Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy Conditions by Mauro Larrat, Claudomiro Sales

“…This study evaluates different machine learning algorithms in detecting and identifying drones using radar data from a 60 GHz millimeter-wave sensor. …”
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Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization by Vasavi Chithanuru, Mangayarkarasi Ramaiah

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Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…Experimental results demonstrate that combining Convolutional Neural Network (CNN)-based feature extraction with OCSVM significantly improves anomaly detection performance compared with simpler handcrafted approaches. …”
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Combined Use of Spectral and Structural Features for Improved Early Detection of Pine Shoot Beetle Attacks in Yunnan Pines by Yujie Liu, Youqing Luo, Run Yu, Lili Ren, Qi Jiang, Shaoshun He, Xinqiang Chen, Guangzhao Yang

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Resource-Efficient Traffic Classification Using Feature Selection for Message Queuing Telemetry Transport-Internet of Things Network-Based Security Attacks by Emmanuel Tuyishime, Marco Martalò, Petru A. Cotfas, Vlad Popescu, Daniel T. Cotfas, Alexandre Rekeraho

“…The aim is to reduce feature dimensionality while maintaining high detection accuracy. …”
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Binocular stereo vision-based relative positioning algorithm for drone swarm by Qing Cheng, Yazhe Wang

“…Abstract To address the challenges of high computational complexity and poor real-time performance in binocular vision-based Unmanned Aerial Vehicle (UAV) formation flight, this paper introduces a UAV localization algorithm based on a lightweight object detection model. …”
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Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance by M. Elhoseny, Deepak Dasaratha Rao, Bala Dhandayuthapani Veerasamy, Noha Alduaiji, J. Shreyas, Piyush Kumar Shukla

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LD-Det: Lightweight Ship Target Detection Method in SAR Images via Dual Domain Feature Fusion by Hang Yu, Bingzong Liu, Lei Wang, Teng Li

“…However, large-scale deep learning algorithm training requires huge computing power support and large equipment to process, which is not suitable for real-time detection on edge platforms. …”
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Multiscale Sample Entropy-Based Feature Extraction with Gaussian Mixture Model for Detection and Classification of Blue Whale Vocalization by Oluwaseyi Paul Babalola, Olayinka Olaolu Ogundile, Vipin Balyan

“…Additionally, MSE is applied to the Gaussian mixture model (GMM) for blue whale call detection and classification. The performance of the proposed MSE-GMM algorithm is experimentally assessed and benchmarked against traditional methods, including principal component analysis (PCA), wavelet-based feature (WF) extraction, and dynamic mode decomposition (DMD), all combined with the GMM. …”
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Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion by Changfan Zhang, Xinliang Hu, Jing He, Na Hou

“…The Yolov4 detection algorithm does not sufficiently extract local semantic and location information. …”
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Utilizing Enhanced Particle Swarm Optimization for Feature Selection in Gender-Emotion Detection From English Speech Signals by Ammar Amjad, Li-Chia Tai, Hsien-Tsung Chang

“…We extract pitch and acoustic-related statistical features from speech samples and develop separate models for gender prediction and emotion detection. …”
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An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments by Mimouna Abdullah Alkhonaini

“…Furthermore, the FOFSDL-SCD model utilizes the Fox optimizer algorithm (FOA) method for the feature selection process to select the most significant features from the dataset. …”
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Android malware detection method based on deep neural network by Fan CHAO, Zhi YANG, Xuehui DU, Yan SUN

“…Android is increasingly facing the threat of malware attacks.It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine,method for Android malware detection and family classification based on deep neural network was proposed.Based on the comprehensive extraction of application components,Intent Filter,permissions,and data flow,the method performed an effective feature selection to reduce dimensions,and conducted a large-sample detection and multi-class classification for malware based on deep neural network.The experimental results show that the method can conduct an effective detection and classification.The accuracy of binary classification between benign and malicious Apps is 97.73%,and the accuracy of family multi-class classification can reach 93.54%,which is higher than other machine learning algorithms.…”
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Intrusion detection model based on fuzzy theory and association rules by Jianwu ZHANG, Jiasen HUANG, Di ZHOU

“…An intrusion detection model based on fuzzy theory and improved Apriori algorithm was proposed.The BV-Apriori algorithm was used to generate the matching rule base,and the problem of excessive boundary in the continuous data partitioning process was solved by fuzzy set technology.The real-time analysis of the relationship between features and the update of the rule base were completed,and the intrusion detection model BVA-IDS (Boolean vector Apriori-intrusion detection system) was built.The results show that the mining efficiency of the BV-Apriori algorithm is significantly improved when compared with the existing Apriori-BR algorithm,in addition,the BVA-IDS model also performs well on intrusion detection indicators with high detection accuracy,and low false positive rate and false negative rate.…”
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Intrusion detection model based on fuzzy theory and association rules by Jianwu ZHANG, Jiasen HUANG, Di ZHOU

“…An intrusion detection model based on fuzzy theory and improved Apriori algorithm was proposed.The BV-Apriori algorithm was used to generate the matching rule base,and the problem of excessive boundary in the continuous data partitioning process was solved by fuzzy set technology.The real-time analysis of the relationship between features and the update of the rule base were completed,and the intrusion detection model BVA-IDS (Boolean vector Apriori-intrusion detection system) was built.The results show that the mining efficiency of the BV-Apriori algorithm is significantly improved when compared with the existing Apriori-BR algorithm,in addition,the BVA-IDS model also performs well on intrusion detection indicators with high detection accuracy,and low false positive rate and false negative rate.…”
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A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study by Maria Habib, Victor Vicente-Palacios, Pablo García-Sánchez

“…This paper introduces a multi-objective bio-inspired, AI-based optimization approach for the automated detection of voice disorders. Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. …”
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An airport apron ground service surveillance algorithm based on improved YOLO network by Yaxi Xu, Yi Liu, Ke Shi, Xin Wang, Yi Li, Jizong Chen

“…The improved algorithm can efficiently extract the information features of small-sized objects, medium-sized objects, and moving objects in large scenes, and it achieves effective detection of activities of ground service in the apron area. …”
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A novel attention based vision transformer optimized with hybrid optimization algorithm for turmeric leaf disease detection by R. Selvaraj, M. S. Geetha Devasena

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YOLO-RDM: A high accuracy and efficient algorithm for magnetic tile surface defect detection with practical applications. by Wei Niu, Cheng Lv, Enxu Zhang, Zhongbin Wei

“…To solve these problems, this study proposes a novel magnetic tile defect detection algorithm called YOLO-RDM. First, we apply DOConv to the neck network. …”
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LI-YOLOv8: Lightweight small target detection algorithm for remote sensing images that combines GSConv and PConv. by Pingping Yan, Xiangming Qi, Liang Jiang

“…We propose a lightweight small target detection algorithm for remote sensing images that combines GSConv and PConv, named LI-YOLOv8. …”
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