Transfer learning for multi-material classification of transition metal dichalcogenides with atomic force microscopy by Isaiah A Moses, Wesley F Reinhart

“…Machine Learning: Science and Technology…”
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Parameter uncertainties for imperfect surrogate models in the low-noise regime by Thomas D Swinburne, Danny Perez

“…Machine Learning: Science and Technology…”
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Accelerated development of multi-component alloys in discrete design space using Bayesian multi-objective optimisation by Osman Mamun, Markus Bause, Bhuiyan Shameem Mahmood Ebna Hai

“…Machine Learning: Science and Technology…”
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Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer by Hengrui Zhao, Biling Wang, Michael Dohopolski, Ti Bai, Steve Jiang, Dan Nguyen

“…Machine Learning: Science and Technology…”
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Deep(er) reconstruction of imaging Cherenkov detectors with swin transformers and normalizing flow models by C Fanelli, J Giroux, J Stevens

“…Machine Learning: Science and Technology…”
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Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery by Charles J Abolt, Javier E Santos, Adam L Atchley, Lucas Wells, Daithi Martin, Russell A Parsons, Rodman R Linn

“…Machine Learning: Science and Technology…”
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Automated Tree Detection Using Image Processing and Multisource Data by Grzegorz Dziczkowski, Barbara Probierz, Przemysław Juszczuk, Piotr Stefański, Tomasz Jach, Szymon Głowania, Jan Kozak

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Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking by Arne Kuderle, Martin Ullrich, Nils Roth, Malte Ollenschlager, Alzhraa A. Ibrahim, Hamid Moradi, Robert Richer, Ann-Kristin Seifer, Matthias Zurl, Raul C. Simpetru, Liv Herzer, Dominik Prossel, Felix Kluge, Bjoern M. Eskofier

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A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method by Bo Chen, Yan Lv, Jinbin Zou, Wensheng Chen, Binbin Pan

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Predicting learning achievement using ensemble learning with result explanation. by Tingting Tong, Zhen Li

“…Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. …”
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Seismic anisotropy prediction using ML methods: A case study on an offshore carbonate oilfield. by Guibin Zhao, Fateh Bouchaala, Mohamed S Jouini, Umair Bin Waheed

“…After thoroughly investigating synthetic data, the amplitudes of direct and reflected waves in the time and frequency domains were selected as input features to train machine learning methods. Optimizing the machine learning hyperparameters allowed the training and testing procedures to be completed with high accuracy. …”
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Effects of data transformation and model selection on feature importance in microbiome classification data by Zuzanna Karwowska, Oliver Aasmets, Estonian Biobank research team, Tomasz Kosciolek, Elin Org

“…Our findings suggest that while classification is robust across different transformations, the variation in feature selection necessitates caution when using machine learning for biomarker identification. This research provides valuable insights for applying machine learning to microbiome data and identifies important directions for future work.…”
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Research on filter-based adversarial feature selection against evasion attacks by Qimeng HUANG, Miaomiao WU, Yun LI

“…With the rapid development and widespread application of machine learning technology, its security has attracted increasing attention, leading to a growing interest in adversarial machine learning.In adversarial scenarios, machine learning techniques are threatened by attacks that manipulate a small number of samples to induce misclassification, resulting in serious consequences in various domains such as spam detection, traffic signal recognition, and network intrusion detection.An evaluation criterion for filter-based adversarial feature selection was proposed, based on the minimum redundancy and maximum relevance (mRMR) method, while considering security metrics against evasion attacks.Additionally, a robust adversarial feature selection algorithm was introduced, named SDPOSS, which was based on the decomposition-based Pareto optimization for subset selection (DPOSS) algorithm.SDPOSS didn’t depend on subsequent models and effectively handles large-scale high-dimensional feature spaces.Experimental results demonstrate that as the number of decompositions increases, the runtime of SDPOSS decreases linearly, while achieving excellent classification performance.Moreover, SDPOSS exhibits strong robustness against evasion attacks, providing new insights for adversarial machine learning.…”
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Data Augmentation-Based Enhancement for Efficient Network Traffic Classification by Chang-Yui Shin, Yang-Seo Choi, Myung-Sup Kim

“…Data augmentation increased the accuracy of the machine learning model by 0.26%, which complemented the machine learning model’s performance in network traffic classification and made the machine learning model outperform the lightweight deep learning model.…”
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Evaluasi Kinerja MLLIB APACHE SPARK pada Klasifikasi Berita Palsu dalam Bahasa Indonesia by Antonius Angga Kurniawan, Metty Mustikasari

“… Machine learning digunakan untuk menganalisis, mengklasifikasikan, atau memprediksi data. …”
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A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method by Ankit Kumar, Kamred Udham Singh, Manish Kumar

“…The correct diagnosis of heart disease can save lives, while the incorrect diagnosis can be lethal. The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches, including deep learning. …”
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Quantum resonant dimensionality reduction by Fan Yang, Furong Wang, Xusheng Xu, Pan Gao, Tao Xin, ShiJie Wei, Guilu Long

“…Quantum computing is a promising candidate for accelerating machine learning tasks. Limited by the control accuracy of current quantum hardware, reducing the consumption of quantum resources is the key to achieving quantum advantage. …”
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Rapid learning with phase-change memory-based in-memory computing through learning-to-learn by Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, Roland Renner, Špela Brglez, Thomas Limbacher, Enrique Piñero, Alejandro Linares-Barranco, Angeliki Pantazi, Robert Legenstein

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Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation by Ruijie Huang, Chenji Wei, Jian Yang, Xin Xu, Baozhu Li, Suwei Wu, Lihui Xiong

“…With the high-speed development of artificial intelligence, machine learning methods have become key technologies for intelligent exploration, development, and production in oil and gas fields. …”
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On the Effectiveness of Graph Statistics of Shareholder Relation Network in Predicting Bond Default Risk by Zhiguo Huang

“…In order to test the effectiveness of the two schemes, seven machine learning methods and three types of prediction tasks are used. …”
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