Optimization of norovirus concentration and infectivity detection methods in seawater by Dongwei LI, Hongxia MING, Jiajie LI, Jiannan LIN, Jingfeng FAN

“…ConclusionsThe norovirus concentration and detection method established and optimized in this study significantly improved the recovery rate and detection accuracy of norovirus, which is of great significance for improving food safety testing and infectious disease prevention and control.…”
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SARE: an optimized method for detection functional regulatory elements in genome by Mohammadjavad Hosseinpoor

“…The SARE method addresses the limitations of traditional fixed-fragment approaches by detecting variable-length regulatory elements. …”
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Universal anomaly detection at the LHC: transforming optimal classifiers and the DDD method by Sascha Caron, José Enrique García Navarro, María Moreno Llácer, Polina Moskvitina, Mats Rovers, Adrián Rubio Jímenez, Roberto Ruiz de Austri, Zhongyi Zhang

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Optimal-Transport-Based Positive and Unlabeled Learning Method for Windshear Detection by Jie Zhang, Pak-Wai Chan, Michael Kwok-Po Ng

“…In this paper, we aim to investigate a machine learning method for windshear detection based on previously collected wind velocity data and windshear records. …”
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Enhancing knee osteoarthritis detection with AI, image denoising, and optimized classification methods and the importance of physical therapy methods by Burak Bugday, Harun Bingol, Muhammed Yildirim, Bilal Alatas

“…Then, feature selection was made using the neighborhood component analysis (NCA) method for the developed system to produce more successful results, and the optimized feature map was classified into six different classifiers. …”
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Optimized driver fatigue detection method using multimodal neural networks by Shengli Cao, Peihua Feng, Wei Kang, Zeyi Chen, Bo Wang

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Strategy optimization method based on UAVs-assisted detection of covert communication by Xiaohan Wang, Wen Tian, Guangjie Liu, Yuwei Dai

“…Specifically, the UAV actively transmitsnoise to Alice’s transmission channels to disrupt covert transmission whenWillie detects a covert communication transmission. Furthermore, we ana-lyze the adversarial process between the detector and Alice under UAV-assisted jamming based on game theory, theoretically verify the conditionsfor the existence of a Nash equilibrium, and formulate optimal strategiesfor both sides.…”
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Low Complexity Optimized AOR Method for Massive MIMO Signal Detection by Muhammad Nauman Irshad, Muhamamd Muzamil Aslam, Rardchawadee Silapunt

“…This approach provides a better starting point that facilitates faster convergence and more accurate detection. These significant modifications amplify the efficiency of the OAOR method. …”
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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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Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments by Rong Xie, Chunjun Chen

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Ship detection optimization method in SAR imagery based on multi-feature weighting by Quanhua ZHAO, Xiao WANG, Yu LI, Guanghui WANG

“…Aiming at the problem that the accuracy of traditional ship detection algorithms is not satisfying in complex scene with many false alarm targets,a ship detection optimization method in SAR imagery based on multi-feature weighting was proposed.Firstly,the marker-based watershed algorithm was employed to remove land from SAR amplitude image.Then,the CFAR algorithm based on log-normal distribution was used to obtain candidate targets from no land image.Furthermore,the length to width ratio,the ship area and the contrast ratio of the candidate targets were extracted.Finally,a variance coefficient method was proposed to distribute the weight of the three features,and the confidence levels were calculated by combining the normalized feature vectors of the candidate targets with the feature weight.By determining the best confidence level,false alarm targets among the candidate targets were removed to optimize ship detection results.In order to verify the proposed method,experiments were carried on with the GF-3 SAR images of different complex scenes.The experimental results show that the proposed method is feasible and effective.…”
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A Joint Optimization Method for Cooperative Detection Resources Based on Channel Capacity by Wenda Yang, Minggong Wu, Xiangxi Wen, Kexin Bi

“…Aiming at the shortcomings of ABC algorithm, its search strategy and parameter optimization method are improved. A joint optimization process of UAV swarm trajectory and radiated power based on improved ABC algorithm is constructed. …”
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Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection by Jian Yang, Zixin Tang, Zhenkai Guan, Wenjia Hua, Mingyu Wei, Chunjie Wang, Chenglong Gu

“…To tackle this issue, this paper proposed an optimized deep feature synthesis (DFS) method in the automatic feature engineering scheme to improve the car loan fraud detection. …”
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A Semantic Segmentation-Based GNSS Signal Occlusion Detection and Optimization Method by Zhe Yue, Chenchen Sun, Xuerong Zhang, Chengkai Tang, Yuting Gao, Kezhao Li

“…To address this issue, we dig into the environmental perception perspective to propose a semantic segmentation-based GNSS signal occlusion detection and optimization method. The approach distinguishes between building and tree occlusions and adjusts signal weights accordingly to enhance positioning accuracy. …”
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An optimization method of multiscale storage tank target detection introducing an attention mechanism by Wenjia Sun, Chunchun Hu, Nianxue Luo, Qiansheng Zhao

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Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders by Cengiz Gunay, Krishan Bhalsod

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An Improved Generalized Flexibility Sensitivity Method for Structural Damage Detection by NIU Zirong, WU Feng, HAN Zhaolong, ZHUO Yang, CHE Ailan, ZHU Hongbo

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Benchmarking and optimization of methods for the detection of identity-by-descent in high-recombining Plasmodium falciparum genomes by Bing Guo, Shannon Takala-Harrison, Timothy D O'Connor

“…Our optimization and high-level benchmarking methods not only improve IBD segment detection in high-recombining genomes but also enhance overall genomic analysis, paving the way for more accurate genomic surveillance and targeted intervention strategies for malaria.…”
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Cervical Cell Nuclear Segmentation Method Based on Optimized MSER Algorithm by HE Yong jun, ZHANG Xue yuan, SHAO Hui li, DING Bo

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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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