Polygraph-based deception detection and Machine Learning. Combining the Worst of Both Worlds? by Kyriakos N. Kotsoglou, Alex Biedermann

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Comprehensive Review of Hybrid Feature Selection Methods for Microarray-Based Cancer Detection by Azka Khoirunnisa, Didit Adytia, Mustafa Mat Deris, Adiwijaya

“…This paper provides a comparative evaluation of hybrid feature selection methods to enhance microarray-based cancer classification. It aims to guide researchers in choosing appropriate strategies that optimize the dataset analysis.…”
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SDMA-Net: Swin Transformer-Based Dynamic Memory-Attention Network for Endoscopic Navigation by Runnan Zhang, Qi Tian, Jinghui Chu, Wei Lu

“…As a conclusion, our proposed SDMA-Net provides an effective and efficient solution for endoscopic motion detection and classification.…”
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Drought and hydrothermal humidity factor as one of the criteria to estimate its intensity degree (literature review) by E. V. Ionova, V. A. Likhovidova, I. A. Lobunskaya

“…The current paper has considered the following issues: determination of the drought phenomenon and its classification (atmospheric, soil and combined), drought harmfulness and spread in the Russian Federation. …”
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Distributed semi-supervised partial multi-dimensional learning via subspace learning by Zhen Xu, Weibin Chen

“…Nowadays, however, the training data are typically distributed at multiple nodes within a network, making it challenging to transmit them to a fusion center for further processing. To address these issues, in this paper, we propose a novel algorithm called distributed semi-supervised partial multi-dimensional learning (dS $$^2$$ 2 PMDL), which is designed to handle distributed classification of a small proportion of partially multi-dimensional (PMD) data and a large proportion of unlabeled data across a network. …”
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Ovarian carcinosarcoma (a literature review) by A. M. Beishembaev, K. I. Zhordania, E. D. Choi, A. A. Turkmenov, A. K. Moldobekova

“…The lack of extensive clinical studies along with observed substantial number of controversial issues may be accounted for by OCS low prevalence and complex pathogenesis. …”
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Limits of competence of a forensic commodity expert in determining the characteristics of goods in cases of customs rules violation by S. О. Diachuk

“…Today, all goods, regardless of the country of origin, are subject to classification, that is, codes are determined for goods in accordance with the classification groupings specified in the UCC of the Economic and Monetary Union. …”
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Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration by Maridu Bhargavi, Sivadi Balakrishna

“…Among the primary challenges in automated skin cancer classification are addressing differences in lesion appearance, occlusions, and data class imbalance that impact model performance and reliability. …”
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DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG by Jing Wu, Nan Zhang, Qilei Ye, Xiaorui Zheng, Minmin Shao, Xian Chen, Hui Huang

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Towards Greater Standardisation in Benthic Trait Research to Support Application to Environmental Management by Irene Susini, Heidi M. Tillin, Louise Anderson, Craig M. Robertson, Sian Rees, Kerry L. Howell

“…Secondly, three distinct trait classification frameworks are identified, of which one presents considerable internal variation; within‐framework trait classification also emerges as inconsistent. …”
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Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

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Stealthy Adversarial Attacks on Machine Learning-Based Classifiers of Wireless Signals by Wenhan Zhang, Marwan Krunz, Gregory Ditzler

“…Machine learning (ML) has been successfully applied to classification tasks in many domains, including computer vision, cybersecurity, and communications. …”
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Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions by Teerath Kumar, Rob Brennan, Alessandra Mileo, Malika Bendechache

“…This survey explores the mitigation of the overfitting issue through a comprehensive examination of image data augmentation techniques, which aim to enhance dataset size and diversity by introducing varied samples. …”
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Contemporary Understanding of Diagnosis and Treatment of Children with Cystic Fibrosi by A. A. Baranov, L. S. Namazova-Baranova, O. I. Simonova, N. Y. Kashirskaya, E. A. Roslavtseva, Y. V. Gorinova, S. A. Krasovskiy, L. R. Selimzyanova

“…The issue of contemporary diagnosis and correct management of patients with cystic fibrosis is relevant not only for Russia, but for the entire world. …”
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Modulation recognition method based on multiscale convolutional fusion coding networks by LI Guojun, ZHU Siyuan, ZHENG Jianzhong, WANG Jie, YE Changrong

“…To address the issue of insufficient feature extraction in existing modulation recognition methods that limited classification accuracy, a Transformer-based modulation recognition method was proposed. …”
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A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data by Zhenzhen Wang, Junde Xie, Jia Zhang

“…Addressing class imbalance in breast cancer data is essential for enhancing detection accuracy, yet traditional machine learning methods often overlook this imbalance, limiting their classification performance. To tackle this issue, we propose a robust enhanced ensemble learning method (REEL). …”
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Coğrafyacılar Tarafından Yapılan Arazi Kullanım Çalışmaları Üzerine Kavramsal ve Yöntemsel Öneriler by Ali Özçağlar

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MVSAPNet: A Multivariate Data-Driven Method for Detecting Disc Cutter Wear States in Composite Strata Shield Tunneling by Yewei Xiong, Xinwen Gao, Dahua Ye

“…However, existing methods that treat the problem as a classification task often overlook the issue of data imbalance. …”
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Career path prediction using XGBoost Model and students’ academic results by Hong Quan Nguyen, Duc Dang Khoi Nguyen, Tan Duy Le, An Mai, Kha Tu Huynh

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Two-Stage Unsupervised Hyperspectral Band Selection Based on Deep Reinforcement Learning by Yi Guo, Qianqian Wang, Bingliang Hu, Xueming Qian, Haibo Ye

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