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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Deguchi, Daiki Nunome, Toshiro Graduate School of Engineering Nagoya Institute of Technology Computer Science Program Department of Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350364866

This paper compares video and audio QoE of OFDMA multi-user transmission and reliable groupcast over wireless LAN. We assume video and audio transmission to several terminals simultaneously. As a reliable groupcast method, we employ Unsolicited Retry, a technique of IEEE 802.11aa GroupCast with Retries. We utilize IEEE 802.11ax for OFDMA transmission. We evaluate application-level QoS by computer simulation. We then assess QoE through a subjective experiment with video and audio streams generated by the output timing obtained from the simulation. We notice that each method has a situation to be applied appropriately. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Comparison between experimental and numerical results for piezoelectric signals generated in water-saturated cancellous bone by ultrasound irradiation

Comparison between experimental and numerical results for pi...

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2023 International Congress on Ultrasonics, ICU Beijing 2023

作者： Hosokawa, A. Department of Electrical and Computer Engineering National Institute of Technology Akashi College Akashi674-8501 Japan

The bone formation is considered to be accompanied by the piezoelectric effects in bone. Therefore, the piezoelectric effect under ultrasound irradiation should be elucidated to realize the effective healing. Then, the piezoelectric properties in cancellous bone have been attempted to clarify by complementing experiments and simulations with each other. In this paper, the piezoelectric signals generated in water-saturated cancellous bone by ultrasound irradiation were experimentally and numerically observed, and the effect of the trabecular orientation was investigated. The experimental observation was performed by the piezoelectric cell (PE-cell), which corresponds to an ultrasound receiver using the cancellous bone specimen as a piezoelectric element. The numerical observation was performed by the piezoelectric finite-difference time-domain (PE-FDTD) method, which is an elastic FDTD method with piezoelectric constitutive equations. In both the experimental and simulated results, the piezoelectric signal amplitude increased with the mean intercept lengths (MILs) of the trabecular elements and the pore spaces or the strength of the trabecular orientation. From the fact that the ultrasound propagation in water-saturated cancellous bone can depend on the trabecular orientation, it was considered that the ultrasound properties could be largely associated with the piezoelectric properties. © 2024 institute of Physics Publishing. All rights reserved.

关键词： Piezoelectricity

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Skin cancer detection using deep learning 10

Skin cancer detection using deep learning

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Arun, K.A. Palmer, Matthew Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Karunya Nagar Coimbatore641114 India

ISBN: (纸本)9798350353068

Skin cancer is a prevalent and sometimes lethal condition for which early detection is essential to the best course of treatment. Using dermatoscopic images, convolutional neural networks (CNNs) have demonstrated potential as automated methods for identifying skin cancer. This research presents a unique CNN-based method that uses a carefully chosen dataset that includes a wide range of benign and malignant lesions to diagnose skin cancer. Preprocessing the pictures, training CNN models, and assessing the outcomes using standard metrics like accuracy, precision, recall, and F1-score are the steps in the process. The method works effectively in accurately identifying skin lesions and distinguishing between instances that are benign and those that are malignant, according to extensive testing. Furthermore, a comparison with more traditional approaches highlights the benefit of deep learning in dermatological applications. Furthermore, the results' clinical implications and Possible approaches to incorporating automated technologies into dermatological practice are looked at. By demonstrating the ground-breaking potential of deep learning for skin cancer diagnosis, this work adds to the growing body of research in the field of healthcare. The code created for this project is noteworthy for achieving an astounding 95% accuracy rate, which further confirms the effectiveness of the suggested CNN-based strategy. This work highlights the need of multidisciplinary collaboration between computer science and medicine to address urgent healthcare concerns and develop computer-aided diagnostics. © 2024 IEEE.

关键词： Diagnosis

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Image Processing: Unlocking the Secrets of Visual Information 2

Image Processing: Unlocking the Secrets of Visual Informatio...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Lavanya, D. Bhushan, Kavi Kant, Gur Sharan Sountharraj, A.C. Srivastav, Akanksha Begum, M. Sharmila Sri Venkateswara College of Engineering Computer Science and Engineering Department Andhra Pradesh Tirupati India Ccs University Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science U.P Meerut India Dr. Ngp Art's and Science College Department of Computer Science with Data Analytics Coimbatore-48 India Tula's Institute Department of Computer Science and Engineering Dehradun India Maharaja Institute of Technology Department of Computer Science Mysore India

ISBN: (纸本)9798350384277

In the age of image processing, the hidden ability can be encoded with visual information that is not only observed but also painstakingly carried out;in this way, the complex secrets that are encoded in each pixel have been made clear. Image processing is the magic that has increased the amount of clarity, significance, and utility while transforming stationary arrays of images into dynamic narratives. The image's quality is typically influenced by several variables, such as temperature, light, and noise. Due to its persistent presence in various digital photographs, noise plays a crucial function in the image. Throughout the image coding, transmission, and development procedure, noise consequences could arise. The field of noise removal in image processing has grown to be quite interesting and vibrant. Recently, there has been a notable advancement regarding the filtering of various noise types for image processing in digital format. This research paper aims to discuss image processing and denoising various noises in images to improve overall quality. In terms of methodology systematic qualitative research has been conducted that helps the research gather information from a wide range of sources. Moreover, this paper finds that image processing has significant potential in this digital age. By denoising images, this phenomenon can be applied in different applications in every sector. © 2024 IEEE.

关键词： Image denoising

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Envisaging the patterns in Blockchain 7

Envisaging the patterns in Blockchain

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

作者： Nagarajan, G. Atmakuri, Sukeerth Nalamati, Sri Satyendra Minu, R.I. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350372816

Cryptocurrency is becoming more mainstream, and experts predict that its market size will reach dollar 2.2 trillion by 2026. However, it's also an intangible and highly volatile asset that poses significant challenges for governments, anti-money laundering analysts, and financial crime investigators. Compliance with strict regulations is also a challenge facing virtual asset service providers. The price of Bitcoin hit an all-time high in January 2021, making headlines worldwide. As the popularity of cryptocurrency continues to grow, AML analysts and financial crime investigators face tougher challenges. VASPs are under pressure to comply with tighter regulations, and both parties need to take a proactive approach. Bitcoin visualization is a solution that involves visualizing a Bitcoin block, enabling analysts to clarify complex and dense blocks of transaction data quickly to identify high-risk activity. Instead of looking at historic Bitcoin transactions, analysts can now examine Bitcoin activity in real-time using a powerful Bitcoin network visualization chart showing key connections, combined with a dedicated Bitcoin timeline view featuring precise transaction details as they occur. We conducted a thorough and quantitative assessment of 76 analytics tools, including those proposed in academic research and those endorsed by professionals and blockchain enthusiasts online, to provide a comprehensive analysis of the facets of blockchain data that have been thoroughly investigated. We also identified areas that warrant further exploration in future research. © 2024 IEEE.

关键词： Blockchain Blockchain tools Data parsing Data Visualization Graph Modelling Graph-based analysis

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Defense against distributed DoS attack detection by using intelligent evolutionary algorithm

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International Journal of computers and Applications 2022年第3期44卷 219-229页

作者： Dwivedi, Shubhra Vardhan, Manu Tripathi, Sarsij Department of Computer Science & Engineering National Institute of Technology Raipur Chhattisgarh Raipur India

Modern society is prominently dependent on information and communication technology over the last few decades has carried it with more vulnerability to an enormous variety of cyber-attacks. One of the attacks is a Distributed Denial-of-Service (DDoS) that exploits the power of thousands and sometimes hundreds of thousands of compromised computers to attack information-providing services and online commercial sites, often resulting in significant downtime and financial losses and thereby denying services of legitimate clients. The study of DDoS attacks is a significant area of research;there are a number of techniques that have been proposed such as evolutionary algorithm and artificial intelligence in the literature for detecting DDoS attacks. Unfortunately, the modern well-known DDoS detection schemes are deteriorating to validate the objective and prior recognition of DDoS attacks. In order to mitigate denial of service attacks, in this paper, we use grasshopper optimization algorithm (GOA) with machine learning algorithm called GOIDS. This approach is based on creating an intrusion detection system (IDS) to fulfill the requirements of the monitored environment and able to distinguish between normal and attack traffics. Furthermore, GOIDS selects the most relevant features from the original IDS dataset that can help to distinguish typical low-speed DDoS attacks and then, selected features are passed to the classifiers, i.e. support vector machine, decision tree, naïve Bayes, and multilayer perceptron to identify type of attack. The publicly available dataset as KDD Cup 99 and CIC-IDS 2017 are used for our experimental study. From the results of the simulation, it is clear that GOIDS with decision tree acquires high detection and accuracy with a low false–positive rate. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Denial-of-service attack

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A Comparative Analysis of Advanced Machine Learning Algorithms to diagnose Parkinson's Disease 2

A Comparative Analysis of Advanced Machine Learning Algorith...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Roy, Sourabarna Pal, Tannistha Debbarma, Swapan Department of Computer Science and Engineering National Institute of Technology Tripura Barjala Jirania Agartala799046 India

Parkinson's disease (PD) is a prevalent and debilitating neurological disorder affecting millions worldwide, necessitating early and precise diagnosis for effective management and therapy. Recent breakthroughs in artificial intelligence (AI) applications within the medical field have showcased promising potential in disease identification and diagnosis. This study presents a comprehensive analysis of cutting-edge AI-based methods for the identification of Parkinson's disease. This paper encompasses extensive testing on substantial datasets containing both PD patients and healthy individuals, aiming to assess the efficacy of AI models. Performance metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC-ROC) are employed to evaluate model performance rigorously. This paper is not only reviewing the state-of-the-art AI techniques for Parkinson's disease identification but also introduces novel contributions in the field. Our results demonstrate the potential of AI in achieving early and accurate PD diagnosis, offering hope for improved patient care and treatment outcomes. Furthermore, this study paves the way for future research and development in the domain of AI-driven healthcare, with the ultimate goal of enhancing the quality of life for individuals suffering from Parkinson's disease and similar neurodegenerative conditions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Magnetic resonance imaging

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State-of-the-Art Deep Learning Architectures for Identifying Diseases in Cucumber and Black Gram Plants 4

State-of-the-Art Deep Learning Architectures for Identifying...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560056 India

ISBN: (纸本)9798350393354

Tackling the imperative task of plant disease identification, this paper proposes a holistic deep learning procedure applicable to cucumber and black gram crops. The study employs a diverse set of models, including the efficient Extreme Learning Machine (ELM), intricate Feedforward Neural Network (FNN), Deep Residual Network for hierarchical Convolutional Neural Network (CNN), feature extraction for spatial features, and the lightweight MobileNet, tailored for resource *** study leverages the Cucumber and Black Gram Disease Classification Dataset, a comprehensive repository encompassing diverse disease stages. Employing a holistic methodology, the integrated models notably enhance the accuracy of disease classification. The Extreme Learning Machine enables swift learning, the Feedforward Neural Network captures intricate patterns, the Deep Residual Network explores hierarchical features, and the CNN excels in spatial feature extraction. Additionally, the lightweight MobileNet proves adaptable to resource *** establishing a baseline, the collective performance of ELM, FNN, Deep Residual Network, CNN, and MobileNet was assessed. While MobileNet led with high classification accuracy at 97% for cucumber and 95% for black gram diseases, the baseline models collectively demonstrated commendable pathogen detection, maintaining an average precision of over 90% for both crops. MobileNet's superior accuracy sets a benchmark for the models, indicating its efficacy in disease classification. © 2024 IEEE.

关键词： Feedforward neural networks

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Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

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Intelligent Automation & Soft Computing 2023年第2期35卷 1873-1887页

作者： S.Siamala Devi Chandrakala Kuruba Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science and Engineering Vignan Nirula Institute of Technology and ScienceGuntur522009India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Wireless sensor networks(WSNs)are projected to have a wide range of applications in the *** fundamental problem with WSN is that it has aﬁnite *** a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data *** cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunﬂower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is *** does not provide better competence and out-put in the ***,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the *** hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used toﬁnd the route among origin CH and *** open cache of nodes,trans-mission power,and proximity are used to improve the CH *** grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle *** GWO method was inspired by this hunting *** proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale *** HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.

关键词： Energy efﬁciency power consumption lifespan of the network black hole method ant colony optimisation routing and cluster heads(CHs)

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Single/Multi-band graphene-based disk THz absorbers with single graphene layer: conceptual design

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Optical and Quantum Electronics 2024年第11期56卷 1-26页

作者： Mohsen Daraei, Omid Rezaei, Pejman Zamzam, Pouria Bozorgi, S. Amir Amiri, Nafiseh Ebrahimi, Farshad Radpour, Hamed Rastkar Mirzaei, Milad Mohapatra, Saswat Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Electrical and Computer Engineering Faculty Semnan University Semnan Iran Center for Digital Safety and Security AIT Austrian Institute of Technology Seibersdorf Austria The School of Electrical and Computer Engineering University of Oklahoma NormanOK73019 United States

This article introduces a unified structure for broadband and multi-band graphene-based absorbers. The absorption process relies on multiple circular graphene disks positioned on a glass surface. The absorber’s functionality can be adjusted from broad-band to multi-band by varying its height. To thoroughly assess its performance, we employed two theoretical frameworks: the transmission line model (TLM) and the Finite Element Method (FEM). Additionally, we conducted optical analysis using various incident wave polarizations. To validate our findings, we simulated the structure in the CST Studio simulator. The analysis reveals that the structure achieves over 90% absorption in the broad-band phase. Furthermore, it exhibits five absorption peaks: 28.1%, 99.9%, 98.1%, 94%, and 76% at frequencies of 0.3, 1, 2, and 2.7 THz, respectively, in the multi-band absorption phase. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Terahertz waves

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