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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： COVID-19

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Noisy Computing of the Threshold Function 36

Noisy Computing of the Threshold Function

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Wang, Ziao Ghaddar, Nadim Zhu, Banghua Wang, Lele Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Department of Electrical and Computer Engineering University of Toronto Canada Department of Electrical and Computer Engineering University of Washington United States Department of Electrical and Computer Engineering University of British Columbia Canada

Coping with noise in computing is an important problem to consider in large systems. With applications in fault tolerance (Hastad et al., 1987;Pease et al., 1980;Pippenger et al., 1991), noisy sorting (Shah and Wainwright, 2018;Agarwal et al., 2017;Falahatgar et al., 2017;Heckel et al., 2019;Wang et al., 2024a;Gu and Xu, 2023;Kunisky et al., 2024), noisy searching (Berlekamp, 1964;Horstein, 1963;Burnashev and Zigangirov, 1974;Pelc, 1989;Karp and Kleinberg, 2007), among many others, the goal is to devise algorithms with the minimum number of queries that are robust enough to detect and correct the errors that can happen during the computation. In this work, we consider the noisy computing of the threshold-k function. For n Boolean variables x = (x1, ..., xn) ∈ {0, 1}n, the threshold-k function THk(·) computes whether the number of 1's in x is at least k or not, i.e., (Equation presented) The noisy queries correspond to noisy readings of the bits, where at each time step, the agent queries one of the bits, and with probability p, the wrong value of the bit is returned. It is assumed that the constant p ∈ (0, 1/2) is known to the agent. Our goal is to characterize the optimal query complexity for computing the THk function with error probability at most δ. This model for noisy computation of the THk function has been studied by Feige et al. (1994), where the order of the optimal query complexity is established;however, the exact tight characterization of the optimal number of queries is still open. In this paper, our main contribution is tightening this gap by providing new upper and lower bounds for the computation of the THk function, which simultaneously improve the existing upper and lower bounds. The main result of this paper can be stated as follows: for any 1 ≤ k ≤ n, there exists an algorithm that computes the THk function with an error probability at most δ = o(1), and the algorithm uses at most (Equation presented) queries in expectation. Here we define m (Eq

关键词： Active learning

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Occupancy Map Guided Attributes Artifacts Removal for Video-Based Point Cloud Compression

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第12期20卷 1-20页

作者： Chen, Peilin Wang, Shiqi Li, Zhu Department of Computer Science City University of Hong Kong Kowloon Hong Kong Department of Computer Science and Electrical Engineering University of Missouri-Kansas City KansasMO United States

Point clouds offer realistic 3D representations of objects and scenes at the expense of large data volumes. To represent such data compactly in real-world applications, Video-Based Point Cloud Compression (V-PCC) converts their texture into 2D attributes and occupancy maps before applying lossy video compression. Unfortunately, the coding artifacts introduced in the decoded attribute maps eventually degrade the quality of the reconstructed point cloud, thereby influencing its immersive experience. This article proposes a deep learning-based attribute map enhancement method that fully leverages the occupancy map's guidance. The design philosophy is that the cross-modality guidance from occupancy can be leveraged as critical information to enhance the attribute. Therefore, instead of treating attribute and occupancy as two separate sources of signals, occupancy serves as an indispensable auxiliary, such that the proposed framework explicitly provides the model with abundant clues by conducting local feature modification and global dependencies aggregation. In particular, the proposed framework is compatible with existing V-PCC bitstreams and can be feasibly incorporated into the standardized decoder pipeline. Extensive evaluations show the effectiveness of the proposed framework in attribute enhancement, with equivalently 6.0% Bjontegaard Delta-rate (BD-rate) savings obtained. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Image compression

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Tsunami tide prediction in shallow water using recurrent neural networks: model implementation in the Indonesia Tsunami Early Warning System

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Journal of Reliable Intelligent Environments 2024年第2期10卷 177-195页

作者： Dharmawan, Willy Diana, Mery Tuntari, Beti Astawa, I. Made Rahardjo, Sasono Nambo, Hidetaka Centre of Electronics BRIN Puspiptek Serpong South Tangerang15314 Indonesia Electrical Engineering and Computer Science Kanazawa University Kakuma Kanazawa Ishikawa9201192 Japan Computer Science and Electrical Engineering Kumamoto University Kumamoto Kurokami City8600862 Japan

Near-field tides prediction for tsunami detection in the coastal area is a significant problem of the cable-based tsunami meter system in north Sipora, Indonesia. The problem is caused by its shallow water condition and the unavailability of an applicable model or research for tsunami detection in this area. The problem foundation of shallow water area is its ambient noise level-dependent property that requires preprocessing to improve its feature representation. Moreover, because this shallow water is close to the land area, we must consider a model that can accommodate low prediction time for a Tsunami Early Warning System. Therefore, we propose a recurrent neural network (RNN) model because of its reliable performance for time series forecasting. Our report evaluates variants of the RNN model (the vanilla RNN, LSTM and GRU models) in tides prediction and z-score analysis for tsunami identification. The GRU model overwhelms the other two variants in error scores and time processed (training and prediction). It can achieve median error score distribution of 7.8×10-5 on the L1000-P250 configuration with time prediction under 0.1 s. This lower-time prediction is necessary to ensure the early warning system is going well. Moreover, the GRU model can identify all synthetic tsunami tide spikes (compared with the ground truth result) from magnitude 7.2–8.2 by applying a z-score on the GRU’s prediction. © The Author(s) 2023.

关键词： Deep neural networks

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computer vision in smart agriculture and precision farming:Techniques and applications

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Artificial Intelligence in Agriculture 2024年第3期13卷 64-83页

作者： Sumaira Ghazal Arslan Munir Waqar S.Qureshi Department of Computer Science Kansas State University66506 ManhattanKSUSA Department of Electrical Engineering and Computer Science Florida Atlantic University33431 Boca RatonFLUSA School of Computer Science University of GalwayH91 TK33 GalwayIreland

The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence(AI)*** transformation not only promises increased productivity and economic growth,but also has the potential to address important global issues such as food security and *** survey paper aims to provide a holistic understanding of the integration of vision-based intelligent systems in various aspects of precision *** providing a detailed discussion on key areas of digital life cycle of crops,this survey contributes to a deeper understanding of the complexities associated with the implementation of vision-guided intelligent systems in challenging agricultural *** focus of this survey is to explore widely used imaging and image analysis techniques being utilized for precision farming *** paper first discusses various salient crop metrics used in digital *** this paper illustrates the usage of imaging and computer vision techniques in various phases of digital life cycle of crops in precision agriculture,such as image acquisition,image stitching and photogrammetry,image analysis,decision making,treatment,and *** establishing a thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture,the survey concludes by outlining the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms.

关键词： Digital agriculture computer vision Smart agriculture Image analysis Vision-guided intelligent systems

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ATM: Adaptive transformer model for reconstruction of remote sensing multi-temporal non-equispaced sequence imagery

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Journal of King Saud University - computer and Information sciences 2025年第4期37.0卷 1-13页

作者： Qi, Zhi-Peng Zhao, Zi-Yi Jin, Xing Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China

Due to the influence of the imaging characteristics of the solar orbiting satellite and atmospheric conditions, the multi-spectral observation data often have the missing of phase image, which brings difficulties to the efficient and automatic processing of regional remote sensing data. However, the observation sequence contain complex and diverse information, it is difficult for the conventional method to characterize this information. To address this issue, we perform an adaptive transformer model (ATM) for reconstruction of remote sensing multi-temporal non-equispaced sequence imagery. To realize efficient information processing by dynamically adjusting the weight distribution of the internal relations of data, the core workflow of ATM is divided into three steps: feature coding, relationship evaluation and adaptive focusing. Firstly, the input data is encoded as a sequence of vectors, each representing a local feature of the data. Then, the model generates an attention score by calculating the similarity between the query and the key, quantifying the strength of the association between the different features. Finally, a context-aware feature representation is formed by using the normalized attention score weighted aggregation vector. Our experiments, conducted utilizing unmanned aerial vehicle (UAV) datasets and the Landsat-8 datasets, demonstrate that the proposed methodology achieves promising performance in terms of both precision (RMSE decreases by about 2.5 points, and SSIM increases by about 0.05 points) and effectiveness (time cost savings of about 50 %), and the proposed model has wide application prospects in intelligent agriculture, water color inversion and vegetation phenology detection. © The Author(s) 2025.

关键词： Adaptive transformer model Missing of phase image Multi-spectral observation data Non-equispaced sequence imagery

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A Deepfake Detection Algorithm Based on Fourier Transform of Biological Signal

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computers, Materials & Continua 2024年第6期79卷 5295-5312页

作者： Yin Ni Wu Zeng Peng Xia Guang Stanley Yang Ruochen Tan School of Electrical and Electronic Engineering Wuhan Polytechnic UniversityWuhan430023China School of Mathematics and Computer Science Wuhan Polytechnic UniversityWuhan430048China Paul G.Allen School of Computer Science and Engineering University ofWashingtonSeattleWA98195USA School of Computer Science and Engineering University of CaliforniaSanDiegoCA92093USA

Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal *** current deepfake generators strive for high realism in visual effects,they do not replicate biometric signals indicative of cardiac *** this gap,many researchers have developed detection methods focusing on biometric *** methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography(rPPG)signal,resulting in high detection ***,in the spectral analysis,existing approaches often only consider the power spectral density and neglect the amplitude spectrum—both crucial for assessing cardiac *** introduce a novel method that extracts rPPG signals from multiple regions of interest through remote photoplethysmography and processes them using Fast Fourier Transform(FFT).The resultant time-frequency domain signal samples are organized into matrices to create Matrix Visualization Heatmaps(MVHM),which are then utilized to train an image classification ***,we explored various combinations of time-frequency domain representations of rPPG signals and the impact of attention *** experimental results show that our algorithm achieves a remarkable detection accuracy of 99.22%in identifying fake videos,significantly outperforming mainstream algorithms and demonstrating the effectiveness of Fourier Transform and attention mechanisms in detecting fake faces.

关键词： Deepfake detector remote photoplethysmography fast fourier transform spatial attention mechanism

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Zero-dynamics attack detection based on data association in feedback pathway

Cognitive Robotics

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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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False Data Injection Attack Detection and Localization Framework in Power Distribution Systems Using a Novel Ensemble of CNNs and Explainable Artificial Intelligence

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IEEE Transactions on Industry Applications 2025年第3期61卷 4801-4811页

作者： Dehbozorgi, Mohammad Reza Rastegar, Mohammad Arani, Mohammadreza F. M. Shiraz University School of Electrical and Computer Engineering Shiraz Iran Toronto Metropolitan University Faculty of Engineering and Architectural Science Toronto Canada

Cyber-physical power systems are vulnerable to cyber-attacks, especially false data injection attacks (FDIAs). FDIAs against distribution system state estimation (DSSE), which alter state estimation (SE) by changing meter readings, have received researchers' attention in recent years. A common defense against FDIAs in the literature is the use of labeled data to train classifiers as FDIA detectors. However, this approach's performance can be limited by the highly imbalanced nature of FDIA datasets. The black box characteristics of the machine learning models can make them hard to trust and adopt in important applications. Hence, we propose an innovative explainable artificial intelligence (XAI)-enhanced ensemble-based detection and localization model that leverages convolutional neural networks (CNNs) and support vector machine (SVM). The ensemble model uses SVM to merge various spatiotemporal CNNs' outputs. Training these CNNs on under-sampled subsets of the majority class and using their ensemble addresses class imbalance. This paper leverages XAI to enhance the interpretability of the detection process and improve localization accuracy. The localization process uses the outputs of an XAI technique, gradient-weighted class activation mapping, to aid the majority-voting-based localization ensemble model. Our model can also detect FDIAs during the distribution feeder's topology changes. Extensive simulations on IEEE 13-bus and IEEE 123-bus feeders prove the proposed under-sampling-based detection approach as an alternative to prevalent over-sampling methods like generative adversarial networks (GANs), offering a novel solution to class imbalance challenges. The paper also provides a comprehensive analysis of the proposed spatiotemporal model's performance, demonstrating its superiority over temporal CNNs. © 1972-2012 IEEE.

关键词： DC distribution systems

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computer-aided diagnosis for multi-class classification of brain tumors using CNN features via transfer-learning

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Multimedia Tools and Applications 2025年 1-24页

作者： Yadav, Agnesh Chandra Shah, Krish Purohit, Aaryan Kolekar, Maheshkumar H. Electrical Engineering IIT Bihta Bihar Patna801106 India Computer Science Engineering SPIT Andheri Maharashtra Mumbai400058 India

Brain tumor classification is essential for accurate diagnosis and treatment planning, significantly enhancing patient outcomes and survival rates. The complexity of multi-class classification, which includes Glioma, Meningioma, Pituitary tumors, and healthy cases (No Tumor), presents considerable challenges. This study addresses these challenges through advanced preprocessing techniques such as resizing, normalization, object-centric image extraction, and contrast enhancement. Additionally, a segregation approach is employed, processing MRI images from three perspectives-Axial, Sagittal, and Coronal-to further improve classification accuracy. A modified VGG-16 model, enhanced with transfer learning, is used to extract meaningful features while optimizing the number of trainable parameters for improved efficiency. The proposed approach demonstrates impressive results, achieving 98.5% accuracy and strong performance metrics, including an Area Under the Curve of 99.0%, recall of 98.4%, specificity of 99.4%, precision of 98.3%, and an F1-score of 98.3%. These outcomes underscore the potential of the proposed method in aiding medical professionals to make more informed decisions, ultimately improving patient care and clinical efficacy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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