this paper also proposes potential solutions, such as improving algorithm interpretability through interpretable tools, and utilizing transfer learning and domain adaptation methods to enhance model effectiveness across different datasets. …”
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FORMATION OF STUDENTS’ SCIENTIFIC THINKING BASED ON THE LEARNING OF METHODS OF THE SUBSTANCE ANALYSIS by M. A. Kosareva, L. A. Baikova, E. A. Nikonenko, V. V. Vaitner, A. N. Gabdullin

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Accelerating Subglacial Hydrology for Ice Sheet Models With Deep Learning Methods by Vincent Verjans, Alexander Robel

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Gravity Predictions in Data-Missing Areas Using Machine Learning Methods by Yubin Liu, Yi Zhang, Qipei Pang, Sulan Liu, Shaobo Li, Xuguo Shi, Shaofeng Bian, Yunlong Wu

“…The results indicate that machine learning methods exhibit a marked advantage in gravity data prediction, significantly enhancing the predictive accuracy.…”
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Research of Pedestrian Detection Methods with Anchor Frame Based on Deep Learning by Yan Tao

“…The YOLOv3 model in the YOLO model exhibits notable modifications in the overall architecture of pedestrian identification in the single-stage pedestrian recognition method using anchor frames, greatly improving its capacity to handle scale changes and occlusion issues.…”
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Analysis of soil suitability for agricultural needs using machine learning methods by Kurashkin Sergei, Kravtsov Kirill, Kukartsev Anatoly, Boyko Andrey, Volneikina Ekaterina

“…This study explores the application of machine learning methods to assess soil suitability for agricultural purposes, focusing on identifying and analysing key factors that influence soil productivity under drought conditions. …”
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Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction. by Akash Bahai, Chee Keong Kwoh, Yuguang Mu, Yinghui Li

“…This study systematically benchmarks state-of-the-art deep learning methods for RNA structure prediction across diverse datasets. …”
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Research on machine learning methods for detecting objects in difficult shooting conditions by Vitalii Serdechnyi, Olesia Barkovska, Andriy Kovalenko, Anton Havrashenko, Vitalii Martovytskyi

“…The subject matter of the article is research into machine learning methods for object detection in images and videos under complex urban conditions, particularly under poor lighting, the presence of precipitation, high scene complexity, and limited computational resources. …”
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Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods by Di Wang, Dingyu Jiao, Zihang Zhang, Runze Zhou, Weize Guo, Huai Su

“…In this paper, an interpretable combinatorial machine learning shale fracability evaluation method is proposed, which combines XGBoost and Bayesian optimization techniques to mine the non-linear relationship between the influencing factors and fracability, and to achieve more accurate fracability evaluations with a lower error rate (maximum MAPE not more than 20%). …”
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mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R by Patrick Schratz, Marc Becker, Michel Lang, Alexander Brenning

“…This is made possible by integrating the package directly into the mlr3 machine-learning framework, which already has support for generic non-spatiotemporal resampling methods such as random partitioning. …”
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Comparative Study on Performance Evaluation of Eager versus Lazy Learning Methods by Selvi Lukman, Jimmy Loekito, Pin Panji Yapinus

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Point Cloud Deep Learning Methods for Particle Shower Reconstruction in the DHCAL by Borysova Maryna, Bressler Shikma, Gross Eilam, Kakati Nilotpal, Zavazieva Darina

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