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DTAIS:Distributed trusted active identity resolution systems for the Industrial Internet

Digital Communications and Networks 2024年第4期10卷 853-862页

作者： Tao Huang Renchao Xie Yuzheng Ren F.Richard Yu Zhuang Zou Lu Han Yunjie Liu Demin Cheng Yinan Li Tian Liu State Key Lab.of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijingChina Department of Systems and Computer Eng. Carleton UniversityOttawaONCanada

In recent years,the Industrial Internet and Industry 4.0 came into *** the development of modern industrial intelligent manufacturing technology,digital twins,Web3 and many other digital entity applications are also *** applications apply architectures such as distributed learning,resource sharing,and arithmetic trading,which make high demands on identity authentication,asset authentication,resource addressing,and service ***,an efficient,secure,and trustworthy Industrial Internet identity resolution system is ***,most of the traditional identity resolution systems follow DNS architecture or tree structure,which has the risk of a single point of failure and DDoS *** they cannot guarantee the security and privacy of digital identity,personal assets,and device *** we consider a decentralized approach for identity management,identity authentication,and asset *** this paper,we propose a distributed trusted active identity resolution system based on the inter-planetary file system(IPFS)and non-fungible token(NFT),which can provide distributed identity resolution *** we have designed the system architecture,identity service process,load balancing strategy and smart contract *** addition,we use Jmeter to verify the performance of the system,and the results show that the system has good high concurrent performance and robustness.

关键词： Industrial Internet NFT IPFS Trust Identity resolution system

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Detection and Prevention of Black Hole Attack using Tree Hierarchical Deep Convolutional Neural Network and Enhanced Identity-based Encryption in Vehicular Ad Hoc Network

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IEIE Transactions on Smart Processing and Computing 2024年第1期13卷 41-50页

作者： Narayanan, K. Lakshmi Naresh, R. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamilnadu Chennai603203 India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamilnadu Chennai603203 India

A method for the recognition and prevention of a black hole attack is proposed using a tree hierarchical deep convolutional neural network (THDCNN)and enhanced identity based encryption in a vehicular ad hoc network (VANET). Automobiles are organized with a cluster formula using an improved k-means clustering algorithm. Following the cluster formation, cluster head (CH) selection is done using a balancing composite motion optimization (BCMO) algorithm. After selecting the cluster head, the entrance of a spiteful node occurs in the cluster. The THDCNN is proposed for classifying a cluster node as i) a black hole attack node or ii) a normal node. If a black hole attack node is found, the attack node information is communicated to an individual CH, which makes a final decision. Otherwise, the standard node data is encrypted with the enhanced identity-based encryption algorithm. Finally, the proposed method prevents the normal node data from the attacker. Thus, the proposed method attains higher accuracy and lower computational time than other methods. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： K-means clustering

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Lung Cancer Detection Using Machine Learning and Deep Learning Models 21

Lung Cancer Detection Using Machine Learning and Deep Learni...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alheeti, Khattab M. Ali Al-Shouka, Tabreer T. Majeed, Saleem Hamad Ahmed, Amjed Abbas College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq College of Computer and Information Technology University of Anbar Computer Sciences Department Anbar Iraq Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9798350374131

technology and artificial intelligence play a significant role in improving healthcare and enable tasks to be automated. In addition, the diseases can be better understood and diagnosed faster, saving time and reducing costs. This study examines the impact of transfer learning models on the effectiveness of deep learning models in classifying lung cancer through the analysis of CT scan images. Additionally, it investigates the relative performance of various machine learning and deep learning models, encompassing Support Vector Machine (SVM) and convolutional neural net-works (CNN) such as InceptionV3, VGG16, Xception, ResNet50, and MobileNetV2, in the early detection of lung cancer based on CT scan images. The SVM model achieved an overall accuracy of 89% after preprocessing, the proposed approach was applied to five pre-trained models (ResNet50, In-ceptionV3, VGG16, Xception, MobileNetV2) using the dataset: Chest CT-Scan;Among the pre-trained CNN models, the Mo-bileNetV2 model achieved the highest accuracy of 98% and the lowest test loss, indicating it performed the best. The Xception model achieved the second-highest accuracy of 97%. The image pre-processing phase plays a significant role in im-proving system performance in terms of improving image contrast and increasing processing speed. © 2024 IEEE.

关键词： Support vector machines

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Real-Time On-Site Ground-Motion Prediction Using ResNet

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Seismological Research Letters 2025年第3期96卷 1834-1845页

作者： Yang, Tzu-Yi Chen, Da-Yi Chin, Tai-Lin Mobile Computing and Networking Lab Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Seismological Center of Central Weather Administration Taipei Taiwan

On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023. © Seismological Society of America.

关键词： Convolutional neural networks

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An efficient extraction of the maximum power in a photovoltaic system based on enhanced P&O-Fuzzy logic controller

An efficient extraction of the maximum power in a photovolta...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Elzein, I.M. Kurdi, M. Department of Telecommunication and Networking Engineering University of Doha for Science and Technology Doha Qatar Department of Computer Science City University Lebanon

ISBN: (纸本)9781032803722

The concept of solar cell is changing the world in obtaining a clean energy that is an environment user friendly source of energy. Solar arrays process and convert the irradiance which is representing the quantity of light that comes from sun to a usable energy. The maximum power point which is denoted by Maximum power (Pmax) can be efficiently extracted from solar panels through the implementation of a tracking system denoted by the maximum power point tracking algorithm (MPPT). Such a tracking system is well known and utilized in detecting the ultimate maximum power that can be taken from the PV solar arrays under the circumstances of the various changes in both the temperature and irradiance. Through this analysis study, the Perturb and Observe (P&O) algorithm will be proposed to adjust the perturbation steps that will be controlled by an enhanced fuzzy logic controller. It will utilize the fuzzy concepts to extract the maximum power. In this research paper an analysis will be conducted to find the unknown parameters of the PV module along in selecting the appropriate set of fuzzy rules/fuzzy table as well as introducing a high gain dc-dc boost converter to improve the efficiency of power, response time, and reducing the oscillation around the Pmax in the proposed photovoltaic system. © 2025 The Author(s).

关键词： Solar irradiance

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Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

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Digital Communications and Networks 2022年第5期8卷 843-852页

作者： Dinkisa A.Bulti Jyri Hämäläinen Beneyam B.Haile Yihenew Wondie School of Electrical and Computer Engineering Addis Ababa Institute of Technology(AAiT)Ethiopia Department of Communication and Networking Aalto UniversityFinland

Heterogeneous Networks(HetNets)and cell densification represent promising solutions for the surging data traffic demand in wireless *** dense HetNets,user traffic is steered toward the Low-Power Node(LPN)when possible to enhance the user throughput and system capacity by increasing the area spectral ***,because of the transmit power differences in different tiers of HetNets and irregular service demand,a load imbalance typically exists among different serving *** offload more traffic to LPNs and coordinate the Inter-Cell Interference(ICI),Third-Generation Partnership Project(3GPP)has facilitated the development of the Cell Range Expansion(CRE),enhanced Inter-Cell Interference Coordination(eICIC)and Further enhanced ICIC(FeICIC).In this paper,we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe(LPS)*** solution allows the separation of User Association(UA)functions at the User Equipment(UE)and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength(max-RSS)based UA scheme,where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the *** proposed solution is evaluated using system-level simulations wherein the results correspond to performance changes in different service *** show that our method effectively solves the offloading and interference coordination problems in dense HetNets.

关键词： Heterogeneous networks(HetNets) 5G LTE-AUser association Interference coordination Load balancing Cell range expansion Almost blank subframe Further enhanced ICIC Low-power subframe

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Block chain-based trusted smart contract for secure mobile user payment gateway in e-health systems

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Soft Computing 2024年第17-18期28卷 10139-10151页

作者： Saranya, A. Naresh, R. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu Chennai603203 India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu Chennai603203 India

In other instances, the cloud transfers payment information directly to the merchant’s server without first doing any fraud checks. Block authentication between the cloud and a healthcare merchant server is the goal of this study, which is designed to prevent the deployment of fraudulent servers. Smart contract verification on the blockchain will be used to this end. The cloud assault must be prevented from accessing personal payment and card information. It is via the employment of cryptography methods that this concept of privacy reservation is made possible. The Boolean crossover depth first search (DFS) technique is used to encrypt data in the payment gateway protocol at its inception. When the hospital administrator solicits card data from patients or users, our innovative approach swiftly initiates data encryption using the DFS technique. This method meticulously traverses the data structure in a depth-first manner, employing Boolean operations to systematically encrypt sensitive information. Through this process, the patient's payment and card data undergo secure transformation, rendering it unreadable to unauthorized entities. Additionally, an authentication key is generated concurrently with the encryption process, enabling verification between the system and the user before any data transmission occurs. The integration of the DFS technique serves as a fundamental layer of defense against potential cloud-based attacks. Its application ensures the utmost protection of personal payment and card details throughout the transaction process, bolstering the security infrastructure of our proposed system. A comprehensive analysis comparing our approach with existing methods showcases the efficiency and reliability of our proposed system in terms of Execution Time (ms), Encryption Time (ms), Decryption Time (ms), and Memory (bits). This study aims to bridge the gap in transactional security, ensuring robust protection against unauthorized access and data breaches in

关键词： Smart contract

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An energy-efficient task and virtual machine placement in virtualised cloud server using FY-SFLA and RMMS-DLVQ

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International Journal of networking and Virtual Organisations 2023年第2期29卷 144-167页

作者： Sudhakar, E.P. Saravanan, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Creating infrastructures, virtual servers, computing resources, along with devices is termed virtualisation. In this methodology, to augment resource usage along with to mitigate the total power consumption, mapping of a group of virtual machine (VM) onto a set of physical machines (PM) is performed in a data centre (DC). Nevertheless, a crucial challenge is presented by the VM allocation together with the higher energy consumption (EC) of cloud data centres (CDC). Thus, to alleviate the resource wastage along with to mitigate the DCs' EC, an effectual Fisher Yates-Shuffled frog leaping algorithm (FY-SFLA) is proposed here: 1) task feature extraction;2) resource information extraction;3) task separation by utilising cosine distance - K means algorithm (CD-KMA);4) task placement in VM by employing the FY-SFLA task;5) VM status identification by deploying random mutation monkey search deep learning vector quantisation (RMMS - DLVQ) are '5' phases comprised in the proposed methodology. © 2023 Inderscience Enterprises Ltd.

关键词： Virtual machine

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Single- and multi-GPU computing on NVIDIA- and AMD-based server platforms for solidification modeling application

Single- and multi-GPU computing on NVIDIA- and AMD-based ser...

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作者： Halbiniak, Kamil Meyer, Norbert Rojek, Krzysztof Department of Computer Science Czestochowa University of Technology Czestochowa Poland Poznan Supercomputing and Networking Center Poznan Poland

This work explores the performance of single- and multi-GPU computing on state-of-the-art NVIDIA- and AMD-based server-class hardware using various programming interfaces to accelerate a real-world scientific application for solidification modeling based on the phase-field method. The main computations of this memory-bound application correspond to 20 stencils computed across grid nodes. We investigate the application's scalability for two basic schemes of organizing computation: without and with hiding data transfers behind computation, combined with using either peer-to-peer inter-GPU data transfers through NVIDIA NVLink and AMD Infinity interconnects or communication over the PCIe and main memory. Among the studied programming interfaces is CUDA, HIP, and OpenMP Accelerator Model. While the first two are designed to write the codes for a specific hardware platform, OpenMP enables code portability between NVIDIA and AMD GPUs. The resulting performance is experimentally assessed on computing platforms containing NVIDIA V100 (up to 8 GPUs) and A100 (one GPU), as well as AMD MI210 (one device) and MI250 (up to 8 logical GPUs). © 2023 John Wiley & Sons Ltd.

关键词： Graphics processing unit

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A Transfer Learning for Intelligent Prediction of Lung Cancer Detection

A Transfer Learning for Intelligent Prediction of Lung Cance...

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2023 Al-Sadiq International Conference on Communication and Information technology, AICCIT 2023

作者： Al-Shouka, Tabreer T. Alheeti, Khattab M. Ali University of Anbar College of Computer and Information Technology Department of Computer Sciences Anbar Iraq Technology University of Anbar College of Computer and Information Department of Computer Networking Systems Iraq

ISBN: (纸本)9798350341881

Cancer is a widespread and potentially lethal illness. There are many different types of cancer. The most common type of cancer is lung cancer. High mortality rates from lung cancer are major public health concerns. Therefore, early detection of cancer reduces the possibility of dying from the disease. A CT scan is used to determine the best course of treatment. Screening with CT has been shown to reduce mortality from lung cancer by detecting the disease at its earliest stages when it manifests as pulmonary nodules. Artificial Intelligence (AI) systems, which employ transfer-learning models, have shown great potential in enhancing the precision and speed of lung cancer diagnosis and treatment. Transfer learning enables adapting pre-existing models to new tasks, facilitating more efficient and effective medical data analysis. Additionally, these systems can assist clinicians in identifying the specific type and stage of lung cancer, which can guide treatment decisions. Our study explores the use of CNN transfer learning with RESNET, MobileNetV2, Xception, and VGG16 models for medical image analysis tasks;res-net achieved the highest testing accuracy in our research, at 0.94, with a testing loss of0.16, which has demonstrated the potential to enhance the accuracy and efficiency of healthcare AI systems. © 2023 IEEE.

关键词： Health care

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