MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 by Xupeng Yin, Zikai Zhao, Liguo Weng

“…To overcome these challenges, this paper proposes MAS-YOLO, a lightweight detection algorithm for PCB defect detection based on improved YOLOv12 architecture. …”
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A Dual-Strategy Framework for Cyber Threat Detection in Imbalanced, High-Dimensional Data Across Heterogeneous Networks by T. Saranya, S. Indra Priyadharshini

Subjects: “…Intrusion detection system (IDS)…”
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An Algorithm for Extracting Features of Basketball Players' Foul Actions Based on an Attention Mechanism by Peng Wang

“…The experimental results show that the algorithm can effectively identify the identification of basketball players, the cumulative matching feature value of foul action recognition can reach more than 95%, and the average accuracy is more than 70%; the F1 value and stability are high, which can reduce the error caused by data fluctuation and noise; the error rate of real-time detection is less than 4.5%, the omission rate is less than 4.7%, the detection time is lower than 14 ms, and the application effect is good.…”
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Optimization and Validation of Wafer Surface Defect Detection Algorithm Based on RT-DETR by Ao Xu, Yanwei Li, Hongbo Xie, Rui Yang, Jianjie Li, Jiaying Wang

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RF-SFAD: A RANDOM FOREST MODEL FOR SELECTIVE FORWARDING ATTACK DETECTION IN MOBILE WIRELESS SENSOR NETWORKS by N Usha Bhanu, Soubhagya Ranjan Mallick, Sreenivasa Rao Chappidi, K Sangeethalakshmi

Subjects: “…mobile wireless sensor networks, random forest algorithm, selective forwarding attack detection, clustering, feature selection.…”
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Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments by Hanan Abbas Mohammad

Subjects: “…CICIoT2023 Dataset, Feature scaling, IoT Security, Deep learning, LSTM Models, Whale optimization algorithm (WOA), Detect IoT Attacks.…”
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Algorithm for Recognition of Small Air Targets by Trajectory Features in Passive Bistatic Radar by Dao Van Luc, A. A. Konovalov, Le Minh Hoang

“…Aim. Development of an algorithm for recognizing small air targets by trajectory features based on machine learning. …”
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Improving Attack Detection in IoV with Class Balancing and Feature Selection by Thierry Widyatama, Ifan Rizqa, Fauzi Adi Rafrastara

“…Moreover, the application of feature selection significantly reduced computational time without compromising detection performance. …”
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Hyperbolic geometry enhanced feature filtering network for industrial anomaly detection by Yanjun Feng, Jun Liu, Yonggang Gai

“…The visual perception algorithms have become extensively utilized in surface defect detection, progressively replacing manual inspection methods. …”
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An optimized ensemble model with advanced feature selection for network intrusion detection by Afaq Ahmed, Muhammad Asim, Irshad Ullah, Zainulabidin, Abdelhamied A. Ateya

“…To address this challenge, our study presents the “Optimized Random Forest (Opt-Forest),” an innovative ensemble model that combines decision forest approaches with genetic algorithms (GAs) for enhanced intrusion detection. The genetic algorithms based decision forest construction offers notable benefits by traversing a wider exploration space and mitigating the risk of becoming stuck in local optima, resulting in the discovery of more accurate and compact decision trees. …”
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Dual-modal edible oil impurity dataset for weak feature detection by Huiyu Wang, Qianghua Chen, Jianding Zhao, Liwen Xu, Ming Li, Ying Zhao, Qinpei Zhao, Qin Lu

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PVLF: point-voxel local feature fusion for 3D detection by Haowei Zhao, Zhuolei Xiao

“…PVLF explores local spatial features to improve accuracy regarding tiny object detection. …”
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A novel feature selection technique: Detection and classification of Android malware by Sandeep Sharma, Prachi, Rita Chhikara, Kavita Khanna

“…Experimental results using machine learning algorithms demonstrate that the technique proposed in this research effectively integrates the advantages of individual feature selection techniques and exhibits the potential to identify a brief set of pivotal features for detecting Android malware. …”
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Optimizing Feature Selection for IOT Intrusion Detection Using RFE and PSO by zahraa mehssen agheeb Alhamdawee

“…Two feature selection mechanisms, which are Particle Swarm Optimization Algorithm (PSO) and Correlation-based Feature Selection Recursive Feature Elimination (RFE) have been used to compare their performances. …”
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Infrared dim tiny-sized target detection based on feature fusion by Peng Zhang, Yaman Jing, Guodong Liu, Ziyang Chen, Xiaoyan Wu, Osami Sasaki, Jixiong Pu

“…These two main modules are employed as the core to form a feature fusion network to realize the detection of infrared dim tiny-sized targets. …”
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Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking by Harry Hoa Huynh, Cheol-Hong Min

“…Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. …”
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An efficient fire detection algorithm based on Mamba space state linear attention by Yuming Li, Yongjie Wang, Xiaorui Shao, Anbo Zheng

“…Abstract As an emerging State Space Model (SSM), the Mamba model draws inspiration from the architecture of Recurrent Neural Networks (RNNs), significantly enhancing the global receptive field and feature extraction capabilities of object detection models. …”
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Two-level feature selection method based on SVM for intrusion detection by Xiao-nian WU, Xiao-jin PENG, Yu-yang YANG, Kun FANG

“…To select optimized features for intrusion detection，a two-level feature selection method based on support vector machine was proposed.This method set an evaluation index named feature evaluation value for feature selection，which was the ratio of the detection rate and false alarm rate.Firstly，this method filtrated noise and irrelevant features to reduce the feature dimension respectively by Fisher score and information gain in the filtration mode.Then，a crossing feature subset was obtained based on the above two filtered feature sets.And combining support vector machine，the sequential backward selection algorithm in the wrapper mode was used to select the optimal feature subset from the crossing feature subset.The simulation test results show that，the better classification performance is obtained according to the selected optimal feature subset，and the modeling time and testing time of the system are reduced effectively.…”
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The Impact of Feature Extraction in Random Forest Classifier for Fake News Detection by Dhani Ariatmanto, Anggi Muhammad Rifai

“…This research focuses on detecting and classifying fake news using the Random Forest algorithm by investigating the impact of feature extraction techniques on classification accuracy, this study specifically employs the TF-IDF method. …”
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Denoising and Feature Enhancement Network for Target Detection Based on SAR Images by Cheng Yang, Chengyu Li, Yongfeng Zhu

“…To address these issues, this paper proposes an algorithm based on YOLOv8 for detecting ship targets in complex backgrounds using SAR images, named DFENet (Denoising and Feature Enhancement Network). …”
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