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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Saad, Rasha A. Abd El-Samie, Fathi E. Abdel-Azim, Mohammed El-Hoseny, Heba M. Obour Higher Institute for Engineering and Technology Department of Electronics and Electrical Communications Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Egypt Mansoura University Faculty of Engineering Department of Electronics and Electrical Communications Egypt The Higher Future Institute for Specialized Technological Studies Department of Computer Science Egypt

ISBN: (纸本)9798350323511

A count of crowds tries to determine the total number of persons in crowded places in order to avoid disruption of the safety system and maintain crowd safety. Accurate estimation of the size of crowds is a challenging process because of numerous challenges like occlusion, complicated backgrounds, varying sizes, uneven distribution, alteration of lighting, and adaptable weather. In this study, we propose a solution to two crowd counting challenges: poor weather such as rain and image scaling. We investigate crowd counting in the rainy Shanghai-Tec images. We adopt the GAN-U-Net de-raining model and the SAS-Net [1] and SGA-Net [2] crowd-counting models in our study. © 2023 IEEE.

关键词： computer vision

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Comparative Analysis of Deep Learning and Machine Learning Analysis for Kidney Tumor Analysis 7

Comparative Analysis of Deep Learning and Machine Learning A...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Yazhini, A. Kalpana Devi, M. Anitha, M. Praveena, R. Mol, Maheswari K Lawrence, Jinsha Anna University Faculty of Electrical Engineering College of Engineering Chennai600025 India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore10 India KIT - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore641402 India Jai Shriram Engineering College Department of Computer Science and Engineering Tirupur638660 India Veterinary College and Research Institute Thanjavur614625 India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

In order to lower death risks, provide the most effective course of treatment, and improve community healthcare, the majority of recent research has concentrated on examining prevalent illnesses in the population. One of the prevalent ailments that has impacted our society is kidney disease. Around the world, kidney tumors (KT) rank tenth in terms of frequency in both men and women. The organization of cancer is a problem that is attracting increasing attention in the domains of bioinformatics and computational biology. This study presents a comparative analysis of the latest developments in deep learning (DL) and machine learning (ML). While several methods have been proposed to tackle the cancer classification challenge, new research indicates that supervised and deep learning-based methods are the most effective. Healthcare has advanced as a result of the creation of numerous machine learning techniques for awareness evaluation in cancer categorization. It seems that there is a high requirement for the ongoing development of efficient categorization algorithms in order to handle the growth in healthcare applications. The comparative analysis of the proposed classification shows the accuracy as 0.92%. © 2024 IEEE.

关键词： Adversarial machine learning

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AI-Aided Height Optimization for NOMA-UAV Networks

AI-Aided Height Optimization for NOMA-UAV Networks

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2023 International Conference on computer Science, Information technology and engineering, ICCoSITE 2023

作者： Hashesh, Amira O. Tag Eldien, Adly S. Fouda, Mostafa M. Zaki, Rokaia M. Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Cairo Egypt High Institute for Engineering and Technology - Obour Egypt Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho FallsID United States Higher Institute of Engineering and Technology Kafrelsheikh Egypt

ISBN: (纸本)9798350320954

Currently, Unmanned Aerial Vehicles (UAVs) are gaining significant attention due to their potential to effectively carry out a variety of tasks with superior performance through the use of fifth-generation (5G) and sixth-generation (6G) networks. Non-orthogonal multiple access (NOMA) techniques can further improve the performance and efficiency while reducing the interference. In this paper, we propose the application of machine learning (ML) techniques to evaluate the outage performance of a NOMA-enabled UAV network. Specifically, this study investigates the optimal UAV height that allows two users on the ground to receive the best service when they are simultaneously served by one UAV. We generated our own dataset which included several network parameters. We then trained various machine learning techniques on this dataset, including artificial neural networks (ANN), support vector regression (SVR), and linear regression (LR). Our results indicate that ANN provides the best accuracy compared with SVR and LR, with an average root mean squared error (RMSE) of 0.0931. © 2023 IEEE.

关键词： Mean square error

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A Double Layer Interdigital CPW Metamaterial Director for LTE Antenna System

A Double Layer Interdigital CPW Metamaterial Director for LT...

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2023 International electrical engineering Congress, iEECON 2023

作者： Chomtong, Pongsathorn Krachodnok, Piyaporn Akkaraekthalin, Prayoot King Mongkut's University of Technology North Bangkok Faculty of Technical Education Department of Teacher Training in Electrical Engineering Bangkok Thailand Institute of Engineering School of Telecommunication Engineering Suranaree University of Technology Nakorn Ratchasima Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350332360

This paper proposes the design of metamaterial director for antenna, which has the feature of double layer based on CPW structure by using interdigital CPW technique. The designed unit cell of metamaterial director has resonance frequency of 1.8 GHz, covering the frequency band of LTE system (1.72 GHz-1.83GHz). By using this technique, the unit cell can have negative permeability, the required property in design at the resonance frequency. It is found that size of the designed interdigital CPW unit cell has been reduced to λ8. The result causes from the slow wave effect on transmission line in the structure, which electrical and physical lengths of transmission line are not equal from that effect. The unit cell size of 14 mm was designed and used to form a 7 × 7 array as a result of metamaterial director with overall size of 98 mm. The 1.8 GHz dipole antenna was then used with the metamaterial director. The results show that the simulation impedance matching result less than-10 dB with the bandwidth of 183 MHz. Using the designed director, the antenna gain has been increased from 2 dB to 5.91 dB with directional radiation pattern. The proposed metamaterial director is appropriate for modern wireless communication systems. © 2023 IEEE.

关键词： Metamaterials

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Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images 23rd

Robust Multiresolution and Multistain Background Segmentatio...

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Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical engineering, PCBEE 2023

作者： Jurgas, Artur Wodzinski, Marek Atzori, Manfredo Müller, Henning Information Systems Institute Sierre Switzerland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

ISBN: (纸本)9783031384295

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pathology

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A Multi-Agent Deep Reinforcement Learning Framework for Detecting and Mitigating DDoS Attacks in IoT Networks 4

A Multi-Agent Deep Reinforcement Learning Framework for Dete...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Shaharkar, Bhushan Mittal, Tanusha Sunkara, Krishna Chaitanya Shukla, Vishal Desai, Ishan Mookherjee, Ujal Walchand Institute of Technology Department of Information Technology Solapur India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Oracle Cloud Engineering Data Center Design & Modeling Raleigh United States Ramdeobaba University Department of Mechanical Engineering Nagpur India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The rise of Internet of Things (IoT) networks has introduced new opportunities for innovation but also increased vulnerability to cyberattacks, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection and mitigation mechanisms often struggle to handle the dynamic and distributed nature of IoT systems. In this study, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) framework designed to detect and mitigate DDoS attacks in IoT networks in real-time. The framework consists of multiple collaborative agents, each deployed at different network layers or nodes, trained using deep reinforcement learning to identify attack patterns and take immediate mitigation actions. Through cooperative learning, these agents adapt to evolving attack strategies and optimize defense policies. Experimental evaluations demonstrate that the proposed MADRL framework outperforms traditional techniques by achieving higher detection accuracy, faster response times, and reduced false positive rates. Our results indicate that this framework can effectively secure IoT networks against complex DDoS attacks, maintaining network stability and availability. © 2024 IEEE.

关键词： Deep reinforcement learning

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Opto-Video Encryption Based on Logistic Adjusted Sine map in FrFT

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computers, Materials & Continua 2022年第8期72卷 3651-3663页

作者： Osama S.Faragallah Ashraf Afifi Ibrahim F.Elashry Ensherah A.Naeem Heba M.El-Hoseny Ahmed I.Sallam Hala S.El-sayed Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering Kafrelsheikh UniversityKafrelsheikh 61519Egypt Electrical Department Faculty of Industrial EducationSuez UniversitySuez 43527Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology 3036Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom 32511Egypt

In the last few years,videos became the most common form of information transmitted over the internet,and a lot of the traffic is confidential and must be protected and delivered safely to its intended *** introduces the challenges of presenting encryption systems that can encode videos securely and efficiently at the same *** paper presents an efficient opto-video encryption system using Logistic Adjusted Sine map(LASM)in the Fractional Fourier Transform(FrFT).In the presented opto-video LASM-based FrFT scheme,the encoded video is split into distinct frames and transformed into optical signals utilizing an optical *** of the developed optical video frames is ciphered by utilizing the LASM in optical FrFT system using two-phase modulation forms on the video frame,the first in the time-domain and the second in the FrFT *** the end,the ciphervideo frame is spotted utilizing a CCD digital camera and transformed into a digital structure that can be managed using a *** test the proposed opto-video LASM-based FrFT scheme using various security *** outcomes demonstrate that the presented scheme can effectively encrypt and decrypt video *** addition,it encrypts videos with a high level of encryption qualitywithout sacrificing its resistance to noise ***,the test outcomes demonstrate that the presented scheme is immune to known attacks.

关键词： Optical encryption FrFT logistic-adjusted sine map

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Compressed Image Generation with Denoising Diffusion Codebook Models

arXiv

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arXiv 2025年

作者： Ohayon, Guy Manor, Hila Michaeli, Tomer Elad, Michael Faculty of Computer Science Technion – Israel Institute of Technology Haifa Israel Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa Israel

We present a novel generative approach based on Denoising Diffusion Models (DDMs), which produces high-quality image samples along with their losslessly compressed bit-stream representations. This is obtained by replacing the standard Gaussian noise sampling in the reverse diffusion with a selection of noise samples from pre-defined codebooks of fixed iid Gaussian vectors. Surprisingly, we find that our method, termed Denoising Diffusion Codebook Model (DDCM), retains sample quality and diversity of standard DDMs, even for extremely small codebooks. We leverage DDCM and pick the noises from the codebooks that best match a given image, converting our generative model into a highly effective lossy image codec achieving state-of-the-art perceptual image compression results. More generally, by setting other noise selections rules, we extend our compression method to any conditional image generation task (e.g., image restoration), where the generated images are produced jointly with their condensed bit-stream representations. Our work is accompanied by a mathematical interpretation of the proposed compressed conditional generation schemes, establishing a connection with score-based approximations of posterior samplers for the tasks considered. Code and demo are available on our project’s website. Copyright © 2025, The Authors. All rights reserved.

关键词： Image denoising

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Two-dimensional Parameter Relationships for W UMa-type Systems Revisited

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Research in Astronomy and Astrophysics 2024年第1期24卷 51-58页

作者： Atila Poro Ehsan Paki Ailar Alizadehsabegh Mehdi Khodadadilori Selda Ranjbar Salehian Mahya Hedayatjoo Fatemeh Hashemi Yasaman Dashti Fatemeh Mohammadizadeh Astronomy Department of the Raderon AI Lab. BurnabyBCCanada Faculty of Electrical and Computer Engineering Department of PhotonicsUniversity of TabrizTabriz 51664Iran Department of Electrical Engineering Amirkabir University of TechnologyTehranIran Astronomy Students'Scientific Association University of TabrizTabriz 51664Iran Department of Physics Iran University of Science and TechnologyTehranIran Institute for the Intellectual Development(Kanoon) Astronomy GroupBojnordIran Department of Mechanical Engineering Amirkabir University of TechnologyTehranIran Aerospace Engineering Department Science and Research Branch of Islamic Azad UniversityTehranIran

Reviewing the empirical and theoretical parameter relationships between various parameters is a good way to understand more about contact binary *** this investigation,two-dimensional(2D)relationships for P–MV(system),P–L1,2,M1,2–L1,2,and q–Lratiowere *** sample used is related to 118 contact binary systems with an orbital period shorter than 0.6 days whose absolute parameters were estimated based on the Gaia Data Release 3 *** reviewed previous studies on 2D relationships and updated six parameter ***,Markov chain Monte Carlo and Machine Learning methods were used,and the outcomes were *** selected 22 contact binary systems from eight previous studies for comparison,which had light curve solutions using spectroscopic *** results show that the systems are in good agreement with the results of this study.

关键词： (stars:)binaries(including multiple):close stars:fundamental parameters methods:data analysis

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Video Streaming QoS Analysis with TIPHON Standard NDNts 9

Video Streaming QoS Analysis with TIPHON Standard NDNts

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： J.d, I Made Aditya Putra Alifa, Lisdatul Fawwaz, Muhammad Hilmi Mauren Sibayak, Nissha Kinaya Mayasari, Ratna Arifin, Hasan Nur Ahdan, Syaiful Negara, Ridha Muldina Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

Named data networks are a new concept in network architecture that can process data transmission by name. This concept can be utilized in the case of video streaming. TIPHON is a standard used to support streaming multimedia activities. According to TIPHON, three factors-delay, packet loss, and throughput-can influence how a stream behaves. This study's goal is to evaluate the quality of service (QoS) of video streaming services using an NDN network that adheres to the TIPHON NDN standard. Several things can affect throughput, such as bottlenecks on the computer or network device used. © 2023 IEEE.

关键词： Quality control

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