Multi-scale feature fusion and feature calibration with edge information enhancement for remote sensing object detection by Lihua Yang, Yi Gu, Hao Feng

“…EMF-DETR introduces a multi-scale edge-aware feature extraction network named MEFE-Net. The network improves object recognition and localization capabilities by extracting multi-scale features and enhancing edge information for targets at each scale, demonstrating exceptional performance in small object detection. …”
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Deception detection based on micro-expression and feature selection methods by Shusen Yuan, Zilong Shao, Zhongjun Ma, Ting Cao, Hongbo Xing, Yong Liu, Yewen Cao

“…Feature importance analysis indicated that micro-expression (ME) information had a significant impact on the deception detection task. …”
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Comparison of Various Feature Extractors and Classifiers in Wood Defect Detection by Kenan Kiliç, Kazım Kiliç, İbrahim Alper Doğru, Uğur Özcan

“…The features of wood images in the dataset taken from literature are extracted separately with six texture feature extractors to detect defective wood. …”
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Integration of Arc and Microstructural Analysis for Anomaly Detection in Walls Manufactured by GMA-Based WAAM by Lucas J. E. B. Santos, Joyce I. V. Souto, Igo J. S. Azevedo, Walman B. Castro, Jefferson S. Lima, João M. P. Q. Delgado, Renato A. C. Santana, Ricardo S. Gomez, André L. D. Bezerra, Antonio G. B. Lima

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Research on Detection Technology of Wind Turbine Blade AnomalyBased on Audio Data by HU Kaikai, CHEN Yanan, CHEN Gang, SHU Hui, LI Ziyuan

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Research on defect perception model of distribution network based on big data analysis and waveform matching algorithm by LIN Kaifeng, LI Yiming, ZHANG Bo, YANG Changyu, ZHU Zeting, YANG Zhenda

Subjects: “…data analysis…”
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MicrobeNet: An Automated Approach for Microbe Organisms Prediction Using Feature Fusion and Weighted CNN Model by Khaled Alnowaiser

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Detecting emotional disorder with eye movement features in sports watching by Wei Qiang, Wei Qiang, Lin Yang, Xucheng Zhang, Na Liu, Yanyong Wang, Jipeng Zhang, Yixin Long, Weiwei Xu, Wei Sun

“…Based on prior research and collected data, four primary eye movement behaviors were identified, along with 14 associated features. Statistical significance was assessed using t-tests and U-tests, and machine learning models were employed for classification (SVM for single-feature analysis and a decision tree for significant features) with k-fold validation. …”
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Bacterial Disease Detection of Cherry Plant Using Deep Features by Hatice Kayhan, Emrah Dönmez, Yavuz Ünal

“…These machine learning-based features have been used for the detection of bacteria-based diseases commonly seen on the leaves of cherry plants. …”
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COMPARATIVE ANALYSIS OF NON-COOPERATIVE SPECTRUM SENSING TECHNIQUES IN COGNITIVE RADIO NETWORK by HAMMED OYEBAMIJI LASISI, OLAJIDE MICHEAL ODOFIN, MUHAMMED BABAJIDE HAMMED, IDOWU OLAMIDE HUSSEIN

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Uncovering the Diagnostic Power of Radiomic Feature Significance in Automated Lung Cancer Detection: An Integrative Analysis of Texture, Shape, and Intensity Contributions by Sotiris Raptis, Christos Ilioudis, Kiki Theodorou

“…These performed excellently in diagnosis, with DenseNet-201 producing an accuracy of 92.4% and XGBoost at 89.7%. The analysis of feature interpretability ascertains its potential in early detection and boosting diagnostic confidence. …”
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MicrocrackAttentionNext: Advancing Microcrack Detection in Wave Field Analysis Using Deep Neural Networks Through Feature Visualization by Fatahlla Moreh, Yusuf Hasan, Bilal Zahid Hussain, Mohammad Ammar, Frank Wuttke, Sven Tomforde

“…This study proposes an asymmetric encoder–decoder network with an adaptive feature reuse block for microcrack detection. The impact of various activation and loss functions are examined through feature space visualisation using the manifold discovery and analysis (MDA) algorithm. …”
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Features of Unmanned Aircraft Detection Using Precision Approach Radar by E. A. Rubtsov, A. V. Fedorov, N. V. Povarenkin, M. Al-Rubaye

“…The small radar cross-section (RCS) of UAVs leads to a decrease in the maximum range and the appearance of blind spots, within which the vehicle cannot be detected.Aim. Analysis of the possibility of detecting UAVs using a precision approach radar, assessing the maximum detection range, blind spots and developing recommendations for their reduction.Materials and methods. …”
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Detection of Disease Features on Retinal OCT Scans Using RETFound by Katherine Du, Atharv Ramesh Nair, Stavan Shah, Adarsh Gadari, Sharat Chandra Vupparaboina, Sandeep Chandra Bollepalli, Shan Sutharahan, José-Alain Sahel, Soumya Jana, Jay Chhablani, Kiran Kumar Vupparaboina

“…Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. …”
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Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images by Taojuan Li, Wen Liu, Mingxian Song, Zheng Gu, Ling Hai

“…Real-time analysis of ultrasound videos using embedded terminals enables the rapid detection of breast masses and plays a crucial role in early breast cancer screening and diagnosis. …”
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EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA by Yousra Berrich, Zouhair Guennoun

“…Abstract This study focuses on epilepsy detection using hybrid CNN-SVM and DNN-SVM models, combined with feature dimensionality reduction through PCA. …”
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Assessing the quality of whole slide images in cytology from nuclei features by Paul Barthe, Romain Brixtel, Yann Caillot, Benoît Lemoine, Arnaud Renouf, Vianney Thurotte, Ouarda Beniken, Sébastien Bougleux, Olivier Lézoray

“…The quality of a preparation protocol is evaluated according to several reference preparation protocols, by comparing their feature distributions with a weighted distance. Results: Through empirical analysis conducted on seven distinct preparation protocols, we demonstrated that the proposed method build a quality module that clearly discriminates each preparation. …”
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Comparative analysis of random forest and deep learning approaches for automated acute lymphoblastic leukemia detection using morphologicaland textural features by Windra Swastika, Kestrilia Rega Prilianti, Paulus Lucky Tirma Irawan, Hendry Setiawan

“…Using 10,661 images from the ALL Challenge dataset, we evaluated both approaches on training (70%), validation (15%), and test (15%) sets. Feature importance analysis revealed cell area (10.71%), energy (10.67%), and skewness (10.50%) as the mostsignificant discriminative features. …”
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Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms by Lal Hussain, Shahzad Ahmad Qureshi, Amjad Aldweesh, Jawad ur Rehman Pirzada, Faisal Mehmood Butt, Elsayed Tag eldin, Mushtaq Ali, Abdulmohsen Algarni, Muhammad Amin Nadim

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Enhanced detection of headache presentation in unruptured brain arteriovenous malformation through combined radiologic features: A cross-sectional study by Chia-Yu Liu, Chia-Feng Lu, Jr-Wei Wu, Yong-Sin Hu, Jih-Yuan Lin, Huai-Che Yang, Jing-Kai Loo, Feng-Chi Chang, Kang-Du Liu, Chung-Jung Lin

“…Adding two significant radiomic features and one angioarchitecture feature enhanced the models. …”
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