Real-Time Vehicle Detection Using Cross-Correlation and 2D-DWT for Feature Extraction by Abdelmoghit Zaarane, Ibtissam Slimani, Abdellatif Hamdoun, Issam Atouf

“…In the first step, potential vehicles locations are detected based on template matching technique using cross-correlation which is one of the fast algorithms. …”
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Chronic liver disease detection using ranking and projection-based feature optimization with deep learning by Sumaiya Noor, Salman A. AlQahtani, Salman Khan

“…The approach integrates multiple ranking and projection techniques for features, utilizing deep learning to detect early signs of liver disease. …”
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Enhanced Intrusion Detection in In-Vehicle Networks Using Advanced Feature Fusion and Stacking-Enriched Learning by Ali Altalbe

“…This work implements and validates the FFS-IDS using real-time car hacking data sets and achieves better performance than individual decision tree classifiers and popular ensemble learning methods such as Random Forest, LightGBM, AdaBoost, and ExtraTree algorithms. The results demonstrate that FFS-IDS can detect Denial of Service (DoS), Gear spoofing, and RPM spoofing attacks with up to 99% accuracy and Fuzzy attacks with up to 97.5% accuracy using benchmark datasets. …”
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A Hybrid Algorithm for Contour Thinning in Image Processing by Mamatov Narzullo, Niyozmatova Nilufar, Jalelova Malika, Samijonov Abdurashid, Samijonov Boymirzo

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An IoT intrusion detection framework based on feature selection and large language models fine-tuning by Huan Ma, Wan Zhang, Dalong Zhang, Baozhan Chen

“…This algorithm utilizes the CMA-ES algorithm for feature search while also taking into account the mutual information and collinearity among features, thereby more effectively reducing redundancy features. …”
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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images by Yuhang Zhang, Wuxia Zhang, Songtao Ding, Siyuan Wu, Xiaoqiang Lu

“…The “from-to” information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multilevel features, leading to challenges in extracting LCLU features in complex scenes. …”
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Local Sub-Block Contrast and Spatial–Spectral Gradient Feature Fusion for Hyperspectral Anomaly Detection by Dong Zhao, Xingchen Xu, Mingtao You, Pattathal V. Arun, Zhe Zhao, Jiahong Ren, Li Wu, Huixin Zhou

“…However, they often overlook the spatial–spectral gradient information inherent in hyperspectral images, which can lead to decreased detection accuracy. To address this limitation, we propose a novel hyperspectral anomaly detection algorithm that incorporates both local sub-block contrast and spatial–spectral gradient features. …”
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Advanced genetic algorithm (GA)-independent component analysis (ICA) ensemble model for predicting trapped humans through hybrid dimensionality reduction by Enoch Adama Jiya, Ilesanmi B. Oluwafemi

“…To choose relevant subset features from the data and for better generalization in various contexts, this work uses an adaptive human presence detector algorithm that hybridizes dimensionality reduction techniques genetic algorithm (GA), which maximizes feature selection, and independent component analysis (ICA), which lowers the dimensionality of the chosen features. …”
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Spatial Shape-Aware Network for Elongated Target Detection by Shaowen Xu, Der-Horng Lee

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A Hybrid PSO-GA Optimized Approach for COVID-19 Detection Using CT Scan by Abdelghany Fathy, Hatem Abdelkader, Samia Allaoua Chelloug, Mohammed Saleh Ali Muthanna, Amira Abdelatey

“…In the first step, pre-trained convolutional neural networks (CNNs), including VGG-16, ResNet-50, and MobileNet-v2, are utilized to extract critical features from COVID-19-affected lung images. In the second step, a hybrid particle swarm optimization (PSO) and genetic algorithm (GA) optimized approach, called Hybrid PSO-GA, is developed and used to select the optimal features that can increase the accuracy of COVID-19 detection. …”
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Insulator discharge severity assessment algorithm based on RDIDSNet by Cheng Chi, Li Keyu, Yanhui Meng, Yang Yang, JiNing Zhao, Shaotong Pei, Haosen Sun

“…Abstract For the insulator discharge severity assessment at the line inspection site using edge-end computing equipment and UV cameras, this paper proposes an improved assessment algorithm based on the YOLOv8 algorithm. Firstly, LDConv is introduced to replace the convolution of the backbone network part of the network feature extraction, which effectively realizes the enhancement of the feature extraction ability of the algorithm in the case of model lightweighting; and then ACMix attention mechanism is introduced, which realizes better focusing of the model on the target with a very small performance loss; and finally, Shape-IoU is introduced to replace the loss function of the CIoU, which effectively improve the detection accuracy of the algorithm. …”
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The Detection Optimization of Low-Quality Fake Face Images: Feature Enhancement and Noise Suppression Strategies by Ge Wang, Yue Han, Fangqian Xu, Yuteng Gao, Wenjie Sang

“…To address these limitations, this paper proposes a novel algorithm, YOLOv9-ARC, which is designed to enhance the accuracy of detecting low-quality fake facial images. …”
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Green Apple Detection Method Based on Multidimensional Feature Extraction Network Model and Transformer Module by Wei Ji, Kelong Zhai, Bo Xu, Jiawen Wu

“…To enhance the fast and accurate detection of pollution-free green apples for food safety, this paper uses the DETR network as a framework to propose a new method for pollution-free green apple detection based on a multidimensional feature extraction network and Transformer module. …”
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Pillar-X: Integrating Self-Learned Image Features to Improve 3D Object Detection by Mihaly Csontho, Andras Rovid

“…The proposed model achieves this while maintaining a speed similar to the baseline algorithm (¿20 Hz). By comparing the network using both PASCAL and a specific criterion, it can be concluded that Pillar-X is able to improve accuracy and reliability in 3D object detection tasks.…”
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Intelligent Cyber-Attack Detection in IoT Networks Using IDAOA-Based Wrapper Feature Selection by Mohammed Abdullah, Ryna Svyd

“…This study presents an innovative framework that integrates the Improved Dynamic Arithmetic Optimization Algorithm (IDAOA) with a Bagging technique to enhance the performance of intelligent cyber intrusion detection systems. …”
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An Intrusion Detection Model Based on Feature Selection and Improved One-Dimensional Convolutional Neural Network by Qingfeng Li, Bo Li, Linzhi Wen

“…Traditional machine learning techniques to intrusion detection rely on expert experience to choose features, and deep learning approaches have a low detection efficiency. …”
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A Multi-Index Fusion Adaptive Cavitation Feature Extraction for Hydraulic Turbine Cavitation Detection by Yi Wang, Feng Li, Mengge Lv, Tianzhen Wang, Xiaohang Wang

“…A multi-index fusion adaptive cavitation feature extraction and cavitation detection method is proposed to solve the above problems. …”
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A novel similarity-constrained feature selection method for epilepsy detection via EEG signals by Chunlei Shi, Jun Gao, Jian Yu, Lingzhi Zhao, Faxian Jia

“…Finally, a heuristic search strategy-based algorithm is designed to select features for epileptic EEG signals. …”
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Edge-Guided Feature Pyramid Networks: An Edge-Guided Model for Enhanced Small Target Detection by Zimeng Liang, Hua Shen

“…We conducted comparative experiments on multiple datasets using the proposed algorithm and existing advanced methods. The results show improvements in the IoU, nIoU, and F1 metrics, while also showcasing the lightweight nature of EG-FPNs, confirming that they are more suitable for drone detection in resource-constrained infrared scenarios.…”
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MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection by Jingcui Ma, Nian Pan, Dengyu Yin, Di Wang, Jin Zhou

“…To address the issues of sparse feature loss for small targets during the down-sampling phase of the traditional U-Net network and the semantic gap in the feature fusion process, a multilevel feature extraction and fusion attention network (MEFA-Net) is designed. …”
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