Bankruptcy Risk Factors of Russian Companies by A. A. Zhukov, E. D. Nikulin, D. A. Shchuchkin

“…For the study, one of the machine learning methods was used – the random forest method. …”
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Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on <i>k</i>-Means and <i>k</i>-Nearest Neighbors Algorithms by P. V. Matrenin, A. I. Khalyasmaa, V. V. Gamaley, S. A. Eroshenko, N. A. Papkova, D. A. Sekatski, Y. V. Potachits

“…In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. …”
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Towards Synthetic Augmentation of Training Datasets Generated by Mobility-on-Demand Service Using Deep Variational Autoencoders by Martin Gregurić, Filip Vrbanić, Edouard Ivanjko

“…This augmentation by synthetic samples can potentially enable larger, balanced, and more consistent datasets for machine learning analysis of MoD-based data. The proposed VAE approaches are compared with common dimensionality reduction techniques and standard autoencoders concerning their efficiency in 2-dimensional clustering based on collected MoD-based data. …”
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Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction by Qinhe Zhang, Jiachen Zhang, Tie Feng, Jialang Xue, Xinxin Zhu, Ningyang Zhu, Zhiheng Li

“…Moreover, without proper feature reduction, the interpretability and generalization ability of machine learning models in SDP may be compromised, hindering their practical utility in diverse software development environments. …”
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Semantic segmentation of optical satellite images for the illegal construction detection using transfer learning by Yashasvi Mehta, Abdullah Baz, Shobhit K. Patel

“…It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and incorporates customized evaluation metrics that are not typically found in earlier research. …”
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HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley Data by K.S. Koushik, Bipin Nair B J, N. Shobha Rani

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Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing by Shavbo Salehi, Hao Zhou, Medhat Elsayed, Majid Bavand, Raimundas Gaigalas, Yigit Ozcan, Melike Erol-Kantarci

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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

“…Notably, this surpasses the capabilities of other models that rely on amalgamations of machine learning algorithms and feature dimensionality reduction methods. …”
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Joint learning equation of state surfaces with uncertainty-aware physically regularized neural networks by Dongyang Kuang, Shiwei Li, Buxuan Wang, Chao Xiong, Shichang Zhang, Yanyao Zhang

“…Abstract The equation of state (EOS) is essential for understanding material behavior under different pressure-temperature-volume (P-T-V) conditions across various disciplines. Traditional models, such as the Mie-Gr $$\ddot{\text {u}}$$ neisen-Debye equation, rely on thermodynamic assumptions and expert knowledge, while classical Gaussian process based machine learning approaches can be sensitive to choice of kernels and are limited by scalability and extrapolability. …”
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Federated Learning-Driven IoT Request Scheduling for Fault Tolerance in Cloud Data Centers by Sheeja Rani S, Raafat Aburukba

“…At first, radial kernelized support vector regression is applied in the local training model to identify resource-efficient virtual machines. …”
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A Hubness Information-Based k-Nearest Neighbor Approach for Multi-Label Learning by Zeyu Teng, Shanshan Tang, Min Huang, Xingwei Wang

“…Hubness, a phenomenon in which a few points appear in the k-nearest neighbor (kNN) lists of many points in high-dimensional spaces, may significantly impact machine learning applications and has recently attracted extensive attention. …”
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AI-driven diagnosis and health management of autonomous electric vehicle powertrains: An empirical data-driven approach by Hicham El hadraoui, Adila El maghraoui, Oussama Laayati, Erroumayssae Sabani, Mourad Zegrari, Ahmed Chebak

“…The approach leverages vibration signals acquired from accelerometers and employs a hybrid machine learning (ML) framework. The study focuses on identifying the most informative features from time, frequency, and wavelet domains, followed by dimensionality reduction using Principal Component Analysis (PCA) and Correlation Analysis (CA) to enhance classification performance, reduce complexity, and improve model interpretability. …”
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Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

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A transformer-based embedding approach to developing short-form psychological measures by Se-Jin Jung, Jang-Won Seo

“…However, existing short-form development approaches typically require full-scale administration and rely on factor analysis or machine learning techniques based on response data.MethodsThis study proposes a novel, data-independent method for item reduction using transformer-based semantic embeddings. …”
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Random forest models highlight early Homo sapiens habitats and their relationship to lithic assemblage composition by Lucy Timbrell, James Blinkhorn, Matt Grove

“…We apply random forests, a powerful and highly flexible machine-learning tool for niche modelling, in combination with palaeoclimatic simulations at high temporal resolution. …”
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A Dual-Strategy Framework for Cyber Threat Detection in Imbalanced, High-Dimensional Data Across Heterogeneous Networks by T. Saranya, S. Indra Priyadharshini

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Multistage Training of Fuzzy Cognitive Maps to Predict Preeclampsia and Fetal Growth Restriction by William Hoyos, Rodrigo Garcia, Jose Aguilar

“…Also, the comparison with other machine learning models demonstrates that our approach is competitive to predict PE and FGR. …”
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Robust Driving Control Design for Precise Positional Motions of Permanent Magnet Synchronous Motor Driven Rotary Machines with Position-Dependent Periodic Disturbances by Syh-Shiuh Yeh, Zhi-Hong Liu

“…However, problems with learning period convergence and rotary machine dynamics significantly affect transient motion, further constraining the overall motion performance. …”
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Post-processing methods for mitigating algorithmic bias in healthcare classification models: An extended umbrella review by Shaina Mackin, Vincent J. Major, Rumi Chunara, Remle Newton-Dame

“…This umbrella review sought to identify post-processing bias mitigation methods and tools applicable to binary healthcare classification models in healthcare and summarize bias reduction effectiveness and accuracy loss. …”
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Predicting Wind Turbine Blade Tip Deformation With Long Short‐Term Memory (LSTM) Models by Shubham Baisthakur, Breiffni Fitzgerald

“…ABSTRACT Driven by the challenges in measuring blade deformations, this study presents a novel machine learning methodology to predict blade tip deformation using inflow wind data and operational parameters. …”
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