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Feature Reduction-based Intrusion Detection (FRID) in Satellite-Terrestrial Integrated networks 5

Feature Reduction-based Intrusion Detection (FRID) in Satell...

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5th international conference on High Performance Big Data and Intelligent Systems, HDIS 2023

作者： Yao, Lin Tian, Ziyuan Deng, Jing Wu, Guowei Li, Li DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China School of Software Dalian University of Technology Dalian China The Department of Computer Science UNC Greensboro UNC NC Greensboro27412 United States Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798350394160

Satellite-Terrestrial Integrated networks (STINs) integrate satellite networks, the Internet, and mobile wireless networks and are able to provide powerful services to users. In STIN, satellite gateways play an important role of connecting space-based networks to terrestrial-based networks, but can be under frequent attacks such as Distributed Denial of Services (DDoS) attack. therefore, intrusion detections become essential in STINs. In this work, we propose a feature reduction-based intrusion detection (FRID) technique with the help from deep reinforcement learning. In FRID, only features that are highly distinguishable, robust, and independent are selected for the later stages. Experimental results show the accuracy, recall, FAR, and F1 scores comparing FRID with several state-of-the-art schemes and demonstrate its superiority. ©2023 IEEE.

关键词： Intrusion detection

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A Development of 5G Wireless sensor Network using Man Design 4

A Development of 5G Wireless Sensor Network using Man Design

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Vennila, C. Almusawi, Muntather Kumar, K. Sriram Priya, B. Rawat, Vidhi Amirthayogam, G. Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h TN India The Islamic University of Babylon College of Technical Engineering Department of Computers Techniques Engineering Babylon Iraq Karpagam Institute of Technology Department of Information Technology TN India Nehru Institute of Engineering and Technology Department of Computer Science and Business System Tamil Nadu T.M.Palayam India Ies College of Technology Department of Electrical & Electronics Engg Madhya Pradesh Bhopal India Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

this article investigates the key drivers influencing the design of the forthcoming Metropolitan Area Network (MAN), focusing on the integration of two pivotal elements: Cloud Radio Access Network (C-RAN) for 5G implementation and the deployment of edge computing. As we delve into these aspects, the article systematically uncovers the crucial architectural components essential for effectively addressing the challenges inherent in implementing C-RAN and edge computing. through this strategic approach, the objective is to ensure a cost-effective solution that aligns with the specific requirements of these applications. the article aims to provide insights into the symbiotic relationship between CRAN and 5G, emphasizing their collaborative role in enhancing network efficiency. Simultaneously, it underscores the significance of edge computing, highlighting its potential to optimize data processing and reduce latency within the evolving landscape of metropolitan networks. this comprehensive exploration contributes to a nuanced understanding of the future MAN design, grounded in innovative solutions and considerations for seamless integration. © 2024 IEEE.

关键词： Radio access networks

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A Survey on Image Denoising Techniques 5

A Survey on Image Denoising Techniques

引用

5th international conference on Control, Communication and Computing, ICCC 2023

作者： Mary Mathew, Irene Akhilaraj, D. Zacharias, Joseph College of Engineering Trivandrum Department of CSE Thiruvananthapuram India College of Engineering Trivandrum Department of ECE Thiruvananthapuram India

ISBN: (纸本)9798350334128

the primary objective of image denoising is to suppress or discard noise or distortions from a noisy image. Images may become noisy during various processes such as image acquisition, compression, etc. Noise present in images degrades its quality;hence different techniques have been developed to successfully remove noise, without affecting image features. therefore, image denoising is a very important task in the area of image processing. In this paper, various image denoising techniques have been explained, that have been prominent in recent times. Also, the different metrics used to evaluate these denoising models, and the loss functions that have been used in these models are also being discussed. © 2023 IEEE.

关键词： Generative adversarial networks

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Sign Language Translation and Voice Impairment Support System using Deep Learning 5

Sign Language Translation and Voice Impairment Support Syste...

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5th international conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shajeena, J. Shiny, R.M. Mary Vespa, M. Kavitha, R. Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

the Sign Language Translation and Voice Impairment Support System (SLT-VISS) represents a groundbreaking application of deep learning methodology aimed at facilitating communication for individuals with hearing impairments or speech disabilities. By utilizing Convolutional Neural Network (CNN), OpenCV for image processing, and Pyttsx3 for text-to-voice conversion, SLT-VISS offers real-time translation of sign language gestures into both text and speech output. Using a camera as input, the CNN algorithm accurately identifies and classifies sign language gestures displayed in front of it. OpenCV complements this process by preprocessing the input images for gesture segmentation and feature extraction, enhancing the accuracy of gesture recognition. Once a gesture is recognized, Pyttsx3 converts the translated text into synthesized speech, providing immediate auditory feedback to the user. SLT-VISS is designed to bridge the communication gap faced by individuals with communication disabilities, enabling them to express themselves effectively in various social and professional settings. By seamlessly translating sign language into both text and speech, this system promotes inclusivity and accessibility, empowering users to communicate with confidence and independence. the integration of CNN, OpenCV, and Pyttsx3 technologies within SLT-VISS signifies a significant advancement in assistive technology. As technology continues to evolve, further enhancements to SLT-VISS holds the potential to revolutionize communication accessibility and enable greater inclusivity in society. © 2024 IEEE.

关键词： Convolutional neural networks

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Multivariate time series anomaly detection based on sparse autoencoder and dynamic graph attention network 5

Multivariate time series anomaly detection based on sparse a...

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5th international conference on Computer Vision and Data Mining, ICCVDM 2024

作者： Li, Hui Zhang, Suli School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Computer Technology and Engineering Changchun Institute of Technology Changchun130012 China

ISBN: (纸本)9781510682863

With the further advancement of industrial technology, the data generated by sensors is gradually becoming more complex. Deep learning approaches have made notable strides in the domain of anomaly detection, especially when it comes to high-dimensional datasets. However, most existing methods neglect the complex relationships between feature dimensions and the dependencies between multiple time steps, resulting in insufficient extraction of key feature information. In response to this problem, our paper introduces a model for detecting anomalies in multivariate time series, utilizing Sparse Autoencoder and Dynamic Graph Attention Network (SAE-GATv2). the outcomes of experiments conducted on two actual sensor datasets reveal that our suggested approach is capable of identifying anomalies with greater precision and showcases superior overall performance in comparison to the baseline methods. © 2024 SPIE.

关键词： sensors Data modeling Education and training Feature extraction Neurons Performance modeling Artificial neural networks Deep learning Matrices Engineering

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A Hybrid System for Integrating Cross-Correlation, DNNs and LSTMs for Enhanced Intrusion Detection System 5

A Hybrid System for Integrating Cross-Correlation, DNNs and ...

引用

5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Niharika Prakash, Jay Mmmut Department of Itca Gorakhpur India

ISBN: (纸本)9798350383522

A WSN is a network of wireless sensors that are becoming increasingly vulnerable to cyberattacks due to their widespread deployment and resource constraints. Traditional intrusion detection systems (IDS) often struggle to keep up with the evolving nature of threats, necessitating the adoption of more sophisticated techniques. We do this by using a combination of three powerful tools: Cross correlation, which can understand words from both directions, and two types of neural networks called DNNs and LSTMs. Basically, Cross-correlation measures the similarity between two time series, which can be used to identify patterns in sensor data that deviate from normal behaviour. It used for a variety of purposes, including feature extraction, recognition, filtering, and synchronization, it can help to protect WSNs from a variety of threats, including Denial-of-Service (DoS) attacks, data manipulation attacks, and malicious control attacks. LSTM-DNN models excel at extracting temporal patterns from sensor data, making them well-suited for intrusion detection tasks. Cross-correlation, a statistical measure of similarity between two time series, can further enhance anomaly detection by identifying deviations from normal behaviour patterns. Our tests on real intrusion detection data show that this hybrid model works well. extract complex patterns, capture temporal dependencies, and identify anomalies makes it well-suited for detecting a wide range of intrusions in real-time. Mixing Cross-correlation language skills with DNN and LSTM's monitoring network traffic and system activities for suspicious behaviour that could indicate an intrusion. © 2024 IEEE.

关键词： Cross-correction Deep learning DNN Intrusion detection LSTM Wireless sensor Network

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Research on University Sports Information Management System Based on Big Data and Cloud Computing 5

Research on University Sports Information Management System ...

引用

5th international conference on Computers, Information processing and Advanced Education, CIPAE 2024

作者： Lei, Ming Wang, Yong YiBin Vocational and Technical College Sichuan Yibin China

ISBN: (纸本)9798331527662

this paper proposes a sports information management system based on Hadoop platform, which integrates convolutional neural network (CNN) technology to achieve efficient data management and intelligent decision support. through this architecture system can effectively process and analyze the massive data from university sports activities, so as to improve the efficiency and accuracy of data processing. Secondly, Convolutional Neural Network (CNN) technology is introduced to realize face recognition function. the application of this technology not only enhances the security of the system, but also improves the speed and accuracy of athlete identity verification. the automatic feature extraction capability of CNN enables the system to quickly identify key information in complex image data, which provides a strong technical support for the intelligence of sports management. In addition, the system provides rich data API interfaces, which can seamlessly connect various sports resources in colleges and universities, and realize data sharing and exchange. By building a unified sports big data environment, the system promotes the optimal allocation and efficient use of resources, and provides comprehensive data support for sports teaching, training and competition. this system builds a powerful sports information management platform by integrating Hadoop platform and CNN technology. this not only improves the ability of data processing and storage, but also greatly promotes the modernization process of college sports management through intelligent decision support. © 2024 IEEE.

关键词： Convolutional neural networks

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A Non-invasive Planar Resonant Microwave sensor for Unknown Liquid Permittivity Estimation 5th

A Non-invasive Planar Resonant Microwave Sensor for Unknown ...

引用

5th international conference on VLSI, Communication and Signal processing, VCAS 2022

作者： Mankar, Ojaswita Agarwal, Smriti Department of Electronics and Communications Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India

ISBN: (纸本)9789819909728

this paper presents a split ring-based resonant microwave sensor for precise measurement of liquid sample’s permittivity. the modified SRR sensor is etched on the substrate top plane and is excited by electromagnetic coupling by the microstrip line on the same plane. the liquid sample having unknown permittivity value when placed over sensor alters the field distribution near the sensor region which shifts the sensor’s natural resonant frequency. the correlation between frequency shift and the permittivity of the liquid sample has been used to estimate permittivity of unknown sample. Further, a closed form numerical expression has been developed using curve fitting tool. Validation of the developed numerical model has been successfully done utilizing different independent liquid samples, and a slight error (less than 6%) was reported between actual and calculated permittivity values. the developed sensor is quite simple, compact, cheap, easy to fabricate and highly sensitive, covering the wide permittivity range of liquids for permittivity estimation. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Microwave sensors

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Image processing For LDR To HDR Image Conversion Using Deep Learning 5

Image Processing For LDR To HDR Image Conversion Using Deep ...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Soundari, V.D. Rabin, G. Rishiyugan, S. Pravinkumar, P. Sri Krishna College of Engineering and Technology Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350321425

High Dynamic Range images and High Dynamic Range displays are getting popular nowadays. To attain the benefit of the High Dynamic Range, a lot of Low Dynamic Range images are to be converted to images with better ranges. Due to the reflection, the image may lose its visibility in some specific area and such area is called a highlight. the information will not be clear for the areas in highlight. the highlight should be removed in order to increase the image's visibility. It is not easy to obtain an image having dynamic range at high level for most of the people as it requires a pricey tool which can be used to function beyond the parameters that are present in the sensor technology of the conventional camera. Many low dynamic range images are combined to create a single HDR image. In the existing system the bright regions are brightened and the dark regions are darkened and the ranges with the extended brightness are merged in order to construct an image with High Dynamic Range. For Example, while capturing a crime scene in the camera with certain details that are absent in the image captured, the criminal investigation could be handled better by reconstructing the lost details of the image using this strategy. the exposure of the image is increased along with the brightness of the pixels that are highly dense and the output image is constructed with the properties necessary for a HDR image. © 2023 IEEE.

关键词： Visibility

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Layout Analysis of Malayalam Palm Leaf Images with Fully Convolutional networks 5

Layout Analysis of Malayalam Palm Leaf Images with Fully Con...

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5th IEEE international conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Boyina, Kamal Swetchana, Dutta Reddy, Gujja Manaswi Sushrutha, Karru Pati, Peeta Basa Jyotsna, C. Sivan, Remya Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350305449

Palm leaf manuscripts have been used for centuries as a medium for preserving ancient knowledge and cultural heritage. To prevent their loss, digitization of these manuscripts has become crucial. Layout analysis specifically involves the localization and classification of various objects found in the document, such as text, tables, images, graphics, document boundaries, and background elements. However, layout analysis of palm leaf images presents significant challenges due to their irregular shapes and sizes, complex layouts, and varying degrees of deterioration. this research paper proposes the utilization of Fully Convolutional networks (FCN) for the layout analysis of Malayalam palm leaf images. Multiple variations of FCN, including FCN-8, FCN-16, FCN-16, and FCN-8 with skipped connections, are explored to accurately extract and classify layout components such as background, text, and punch holes. the research uses a manually crafted masked dataset and combines manual research, deep learning, and image processing techniques. the results demonstrate the potential of FCN-based models for analyzing palm leaf manuscript layouts and contribute to their preservation and understanding. Significantly, FCN-8 yielded the best score, achieving a Mean Average Precision (MAP) of 0.79 at Intersection over Union (IOU). the paper provides an in-depth analysis of the methodology, dataset, and results, offering new avenues for research and preservation in this domain. © 2023 IEEE.

关键词： Deterioration

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