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image Classification Based on Quality of Pixels 2nd

Image Classification Based on Quality of Pixels

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2nd International conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Saudagar, Onkar Rananavare, Pranav Shaikh, Zain Ul Abedin Sirvi, Manojkumar Godbole, Mihir Pune Institute of Computer Technology Pune411043 India AlgoAnalytics Pune India

ISBN: (纸本)9789819977109

Anomaly detection for CCTV Surveillance Systems to give Alerts and Auto-Alerts when no one monitors the CCTV is most important, making it easy to keep track of events. The vital component of training a machine learning model is data. The data here are images from various timestamps and from every camera present on the industry campus. If the data fed is flawed then the model can generate false alarms, making it challenging to identify true anomalies. So, in this paper, there are techniques to either discard the images that cannot be enhanced or enhance them in order to increase the quality of images fed to the anomaly detection model. In addition to this, we have used Kurtosis and Brisque values as parameters of every image and trained the values on different ML and DL models such as Logistic Regression, Decision Tree, Naïve Bayes, VGG16, CNN, AlexNet, and LeNet5. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Ethical Hacking and Demonstrating Network Exploits using Wireshark 2

Ethical Hacking and Demonstrating Network Exploits using Wir...

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2nd International conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Sri Vijaya, P. Neelima, N. Shahin, Sk Gopi Krishna, Y. Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350340235

Internet traffic analysis is a practice that records information about user behavior on the network. In order to determine how many people access the internet and consume bandwidth, this research seeks to collect data regarding traffic outcomes in a graphical format. Researchers in this study also recorded the peak internet usage hours. The study's results were obtained using the packet sniffing approach. data packets from the http protocol application can be filtered by researchers. Since then, user activity on the Internet has become more dominating in terms of locating and downloading sites. Wireshark is the programme utilized;it has several capabilities that are quite beneficial and simple to use. © 2023 IEEE.

关键词： Denial of service image sniffing Network traffic analysis Password sniffing Wireshark

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Generating Synthetic Brain Tumor data Using StyleGAN3 for Lower Class Enhancement 2nd

Generating Synthetic Brain Tumor Data Using StyleGAN3 for Lo...

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2nd Pan-African conference on Artificial Intelligence (PanAfriCon AI)

作者： Abdalaziz, Ahmed Schwenker, Friedhelm Univ Ulm Inst Neural Informat Proc D-89081 Ulm Germany German Univ Cairo New Cairo 11835 Egypt

ISBN: (纸本)9783031576232;9783031576249

Accurate simulation of brain tumor data is crucial in medical imaging research for developing and assessing novel image enhancement techniques. We propose an approach to generate synthetic brain tumor data using StyleGAN3, a GAN model. Our objective is to explore the impact of varying gamma values and dataset sizes on the quality of synthetic brain tumor images via transfer learning from a pre-trained 512 X 512 model. We conducted three experiments with different gamma values and dataset sizes. The first two experiments employed gamma values of 6.6 and 8.2, respectively, using a dataset of 310 brain tumor images. The third experiment maintained the gamma value at 8.2 but reduced the dataset size to 155 images. We used FID and KID as evaluation metrics. Results show varying gamma values and dataset sizes significantly influence synthetic image quality. Higher gamma values generally yielded improved image quality. In another experiment, we obtained FID and KID scores of 67.856 and 0.013, respectively [1]. These findings impact brain tumor imaging enhancement techniques. StyleGAN3 and transfer learning can efficiently generate synthetic brain tumor datasets, advancing medical image analysis and diagnosis. Our study underscores synthetic data generation's potential for enhancing medical image research.

关键词： Brain Tumor Imaging Evaluation Metrics image Enhancement StyleGAN3 Synthetic data Generation Transfer Learning

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Comparative Study of Low-Light image Enhancement Performance: DCE-Net vs. MirNetv2 2

Comparative Study of Low-Light Image Enhancement Performance...

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2nd International conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Bharathi Mohan, G. Prasanna Kumar, R. Mithilesh, A. Sriram, S. Surya Santhosh, V.R. Balaji, M. Sameer, N.M.D. Logeshwar, B.S. Doss, Srinath Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Cse Chennai India Botho University Faculty of Engineering and Technology Botswana

ISBN: (纸本)9798350328202

The difficult computer vision task of low-light image augmentation tries to raise the standard of photographs taken in low light. Deep learning-based low-light picture enhancing systems have recently attained modern outcomes. In this article, we compare the effectiveness of two cutting-edge deep learning-based algorithms-DCE-Net and MirNetv2-to enhance the quality of images in low light. A deep cross-enhancement network called DCE-Net uses a cutting-edge cross-connection module to take advantage of the complementing data that exists between the network's various tiers. A multi-scale feature extraction module is used by the multi-scale illumination recovery network MirNetv2 to extract features from various scales of the picture. On the LOLdataset benchmark datasets for enhancing low-light images, we compare the execution of DCE-Net and MirNetv2. The outcomes demonstrate that DCE-Net and MirNetv2 provides cutting-edge outcomes. On the LOLdataset, DCE-Net performs better than MirNetv2 in terms of improving contrast and brightness. The findings of this study indicate that the deep learning-based Improving visibility in low-light images method DCE-Net- a promising one. In a wide range of applications, including surveillance, night photography, and medical imaging, DCE-Net can be utilized to improve low-light photos. © 2024 IEEE.

关键词： image enhancement

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Feature Integration-based Residual Deep Learning Model for Fake News Detection using Multimodal data Sources 2

Feature Integration-based Residual Deep Learning Model for F...

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2nd International conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Shah, Minal Nirav Ganatra, Amit Charotar University of Science and Technology Department of Computer Science Cspit Charusat Gujarat Changa388421 India Parul University Ta.Waghodia Gujarat 391760 India

ISBN: (纸本)9798350368413

The rapid growth of the modern digital world has supported to spread the news rapidly on the social media sectors including Weibo, Facebook, and Twitter. Unconfirmed news is frequently spread in multimedia content form such as video, audio, picture, or text. The propagation of certain false news cheats the public results in protests and generates issues. Fake news propagation has become a significant issue in modern data because of the huge utilization of social media entities. One of the important operations is false news identification, yet the difficulty of some languages makes the identification of fake news complex. The conventional approaches cannot gather many contextual and semantic features from the documents in a specific multilingual text corpus. To overcome such issues, a residual-based deep learning model is introduced for fake news detection using multimodal information. Firstly, multimodal information like text and images are gathered from publicly available data sources. Further, the input text is undergone for pre-processing and it is used for extracting the features. Here, the Bidirectional Encoder Representations from Transformers (BERT) is employed as feature extraction as F1. Similarly, the features from the raw image are achieved by applying the Visual Geometry Group (VGG16), designated as F2. Finally, these two resultant features are concatenated together and subjected to the Residual Recurrent Neural Network (Res-RNN) for detecting fake news. Hence, the proposed system is examined with different metrics and compared with various approaches. Thus, the results declare that the suggested system achieves higher accuracy in avoiding the spreading of fake news over the media. © 2024 IEEE.

关键词： Recurrent neural networks

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Lightweight TransUNet Informal Residential Area segmentation 2

Lightweight TransUNet Informal Residential Area segmentation

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2nd International conference on Machine Vision, image Processing and Imaging Technology, MVIPIT 2024

作者： Zhang, Shuo Jia, Yiru Liu, Zhaoying Yang, Fei School of Computer science Beijing University of Technology Beijing China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331543037

Remote sensing image informal residential area segmentation plays an important role in urban planning, environmental detection, and disaster assessment. With the development of deep learning, the performance of informal residential area extraction has been improved. However, trying to accurately extract the boundaries between different buildings in the same complex is still a challenging task, and at the same time, segmentation model training takes a lot of time. To address the above problems, we propose a deep learning-based segmentation method for remote sensing images of informal residential area instances, aiming to further improve the accuracy of informal residential area segmentation. This paper has the following three main contributions. First, this paper designs and implements a remote sensing image informal residential area instance segmentation method called TransUNet-IRA, which is a model based on TransUNet, and reduces the training file weights by reducing the number of encoder layers and changing the format of the dataset, which increases the computing speed and improves the performance of the model. Second, we combine DiceCE Loss with IoU Loss to form DiceCE-IoU Loss to further improve the model’s performance in informal residential area segmentation. Finally, we collect a large amount of meter-level remote sensing image data from Guilin City, Guangxi Province, and Chenzhou City, Hunan Province, and construct a remote sensing image informal residential area instance segmentation dataset for these two regions based on these remote sensing images, which is annotated with three segmentations of the background, informal residential area, and single building in the formal residential area. After experimental validation, our method demonstrates performance advantages on the constructed dataset, not only outperforming classical models such as UNet, ResUNet, AttUNet, and SwinUNet but also improving on the original TransUNet. ©2024 IEEE.

关键词： image segmentation

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Deep Learning-Based image Analysis to Track Vehicles 2

Deep Learning-Based Image Analysis to Track Vehicles

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2nd International conference on Disruptive Technologies, ICDT 2024

作者： Prabakaran, P. Jayasudha, R. Sandhu, Mukta Gupta, Sandeep School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Dr.N.G.P.Institute of Technology Department of Mathematics Coimbatore India Shri Vishwakarma Skill University Skill Department of Computer Science and Engineering Haryana Palwal India Department of Electrical Engineering Dehradun India

ISBN: (纸本)9798350371055

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches. © 2024 IEEE.

关键词： Artificial Intelligence HD Map Traffic Monitoring System Vehicle Type Classification YOLOv4

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Robust Gray-Scale image Watermarkingusing Symlet Wavelet Decomposition using Hybrid Approach 2

Robust Gray-Scale Image Watermarkingusing Symlet Wavelet Dec...

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2nd IEEE International conference on Futuristic Technologies, INCOFT 2023

作者： Savakar, Dayanand Biradar, Pandurang Ghuli, Anand Shirur, Shreya Rani Channamma University Department of Computer Science Karnataka Belagavi India BLDEA's V.P.Dr.P.G.Halakatti College of Engineering & Technology Department of M.C.A Karnataka Vijayapur India

ISBN: (纸本)9798350308846

Amongst digital requirements, gray-scale image watermarking-capacity is the crucial issue that has to be effectively handled. A method of utilizing a blind watermarking scheme to insert a gray-scale image into another gray-scale image of a different size is proposed in order to mark the same. This method achieves a logically good perceptual analogy by measuring the appropriate (PSNR) and (SSIM) index. To extract LL bands, the technique uses sym 4 discrete wavelet transformation at a single level (DWT), where most of the image's features are kept. A trustworthy rectangular matrix of computationally viable areas of the updated image is then obtained using a singular value decomposition (SVD), first utilized to obtain a set of elements sufficient to describe the full image. The technique is resistant to attacks from motion blur, JPEG compression, pepper and salt, Gaussian, speckle, median, and Gaussian lowpass filters, as well as attacks from sharpening and histogram equalization. An increasingly popular strategy for securing copyright protection and identifying data owners is watermarking. The representation of the image determines the watermark embedding techniques, and each domain has its own set of advantages and disadvantages. © 2023 IEEE.

关键词： DWT gray-scale image nonblind PSNR SSIM SVD (keywords)

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Visionary Narratives: Unveiling the Potential of an Automated image Caption Generator through Deep Learning and Multidimensional Analysis 2

Visionary Narratives: Unveiling the Potential of an Automate...

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2nd IEEE International conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Keshamoni, Kumar Priyanka, Pabbala Vaagdevi Engineering College Department of Ece Telanagana Warangal India

ISBN: (纸本)9798350360523

This paper introduces an image caption generator, implemented from Yumi's Blog, which employs a deep learning model trained on the FLICKR-8K dataset. The generator leverages computer vision and natural language processing to produce descriptive captions for images. Potential applications range from aiding visually impaired individuals to medical and geospatial image analysis. The project encompasses a comprehensive workflow;including data cleaning, feature extraction using VGG-16, LSTM model building, and evaluation through BLEU scores. The model's performance is showcased through use cases, demonstrating its potential impact on diverse fields such as accessibility, advertising, and healthcare. The study concludes with insights into hyperparameter tuning and the acknowledgment that while the model yields promising results, further refinement is possible for enhanced caption generation. © 2024 IEEE.

关键词： image analysis

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Securing data in the image Using SHA & ECC 2

Securing Data in the Image Using SHA & ECC

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2nd International conference on Edge Computing and Applications, ICECAA 2023

作者： Hamsanandhini, S. Balusubramanie, P. Abinaya, A.B. Kongu Engineering College Cse Tamilnadu Perundurai India Kongu Engineering College Computer Technology - Pg Tamilnadu Perundurai India

ISBN: (纸本)9798350347579

Networks have expanded globally, and information is now available digitally. Due to the volume of data being exchanged online, data security has become a difficult problem. Cryptography provides integrity, confidentiality, non-repudiation, and authenticity to the secret data. Symmetric & asymmetric cryptographic techniques are used today for hiding any confidential information from unauthorized persons. The combination proposed is the asymmetric algorithm (ECC) and a hash function (SHA). This research study introduces a new technique for encrypting and decrypting text that uses elliptical curve cryptography (ECC) to improve data security and a secure hash function (SHA-3) to ensure data integrity. When compared to the previous method for text encryption (RSA), the suggested solution of SHA with ECC is more effective. Compared to other cryptographic algorithms, it offers a higher level of security with a smaller key size. © 2023 IEEE.

关键词： Hash functions

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