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An image Region Hierarchical Encryption Approach for Edge Intelligence Perception 24

An Image Region Hierarchical Encryption Approach for Edge In...

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6th international conference on Telecommunications and Communication Engineering

作者： Hou, Chengyu Li, Wenzao Xiao, Linsong Yao, Sai Chengdu Univ Informat Technol Chengdu Peoples R China

ISBN: (纸本)9798400709630

In the context. of smart cities, edge-aware machine are widely used. These systems involve scenarios where large volumes of image data are stored locally. They also involve scenarios where image data is uploaded to edge clouds, posing significant privacy risks. 'therefore, it is necessary to encrypt images containing sensitive information. However, edge computational devices typically have limited computational ability. To address the need t'or privacy protection, this paper proposes a partial image encryption algorithm based on object detection. First, our approach uses an object detection model to identify private areas in images (such as license plates) and applies a specific encryption strategy to license plate areas. At the same time, the computational burden on edge devices is reduced. Additionally, we introduce a chaotic mapping algorithm based on image segmentation and compare its performance with traditional chaotic mapping algorithms. Experimental results show that the improved algorithm performs better in encrypting sensitive areas while also exhibiting superior performance in gray value histogram analysis and scatter plot analysis.

关键词： machine vision encryption and decryption, image segmentation chaotic mapping

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Research on image Recognition Method and Personalized Recommendation Model based on Deep Learning 5

Research on Image Recognition Method and Personalized Recomm...

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5th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Liu, Feng Guo, Weiwei Department of Electrical and Information Engineering Heilongjiang University of Technology JiXi China

ISBN: (纸本)9781839539848

In response to the problems of low accuracy and poor user experience caused by cold start and incomplete consideration of user timing preferences in traditional recommendation systems, the article proposes an image recognition method and personalized recommendation model based on deep learning. The model is based on a short-term and short-term memory network model, first extracting keywords from user behavior item features, and establishing feature vectors between user behavior and images;Then, the feature vectors of user behavior and images are sorted in chronological order to obtain a list of user interest points. Then, feature maps and cosine similarity are used to calculate the similarity between images, and the target user's preference for different feature keywords is obtained, providing a data basis for personalized recommendations;finally, by repeatedly adjusting the training parameters through the constructed deep training model, a more accurate recommendation list is obtained. The recommendation algorithm is linearly combined with image similarity to provide personalized recommendations for users. Obtaining test data through the internet, identifying selected images, obtaining personalized recommendation lists through recognition results, and comparing them with traditional methods. After comparative analysis, the proposed image recognition method has higher recommendation accuracy compared to the recommendation model. © The Institution of Engineering & technology 2023.

关键词： Recommender systems

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RF signal Classification-based Drone Identification with Multimodal Database 23

RF Signal Classification-based Drone Identification with Mul...

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Mao, Shengjian Yu, Ningning Li, Miao Zhou, Chengwei Shi, Zhiguo College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China International Joint Innovation Center Zhejiang University Haining314400 China

ISBN: (纸本)9798350325959

The aim of this paper is to conduct a comprehensive review of existing UAV classification and identification methods. We will analyze the evolution of these methods and propose an anti-UAV RF detection and identification technique based on a self-constructed UAV RF signal dataset. This dataset utilizes a Universal Software Radio Peripheral (USRP) to capture the communication RF signals of 24 UAVs within an urban environment. Subsequently, we will undertake feature extraction and time-frequency signal statistical analysis of various UAV RF signals. We will employ machine learning methods for Classification-based Drone Identification. Our ultimate objective is to achieve UAV signal identification and classification using optimization techniques within a reasonable time frame. © 2023 IEEE.

关键词： RF database RF signal signal identification UAV identification USRP

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Deep Learning-based Noise Reduction Techniques in Electronic signal Processing 9

Deep Learning-Based Noise Reduction Techniques in Electronic...

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9th international conference on Science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Johari, Lalit Ramya, P. Suganya, M. Praveena, Hirald Dwaraka Lathamageshwari, P.S. Rajendiran, M. Iftm University Department of Computer Applications Uttar Pradesh Moradabad244102 India Bannari Amman Institute of Technology Department of Electronics And Communication Engineering Tamil Nadu Sathyamangalam638401 India Annapoorana Engineering College Department of Electronics And Communication Engineering Tamil Nadu Salem636308 India Department of Electronics And Communication Engineering Andhra Pradesh Tirupati517102 India Tamil Nadu Kavaraipettai601206 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

This paper explores the operation of deep literacy- grounded noise reduction ways in electronic signal processing. Noise hindrance poses a significant challenge in colorful signal processing operations, impacting the delicacy and trustability of data analysis and interpretation. Traditional noise reduction styles frequently calculate on heuristic approaches or signal processing algorithms, which may have limited effectiveness, especially in complex and dynamic surroundings. In discrepancy, deep literacy algorithms, similar as convolutional neural networks(CNNs) and intermittent neural networks(RNNs), offer promising results for noise reduction by learning complex patterns and connections directly from data. This paper determines the state- of- the- art deep literacy- grounded noise reduction ways, including denoising autoencoders, generative inimical networks(GANs), and intermittent neural network- grounded models. Case studies and experimental results demonstrate the effectiveness and performance of these ways in colorful signal processing tasks, similar as audio denoising, image restoration, and biomedical signal processing. The integration of deep literacy- grounded noise reduction styles into electronic signal processing systems shows significant eventuality for perfecting signal quality and enhancing the delicacy of data analysis in different operations. © 2024 IEEE.

关键词： Recurrent neural networks

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HEECCNB: An Efficient IoT-Cloud Architecture for Secure Patient Data Transmission and Accurate Disease Prediction in Healthcare systems 7

HEECCNB: An Efficient IoT-Cloud Architecture for Secure Pati...

引用

7th international conference on image Information Processing, ICIIP 2023

作者： Veena, C. Sridevi, Mulagundla Liyakat, Kazi Kutubuddin Sayyad Saha, Bishal Reddy, Sheri Ramchandra Shirisha, N. Nandikotkur road Andhra Pradesh Kurnool518452 India Cvr College of Engineering Department of Cse Vasthunagar Ibrahimpatnam R.R District Telangana Mangalpally India Brahmdevdada Mane Institute of Technology Department of Electrical Engineering Maharashtra Solapur India Vellore Institute of Technology Department of Computer Science And Engineering Tamilnadu Vellore India Telangana Hyderabad501401 India Mlr Institute of Technology Department of Cse Hyderabad Dundigal India

ISBN: (纸本)9798350371406

The rapid expansion of the internet of Things (IoT) in various fields has brought about significant advancements in data processing capabilities, in particular with the advent of IoT-cloud-based devices. Among these advancements, the healthcare system stands as one of the most intriguing IoT-Cloud integration uses cases. However, ensuring the security and privacy of patient data has become a critical concern, necessitating the development of robust protective measures. This research addresses the challenges of data security and computation overhead in the context of an IoT-cloud-based health system. A novel strategy dubbed Homomorphic Encryption with Elliptical Curve Cryptography integrated with Naive Bayes (HEECCNB) is put forth to address these problems. This approach is intended to enable precise disease prediction based on the gathered IoT sensor data while simultaneously providing good patient data privacy inside the IoT Health Cloud system. By employing homomorphic encryption, sensitive patient information can be securely processed and analyzed within the cloud environment without the need for decryption, preserving the confidentiality of the data. Furthermore, the integration of Elliptical Curve Cryptography enhances the security of the system by providing efficient and robust cryptographic operations. The proposed HEECCNB approach demonstrates promising results in both data protection and disease prediction. Through experiments conducted on the IoT Health Cloud scheme, the research validates the effectiveness of the projected method in safeguarding patient data and accurately predicting diseases based on IoT sensor data. Hence, the HEECCNB approach presents a compelling solution to the data security and computation overhead challenges faced in IoT-cloud-based health systems. Its successful implementation could significantly enhance the overall functionality and trustworthiness of such systems, facilitating the integration of IoT technologies in healthcare fo

关键词： internet of things

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Advancements in Intrusion Detection systems for internet of Things Using Machine Learning 5

Advancements in Intrusion Detection Systems for Internet of ...

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5th international conference on Multimedia, signal Processing and Communication Technologies (IMPACT)

作者： Ul Haq, Shahid Abbas, Ash Mohammad Aligarh Muslim Univ Zakir Hussain Coll Engn & Technol Dept Comp Engn Aligarh 202002 Uttar Pradesh India

ISBN: (纸本)9781665476478

Advancement in technology leads to connecting different types of devices or things to the internet and enables the formation of a special kind of network called the internet of Things (IoT). Intrusion detection in an IoT is a challenging task due to its unique characteristics. Machine learning schemes possess the potential to improve intrusion detection systems in case of an IoT. In this paper, we present a survey of advancements in research on the use of machine learning approaches for intrusion detection in an IoT. Our focus is on architectures, schemes, and the types of machine learning approaches used for intrusion detection. We compare different schemes based on their basis and features.

关键词： Intrusion detection IoT machine learning

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Research on IR-RGB image Fusion Method based on Target Perception and Hue Correction

Research on IR-RGB Image Fusion Method Based on Target Perce...

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international conference on Communications, signal Processing, and systems, CSPS 2023

作者： Jiang, Qianjun Lin, Di Hao, Zongbo Chang, He Li, Xin University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9789819975396

In this paper, a hue-corrected IR-RGB fusion method is proposed based on object perception. We use the salient object detection network to detect the original IR image to obtain the salient regions in the image and then use the object bounding box in the dataset label to remove the noise information and retain the object area of interest. In order to implement different fusion schemes for the target area and the background area in the model, this paper trains a generator to synthesize the target fusion image, trains the target discriminator to identify the pixel intensity and hue information of the target *** trains the background discriminator to identify Gradient information for the background. To improve the performance of downstream object detection tasks, an object detection network is added to the generative model, and an object detection loss is used to guide image fusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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Burrows–Wheeler Transform for Enhancement of Lossless Document image Compression Algorithms

Burrows–Wheeler Transform for Enhancement of Lossless Docum...

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international conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2022

作者： Paikrao, Prashant Doye, Dharmpal Bhalerao, Milind Vaidya, Madhav Shri Guru Gobind Singhaji Institute of Engineering and Technology Nanded India

ISBN: (纸本)9789819932498

images compression has always been important topic, and nowadays, it becomes essential due to the huge requirement for storage and transfer images over internet in abundance. Several diverse image compression methods have been proposed. In this research, a method using Burrows–Wheeler transform algorithm is proposed that restructures the compression data to enhance the performance of subsequent image compression algorithms. A two-dimensional grayscale image is reshaped to one-dimensional data array. Here, the most suitable linearization procedure is used during this conversion. Then the Burrows–Wheeler transform is carried out over this data that restructure the data in such a way that the resultant data became further compressible. Then the enhanced run length encoding method is applied, and its result is stored in two variables. The first variable consists of various gray levels encountered in the image, and the other variable stores their consecutive repetitions called as runs. Then over the second variable consisting of run counts, the Huffman encoding algorithm is applied. Similarly, a dictionary-based method is used in the second approach considering these run counts as the input. Finally, these achieved compression outcomes are evaluated and compared. The performance of the proposed method is compared with the different existing methods like discrete cosine transform block code truncation, singular vector sparse reconstruction, discrete cosine transforms, and Gaussian pyramid methods. The performance metrics used are mean square error, compression ratio, signal-to-noise ratio, and peak signal-to-noise ratio. Compared to these existing methods, the proposed method performs better. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete cosine transforms

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A Predictive Model of Early Alerts to Improve the Availability of internet and Data Service of a Telecommunications Company Through the XGBoost Algorithm

A Predictive Model of Early Alerts to Improve the Availabili...

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international conference on Information technology and systems, ICITS 2024

作者： Barrionuevo, David Tapia, Freddy Universidad de Las Fuerzas Armadas - ESPE Sangolquí Ecuador

ISBN: (纸本)9783031542558

The evolution of information and knowledge technologies has caused disruptive business change and new business models, which are based on agile and secure access to data. Therefore, telecommunications companies need to provide quality services that guarantee continuity, efficiency, and privacy in the transmission of information. This paper addresses the problem of a national telecommunications company, which has detected low rates of availability and intermittence in the data and internet service of its corporate clients, partly generated by the inadequate management of link monitoring, with alerts and corrective actions, which have caused effects to the company such as loss of customers, fines, increase in operating costs, breach of service agreements, decrease in trust and corporate image, etc. In this virtue, we have proposed a predictive model based on data mining and machine learning techniques, which identifies patterns in the historical information of the network equipment. This model can be used to: predict link behavior, detect outages, generate early warnings, and proactively support the company’s decision-making. The CRISP-DM methodology has been used for the development and evaluation of the predictive model XGBoost (evolved technique of Decision Trees) in Python Jupyter Notebook. The results are encouraging with an effectiveness of up to 95.5% prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data mining

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Performance Evaluation of Biharmonic Function-based image Inpainting Approach 6th

Performance Evaluation of Biharmonic Function-Based Image In...

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6th international conference on Information and Communication technology for Intelligent systems, ICTIS 2021

作者： Hudagi, Manjunath R. Soma, Shridevi Biradar, Rajkumar L. Tatyasaheb Kore Institute of Engineering and Technology Warananagar India Poojya Doddappa Appa College of Engineering Gulbarga India Hyderabad India

ISBN: (纸本)9789811935701

image inpainting states to restoring misplaced procedure or damaged regions in an image. image inpainting is a procedure of returning corrupted and old images. The major intention of this research is to analyze and justify the effectiveness of the image inpainting technique. Accordingly, performance analysis of image inpainting model is performed through Biharmonic functions. Moreover, the analysis is done by varying domain size in the Biharmonic function based on the percentage of images. Here, the performance of Biharmonic functions is evaluated using Structural Similarity Index Measure (SSIM), peak signal to noise ratio (PSNR), universal image quality index (UQI), second-derivative-like measure of enhancement (SDME), multi-scale structural similarity (MS-SSIM), and Mean Square Error (MSE). Thus, from this analysis, it is shown that the Biharmonic functions obtained better performance for the image inpainting process. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mean square error

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