Electrochemical Detection of Sequence-Specific DNA with the Amplification of Gold Nanoparticles by Yuzhong Zhang, Zhen Wang, Yuehong Wang, Lei Huang, Wei Jiang, Mingzhu Wang

“…Scanning electron microscopy (SEM) and electrochemical impedance spectra (EIS) were used to investigate the film assembly process. The DNA hybridization events were monitored by differential pulse voltammetry (DPV), and adriamycin was used as the electrochemical indicator. …”
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Road Damage Detection Using Yolov9-Based Imagery by Febrian Akbar Azhari, Tatang Rohana, Kiki Ahmad Baihaqi, Ahmad Fauzi

“…An annotated dataset of road images was used during the model training and testing process. The results show that the YOLOv9 model can accurately detect road damage. …”
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Calibration and uncertainty quantification for deep learning-based drought detection by Mengxue Zhang, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Gustau Camps-Valls

“…Deep learning models have recently succeeded in detecting extreme climate events and promise to uncover and understand droughts further. …”
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Prospects for detecting the couplings of axion-like particle with neutrinos at the CEPC by Chong-Xing Yue, Xin-Yang Li, Xiao-Chen Sun

“…Abstract We explore the possibility of detecting the couplings of axion-like particle (ALP) with leptons from their loop-level impact on the ALP couplings to electroweak (EW) gauge bosons via the signal process at the circular electron positron collider (CEPC) and obtain prospective sensitivities to the ALP-lepton couplings. …”
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Detecting phishing gangs via taint analysis on the Ethereum blockchain by Kangrui Huang, Weili Chen, Zibin Zheng

“…Experimental results indicate that the proposed framework can be used to build a uniform platform to monitor every account on the Ethereum blockchain for early warning of phishing scams and detection of the phishers' money laundering and cashing process.…”
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Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments by Rong Xie, Chunjun Chen

“…The inertial reference method was then applied to process the acceleration and relative displacement data between the detection beam and the track, yielding virtual irregularities. …”
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Study on PV Defect Detection Based on CWE YOLOv8 by Xiaojuan Zhang, Ruixu Yao, Bo Jing, Xiaoxuan Jiao, Mengdi Ren

“…A lightweight EfficientHead detection head is also introduced to boost the model’s accuracy and robustness in complex detection scenarios. …”
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Hard-coded backdoor detection method based on semantic conflict by Anxiang HU, Da XIAO, Shichen GUO, Shengli LIU

“…The current router security issues focus on the mining and utilization of memory-type vulnerabilities, but there is low interest in detecting backdoors.Hard-coded backdoor is one of the most common backdoors, which is simple and convenient to set up and can be implemented with only a small amount of code.However, it is difficult to be discovered and often causes serious safety hazard and economic loss.The triggering process of hard-coded backdoor is inseparable from string comparison functions.Therefore, the detection of hard-coded backdoors relies on string comparison functions, which are mainly divided into static analysis method and symbolic execution method.The former has a high degree of automation, but has a high false positive rate and poor detection results.The latter has a high accuracy rate, but cannot automate large-scale detection of firmware, and faces the problem of path explosion or even unable to constrain solution.Aiming at the above problems, a hard-coded backdoor detection algorithm based on string text semantic conflict (Stect) was proposed since static analysis and the think of stain analysis.Stect started from the commonly used string comparison functions, combined with the characteristics of MIPS and ARM architectures, and extracted a set of paths with the same start and end nodes using function call relationships, control flow graphs, and branching selection dependent strings.If the strings in the successfully verified set of paths have semantic conflict, it means that there is a hard-coded backdoor in the router firmware.In order to evaluate the detection effect of Stect, 1 074 collected device images were tested and compared with other backdoor detection methods.Experimental results show that Stect has a better detection effect compared with existing backdoor detection methods including Costin and Stringer: 8 hard-coded backdoor images detected from image data set, and the recall rate reached 88.89%.…”
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Knowledge Error Detection via Textual and Structural Joint Learning by Xiaoyu Wang, Xiang Ao, Fuwei Zhang, Zhao Zhang, Qing He

“…Current methods for knowledge graph error detection primarily focus on graph structure and overlook the importance of textual information in error detection. …”
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Can artificial ıntelligence detect the anti-aging effect of rhinoplasty? by Muhammed Zeki Yalçın, Yuksel Toplu, Osman Kurt

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A Method for Detecting Tomato Maturity Based on Deep Learning by Song Wang, Jianxia Xiang, Daqing Chen, Cong Zhang

“…The modeling of global and local information is realized through the self-attention mechanism, which improves the generalization ability and feature extraction ability of the model, thereby bringing higher detection accuracy. Secondly, the C2f convolution in the neck section is replaced with Distribution Shifting Convolution, so that the model can better process spatial information and further improve the object detection accuracy. …”
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Myocarditis Detection Using Proximal Policy Optimization and Mutual Learning by Asadi Srinivasulu, Sivaram Rajeyyagari

“…To address class imbalance, a proximal policy optimization (PPO)-based algorithm is utilized, significantly improving the training process by preventing abrupt policy shifts and stabilizing them. …”
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Early detection of risky spatio-temporal congestion in urban traffic by Shaobo Sui, Dan Xu, Mingyang Bai, Xiaoke Zhang, Zhaojun Mao, Daqing Li

“…In this article, we develop a detection method for risky congestion based on its spatio-temporal evolution feature, which can detect risky spatio-temporal congestion clusters (SCCs) when they are small. …”
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Detecting differential transmissibilities that affect the size of self-limited outbreaks. by Seth Blumberg, Sebastian Funk, Juliet R C Pulliam

“…Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases. …”
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Mixed Gas Detection and Temperature Compensation Based on Photoacoustic Spectroscopy by Sun Chao, Hu Runze, Liu Niansong, Ding Jianjun

“…Experimental results show that, compared to the traditional SVM algorithm, the KNN-SVM algorithm performs better in gas classification prediction, with an accuracy rate of 99.167% and an AUC indicator of 99.375%, enhancing the accuracy of gas detection. In response to the impact of temperature on the system during the experimental process, a WOA-BP temperature compensation model was established to compensate for temperature in gas concentration detection. …”
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Capacity Constraint Analysis Using Object Detection for Smart Manufacturing by Hafiz Mughees Ahmad, Afshin Rahimi, Khizer Hayat

“…The increasing adoption of Deep Learning (DL)-based Object Detection (OD) models in smart manufacturing has opened up new avenues for optimizing production processes. …”
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Automated detection of hospital outbreaks: A systematic review of methods. by Brice Leclère, David L Buckeridge, Pierre-Yves Boëlle, Pascal Astagneau, Didier Lepelletier

“…<h4>Results</h4>Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). …”
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Sysmon event logs for machine learning-based malware detection by Riki Mi’roj Achmad, Dyah Putri Nariswari, Baskoro Adi Pratomo, Hudan Studiawan

“…Malware poses a significant threat to modern computing environments, necessitating advanced detection techniques that can adapt to evolving attack methods. …”
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Deepfake detection method based on patch-wise lighting inconsistency by Wenxuan WU, Wenbo ZHOU, Weiming ZHANG, Nenghai YU

“…The rapid development and widespread dissemination of deepfake techniques has caused increased concern.The malicious application of deepfake techniques also poses a potential threat to the society.Therefore, how to detect deepfake content has become a popular research topic.Most of the previous deepfake detection algorithms focused on capturing subtle forgery traces at pixel level and have achieved some results.However, most of the deepfake algorithms ignore the lighting information before and after generation, resulting in some lighting inconsistency between the original face and the forged face, which provided the possibility of using lighting inconsistency to detect deepfake.A corresponding algorithm was designed from two perspectives: introducing lighting inconsistency information and designing a network structure module for a specific task.For the introduction of lighting task, a new network structure was derived by designing the corresponding channel fusion method to provide more lighting inconsistency information to the network feature extraction layer.In order to ensure the portability of the network structure, the process of feature channel fusion was placed before the network extraction information, so that the proposed method can be fully planted to common deepfake detection networks.For the design of the network structure, a deepfake detection method was proposed for lighting inconsistency based on patch-similarity from two perspectives: network structure and loss function design.For the network structure, based on the characteristic of inconsistency between the forged image tampering region and the background region, the extracted features were chunked in the network feature layer and the feature layer similarity matrix was obtained by comparing the patch-wise cosine similarity to make the network focus more on the lighting inconsistency.On this basis, based on the feature layer similarity matching scheme, an independent ground truth and loss function was designed for this task in a targeted manner by comparing the input image with the untampered image of this image for patch-wise authenticity.It is demonstrated experimentally that the accuracy of the proposed method is significantly improved for deepfake detection compared with the baseline method.…”
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Weed detection on embedded systems using computer vision algorithms by D. Shadrin, S. Illarionova, R. Kasatov, M. Akimenkova, G. Rudensky, E. Erhan

“…They allow one to automate data analysis process and to make decisions faster. However, the weed detection task in agriculture requires not only high recognition accuracy, but also fast computations on portable devices with low memory availability that makes it possible to embed computer vision systems on unmanned aerial vehicles (UAVs). …”
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