Enhancing credit card fraud detection: highly imbalanced data case by Dalia Breskuvienė, Gintautas Dzemyda

“…The uniqueness of the proposed method is in forming a new dataset containing the Best-Matching Units of the trained SOM as vectors of attributes corresponding to the initial features. …”
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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning. by Fayaz Hassan, Zafi Sherhan Syed, Aftab Ahmed Memon, Saad Said Alqahtany, Nadeem Ahmed, Mana Saleh Al Reshan, Yousef Asiri, Asadullah Shaikh

“…We proposed a hybrid approach uses automated feature engineering via correlation-based feature selection (CFS) and principal component analysis (PCA)-based dimensionality reduction to reduce feature matrix size before a series of dense layers are used for classification. …”
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PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs) by Wafa Alanazi, Di Meng, Gianluca Pollastri

“…Inspired by the remarkable success of NLP techniques, this study leverages pre-trained language models (PLMs) to enhance RSA prediction. …”
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An enhanced BERT model with improved local feature extraction and long-range dependency capture in promoter prediction for hearing loss by Jing Sun, Yangfan Huang, Jiale Fu, Li Teng, Xiao Liu, Xiaohua Luo

“…The CNN module is able to capture local regulatory features, while the BiLSTM module can effectively model long-distance dependencies, enabling efficient integration of global and local features of promoter sequences. …”
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May Patients with Chronic Stroke Benefit from Robotic Gait Training with an End-Effector? A Case-Control Study by Mirjam Bonanno, Paolo De Pasquale, Antonino Lombardo Facciale, Biagio Dauccio, Rosaria De Luca, Angelo Quartarone, Rocco Salvatore Calabrò

“…The data of these subjects were compared with those coming from a sample of twelve individuals (control group, CG) matched for clinical and demographic features who underwent the same amount of conventional gait training (CGT), in addition to standard rehabilitation therapy. …”
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A Dynamic Adaptive Framework for Remote Sensing Imagery Superpixel Segmentation and Classification via Dual-Branch Feature Learning by Wangtun Yang, Yang Zhang, Heng Zhang, Liangzhi Li

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Smart Grid Intrusion Detection for IEC 60870-5-104 With Feature Optimization, Privacy Protection, and Honeypot-Firewall Integration by Pedamallu Sai Mrudula, Rayappa David Amar Raj, Archana Pallakonda, Yanamala Rama Muni Reddy, K. Krishna Prakasha, V. Anandkumar

“…Defences against adversarial attacks use FGSM-based training, feature smoothing, and ensemble-based defences, to reduce susceptibility to evasion tactics. …”
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DaNet: Domain-adaptive white blood cell classification through synthetic augmentation and cross-domain feature alignment by Wenpeng Gao, Liantao Lan, Xiaomao Fan

“…Novelty:: The proposed BMDM facilitates a more continuous latent space for domain-invariant features across multiple source domains, which effectively alleviates the alignment challenges commonly encountered by DDA-based methods in WBC image classification tasks, particularly in the presence of subtle inter-class differences and class imbalance. …”
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TransRNetFuse: a highly accurate and precise boundary FCN-transformer feature integration for medical image segmentation by Baotian Li, Jing Zhou, Fangfang Gou, Jia Wu

“…Nonetheless, the majority of existing models prioritize overall high accuracy, often overlooking the sensitivity to local salient features and the precision of segmentation boundaries. …”
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Fusion of non-iterative deep neural network feature extraction with kernel extreme learning machine for plant disease classification by Kirti Kirti, Navin Rajpal, Virendra P. Vishwakarma, Pramod Kumar Soni

“…The method extracts deep, discriminative features via ResNet-50 and feeds them into a lightweight KELM for final classification. …”
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Prediction of BTEX concentrations in the air of Southern East Azerbaijan province, Iran using ensemble machine learning and feature analysis by Mansour Baziar, Negar Jafari, Ali Oghazyan, Amir Mohammadi, Ali Abdolahnejad, Ali Behnami

“…To further optimize the stacking ensemble, CatBoost, a high-performing model not included in LazyRegressor, was incorporated. A thorough feature analysis identified key predictors influencing BTEX concentrations, including PM10, PM2.5, humidity, temperature, wind speed, and UV index. …”
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A novel multiscale feature enhancement network using learnable density map for red clustered pepper yield estimation by Chenming Cheng, Chenming Cheng, Jin Lei, Jin Lei, Zicui Zhu, Lijian Lu, Lijian Lu, Zhi Wang, Zhi Wang, Jiali Tao, Jiali Tao, Xinyan Qin, Xinyan Qin

“…Then, a novel MFEN was developed to improve feature extraction from these density maps. This network combines dilation convolution, residual structures, and an attention mechanism to effectively extract features. …”
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Optimizing feature selection and deep learning techniques for precise detection of low-rate distributed denial of service (LDDoS) attack by Naeem Ali Al-Shukaili, Miss Laiha M. Kiah, Ismail Ahmedy

“…Feature encoding to train the model with k-best feature is done by label encoder. …”
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Prognostic and predictive value of pathohistological features in gastric cancer and identification of SLITRK4 as a potential biomarker for gastric cancer by Yuzhe Zhang, Yuhang Xue, Yongju Gao, Ye Zhang

“…Abstract The aim of this study was to develop a quantitative feature-based model from histopathologic images to assess the prognosis of patients with gastric cancer. …”
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Improving machine learning algorithm for risk of early pressure injury prediction in admission patients using probability feature aggregation by Shu-Chen Chang, Shu-Mei Lai, Mei-Wen Wu, Shou-Chuan Sun, Mei-Chu Chen, Chiao-Min Chen

“…Methods The proposed approach receives multiple patients records, selects key features of discrete numerical based on their relevance to PIs, and trains a random forest (RF) machine learning (ML) algorithm to build a predictive model. …”
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MultiNet: A deep neural network approach for detecting breast cancer through multi-scale feature fusion by Saikat Islam Khan, Ashef Shahrior, Razaul Karim, Mahmodul Hasan, Anichur Rahman

“…In the proposed framework, features from microscopy images are extracted using three well-known pre-trained models, including DenseNet-201, NasNetMobile, and VGG16. …”
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Enhanced Feature Selectivity in MobileNetV2 for Skin Cancer Detection through Scaled Dot-Product Attention by Omar Gasmann, Nazmul Shahadat

“… This study presents an enhanced MobileNetV2-based model architecture for efficient and accurate skin cancer classification, suitable for mobile deployment. …”
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TCI-Net: Structural Feature Enhancement and Multi-Level Constrained Network for Reliable Thin Crack Identification on Concrete Surfaces by Shiming Li, Fengtao Yan, Zhijie Li, Qingfeng Hu, Shiqian Xu, Shilian Liu

“…This method builds on a pre-trained multi-scale semantic feature encoding network, integrating various crack edge structure information extraction operators, and a lightweight crack spatial detail feature extraction module is constructed. …”
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Resolution-Aware Deep Learning with Feature Space Optimization for Reliable Identity Verification in Electronic Know Your Customer Processes by Mahasak Ketcham, Pongsarun Boonyopakorn, Thittaporn Ganokratanaa

“…The proposed model is trained on facial images resized to 24 × 24 pixels, with a super-resolution module enhancing feature clarity prior to classification. …”
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Harnessing machine learning for high-throughput screening of high thermal conductivity polyimides: A multiscale feature engineering approach by Jiale Han, Chunhua Ying, Yue Cao, Wen Li, Yuan Feng, Masood Mortazavi, Pingfan Wu, Liang Peng, Jiechen Wang

“…Our workflow involves digitally representing chemical structures using physical insights and refining these representations through the Statistical – Tree – Recursive feature engineering pipeline, which includes three steps--statistical selection, tree-based cascading feature selection, and recursive feature elimination. …”
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