Detection of LUAD-Associated Genes Using Wasserstein Distance in Multiomics Feature Selection by Shaofei Zhao, Siming Huang, Lingli Yang, Weiyu Zhou, Kexuan Li, Shige Wang

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Multi-Scale Feature Pyramid Network With Camera Artifact Augmentation for Pedestrian Detection by Ankit Shrivastava, Shanmugam Poonkuntran

“…This paper presents a novel hybrid model termed as multi-scale feature pyramid network with camera artifact augmentation (MSFPN-CAA) that integrates Feature Pyramid Networks (FPN) with a fine-tuned YOLOv10 model for enhanced pedestrian detection. …”
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Novel Matching Algorithm for Effective Drone Detection and Identification by Radio Feature Extraction by Teng Wu, Yan Du, Runze Mao, Hui Xie, Shengjun Wei, Changzhen Hu

“…To address this challenge, this paper proposes a novel drone detection and identification algorithm based on transmission signal analysis. …”
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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

“…Even with such usefulness, these methods are bound to restrictive elements individually that could compromise the precision of cancer detection systems. More recently, the focus of research has shifted to hybrid approaches that merge several feature selection techniques to mitigate the weaknesses of one method while maximizing the strengths of others. …”
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Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System by Roya Alizadeh, Yvon Savaria, Chahe Nerguizian

“…This allows identifying and recommending the most effective features for the explored detection task according to observed variability, information gain, and correlation between features. …”
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Random forest–based feature selection and detection method for drunk driving recognition by ZhenLong Li, HaoXin Wang, YaoWei Zhang, XiaoHua Zhao

“…A method for drunk driving detection using Feature Selection based on the Random Forest was proposed. …”
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Domain Alignment Dynamic Spectral and Spatial Feature Fusion for Hyperspectral Change Detection by Xuexiang Qin, Yuxiang Zhang, Yanni Dong

“…Change detection is an important task in geospatial analysis that aims to identify noticeable variations in geographic elements between images captured at different periods. …”
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Graph-contrast ransomware detection (GCRD) with advanced feature selection and deep learning by Suneeta Satpathy, Pratik Kumar Swain

“…To overcome the limitations of conventional detection strategies, this study proposes the Graph-Contrast Ransomware Detection (GCRD) model comprising Graph-Based Feature Selection (GFS), Contrastive Learning (CLR), and Transformer-Based Classification (FT-Transformer + MLP). …”
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Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

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Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review by Kholoud Elnaggar, Mostafa M. El-Gayar, Mohammed Elmogy

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Quantitative detection of trace nanoplastics (down to 50 nm) via surface-enhanced Raman scattering based on the multiplex-feature coffee ring by Xinao Lin, Fengcai Lei, Xiu Liang, Yang Jiao, Xiaofei Zhao, Zhen Li, Chao Zhang, Jing Yu

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An automated extraction of spectral-temporal and spatial-temporal features of EEG for emotion detection by Monira Islam, Tan Lee

“…The spectral energy across multiple EEG channels within the same segment is aggregated while the consecutive frames are stacked to give spectral-temporal feature representation. Again, connectivity analysis is performed at each instant with a non-linear measure named phase locking value (PLV) to construct the connectivity map containing spatial-temporal features. …”
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Fusion of Deep and Time–Frequency Local Features for Melanoma Skin Cancer Detection by Hamidreza Eghtesaddoust, Morteza Valizadeh, Mehdi Chehel Amirani

“…After the fusion of the mentioned features, semisupervised discriminant analysis (SDA) reduces the highly correlated and redundant features. …”
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Detecting Urban Mobility Structure and Learning Functional Distribution with Multi-Scale Features by Jia Li, Chuanwei Lu, Haiyan Liu, Jing Li, Dewei Zhou, Qingyun Liu

“…Urban mobility structure detection and functional distribution learning are significant for urban planning and management. …”
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Quantitative Detection of Water Content of Winter Jujubes Based on Spectral Morphological Features by Yabei Di, Huaping Luo, Hongyang Liu, Huaiyu Liu, Lei Kang, Yuesen Tong

“…This study is based on using spectral morphological features to quantitatively detect the water content of winter jujubes, and it extends the research scope to the composite effect of spectral morphological features on the basis of previous research. …”
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Correlation Analysis of Imaging and Pathological Features of Ependymomas by Sun D, Chen F, Wang Z

“…Imaging analysis included lesion diameter, location, morphology, surrounding oedema and enhancement manifestations; pathological analysis included histopathological examination and immunohistochemical detection, with detection indicators including glial fibrillary acidic protein (GFAP), S-100 protein, EMA, Ki-67 and other markers. …”
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Learning by precedents based on the analysis of the features properties by V. V. Krasnoproshin, V. G. Rodchanka

“…A method of learning based on the analysis of the properties of feature combinations and building feature subspaces where classes do not intersect is proposed. …”
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Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph by Imam Dad, JianFeng He, Zulqarnain Baloch

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A Network Analysis Approach to Detect and Differentiate Usher Syndrome Types Using miRNA Expression Profiles: A Pilot Study by Rama Krishna Thelagathoti, Wesley A. Tom, Chao Jiang, Dinesh S. Chandel, Gary Krzyzanowski, Appolinaire Olou, Rohan M. Fernando

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Machine learning models and dimensionality reduction for improving the Android malware detection by Pablo Morán, Antonio Robles-Gómez, Andres Duque, Llanos Tobarra, Rafael Pastor-Vargas

“…Predictive models based on Random Forest are found to achieve the most promising results. They can detect an average of 91.72% malware samples, with a very low false positive rate of 0.13%, and using only 5,000 features. …”
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