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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Abu-Nab, A.K. Abu-Bakr, A.F. Abushaeer, Zain Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Moscow Institute of Physics and Technology Phystech School of Applied Mathematics and Informatics Moscow141700 Russia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Higher Institute of Engineering and Technology Department of Basic Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350323511

This work presents a numerical investigation of acoustic cavitation in Newtonian fluid based on frequency ultrasound. The model of ultrasonic cavitation dynamics is formulated by a modified rayleigh-Plesset equation and an acoustic pressure equation. The exponential B-Spline collection method was employed to obtain the numerical solutions of the given cavitation bubble model, considering the effect of acoustic pressure. The behaviour of the cavitation microbubbles' radius and the internal pressure of acoustic cavitation were examined at different values of frequencies. The obtained results reveal the importance of frequencies in ultrasound, which play a vital role in changing the behaviour of cavitation bubbles. © 2023 IEEE.

关键词： Numerical methods

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A Comprehensive View on the Progress of Organoid Research with an Emphasis on its Relevance to Disease Characterization

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Biomedical and Environmental Sciences 2023年第10期36卷 959-971页

作者： Chandra KISHORE Vaishali Ji Saurav Mallik Ayan MUKHERJI Namrata TOMAR Soumen Kumar Pati LI Ai Min Sinthia Roy BANERJEE Soumadip GHOSH raza Ali NAQVI University of California Irvine Medical CenterOrangeCA 92868USA Department of Botany J.D.Women’s CollegeBailey RoadPatna-800023India Harvard T.H.Chan School of Public Health BostonMA 02115USA Department of Information Technology Haldia Institute of TechnologyHaldiaWest BengalIndia Medical College of Wisconsin MilwaukeeWI 53226USA Department of Bioinformatics Maulana Abul Kalam Azad University of TechnologyNadiaWest BengalIndia Center for Precision Health School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX 77030USA School of Computer Science and Engineering Xi’an University of TechnologyXi'an 710048ShaanxiChina Department of Information Technology Guru Nanak Institute of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Sister Nivedita UniversityKolkataWest BengalIndia Center for Precision Health School of Biomedical InformaticsUniversity of IllinoisChicago60612USA

INTRODUCTION Organoids are primary tissue or stem cells derived cell aggregates that have the capacity for self-organization,self-renewal,and the capacity to mimic cellular and tissue level *** can overcome the shortc... 详细信息

关键词： functions. overcome shortcomings

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IPFS: An Off-Chain Storage Solution for Blockchain 5th

IPFS: An Off-Chain Storage Solution for Blockchain

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Manpreet Gupta, Shikha Kumar, Deepak raboaca, Maria Simona Goyal, S.B. Verma, Chaman Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Apex Institute of Technology Chandigarh University Mohali India National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Faculty of Information Technology City University London Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789811998751

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File System (IPFS) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFS that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFS, an efficient file sharing system would be created. This paper presents a comprehensive IPFS architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, and Challenges

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IEEE Communications Surveys and Tutorials 2025年

作者： Satpathy, Anurag Sahoo, Manmath Narayan Swain, Chittaranjan Bellavista, Paolo Guizani, Mohsen Muhammad, Khan Bakshi, Sambit Missouri University of Science and Technology Department of Computer Science RollaMO United States National Institute of Technology Visual Surveillance Laboratory Department of Computer Science and Engineering Odisha Rourkela769008 India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India Department of Computer Science and Engineering Italy Machine Learning Department Abu Dhabi United Arab Emirates Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In addition, this survey originally contributes to the literature by proposing a novel taxonomy for existing VNE solutions and providing a thorough comparative study of their strategies. © 1998-2012 IEEE.

关键词： Virtual machine

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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2023 Progress in Applied Electrical engineering, PAEE 2023

作者： Kawala-Sterniuk, Aleksandra Mikolajewski, Dariusz Leiva, Luis A. Ruotsalo, Tuukka Lysiak, Adam Grochowicz, Barbara Jacek Gorzenariczyk, Edward Luckiewicz, Adrian Wieczorek, Anna Pelc, Mariusz Automatic Control and Informatics Opole University of Technology Faculty of Electrical Engineering Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Luxembourg Luxembourg Department of Computer Science Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared. © 2023 IEEE.

关键词： Information filtering

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Enabling Lora energy awareness: A Multihop One-Hop Routing Protocol for Prolonging Network Lifetime

Enabling LoRa energy awareness: A Multihop One-Hop Routing P...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Ioannis Katsidimas Alexandros–Ioannis Manolopoulos Sotiris Nikoletseas Department of Computer Engineering and Informatics University of Patras Patras Greece Department of Physics National and Kapodistrian University of Athens Athens Greece Department of Computer Engineering and Informatics University of Patras and Computer Technology Institute and Press “Diophantus” Patras Greece

IoT applications often rely on battery-powered nodes that may be challenging to recharge, making energy efficiency a crucial concern. In this context, routing protocols play a vital role as they dictate the paths data packets take between nodes, directly influencing the energy consumption of IoT networks. Energy-aware routing protocols have been proposed to address this issue, with the aim of optimizing energy consumption by selecting paths that minimize the energy required for data transmission and reception. These protocols employ various strategies, such as distance-based, traffic-based, and hybrid schemes, to achieve energy efficiency in IoT networks. LoraWAnprotocol has been adopted for various IoT applications with requirements in low-power and extended coverage. Over the recent years attempts have been made to replace the typical star topology employed by Lora with multihop solutions, in an effort to further increase its capabilities. In this paper, we present a multihop algorithm that is based on an energy-balanced routing scheme adopted by Lora nodes to increase energy efficiency and network lifetime. Multiple one-hop routes are configured for critical nodes and a different route is selected in each transmission. Additionally, critical nodes adjust their Spreading Factor and Transmission Power according to the distance of intermediate nodes. Simulation testing results verify the improved performance of our algorithm over the direct link approach.

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SSANet: Side-by-Side Additive Network for Knee Osteoarthritis Severity Detection from X-ray Images 4th

SSANet: Side-by-Side Additive Network for Knee Osteoarthrit...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Tewari, rakhi Bhattacharjee, Debotosh Roy, Hiranmoy Krejcar, Ondrej Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Information Technology RCC Institute of Information Technology Canal South Road Beliaghata Kolkata700015 India Faculty of Informatics and Management Center for Basic and Applied Science University of Hradec Kralove Rokitanskeho 62 Hradec Kralove50003 Czech Republic

ISBN: (纸本)9789819726103

Knee Osteoarthritis (KOA) is becoming one of the most frequent degenerative and irreversible diseases in elderly people. Its early detection and diagnosis is also a difficult and time-intensive task. The recent advances in Machine Learning (ML) and computer Vision (CV) created new paths to automatically detect KOA in the early stages. Detecting and grading the severity of KOA requires a dedicated ML model. This paper proposes a Side-by-Side Additive Network (SSANet)-based model for detecting and grading the severity of KOA. Since KOA affects the knee joint space by narrowing the gap between the joints, we preprocess the X-ray images to detect the Region of Interest (ROI) before the training of the model. From experimental results, it is evidenced that the ROI selection is really improving the detection accuracy of the model. Unlike sequentially connected convolution layer-based deep learning models, the proposed SSANet is based on a parallel convolution layer to reduce the degradation of feature quality. Again, to enhance the edge feature maps, we apply an additive layer that couples the previous layers’ convolutional output. SSANet captures both high-level and low-level features to predict the KOA severity effectively. The presence of a smaller number of layers and fewer numbers of learnable parameters than the existing popular deep learning models, make SSANet a time and space-efficient network. Moreover, the performance of the proposed SSANet in detecting KOA severity is significantly superior to that of other popular state-of-the-art networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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A Novel End-to-End Ensemble UNet Network Based on Edge Computing for Tool Wear Detection 24

A Novel End-to-End Ensemble UNet Network Based on Edge Compu...

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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Chen, Jianwen Tsai, Meng-Shiun Lin, Wan-Ju Hung, Che-Lun Department of Computer Science National Tsing Hua University Hsinchu300044 Taiwan Department of Mechanical Engineering National Taiwan University Taipei106319 Taiwan Department of Mold and Die Engineering National Kaohsiung University of Science and Technology Kaohsung807618 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei11221 Taiwan

ISBN: (纸本)9798400716416

Automated tool wear condition monitoring is essential for ensuring product quality. However, achieving both high detection precision and utilizing edge computing are critical for advancing intelligent manufacturing. Consequently, this article develops a tool wear detection system utilizing edge computing techniques. Firstly, a novel end-to-end ensemble UNet-based model is proposed. Through end-to-end feature fusion training, it effectively addresses the robustness and accuracy issues inherent in single models. Secondly, multiple neural computing sticks are involved to reduce the need for cloud transmission and preserve data privacy, thereby enabling scalability and flexibility in deployment. Experiments demonstrate that mean intersection over union (mIoU) and mean dice coefficient (mDice) in tool wear detection can be improved by over 3% and 2%, respectively. Furthermore, the execution time can be accelerated by a factor of 4. © 2024 ACM.

关键词： Smart manufacturing

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EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images

EfficientNetB0-based Automated Diabetic Retinopathy Classifi...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Ezekiel Alaric Majaw G. Naveen Sundar D. Narmadha Senthil Kumar Thangavel Venkatesan Adeyemi Abel Ajibesin Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy.

关键词： Diabetic retinopathy Renewable energy sources Accuracy Computational modeling Blindness computer architecture Transformers Retina Real-time systems Diseases

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