Attention U-Net-based semantic segmentation for welding line detection by Hunor István Lukács, Bence Zsolt Beregi, Balázs Porteleki, Tamás Fischl, János Botzheim

“…Abstract In industrial processes, quality assurance through methods such as visual inspection is essential for ensuring process stability. …”
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Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression by K. K. L. B. Adikaram, M. A. Hussein, M. Effenberger, T. Becker

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eDNA‐Based Detection of Invasive Crayfish and Crayfish Plague in Estonia by Michael Oliewo Aluma, Katrin Kaldre, David A. Strand, Margo Hurt, Lilian Pukk

“…ABSTRACT In Estonia, three invasive North American crayfish species—Pacifastacus leniusculus, Faxonius limosus, and Procambarus virginalis—have been detected through the annual monitoring program. To protect Astacus astacus, the only native freshwater crayfish species in Estonia, rapid and effective conservation‐based management actions are necessary. …”
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Navigating uncertainty in environmental DNA detection of a nuisance marine macroalga. by Patrick K Nichols, Kaʻuaʻoa M S Fraiola, Alison R Sherwood, Brian B Hauk, Keolohilani H Lopes, Colt A Davis, James T Fumo, Chelsie W W Counsell, Taylor M Williams, Heather L Spalding, Peter B Marko

“…Early detection of nuisance species is crucial for managing threatened ecosystems and preventing widespread establishment. …”
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Design of Display Software for Vehicle-mounted Catenary Detection Device on EMUs by LI Rui, YANG Wuzhou, RAO Tiangui

“…To this end, this paper presented a design of high-definition display software for EMU vehicle-mounted catenary detection device. It adopts the idea of modular design, separating the interface display and data communication, separating the video data processing and video display process. …”
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A Deep Learning and Transfer Learning Approach for Vehicle Damage Detection by Lin Li, Koshin Ono, Chun-Kit Ngan

“…For insurance companies, it is very time-consuming and expensive to process claims for detecting and classifying vehicle damages; thus, deep learning techniques have been used to automate this process to reduce the time and the cost. …”
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Application of Closed Reconstruction Method in Error Detection of RV Cycloidal Gear by Meng Tian, Tianxing Li, Yulong Li, Jinfan Li

“…Cycloidal gear adopts one-dimensional probe sampling mechanism when detecting on the gear measuring center. Due to the sensitive direction of one-dimensional probe is inconsistent with the definition direction of tooth profile normal error， the error calculation process is tedious， and even the error result is inaccurate， which can not truly reflect the machining shape of the actual tooth profile. …”
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A Small Target Pedestrian Detection Model Based on Autonomous Driving by Yang Zhang, Shuaifeng Zhang, Dongrong Xin, Dewang Chen

“…To improve the accuracy of small-target pedestrian detection and the anti-interference ability of the model, a small-target pedestrian detection model that fuses residual networks and feature pyramids is proposed. …”
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Blockchain-Enabled Deep Recurrent Neural Network Model for Clickbait Detection by Abdul Razaque, Bandar Alotaibi, Munif Alotaibi, Fathi Amsaad, Ansagan Manasov, Salim Hariri, Banu B. Yergaliyeva, Aziz Alotaibi

“…The clickbait search process is accomplished by incorporating the binary search features for a faster and more efficient search process for malicious content-detection. …”
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A Classification Data Packets Using the Threshold Method for Detection of DDoS by Sukma Aji, Davito Rasendriya Rizqullah Putra, Imam Riadi, Abdul Fadlil, Muhammad Nur Faiz, Arif Wirawan Muhammad, Santi Purwaningrum, Laura Sari

“…Classification results using the Threshold method after going through the fitting process, namely detecting 8 IP Addresses as computer network users and 6 IP addresses as perpetrators of DDoS attacks with optimal accuracy.…”
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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

“…Abstract Edible oil may be mixed with tiny solid impurities like raw material fragments, hair, metal fragments and etc. during the production and manufacturing process. For food safety reasons, these tiny impurities need to be detected in the quality control process. …”
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Proactive recognition and early detection in communities through targeted HIV screening by Mehdi Nejat, Hamid Reza Marateb, Mehrshad Alirezaei Farahani, Mohammad Zakaria Rajabi, Maryam Nasirian, Miguel Angel Mañanas, Mohammad Javad Tarrahi, Marjan Mansourian

“…In many resource-limited settings, early detection of HIV is hindered by stigma, limited access to testing, and low risk awareness. …”
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Detection of prions in oocytes and ovaries of ewes naturally infected with classical scrapie by Paula A. Marco Lorente, Maialen Zinkunegi, Diego Sola, Nerea Larrañaga, Belén Marín, Bernardino Moreno, Juan J. Badiola, Rosa Bolea, Alicia Otero

“…Prion accumulation appears to be influenced by the host genotype and prion strain, emphasizing the need for ultrasensitive detection techniques. Further research under controlled conditions is necessary to elucidate the mechanisms and implications for disease control and breeding programs.…”
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Hints of Planet Formation Signatures in a Large-cavity Disk Studied in the AGE-PRO ALMA Large Program by Anibal Sierra, Laura M. Pérez, Carolina Agurto-Gangas, James Miley, Ke Zhang, Paola Pinilla, Ilaria Pascucci, Leon Trapman, Nicolas Kurtovic, Miguel Vioque, Dingshan Deng, Rossella Anania, John Carpenter, Lucas A. Cieza, Camilo González-Ruilova, Michiel Hogerheijde, Aleksandra Kuznetsova, Giovanni P. Rosotti, Dary A. Ruiz-Rodriguez, Kamber Schwarz, Benoît Tabone, Estephani E. TorresVillanueva

“…Detecting planet signatures in protoplanetary disks is fundamental to understanding how and where planets form. …”
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Detect Windows Code Injection by Cross-validating Stack and VAD Information by ZHAI Jiqiang, HAN Xu, WANG Jiaqian, SUN Haixu, YANG Hailu

“…Then the data is combined with the process VAD structure to detect the function return address and match the file name to locate the injected code. …”
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Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection by Jinfu Gong, Zhen Huang, Zhengye Yang, Xuezhuan Ding, Fanming Li

“…This method introduces an adaptive coarse detection module, which optimizes the coarse detection process in generative hyperspectral target detection, effectively reducing the background-target misclassification. …”
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Identification and evaluation of the effective criteria for detection of congestion in a smart city by Anita Mohanty, Subrat Kumar Mohanty, Bhagyalaxmi Jena, Ambarish G. Mohapatra, Ahmed N. Rashid, Ashish Khanna, Deepak Gupta

“…This parameter is utilized in analytical hierarchy process to detect the highest priorities parameter and based on that the congestion is detected in particular lane. …”
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Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization by Giulia Veronesi, Nico Curti, Aldo Gardini, Giulia Querzoli, Gastone Castellani, Emi Dika

“…The images were first processed using a multi-resolution image processing pipeline with the aim of segmenting nuclei, evaluating their geometrical and morphological features, as well as their spatial organization. …”
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Malicious code within model detection method based on model similarity by Degang WANG, Yi SUN, Chuanxin ZHOU, Qi GAO, Fan YANG

“…The privacy of user data in federated learning is mainly protected by exchanging model parameters instead of source data.However, federated learning still encounters many security challenges.Extensive research has been conducted to enhance model privacy and detect malicious model attacks.Nevertheless, the issue of risk-spreading through malicious code propagation during the frequent exchange of model data in the federated learning process has received limited attention.To address this issue, a method for detecting malicious code within models, based on model similarity, was proposed.By analyzing the iterative process of local and global models in federated learning, a model distance calculation method was introduced to quantify the similarity between models.Subsequently, the presence of a model carrying malicious code is detected based on the similarity between client models.Experimental results demonstrate the effectiveness of the proposed detection method.For a 178MB model containing 0.375MB embedded malicious code in a training set that is independent and identically distributed, the detection method achieves a true rate of 82.9% and a false positive rate of 1.8%.With 0.75MB of malicious code embedded in the model, the detection method achieves a true rate of 96.6% and a false positive rate of 0.38%.In the case of a non-independent and non-identically distributed training set, the accuracy of the detection method improves as the rate of malicious code embedding and the number of federated learning training rounds increase.Even when the malicious code is encrypted, the accuracy of the proposed detection method still achieves over 90%.In a multi-attacker scenario, the detection method maintains an accuracy of approximately 90% regardless of whether the number of attackers is known or unknown.…”
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Rumor detection model with weighted GraphSAGE focusing on node location by Manfu Ma, Cong Zhang, Yong Li, Jiahao Chen, Xuegang Wang

“…Traditional deep learning models ignore the relationship and topology between nodes in the rumor detection task and use fixed weights or mean aggregation strategies in the feature aggregation process, which fail to capture the complex interactions between nodes and the dynamics of information propagation, limiting the accuracy and robustness of the rumor detection model. …”
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