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Modified PSNR Metric for Watermarked-image Assessment 20

Modified PSNR Metric for Watermarked-Image Assessment

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20th international Multi-conference on systems, signals and Devices, SSD 2023

作者： Taha, Taha Basheer TISHK International University College of Applied Science Information Technology Department Erbil Iraq

ISBN: (纸本)9798350332568

Digital images are exposed to different types of noise due to transformation, compression, or external data embedding. The assessment of these images is important to check their validity before being used in different applications. Because of its simplicity and straightforward calculations, the Peak signal to Noise Ratio (PSNR) is used for evaluating digital images. PSNR, However, does not account for the human eye bias in preserving noise between textured and smooth areas. In this paper, a modified PSNR, named texture-based PSNR (T-PSNR), is proposed to enhance the PSNR metric for better image quality assessment. The metric is designed to be applied for watermarked image evaluation. © 2023 IEEE.

关键词： Textures

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Dual Medical-image Reversible Data Embedding Method with Vector Directions 24

Dual Medical-Image Reversible Data Embedding Method with Vec...

引用

8th international conference on Medical and Health Informatics, ICMHI 2024

作者： Chin-Feng, Lee Ting-Yu, Kang Iuon-Chang, Lin Dept. of Information Management Chaoyang University of Technology Taichung413310 Taiwan Dept. of Management Information Systems National Chung Hsing University Taichung402202 Taiwan

ISBN: (纸本)9798400716874

With the rapid development of the internet, the issue of information security has become increasingly crucial. This study aims to utilize data hiding technology to safeguard confidential information from attacks. This paper focuses on the dual-image reversible data hiding technique and proposes a method based on the concepts of vector directions and diagonal embedding of secret messages. The results show that a good balance between embedding capacity and image quality is achieved under different parameter settings. For instance, at an embedding capacity of 1.44 bpp, the PSNR value is approximately 31.28 dB, while at an embedding capacity of 0.5 bpp, the PSNR value reaches 50.65 dB. The innovative method proposed in this study holds promise for providing new possibilities in the field of data hiding, contributing to the development of techniques for information protection in the future. In addition, the method of this study also has potential for application in the medical field. This implies that the study can be applied to safeguard confidential information in medical images, ensuring patient privacy and fostering opportunities for innovative developments in related industries. © 2024 Copyright owner author(s)

关键词： Data security

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Prediction of Epileptic Seizures based on EEG signal using CNN Model 8

Prediction of Epileptic Seizures based on EEG Signal using C...

引用

8th international conference on Communication and Electronics systems, ICCES 2023

作者： Kumar, Suraj Prakash, Shiva Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9798350396638

Millions of patients suffer from epilepsy each year, a chronic nervous illness with a growing global prevalence. In a lot of situations, it might cause critical injuries or patient deaths. So, the automatic prediction of the epileptic seizure saves the patients from injury as well as from death. In this age of technological advancements, the internet of things (IoT) has started to offer a variety of solutions in the healthcare sector through the use of machine learning, deep learning, and cloud-based services. internet of thing, Edge computing, and Cloud computing helps in the detection of such nervous disorders using deep learning and machine learning. Most of the models were developed preciously but there are many issues related to the prediction and security of the data of patients. This research study proposed an autonomous cloud-edge integrated seizure prediction model based on a convolutional neural network. The proposed model detects and classifying the epileptic seizure using an EEG dataset. The proposed model shows average training accuracy and average validation accuracy are 99.27% and 96.37%, respectively and the average losses for training and validation are 2.10 % and 14.23%, respectively. The result shows that overall accuracies and losses are better than the wellknown existing works. All implementation for seizure prediction is done in Python language. © 2023 IEEE.

关键词： internet of things

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Research on internet of Things image Compression Method based on Convolutional Self coding Fusion 4

Research on Internet of Things Image Compression Method Base...

引用

4th international conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Chen, Shuchang Li, Xinli College of Information Technology Changchun AutoMobile Industry Institute Changchun China College of Instrumentation & Electrical Engineering Jilin University Changchun China

ISBN: (纸本)9798350341690

Objective In order to solve the problem that the cloud platform occupies too much data storage space of the internet of Things in the fields of intelligent manufacturing, intelligent medical treatment and intelligent automobile, and the amount of image data is too large, this paper proposes an internet of Things image compression method based on convolutional self-coding fusion, and researches the compression method of the collected internet of Things images. Method Firstly, the collected internet of things images were grayed to obtain grayscale images. Secondly, the improved convolutional autoencoder (GCNN) model was used to encode, binarize and decode the original color image. Finally, the image decoded by the autoencoder and the gray image were fused and enhanced by the DHSV fusion reconstruction method. Result The proposed method uses the BSDS500 dataset, in which the training set consists of 200 images, the test set consists of 200 images, and the validation set consists of 100 images. The network structure was built according to the way in Section 2.2, the learning rate was set to 0.01 and 0.1 respectively, and the training was performed 100 times with 10 iterations per epoch. Finally, Set8(8 images) is used for testing. The color image is used to evaluate the reconstruction results in visual effect, and the Peak signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are used to evaluate the reconstruction results. The experimental results show that the compression ratio can reach 2.28, and the compressed space is reduced by nearly 1/2. At the same time, the fusion reconstruction image can reduce the distortion, and maintain a good level of clarity, contrast and information *** The network model and fusion reconstruction method proposed in this paper can quickly extract image features while reducing network parameters, and can fuse the high-resolution information of grayscale images to achieve good reconstruction results, w

关键词： Convolutional neural networks

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Classification of Spine images using Texture based DWT for Normal and Scoliosis

Classification of Spine Images using Texture Based DWT for N...

引用

2023 international conference on Artificial Intelligence Robotics, signal and image Processing, AIRoSIP 2023

作者： Ali, Hasimah Abdullah, M.M. Kanafiah, S. N. A. Mohd Jusman, Yessi Elshaikh, M. Universiti Malaysia Perlis Centre of Excellence Unmanned Aerial Systems Faculty of Electrical Engineering & Technology Arau Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Arau Malaysia Universitas Muhammadiyah Yogyakarta Faculty of Electrical Engineering Department of Electrical Engineering Yogyakarta Indonesia Universiti Malaysia Perlis Faculty of Electronic Engineering & Technology Arau Malaysia

ISBN: (纸本)9798350327472

Scoliosis is a common spinal disorder characterized by an abnormal curvature of the spine, typically in a lateral or sideways direction. Early detection and accurate diagnosis of scoliosis are crucial for timely intervention and appropriate treatment planning. This study aims to investigate the characteristic of normal and scoliosis spine images based on the texture features by using Discrete Wavelet Transform (DWT). To achieve this, firstly, the spine images have been subjected to image pre-processing for noise removal and image enhancement. In this study, the DWT were used to decompose the spine image into four sub-bands which are approximation, horizontal, vertical and diagonal. Each of the sub-band reveals the distinct features based on the texture characteristic of the sub-band. Approximation coefficients has been extracted and used as input features to classify the spine images using k-NN classifier. based on the results obtained, the average accuracy, sensitivity and specificity was achieved 97.50%, 95% and 100% respectively. It shows that the texture-based DWT features able to differentiate the characteristic of spine image for both normal and scoliosis class. The findings also demonstrate that the DWT technique offers good accuracy, thus considered as a promising approach. © 2023 IEEE.

关键词： image texture

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Enhancing IoT image Security Through Hybrid Encryption and Optimal Key Generation with Optimization

Enhancing IoT Image Security Through Hybrid Encryption and O...

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2023 international conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2023

作者： Anitha, M. Arulanantham, D. Brinda, G. Vijayakumar, S.D. Prakash, G. Shivaranjani, M. M.P.NachimuthuM.Jaganathan Engineering College Department of Ece Erode India Nandha Engineering College Department of Ece Erode India Builders Engineering College Department of Ece Tirupur India Excel Engineering College Department of Ece Namakkal India Kongu Engineering College Department of Ece Erode India

ISBN: (纸本)9798350300857

In today's world, the internet of Things (IoT), the essential need for safeguarding digital data, namely digital images and videos, has come to the forefront. This study introduces a novel hybrid encryption technique characterized by specific attributes that favor efficiency and optimum key utilization, enhancing information technology security. Safeguarding the authenticity and privacy of digital images is of utmost importance owing to the many security obstacles presented during their processing and transmission. This research presents the IoT-based Hybrid image Security Framework (IoT-HISF) using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), which comprises a comprehensive solution to address the difficulties. The research provides valuable contributions by creating high-speed encryption, focusing on generating vital cryptographic keys. The research introduces multi-layered security measures specifically designed to safeguard images in the IoT using the IoT-HISF framework. The IoT-HISF demonstrates performance outcomes in several metrics, such as peak signal-to-noise ratio (PSNR) with a value of 53.15 dB, Hamming distance of 0.02, bit error rate (BER) of 0.0002, accuracy of 97.82%, payload capacity of 5.32 bits per pixel (bpp), and security index of 97.05. These findings emphasize the exceptional image security capabilities of the IoT-HISF framework, particularly in IoT applications. The results indicate that the IoT-HISF enhances image security in IoT applications, including reliable safeguards for high-quality photos and optimizing resource efficiency. © 2023 IEEE.

关键词： internet of things

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2nd international conference on Next-Generation Computing and Information systems, ICNGCIS 2023

2nd International Conference on Next-Generation Computing an...

引用

2nd international conference on Next-Generation Computing and Information systems, ICNGCIS 2023

ISBN: (纸本)9781032738659

The proceedings contain papers. The special focus in this conference is on . The topics include: Impact of artificial intelligence on high-penetration renewable physical infrastructure;collaborative attacks and defense;building a safe and secure metaverse;distributed-computing based versatile healthcare services framework for diagnostic markers;supervised context-aware Latent Dirichlet allocation-based drug recommendation model;combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment;Time series analysis of vegetation change using remote sensing, GIS and FB prophet;advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution;Towards a serverless computing and edge-intelligence architecture for the Personal-internet-of-Things (PIoT);IOT and developed deep learning based road accident detection system and societal knowledge management;Healthcare CAD model for hierarchical processing of surgical navigation system;applicability of eye tracking technology in virtual keyboard for human-computer interactions;a digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience;a novel approach to glass identification using ensemble learning for forensics;deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification;Multi-class instance segmentation for the detection of cervical cancer cells using modified mask RCNN;dilated convolution model for lightweight neural network;artificial intelligence based Monte Carlo model for epidemic forecasting for societal aspect;efficient data visualization through novel recurrent neural network-based dimension reduction;An optimized CNN-based model for pneumonia detection;EEG-based emotion recognition: Leveraging CNNs for precision;A seven layer DNN approach for social-media multilevel image-based text classification.

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signal Modulation Recognition of Satellite internet based on Dual Input Convolutional Neural Network

Signal Modulation Recognition of Satellite Internet Based on...

引用

IEEE international conference on signal and image Processing (ICSIP)

作者： Yiyu Pan Feng Gao Southwest Institute of Communication China Electronics Technology Group Corporation Chengdu China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In order to identify the main modulation modes used in satellite internet communication systems, a communication signal modulation recognition algorithm based on double-ended convolutional neural network is proposed. The time domain waveform of the signal is transformed into the high-power spectrum and the time-frequency image, and further converted into gray images as the shallow feature expressions of the signal. The modulation recognition problem is converted into an image recognition problem, and a modulation recognition model based on dual input convolutional neural network is designed. Through the training of the network, the shallow features are extracted and mapped deeply, and the modulation recognition of the target signal is finally completed. The simulation results show that the performance of the proposed algorithm is significantly improved compared with the single feature input modulation recognition algorithm, and it has good recognition performance for satellite internet signals

关键词： Training Time-frequency analysis image recognition Satellites Target recognition Modulation Feature extraction

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The Study of Fashion Style Classification: Harajuku-type Kawaii and Street Fashion 16

The Study of Fashion Style Classification: Harajuku-type Kaw...

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16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Woottisart, Poonyawat Sripian, Peeraya Thanasuan, Kejkaew King Mongkuts Univ Technol Thonburi Dept Comp Engn Bangkok Thailand Shibaura Inst Technol Innovat Global Program Tokyo Japan King Mongkuts Univ Technol Thonburi Media Technol Bangkok Thailand

ISBN: (纸本)9781665464956

This work provides a comparison among four fashion image classification models, which are a random forest classification method, a convolutional neural network (CNN), a CNN model trained with preprocessed images that a human head and face are removed, and a CNN model trained with images that a head, face, and background are removed. We are interested in three fashion categories, including Harajuku kawaii style, Thai street style, and European street fashion styles. We retrieved images from Google images, and adopted various preprocessing techniques, which were human detection, clothing segmentation, landmark detection, and colorfulness matrix to locate humans and clothes, and extract fashion features. We grouped the images into 2 classes, which were Harajuku kawaii fashion and other street fashion styles. Results showed that the accuracy score from the CNN model without cropping and background removing process was greater than the others. However, when we validated the models using the fashion dataset that was evaluated by fashion experts, the result indicated that the performance of the CNN model trained with the head-cropped image dataset was greater than the others. We concluded that the deep learning model trained with the head-cropped dataset achieved better results because it learned only clothing features, which are the most significant information in the fashion domain.

关键词： image Classification Kawaii Fashion Street Fashion Deep Learning Convolutional Neural Network

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Research on Urban Photoelectric Sensing Environment Sensor based on Big Data technology of Computer internet of Things

Research on Urban Photoelectric Sensing Environment Sensor B...

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2023 IEEE international conference on image Processing and Computer Applications, ICIPCA 2023

作者： Ma, Chuantao Lu, Jing School of Physics and Electronic Engineering Taishan University Tai'an271000 China

ISBN: (纸本)9798350314670

This paper designs and implements the perception layer, network layer and application layer in the framework of environmental monitoring system. To minimize the bit error rate of free space optical communication (FSO) system under the limitation of multi-mode Gaussian background optical noise field and the energy efficiency of the received optical signal, an adaptive photoelectric array signal processing algorithm for intensity modulation/direct detection (IM/DD) is proposed. Design and implement sensor data acquisition circuit, main control processor and peripheral circuit, system communication interface circuit in environmental monitoring system hardware. The data storage part mainly realizes the storage and processing of data, which involves cloud platform technology. The server platform realizes the final webpage display and large screen display, and the key technologies are communication technology. The analysis and simulation results show that the system can greatly reduce the computational complexity while the performance loss is almost negligible. © 2023 IEEE.

关键词： Big data

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