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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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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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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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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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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MicrobeNet: An Automated Approach for Microbe Organisms Prediction Using Feature Fusion and Weighted CNN Model by Khaled Alnowaiser

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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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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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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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MAHGA: Multi-Aspect Heterogeneous Graph Analysis for Harmful Speech Detection on Social Networks by Ryo Yoshida, Soh Yoshida, Mitsuji Muneyasu

Subjects: “…Harmful speech detection…”
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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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Advances in Automated Voice Pathology Detection: A Comprehensive Review of Speech Signal Analysis Techniques by Anitha Sankaran, Lakshmi Sutha Kumar

“…An in-depth review of the pre-processing steps for a speech signal, the features that parameterize the speech, and the feature extraction process is presented. …”
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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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SDDGRNets: Level–Level Semantically Decomposed Dynamic Graph Reasoning Network for Remote Sensing Semantic Change Detection by Zhuli Xie, Gang Wan, Yunxia Yin, Guangde Sun, Dongdong Bu

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Interpretable graph methods for determining nanoparticles ordering in electron microscopy image by M.Y. Kurbakov, V.V. Sulimova, O.S. Seredin, A.V. Kopylov

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Detection method of LDoS attacks based on combination of ANN ＆ KPCA by Zhijun WU, Liang LIU, Meng YUE

“…Low-rate denial-of-service (LDoS) attack is a new type of attack mode for TCP protocol.Characteristics of low average rate and strong concealment make it difficult for detection by traditional DoS detecting methods.According to characteristics of LDoS attacks,a new LDoS queue future was proposed from the router queue,the kernel principal component analysis (KPCA) method was combined with neural network,and a new method was present to detect LDoS attacks.The method reduced the dimensionality of queue feature via KPCA algorithm and made the reduced dimension data as the inputs of neural network.For the good sell-learning ability,BP neural network could generate a great LDoS attack classifier and this classifier was used to detect the attack.Experiment results show that the proposed approach has the characteristics of effectiveness and low algorithm complexity,which helps the design of high performance router.…”
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Leakage Detection in Subway Tunnels Using 3D Point Cloud Data: Integrating Intensity and Geometric Features with XGBoost Classifier by Anyin Zhang, Junjun Huang, Zexin Sun, Juju Duan, Yuanai Zhang, Yueqian Shen

“…Experimental results demonstrate that the integration of geometric features significantly enhances leakage detection accuracy, achieving an F_1-score of 91.18% and 97.84% on two evaluated datasets, respectively. …”
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