DOG: An Object Detection Adversarial Attack Method by Jinpeng Li, Xiaoyu Ji, Wenyuan Xu, Yushi Cheng

“…This study presents an object detection adversarial attack method (DOG) based on the dynamic optimization of a multi-scale feature grid cluster, aimed at addressing the challenges of poor transferability in white-box attacks and long generation cycles in black-box attacks within the current adversarial example generation techniques. …”
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Defect detection in textiles using back propagation neural classifier by Subrata Das, Amitabh Wahi, Suresh Jayaram

“…This paper presents a classification method to detect defects such as holes and thick places in knitted fabric by applying artificial neural network algorithm. …”
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Blood pressure abnormality detection and interpretation utilizing explainable artificial intelligence by Hedayetul Islam, Md. Sadiq Iqbal, Muhammad Minoar Hossain

“…Principal component analysis (PCA) and recursive feature elimination (RFE) algorithms were used as feature optimizers. …”
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The Fine Feature Extraction and Attention Re-Embedding Model Based on the Swin Transformer for Pavement Damage Classification by Shizheng Zhang, Kunpeng Wang, Zhihao Liu, Min Huang, Sheng Huang

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Comprehensive Evaluation of Techniques for Intelligent Chatter Detection in Micro-Milling Processes by Guilherme Serpa Sestito, Wesley Angelino De Souza, Alessandro Roger Rodrigues, Maira Martins Da Silva

“…This work proposed using feature selection to evaluate the impact of several statistical features on the performance of ML classifiers for chatter detection during micro-milling operations, compare them to the performance of the Convolutional Neural Network algorithm, and discuss the employability of the techniques on the STM32F446RE microcontroller. …”
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SP-Pillars: An Efficient LiDAR 3D Objects Detection Framework With Multi-Scale Feature Perception and Optimization by Tingshuai Chen, Ye Yuan, Bingyang Yin, Yuanhong Liao

“…To address this challenge, this paper proposes a 3D object detection algorithm SP-Pillars that can effectively learn point cloud features. …”
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Improving brain tumor classification: An approach integrating pre-trained CNN models and machine learning algorithms by Mohamed R. Shoaib, Jun Zhao, Heba M. Emara, Ahmed S. Mubarak, Osama A. Omer, Fathi E. Abd El-Samie, Hamada Esmaiel

“…This study provides valuable insights into the interplay between CNN models, feature extraction techniques, and machine learning algorithms for brain tumor classification, highlighting the efficacy of DenseNet201 combined with SVM and MLP.…”
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Active Touch Sensing for Robust Hole Detection in Assembly Tasks by Bojan Nemec, Mihael Simonič, Aleš Ude

“…Unlike general object detection algorithms, our solution is tailored for precise localization of features like hole openings using sparse tactile feedback. …”
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You Only Look Once v8 Cattle Identification Based on Muzzle Print Pattern Using ORB and Fast Library for Approximate Nearest Neighbor Algorithms by Allan Josef Balderas, Kaila Mae A. Pangilinan, Meo Vincent C. Caya

“…Contrast-limited adaptive histogram equalization (CLAHE) was used to enhance the image quality and obtain a prominent and detailed image of the muzzle print. Feature extraction algorithm-oriented FAST and rotated BRIEF (ORB) was applied to extract key points and detect descriptors that are crucial for the cattle identification process. …”
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Quantitatively Detecting Camellia Oil Products Adulterated by Rice Bran Oil and Corn Oil Using Raman Spectroscopy: A Comparative Study Between Models Utilizing Machine Learning Alg... by Henan Liu, Sijia Ma, Ni Liang, Xin Wang

“…In addition to performance fluctuations with varying regression algorithms, the output for feature extraction method also played a vital role in ultimate prediction performance.…”
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Enhancing Cloud Detection in Polar Regions Using Combined Spectral and Textural Features for Landsat 8/9 OLI Imagery by Xinran Shen, Teng Li, Chong Liu, Shaoyin Wang, Lei Zheng, Qi Liang, Xiao Cheng, Jiaqi Yao

“…This study proposes a new cloud detection algorithm for Landsat 8/9 OLI/TIRS images, which combines spectral and texture features to more accurately differentiate clouds from snow. …”
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Insulator Defect Detection in Complex Environments Based on Improved YOLOv8 by Yuxin Qin, Ying Zeng, Xin Wang

“…To solve the problems of its low accuracy, high delay, and large model size in complex environments, following the principle of progressive extraction from high-entropy details to low-entropy semantics, an improved YOLOv8 target detection network for insulator defects based on bidirectional weighted feature fusion was proposed. …”
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SA3C-ID: a novel network intrusion detection model using feature selection and adversarial training by Wanwei Huang, Haobin Tian, Lei Wang, Sunan Wang, Kun Wang, Songze Li

“…Subsequently, the refined data undergoes feature selection employing an improved pigeon-inspired optimizer (PIO) algorithm. …”
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Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods by Saiprasad Potharaju, Swapnali N. Tambe, G. Madhukar Rao, M. V. V. Prasad Kantipudi, Kalyan Devappa Bamane, Mininath Bendre

“…This research demonstrates the effectiveness of a hybrid feature selection approach in improving ICS anomaly detection. …”
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Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach by Rahul Kumar Gupta, Asmaul Hassan, Samir Kumar Majhi, Nikhat Parveen, Abu Taha Zamani, Raju Anitha, Binayak Ojha, Abhinav Kumar Singh, Debendra Muduli

“…This study introduces an innovative machine learning-based fraud detection framework that incorporates sophisticated preprocessing methods like SMOTE-ENN for class imbalance mitigation, autoencoder for dimensionality reduction, and TOPSIS for optimal feature selection. …”
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A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network by Zhuya Huang, Junsheng Yu, Ying Shan, Xiangqing Wang

“…To address the issues of high computational cost, inability to observe fetal heart morphology, and insufficient accuracy associated with the traditional method of calculating the fetal heart rate using a four-channel maternal electrocardiogram (ECG), a method for extracting fetal QRS complexes from a single-channel non-invasive fetal ECG based on a multi-feature fusion neural network is proposed. Firstly, a signal entropy data quality detection algorithm based on the blind source separation method is designed to select maternal ECG signals that meet the quality requirements from all channel ECG data, followed by data preprocessing operations such as denoising and normalization on the signals. …”
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Hypergraph Self-Supervised Learning-Based Joint Spectral-Spatial-Temporal Feature Representation for Hyperspectral Image Change Detection by Ping Jian, Yimin Ou, Keming Chen

“…To address this issue, this article proposes a hypergraph self-supervised learning (HG-SSL) based joint spectral-spatial-temporal feature representation algorithm (HyperSST) for downstream HSI-CD. …”
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An Optimization Method for PCB Surface Defect Detection Model Based on Measurement of Defect Characteristics and Backbone Network Feature Information by Huixiang Liu, Xin Zhao, Qiong Liu, Wenbai Chen

“…This paper proposes the YOLOv8_DSM algorithm for PCB surface defect detection, optimized based on the three major characteristics of defect targets and feature map visualization. …”
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QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals by Veysel Yusuf Cambay, Irem Tasci, Gulay Tasci, Rena Hajiyeva, Sengul Dogan, Turker Tuncer

“…An XFE model has been presented to detect stress automatically. The presented XFE model has four main phases, and these are (i) channel transformer and quadruple transition pattern (QuadTPat)-based feature generation, (ii) feature selection deploying cumulative weighted neighborhood component analysis (CWNCA), (iii) explainable results creation with DLob and (iv) classification with t algorithm-based k-nearest neighbors (tkNN) classifier. …”
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DMF-YOLO: Dynamic Multi-Scale Feature Fusion Network-Driven Small Target Detection in UAV Aerial Images by Xiaojia Yan, Shiyan Sun, Huimin Zhu, Qingping Hu, Wenjian Ying, Yinglei Li

“…The method exhibits superior robustness in dense scenarios, complex backgrounds, and long-range target detection. This approach provides an efficient solution for UAV real-time perception tasks and offers novel insights for multi-scale object detection algorithm design.…”
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