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A Comprehensive Study on Relative Distances of Hand Landmarks Approach for American Sign Language Gesture

Augmented Human Research 2024年第1期9卷 1-12页

作者： Shah, Shail Vaidya, Jaynil Pipariya, Kishan Shah, Manan Department of Computer Science North Carolina State University Raleigh USA School of Computing and Augmented Intelligence Arizona State University Tempe USA Department of Computer Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gandhinagar India

Communication with people with hearing or speaking disabilities is always difficult when there is no knowledge of sign language. The presence of sign language is not enough to communicate smoothly, this process requires another easy medium for communication to make it more efficient, that is, via a digital medium. This paper proposes using Feed-Forward Neural Networks on hand landmarks for real-time sign language identification. The hand landmarks identification was carried out using the MediaPipe Hands library. This approach would make the classification problem efficient by making it faster and requiring less memory. Through this, we aim to bridge the gap between the difficulties that arise during communication between people who do and do not know American Sign Language.

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Design of an Efficient Deep Learning Framework for Covid-19 Image Classification

Design of an Efficient Deep Learning Framework for Covid-19 ...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： J Himasree K. Aravindhan K.P. Keerthana C Gobinath School of Computing and Information and Technology Reva University Bangalore Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The worldwide health crisis caused by COVID-19 and the subsequent control measures have had significant and widespread impacts on various aspects of human life. Detecting COVID-19 early is crucial for effective diagnosis, and ML algorithms have played a vital role in speeding up the process and improving efficiency. However, factors like delivery time and access to training data are also crucial considerations. Extensive analysis of lung ultrasound pictures using the COVID-19 database and deep learning algorithms show that the former is superior to the latter in terms of illness diagnosis. Without specialised testing, it might be difficult to tell COVID-19 from other viral fevers. The research suggests a method of COVID-19 image classification using Convolutional Neural Networks (CNNs). Further evidence of the superiority of the proposed model is provided by a loss and accuracy comparison study.

关键词： COVID-19 Deep learning Ultrasonic imaging Training data Lung Convolutional neural networks Security

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Blockchain-based Secure Storage and Cross-domain Sharing Mechanism for Medical Image Data 24

Blockchain-based Secure Storage and Cross-domain Sharing Mec...

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8th International Conference on Cloud and Big Data computing, ICCBDC 2024

作者： Yao, Chuanjia Jiang, Rong Long, Leijin Dong, Jianhang Wang, Chenguang School of Business Yunnan University of Finance and Economics Yunnan Kunming China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Yunnan Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China School of Information Yunnan University of Finance and Economics Yunnan Kunming China

ISBN: (纸本)9798400717253

With the rapid advancements in medical imaging technologies such as CT, MRI, PET, and ultrasound, these modalities have become pivotal for precise clinical diagnoses and treatment planning. Notably, they facilitate accurate patient evaluations and expedite early disease detection and targeted interventions. However, the voluminous nature of medical image data, coupled with stringent privacy requirements, poses significant challenges to efficient and secure storage and sharing within the realm of big data management. In light of these complexities, this paper introduces a blockchain-centric framework that innovatively integrates InterPlanetary File System (IPFS), cloud storage, AES and blockchain technologies. The framework is meticulously designed to address the dual objectives of secure storage and streamlined sharing of medical image data. A key novelty lies in the strategic differentiation of cloud service roles: the cloud's "chain-on" nodes participate in blockchain's validation and consensus processes without engaging in direct data management or operations, whereas "chain-off" nodes are tasked with establishing transient channels for data transmission upon successful validation of user requests by their chain-on counterparts. In practice, medical image owners encrypt the data using IPFS and create indexes derived from examination reports, facilitating keyword-based searches for data users. Moreover, a Hybrid Encryption scheme with Multiple Public Keys is implemented, necessitating users to utilize their blockchain management system-generated private keys to unlock the AES decryption key for image data access. This multi-faceted technology integration approach not only fortifies the security of storage and ensures privacy preservation but also streamlines retrieval processes and bolsters permission management for medical image data. Therefore, it presents a pioneering solution for medical image data storage and sharing. © 2024 Copyright held by the owner/author

关键词： Storage management

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Security of Medical Multimodality Images for Telemedicine Using PDESNet Encryption Standard

Security of Medical Multimodality Images for Telemedicine Us...

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International Conference on Futuristic Technologies (INCOFT)

作者： P. Shyamala Madhuri D. J. Nagendra Kumar B. Amutha Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India Department of Information Technology Vishnu Institute of Technology Bhimavaram Andhra Pradesh India

The telemedicine approach is a famous medical assistance service in worldwide, in this process diagnosis records like Scan reports, X-ray, CT and all other radiology reports analysis has been done with doctors. Many times, sending and receiving of these images from one point to many points, due to this security has been reduced. The multimodality images are major key diagnosis elements to detect disease of patients, these multimodality images security also been reduced due to low encryption techniques. In this work an advanced cryptographic encryption technique i.e Parallel Data encryption Standard (PDESNet) algorithm. The PDES is a modern novel algorithm can provide security to multimodal images from transmission to storage part. Finally summarize the proposed algorithm with existed models in terms of PSNR, BER, NC, MSE, accuracy and recall.

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Optimized Reading for ADHD and Dyslexia Users In VR

Optimized Reading for ADHD and Dyslexia Users In VR

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Computer Science, Computer engineering, & Applied computing (CSCE), Congress in

作者： Kyler Hoang Leonidas Deligiannidis School of Engineering Wentworth Institute of Technology Boston MA USA School of Computing and Data Science Wentworth Institute of Technology Boston MA USA

To combat the rising population of ADHD and dyslexic patients, current advancements in Unity and Oculus Integration are investigated and employed to design a user-friendly reading experience. Unlike physical novels that lack visual aid, our prototype ensures a distraction-free and minimalistic VR environment. It utilizes *** speech recognition to aid ADHD and dyslexic patients in mechanical and comprehensive reading. Users can also change the background color and music to enhance their facilitated experience. This approach allows the user to constantly focus on the text without averting their gaze. We believe that further development of this prototype can create a powerful tool that redefines reading E-books.

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Gated Fusion and Dual Attention Network for Road Extraction from Satellite Imagery 8

Gated Fusion and Dual Attention Network for Road Extraction ...

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8th International Conference on Image, Vision and computing, ICIVC 2023

作者： Wang, Haijuan Bai, Lin Ji, Bo Shen, Jiaming Jin, Ying Ye, Fei Zhu, Chunlin School of Electronics and Control Engineering Chang'An University Xi'an China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd. Hangzhou China Xi'An Aeronautics Computing Technique Research Institute Xi'an China

ISBN: (纸本)9798350335231

Automatic recognition and extraction of roads from high-resolution satellite images is a crucial task in remote sensing and computer vision. With the continuous development of remote sensing technology, more ground object information is contained in images, making it more challenging to extract roads due to increased interference. This paper proposes a gated fusion and dual attention network with an encoder-decoder structure to address this problem. The full-stage gated fusion module in skip connection selectively fuses feature maps of different scales using the Gated Fusion (GFF) unit, which increases the receptive field of the network and improves the accuracy of road extraction. The context extraction and dual attention module introduce rich global information and simultaneously weights features from both spatial and channel dimensions. This improves the semantic segmentation problem caused by focusing only on local information in current road extraction models. Experimental results on two public datasets show that GFDANet can effectively extract roads in complex scenes. © 2023 IEEE.

关键词： Roads and streets

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Analysis of Cryptocurrency Mining in Gaming Consoles 26th

Analysis of Cryptocurrency Mining in Gaming Consoles

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26th annual International Conference on Advanced computing and Communications, ADCOM 2020

作者： Shanmuga Skandh Vinayak, E. Bhalaji, N. Gao, Xiao-Zhi Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Tamil Nadu Chennai India School of Computing University of Eastern Finland Kuopio Finland

ISBN: (纸本)9789811900181

Ever since the invention of the bitcoin cryptocurrency in the year 2009, many blockchain ledgers have been initiated by several parties, offering over 1600 different types of cryptocurrencies all around the world by them. This increase in popularity has not only moved the masses toward performing trade using these currencies but also start earning them through mining with sophisticated computing hardware. In this article, an analysis is performed to estimate the performance and the profitability of mainstream gaming consoles, developed by the Sony Entertainment Company and Microsoft Corporation when subjected to mine five different types of cryptocurrencies (Bitcoin, Bitcoin Cash, Dogecoin, Litecoin, Dash). The article also provides real-time test results on the mining performance and profitability of the consoles under study. Although the Microsoft Corporation consoles can provide a monthly profit of 0.00096 and 0.00319% of the initial investment, an overall net loss of over 80% of the initial investment is observed in all the consoles. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Profitability

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Buck Boost Converter Control in an IoT-Based Wind System Using Supervised Learning Technique 1

Buck Boost Converter Control in an IoT-Based Wind System Usi...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Anbuselvan, N. Hemalatha, S. Lakshminarayanan, Suresh Sivanantham, E. Mohankumar, N. Thamizhamuthu, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India University of Technology and Applied Sciences - Almusanna Oman Department of Engineering and Electronics South Al Batina Oman Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B.V Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Srm Institute of Science and Technology Kattankulathur Campus-Chengalpattu Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

In recent years, distributed energy resources (DERs) have gained significance as a way of introducing a substantial quantity of renewable energy sources (RESs) into power grids. Wind turbines are accessible in windy areas, giving wind energy systems (WES) a realistic alternative for clients. This work attempts to increase the effectiveness and adaptability of WES by presenting a novel way of managing DC-DC buck-boost converters. The suggested method combines an Internet of Things (IoT) system for monitoring with supervised learning algorithms to make energy conversion more stable, efficient, and able to adapt in real time to changing wind conditions. In this research, creating a reliable supervised learning control method is the primary goal. The second goal is to optimize the most important parameters of the converter. Using the new supervised learning algorithm, the study changes variables like duty cycle, switching frequency, and inductance to get the best results based on past performance data and how the system is working now. It investigates the influence of these parameters on overall system efficiency and stability. The incorporation of an IoT monitoring system into the WES is an essential component of the study. Various sensors monitor variables such as wind speed, converter input and output voltages, and currents in real-time and are shown as results. © 2024 IEEE.

关键词： Buck-Boost converter

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Byzantine-Resilient Federated Learning Leveraging Confidence Score to Identify Retinal Disease

Byzantine-Resilient Federated Learning Leveraging Confidence...

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2023 International Conference on Digital Image computing: Techniques and Applications, DICTA 2023

作者： Eshan, M Sakib Osman Nafi, Md. Naimul Huda Sakib, Nazmus Emon, Mehedi Hasan Reza, Tanzim Parvez, Mohammad Zavid Barua, Prabal Datta Chakraborty, Subrata Brac University Department of Computer Science and Engineering Bangladesh Charles Sturt University School of Computing Mathematics and Engineering Australia University of Southern Queensl School of Management & Enterprise Australia Cogninet Australia Pty Ltd Level 5 29-35 Bellevue St Surry HillsNSW2010 Australia University of New England School of Science and Technology Australia

ISBN: (纸本)9798350382204

Federated learning is a distributed machine learning paradigm that enables multiple actors to collaboratively train a common model without sharing their local data, thus addressing data privacy issues, especially in sensitive domains such as healthcare. However, federated learning is vulnerable to poisoning attacks, where malicious (Byzantine) clients can manipulate their local updates to degrade the performance or compromise the privacy of the global model. To mitigate this problem, this paper proposes a novel method that reduces the influence of malicious clients based on their confidence. We evaluate our method on the Retinal OCT dataset consisting of age-related macular degeneration and diabetic macular edema, using InceptionV3 and VGG19 architecture. The proposed technique significantly improves the global model's precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) for both InceptionV3 and VGG19. For InceptionV3, precision rises from 0.869 to 0.906, recall rises from 0.836 to 0.889, and F1 score rises from 0.852 to 0.898. For VGG19, precision rises from 0.958 to 0.963, recall rises from 0.917 to 0.941, and F1 score rises from 0.937 to 0.952. © 2023 IEEE.

关键词： Computer vision

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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

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Science China(Information Sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph \({\cal C}{\cal O}{\cal N}{\cal G}{\cal E}{\cal S}{\cal T}\) model communication complexity round complexity

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