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HIDS-IoMT: A deep learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things

IEEE ACCESS 2025年 13卷 32863-32882页

作者： Berguiga, Abdelwahed Harchay, Ahlem Massaoudi, Ayman Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka 72388 Saudi Arabia Univ Carthage Mediatron Res Lab Higher Sch Commun Ariana 1054 Tunisia

The expansion of the Internet of Medical Things (IoMT) has enhanced the accuracy, real-time functionality, connectivity, and intelligence of medical examination practices. However, increased interconnectivity of medical equipment has rendered IoMT networks susceptible to several cyberattacks, particularly the Distributed Denial of Service (DDoS) attacks. Current intrusion detection systems (IDS) are insufficiently focused to address and counteract these advanced threats. Furthermore, due to the dynamic nature of IoMT traffic, IDS has considerable difficulty preserving its current threat detection capabilities. This study presents a hybrid deep learning-based intrusion detection system for IoMT networks (HIDS-IoMT). The proposed model hybridizes the Convolutional Neural Network (CNN) for feature extraction and the Long Short Term Memory neural network (LSTM) for sequence data prediction. We implement the designed IDS on a Raspberry Pi device using a fog computing architecture, enabling decentralized processing closer to IoMT devices, thereby enhancing responsiveness and reducing latency. We evaluate the suggested approach for intrusion detection using the IoTID20 and the Edge-IIoTset datasets. These datasets comprise a substantial and varied assortment of traffic flows from actual DDoS attacks, including SYN floods, UDP floods, HTTP floods, and others. We evaluated our novel methodology against distinct DoS attacks such as DDoS, SQL injection, Vulnerability scanner, Scan Host Port, Mirai, etc. Our proposed model attains an accuracy of 99.92%, a precision of 99.91%, a recall rate of 99.99%, and an F1-score of 99.95%, outperforming the latest in advanced methodologies.

关键词： Accuracy image edge detection Convolutional neural networks Intrusion detection Denial-of-service attack Computer architecture Training Telecommunication traffic real-time systems Neural networks Internet of Medical Things DDoS attacks intrusion detection system deep learning convolutional neural network long short term memory neural network

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An automatic fine-grained violence detection system for animation based on modified faster R-CNN

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 237卷

作者： Tang, Yixin Chen, Yu Sharifuzzaman, Sagar A. S. M. Li, Tie Sejong Univ Dept Cooperat Course Performance Film & Animat Seoul 05006 South Korea Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea Sejong Univ Dept Intelligent Mechatron Engn Seoul 05006 South Korea Tiangong Univ Dept Comp Sci & Technol Tianjin Peoples R China

Animation drama and movies are important entertainment sources for children and young people. However, many of these entertainment videos introduce ideas such as violence, fighting, abuse, and car accidents, which are not suitable for children and young people. In order to prevent children from viewing these videos, different countries' government bodies put restrictions on age on these videos. Violence detection is mostly done using manual inspection, which is time and resource consuming and cannot achieve a very good result. Therefore, artificial technologies such as deep learning and machine learning can detect violence in a video. Bleeding is one of the distinct characteristics of violent actions presented in such videos. This paper aims to propose a system that can detect a given video's blood in real-time. Researchers have proposed different image processing-based techniques to detect violence, but their proposed systems lack precision and real-time detection. To this end, a deep learning-based approach is proposed to detect violence in a given video and images. A Faster R-CNN model is modified due to the sophisticated characteristics of the violence in the cartoon and animation images to achieve the highest performance. The backbone of the proposed violence detection model is changed to a modified RegNet model to extract the features from the frame. The standard inner lateral connection is replaced with the modulated deformable convolutional (MDC) layer to extract deformable feature maps. A novel distributed attention module (DAM) is proposed on the feature pyramid network to improve the performance of the feature extraction. The multiscale Region of interest (ROI) Align is adapted to improve the detection performance of violence in a different scenario. Moreover, the classification approach is integrated with the detection model to classify the different levels of violence in a given frame. We have explored state-of-the-art object detection methods such as F

关键词： Violence Fine-grained Animation deep learning CNN Faster R -CNN

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Contrastive learning for deep tone mapping operator

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SIGNAL processing-image COMMUNICATION 2024年 126卷

作者： Li, Di Wang, Mou Rahardja, Susanto Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Singapore Inst Technol Singapore 138683 Singapore

Most existing tone mapping operators (TMOs) are developed based on prior assumptions of human visual system, and they are known to be sensitive to hyperparameters. In this paper, we proposed a straightforward yet efficient framework to automatically learn the priors and perform tone mapping in an end -to -end manner. The proposed algorithm utilizes a contrastive learning framework to enforce the content consistency between high dynamic range (HDR) inputs and low dynamic range (LDR) outputs. Since contrastive learning aims at maximizing the mutual information across different domains, no paired images or labels are required in our algorithm. Equipped with an attention-based U -Net to alleviate the aliasing and halo artifacts, our algorithm can produce sharp and visually appealing images over various complex real -world scenes, indicating that the proposed algorithm can be used as a strong baseline for future HDR image tone mapping task. Extensive experiments as well as subjective evaluations demonstrated that the proposed algorithm outperforms the existing state -of -the -art algorithms qualitatively and quantitatively. The code is available at https://github. com/xslidi/CATMO.

关键词： Generative adversarial net Tone mapping High dynamic range images deep learning Contrastive learning

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Adaptive Feature Aggregation Centric Enhance Network for Accurate and Fast Monocular 3-D Object Detection

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Lin, Peng-Wei Hsu, Chih-Ming Natl Taipei Univ Technol Inst Mech & Elect Engn Taipei 10608 Taiwan Natl Taipei Univ Technol Dept Mech Engn Taipei 10608 Taiwan

Three-dimensional object detection is crucial in autonomous driving. Monocular 3-D object detection has become a popular area of research in autonomous driving because of its ease of deployment and cost-effectiveness. In real-world autonomous driving, detectors should be both real time and accurate. These features can be achieved using deep learning (DL). A one-stage center-based object detector is suitable for real-world applications. However, in center-based object detectors, object-centric estimation plays an important role because it significantly influences detection results. To address this issue, we propose a real-time monocular 3-D object detection neural network called the adaptive feature aggregate centric enhance network. The proposed model is an anchor-free and center-based method. To improve accuracy while maintaining inference speed, we propose an adaptive feature aggregation network that aggregates multiscale features by weighting. Furthermore, we propose a centric enhanced module for heatmap prediction to improve the accuracy of object localization and classification. Our model can achieve 34.48 fps using an Nvidia RTX3070 graphy processing unit (GPU). Extensive experiments on the KITTI benchmark demonstrate that our method achieves good average precision (AP) for cars and pedestrians.

关键词： Three-dimensional displays Heating systems Feature extraction Accuracy Detectors Head Location awareness Convolution Autonomous vehicles Aggregates 3-D object detection attention mechanism autonomous driving deep learning (DL) monocular image real time

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real-time data visual monitoring of triboelectric nanogenerators enabled by deep learning

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NANO ENERGY 2024年 130卷

作者： Zhang, Huiya Liu, Tao Zou, Xuelian Zhu, Yunpeng Chi, Mingchao Wu, Di Jiang, Keyang Zhu, Sijia Zhai, Wenxia Wang, Shuangfei Nie, Shuangxi Wang, Zhiwei Guangxi Univ Coll Light Ind & Food Engn Key Lab Clean Pulp & Papermaking & Pollut Control Nanning 530004 Peoples R China

The rapid advancement of smart sensors and logic algorithms has propelled the widespread adoption of the Internet of Things (IoT) and expedited the advent of the intelligent era. The integration of triboelectric nanogenerator (TENG) sensors with deep learning (DL) leverages unique advantages of TENG such as self-powered sensing, high sensitivity, and broad applicability, along with DL's robust data processing capabilities to effectively, efficiently, and visually monitor various relevant signals. This amalgamation exhibits significantly superior sensing performance and immense developmental potential, finding extensive utility in domains like smart homes, healthcare system, environmental monitoring, among others. Currently, the synergistic working principle of integrating these two technologies remains insufficiently elucidated. This review presents a comprehensive overview of cutting-edge DL techniques and related research aimed at enhancing real-time visual monitoring of TENG. Specifically, it focuses on DL algorithms such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) for processing intricate TENG-generated datasets. Furthermore, this review outlines the advantages and synergistic mechanisms resulting from the integration of DL with TENG sensors, providing a comprehensive summary of their latest applications in various fields requiring real-time data visual monitoring. Finally, it analyzes the prospects, challenges, and countermeasures associated with the integrated development of TENG and DL while offering a comprehensive theoretical foundation and practical guidance for future advancements in this field.

关键词： Triboelectric nanogenerator deep learning Self-powered sensing real-time monitoring

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real-time deep learning-Based Object Recognition in Augmented reality

Real-Time Deep Learning-Based Object Recognition in Augmente...

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Conference on real-time processing of image, Depth, and Video Information

作者： Egipko, V Zhdanova, M. Gapon, N. Voronin, V. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510673199;9781510673182

Augmented reality is a visualization technology that displays information by adding virtual images to the real world. Effective implementation of augmented reality requires recognition of the current scene. Identifying objects in real-time video on computationally limited hardware requires significant effort. One way to solve this problem is to create a hybrid system that, based on machine learning and computer vision technology, processes and analyzes visual data to identify and classify real-world objects. The proposed architecture is based on a combination of the Vuforia augmented system, which provides good performance by balancing prediction accuracy and efficiency. First, the Vuforia neural network architecture allows convenient interaction with AR in Unity and provides initial conditions for detecting 3D objects. The augmented reality construction algorithm is based on the ARCore framework and the OpenGL interface for embedded systems. The system integrates recognition data with an AR platform to display corresponding 3D models, allowing users to interact with them through the functionality of the AR application. This method also involves the development of an enhanced user interface for AR, making the augmented environment more accessible for navigation and control. Experimental research has shown that the proposed method significantly improves the accuracy of object recognition and the ease of working with 3D models in AR.

关键词： deep learning augmented reality computer vision object recognition robotic systems real-time processing

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Temporal signed gestures segmentation in an image sequence using deep reinforcement learning

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 131卷

作者： Kalandyk, Dawid Kapuscinski, Tomasz Rzeszow Univ Technol Doctoral Sch Al Powstancow Warszawy 12 PL-35959 Rzeszow Poland Rzeszow Univ Technol Dept Comp & Control Engn Wincentego Pola 2 PL-35959 Rzeszow Poland

Continuous sign language recognition is challenging due to coarticulatory distortions, which occur at the beginning and end of each gesture. These distortions depend on the temporal context and introduce additional intraclass variability. To address this issue, a new approach is proposed that extracts segments from the image sequence corresponding to undistorted parts of gestures. This should simplify the task by reducing it to the easier problem of isolated gestures recognition. The proposed approach uses deep reinforcement learning for segmentation and a novel image sequence processing scheme to extract gradient changes over time. A dataset recorded by deaf people and annotated according to the proposed approach, was prepared to evaluate the method. The proposed deep learning architectures achieved leave -one -subject -out recognition accuracies in the range of 0.70 to 0.76. Considering the inability to compare with other works, the authors also proposed other evaluation protocols to thoroughly examine the employed approach. This work will be developed, and the main aspiration of the authors will be to create an integrated framework that converts the raw form of RGB video into a string of words representing the Sign Language user's intentions.

关键词： deep reinforcement learning image sequence segmentation Gesture spotting Gesture database

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An Efficient and Fast image Mosaic Approach for Highway Panoramic UAV images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 10454-10467页

作者： Zheng, Haoxin Chang, Zhanqiang Li, Yakai Zhu, Jie Wang, Wei Yang, Qing Xie, Chou Zhang, Jingfa Liu, Jiaxi Capital Normal Univ Coll Resource Environm & Tourism Beijing 100048 Peoples R China China Earthquake Networks Ctr Beijing 100045 Peoples R China Henan Inst Remote Sensing & Surveying Zhengzhou 450000 Peoples R China China Siwei Surveying & Mapping Technol Co Ltd Beijing 100086 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100101 Peoples R China Natl Inst Nat Hazards Beijing 100085 Peoples R China

Nowadays, real-time monitoring of highway operation by unmanned aerial vehicle (UAV) technology is one of the research frontiers for urban remote sensing. In general, the existing stitching algorithms can meet the basic requirements in terms of accuracy, but their splicing speed cannot meet the real-time stitching requirements of UAV. The cause is that the time consumption sharply increases when stitching plenty of UAV images-this is the bottleneck problem. Herein, we proposed a novel splicing method based on the Superpoint network and a self-designed algorithm of matrix iteration. In this method, we take advantage of an advanced deep learning algorithm-Superpoint to efficiently extract image feature points for calculating the geometric transformation matrix, and make the Superpoint model more suitable for highway. More importantly, for the purpose of further improving the stitching speed and realizing real-time stitching for a large number of UAV images, we specially designed an algorithm of matrix iteration to accurately represent the image transformation relationships, i.e., a matrix is iterated through each adjacent transformation matrix relationship. It is the first time that an algorithm of transformation matrix iteration has been designed to address the bottleneck problem in stitching plenty of UAV images. As a result, the experiments indicate that the proposed method has remarkably enhanced the stitching speed and accuracy for plenty of UAV images. Notably, even in the condition of no air triangulation parameters, it can realize real-time stitching.

关键词： Feature extraction Autonomous aerial vehicles real-time systems Road transportation Remote sensing deep learning image stitching image mosaic matrix iteration real-time stitching unmanned aerial vehicle (UAV)

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Spatial interpolation of global DEM using federated deep learning

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Huo, Ziqiang Wen, Jiabao Li, Zhengjian Chen, Desheng Xi, Meng Li, Yang Yang, Jiachen Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Shihezi Univ Coll Mech & Elect Engn Shihezi 832003 Peoples R China

The digital elevation model (DEM) provides important data support for 3D terrain modeling. However, due to the complex and changeable terrain in the real world and the high cost of field measurement, it is extremely difficult to obtain continuous and high-density elevation data directly. Therefore, it is necessary to rely on spatial interpolation technology to restore the DEM overall picture in the original sampling area. The traditional spatial interpolation method usually has the characteristics of low model complexity and high computational cost, which leads to low real-time performance and low precision of the interpolation process. The interpolation operation based on DEM data can be considered as a special image generation process where the input is a DEM image with missing values and the output is a complete DEM image. At present, a large number of studies have proved that deep learning methods are very effective in image generation tasks. However, the training of deep learning models requires the support of a large number of high-quality data sets. DEM data in various countries, especially in key regions, are usually restricted by privacy protection regulations and cannot be disclosed. The emergence of Federated learning (FL) provides a new solution, which supports local training on multiple end nodes, without sending local data to a remote center server for centralized training, effectively protecting data privacy. In this study, we propose a DEM interpolation model based on FL and multiScale U-Net. The experimental results show that compared with the traditional method, this model has faster processing speed and lower interpolation precision. At the same time, this research result provides a new way for efficient and secure use of terrain information, especially in those application scenarios that have strict requirements for DEM data privacy and security.

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Convergence of deep learning and Edge Computing using Model Optimization 13

Convergence of Deep Learning and Edge Computing using Model ...

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13th Iranian/3rd International Machine Vision and image processing Conference (MVIP)

作者： Babaei, Peyman Islamic Azad Univ Comp Dept West Tehran Branch Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Edge systems are undergoing a groundbreaking computing evolution to support artificial intelligence, deep learning, and complex computational algorithms. Using cloud servers to perform deep learning model inference poses challenges such as response delays, increased communication costs, and data privacy concerns. Therefore, significant efforts have been made to push the processing of deep learning models to edge systems, which has led to the creation of edge intelligence as the intersection of learning and edge computing. learning models, especially deep convolutional neural networks, have made significant achievements in machine vision, which provide high accuracy and predictability by spending computing power and memory. If these models are optimized and deployed on edge systems, there will be a revolution in the applications of edge systems in real time. In this paper, by using optimization techniques such as quantization, weight pruning, and weight clustering, the possibility of deploying a typical convolutional neural network model on edge systems that have limited computing resources and memory is investigated. The results show that by using a collaborative algorithm, despite the slight decrease in the accuracy of the model, it is possible to achieve a small-sized model that can even be deployed on microcontrollers.

关键词： Model optimization Quantization Weight pruning Weight clustering Edge computing deep learning

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