Dual threshold cooperative spectrum sensing algorithm based on stochastic resonance by Shunlan LIU, Yide XIAO, Jianrong BAO

“…In view of the poor performance of the existing spectrum sensing algorithm in the low signal-noise ratio (SNR) environment and the limitations of traditional stochastic resonance (SR) detection methods for weak signals in practical applications,a dual threshold cooperative spectrum sensing algorithm based on stochastic resonance (DCSSR) by setting the optimal threshold was proposed and the optimal number of cooperative users was calculated.At last,the performance of the proposed algorithm was analyzed.This algorithm further improves the detection performance of the spectrum sensing algorithm under low signal-to-noise ratio by passing the statistical data located in the double-threshold uncertain region through a stochastic resonance system.Simulation results show that the detection performance of DCSSR algorithm is improved compared with traditional single threshold energy cooperation algorithm,double threshold energy cooperation algorithm and single threshold stochastic resonance cooperation algorithm.When the SNR is -20 dB,the proposed DCSSR algorithm improves the detection probability by 80% compared with the traditional single threshold energy detection cooperation algorithm.…”
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Attention-Based Lightweight YOLOv8 Underwater Target Recognition Algorithm by Shun Cheng, Zhiqian Wang, Shaojin Liu, Yan Han, Pengtao Sun, Jianrong Li

“…The detection frame rate reaches 189 frames per second on the ROUD dataset, thus meeting the high accuracy requirements for underwater object detection algorithms and facilitating lightweight and fast edge deployment.…”
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ALGORITHMS FOR IDENTIFICATION OF CUES WITH AUTHORS’ TEXT INSERTIONS IN BELARUSIAN ELECTRONIC BOOKS by Y. S. Hetsevich, T,. I. Okrut, B. M. Lobanov

“…The algorithms are based on punctuation marking and gender indicators detection, such as past tense verbs and nouns with gender attributes. …”
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Defect identification method for overhead transmission lines based on SIFT algorithm by Qiang Liu, Xi Zheng, Qiuhan Zhang, Hongjie Sun, Jun Yan

“…Tests conducted on various datasets demonstrated a missed detection rate of 4.2 %, a misjudgment rate of 3.5 %, and an overall detection accuracy of 92.3 %. …”
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Improve the robustness of algorithm under adversarial environment by moving target defense by Kang HE, Yuefei ZHU, Long LIU, Bin LU, Bin LIU

“…Traditional machine learning models works in peace environment,assuming that training data and test data share the same distribution.However,the hypothesis does not hold in areas like malicious document detection.The enemy attacks the classification algorithm by modifying the test samples so that the well-constructed malicious samples can escape the detection by machine learning models.To improve the security of machine learning algorithms,moving target defense (MTD) based method was proposed to enhance the robustness.Experimental results show that the proposed method could effectively resist the evasion attack to detection algorithm by dynamic transformation in the stages of algorithm model,feature selection and result output.…”
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Fibre tracing in biomedical images: An objective comparison between seven algorithms. by Youssef Arafat, Cristina Cuesta-Apausa, Esther Castellano, Constantino Carlos Reyes-Aldasoro

“…Trace Ridges is a fully automatic and fast algorithm that combines a series of image-processing algorithms including filtering, watershed transform and edge detection to obtain an accurate delineation of the fibre-like structures in a rapid time. …”
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False alarm rates of fault detection methods by Yusuf Sevim

“…This study focuses on the fault detection (FD) andfalse alarm rates (FAR) of Principal component analysis (PCA) and independent component analysis (ICA)algorithms on the Tennessee Eastman (TE) process. …”
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Review of plant disease image recognition algorithms based on deep learning by Yang Hankun, Zhu Bowei, Zhang Yanmin, Xu Yidong

“…It begins by outlining the importance of plant diseases and the limitations of traditional identification methods, then explores the advantages of deep learning technologies and their application prospects in plant disease identification, especially the use of YOLO series models for real-time detection. Additionally, this article compares the performance of common deep learning algorithms in plant disease identification, and analyzes challenges such as dataset diversity, real-time performance, and catastrophic forgetting. …”
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Multimodal dataset for sensor fusion in fall detection by Carla Taramasco, Miguel Pineiro, Pablo Ormeño-Arriagada, Diego Robles, David Araya

“…The success of developing effective detection algorithms is dependent on the availability of comprehensive datasets that integrate data from multiple synchronized sensors. …”
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Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation by David M Harmon, Ojasav Sehrawat, Maren Maanja, John Wight, Peter A Noseworthy

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Method of Non-Coherent Detection of Repeated Signals by A. P. Los’, A. K. Rozov

“…Algorithms to detect of repeated signals based on the theory of optimal rules of observation stopping are developed. …”
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Nested Pattern Detection and Unidimensional Process Characterization by Gerardo L. Febres

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Improving CNN Fish Detection and Classification with Tracking by Boubker Zouin, Jihad Zahir, Florian Baletaud, Laurent Vigliola, Sébastien Villon

“…Our method fused CNNs and tracking methods, allowing us to detect 12% more individuals compared to CNN alone.…”
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Crowd anomaly estimation and detection: A review by A. Hussein, M.W. Raed, A. Al-Shaikhi, M. Mohandes, B. Liu

“…We present a comprehensive literature review on crowd anomaly detection using disruptive technologies such as radio frequency identification, wireless sensor networks, Wi-Fi, and Bluetooth low energy, employing device-free noninvasive algorithms based on received signal strength indicator variations to detect the speed and direction of a moving crowd to predict the onset of a stampede. …”
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Authorship identification methods in student plagiarism detection by A. I. Paramonov, I. A. Trukhanovich

“…In the modern educational context the problem of plagiarism is urgent and requires the development of effective methods of detection and prevention of this phenomenon. The application of authorship identification methods in the field of student plagiarism detection is considered. …”
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Detection of communities with Naming Game-based methods. by Thais Gobet Uzun, Carlos Henrique Costa Ribeiro

“…Also, our model has shown a degree of accuracy both for non-overlapping and overlapping communities that are comparable with most algorithms specifically designed for topological community detection.…”
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Electronic News Dataset for Native Advertisement Detection by Brian Rizqi Paradisiaca Darnoto, Daniel Siahaan, Diana Purwitasari

“…The resulting, carefully annotated dataset emerges as an invaluable resource, promising to propel the development and evaluation of sophisticated native ads detection algorithms. This advancement holds the potential to augment transparency within online advertising and contribute to the clearer delineation between pure editorial content and sponsored material.…”
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Low-Complexity Gaussian Detection for MIMO Systems by Tianbin Wo, Peter Adam Hoeher

“…Using factor graphs as a general framework and applying the Gaussian approximation, three low-complexity iterative detection algorithms are derived, and their performances are compared by means of Monte Carlo simulations. …”
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Systematic Review: Malware Detection and Classification in Cybersecurity by Sebastian Berrios, Dante Leiva, Bastian Olivares, Héctor Allende-Cid, Pamela Hermosilla

“…These studies cover a variety of detection techniques, including machine learning, deep learning and hybrid models, with a focus on feature extraction, malware behavior analysis and the application of advanced algorithms to improve detection accuracy. …”
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Ensemble Method for Anomaly Detection On the Internet of Things by Kurniabudi Kurniabudi, Eko Arip Winanto, Lola Yorita Astri, Sharipuddin Sharipuddin

“…In this study, the ensemble method is proposed to improve the ability to detect anomalies in unbalanced data. To produce an ideal detection method, a combination of several classification algorithms such as Bayes Network, Naive Bayes, REPtree and J48 is used. …”
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