Unsupervised feature selection and class labeling for credit card fraud by Robert K. L. Kennedy, Flavio Villanustre, Taghi M. Khoshgoftaar

“…In this paper, we present a fully unsupervised approach that combines SHapley Additive exPlanations (SHAP) for feature selection with an autoencoder based method for generating class labels for a widely used credit card fraud detection dataset. …”
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FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation by Yuhan Ding, Jinhui Liu, Yunbo He, Jinliang Huang, Haisu Liang, Zhenglin Yi, Yongjie Wang

“…To solve the problems of existing hybrid networks based on convolutional neural networks (CNN) and Transformers, we propose a new encoder–decoder network FI‐Net based on CNN‐Transformer for medical image segmentation. …”
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Home is where my villa is: a machine learning-based predictive suitability map for Roman features in Northern Noricum (ca. 50–500 CE/Lower Austria/AUT) by Dominik Hagmann

“…The 1161 km² area of interest includes the municipium Aelium Cetium (Sankt Pölten) and the forts Arelape (Pöchlarn), Favianis (Mautern an der Donau), and Augustianis (Traismauer), now part of the UNESCO World Heritage site „Danube Limes.“ Based on 1184 features from 551 findspots grouped into 129 sites, a machine learning-based Archaeological Predictive Model was developed using Maximum Entropy (Maxent), integrating environmental and agency-related factors. …”
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Background-Supported Global Feature Response Image Classification Network by JIANG Wentao, LI Weida, ZHANG Shengchong

“…Then, a full-domain feature response module BGR (background-supported global feature response) is proposed, and BGR is embedded into the residual branch to restore the image full domain features, which reduces the loss of feature information due to the convolution operation to a certain extent. …”
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Integrated Feature-Temporal GAN for Imbalanced Transaction Fraud Detection by Yicen Zheng, Yu Xie, Jiamin Yao

“…The FASS module effectively reduces the amplification of non-discriminative features, while the TD-GAN ensures temporal consistency through adversarial training. …”
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Combination of Feature Selection and Learning Methods for IoT Data Fusion by V. Sattari-Naeini, Zahra Parizi-Nejad

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CT-based AI framework leveraging multi-scale features for predicting pathological grade and Ki67 index in clear cell renal cell carcinoma: a multicenter study by Huancheng Yang, Yueyue Zhang, Fan Li, Weihao Liu, Haoyang Zeng, Haoyuan Yuan, Zixi Ye, Zexin Huang, Yangguang Yuan, Ye Xiang, Kai Wu, Hanlin Liu

“…Abstract Purpose To explore whether a CT-based AI framework, leveraging multi-scale features, can offer a non-invasive approach to accurately predict pathological grade and Ki67 index in clear cell renal cell carcinoma (ccRCC). …”
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Capacity Prognostics of Marine Lithium-Ion Batteries Based on ICPO-Bi-LSTM Under Dynamic Operating Conditions by Qijia Song, Xiangguo Yang, Telu Tang, Yifan Liu, Yuelin Chen, Lin Liu

“…The paper develops a marine lithium-ion battery capacity prognostic method based on ICPO-Bi-LSTM under dynamic operating conditions to address this. …”
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Improving Aerobics Posture Evaluation by Transfer Learning: Humanized Computational Application of BERT-PTA Domain Adaptive Methods by Wenting Zhou, Biao Guo, Feng Cao

“…Second, the BERT-PTA model was used to extract features from the preprocessed posture data. Next, a convolutional neural network was used to construct a key point localization model for aerobics poses, and transfer learning was used to train and fine-tune the model. …”
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Lightweight Dual-Stream SAR–ATR Framework Based on an Attention Mechanism-Guided Heterogeneous Graph Network by Xuying Xiong, Xinyu Zhang, Weidong Jiang, Tianpeng Liu, Yongxiang Liu, Li Liu

“…Additionally, we include a convolutional neural network based feature extraction net to replenish intuitive visual features. …”
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Determinants of Success and Risks of Implementing the “Service Learning” Approach in a Project-Oriented Learning Environment by Ya. A. Aslanov, L. S. Detochenko, A. P. Lepin, E. V. Martynova

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Software Defect Prediction through Neural Network and Feature Selections by Mutasem Shabeb Alkhasawneh

“…To predict the software defect, this study proposed a model consisting of feature selection and classifications. The correlation base method was used for feature selection, and radial base function neural network (RBF) was used for classification. …”
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MFAN: Multi-Feature Attention Network for Breast Cancer Classification by Inzamam Mashood Nasir, Masad A. Alrasheedi, Nasser Aedh Alreshidi

“…Despite various AI-based strategies in the literature, similarity in cancer and non-cancer regions, irrelevant feature extraction, and poorly trained models are persistent problems. …”
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Frobenius deep feature fusion architecture to detect diabetic retinopathy by C. Priyadharsini, Y. Asnath Victy Phamila

“…Methods This work proposes a multi-model architecture by combining the features extracted from convolutional neural networks using novel Frobenius norm-based feature fusion with an ensemble of machine learning classifiers to perform the classification of binary and multi-class stages of Diabetic Retinopathy. …”
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Explainable machine learning and feature engineering applied to nanoindentation data by C.O.W. Trost, S. Žák, S. Schaffer, L. Walch, J. Zitz, T. Klünsner, H. Leitner, L. Exl, M.J. Cordill

“…Features based on dimensional analysis initially aimed to solve the inverse nanoindentation problem were adopted to describe the load–displacement curves. …”
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Internet traffic classification using SVM with flexible feature space by Yaguan QIAN, Xiaohui GUAN, Bensheng YUN, Qiong LOU, Pengfei MA

“…SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,these approaches are both based on a unique feature space.In fact,the separating capacity of a special traffic feature is not similar to different applications.Hence,flexible feature space for extending SVM was proposed,which constructs independent feature space with optimal discriminability for each binary-SVM and trains them under their own feature space.Finally,these trained binary-SVM were ensemble by One-Against-All and One-Against-One approaches.The experiments show that the proposed approach can efficiently improve the precision and callback of the traffic classifier and easily obtain more reasonable optimal separating hyper-plane.…”
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Internet traffic classification using SVM with flexible feature space by Yaguan QIAN, Xiaohui GUAN, Bensheng YUN, Qiong LOU, Pengfei MA

“…SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,these approaches are both based on a unique feature space.In fact,the separating capacity of a special traffic feature is not similar to different applications.Hence,flexible feature space for extending SVM was proposed,which constructs independent feature space with optimal discriminability for each binary-SVM and trains them under their own feature space.Finally,these trained binary-SVM were ensemble by One-Against-All and One-Against-One approaches.The experiments show that the proposed approach can efficiently improve the precision and callback of the traffic classifier and easily obtain more reasonable optimal separating hyper-plane.…”
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Feature fusion with attributed deepwalk for protein–protein interaction prediction by Mei-Yuan Cao, Suhaila Zainudin, Kauthar Mohd Daud

“…Specifically, sequence similarity is computed using Levenshtein distance, while network similarity is measured via a Gaussian kernel-based approach. These complementary features are fused through the weighting mechanism before being processed by the Attributed DeepWalk algorithm, which enhances protein representations by learning low-dimensional embeddings. …”
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A Feature Integration Network for Multi-Channel Speech Enhancement by Xiao Zeng, Xue Zhang, Mingjiang Wang

“…In this study, we propose a novel feature integration network that not only captures spectral information but also refines it through shifted-window-based self-attention, enhancing the quality and precision of the feature extraction. …”
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Human Capital Formation in High-Tech: Features of Government Policy by M. B. Flek, E. A. Ugnich

“…The arsenal of tools of domestic government policy includes a focus on practice-oriented training, the development of digital competencies, advanced training of management personnel, as well as the development of interaction between the academic and high-tech sectors. …”
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