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Secure SSI framework using the optimized Orkey identity schema

International Journal of Information technology (Singapore) 2024年第6期16卷 4007-4014页

作者： Pandu N, K. Kumar, A. Sasi Department of Computer Science and Engineering Srinivas University Institute of Engineering and Technology Srinivas University Department of Computer Science amd Engineering Yenepoya Institute of Technology VTU

In the era of digital transformation and increasing concerns regarding data privacy, the concept of Self-Sovereign Identity (SSI) has attained substantial recognization. SSI offers individuals greater control over their personal information and reduces reliance on centralized authorities for identity verification. However, existing SSI frameworks face challenges in terms of scalability, privacy, and competence. This paper introduces a noivel Secure SSI Framework based on the Optimized OrKey Identity Schema (OIS) for Inter-organizational business processes (IOBP). The Optimized OrKey Identity Schema optimizes data storage and retrieval, reducing the computational overhead associated with identity verification. The pigeon's communicative and mimicry behavior of pigeon-inspired optimization (PIO) as well as the flamingo's foraging and migratory patterns of the Flamingo Search Algorithm (FSA) served as the basis for the proposed Flamieon-inspired optimization method. A Verifiable Data Credential is introduced to combine the elements of verifiable credentials and the Blockchain method to improve the security and trustworthiness of sharing data and verification in a decentralized manner. The proposed OIS methods achieve a value of 4.00% for Anonymization, 9989.57 for Gas ratio, 7445.70 KB for Memory usage, 17.32% for Network hash rate, 5.07 ms for Transaction latency, and 41,234.82 ms for Transaction Time. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： BC technology Flamieon-inspired optimization OrKey identity schema Self-sovereign identity Verifiable data credential

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A Cloud Secure Storage based AES Method of Dispersion 5

A Cloud Secure Storage based AES Method of Dispersion

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Ajitha Lokesh, N. Kumaran, B. Sivasangari, A. Gomathi, R.M. Roobini, M.S. Sathyabama Institute of Science and Technology School of Computing Chennai India

ISBN: (纸本)9798350376685

The popularity of cloud storage services is due to their convenience and fast access to data. However, security incidents like data leakage continue to occur, often caused by negligence or malicious attacks. To address this problem, a Cloud Secure Storage Mechanism (CSSM) has been proposed. The main objective of CSSM is to safeguard the confidentiality of cloud data by utilizing data dispersion and distributed storage. This approach involves encrypting, breaking down, and distributing data, which helps reduce the likelihood of data breaches at the storage *** utilizes a hierarchical management approach that combines user passwords with secret sharing to ensure the security of cryptographic materials. This method helps prevent the leakage of cryptographic materials and enhances the overall security of the cloud storage system. Experimental results have demonstrated the effectiveness of CSSM in safeguarding data security CSSM is a reliable and efficient solution for maintaining cloud data confidentiality and preventing data leakage at the storage layer. It can store a large amount of data in the cloud without significant time overhead. For instance, uploading or downloading a 5GB file with CSSM takes only 646 and 269 seconds, respectively, which is acceptable for users. Overall, CSSM offers a dependable and efficient method for securing cloud data. © 2024 IEEE.

关键词： Cloud security

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Web-Based Framework for Evaluating Clustering Algorithms Across Multiple Datasets

Web-Based Framework for Evaluating Clustering Algorithms Acr...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tiwari, Gyanendar Kumar Singh, Kshitij Dorathi Jayaseeli, J.D. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331509828

This research study develops a web-based framework to compare clustering algorithms based on multiple datasets, assess the performance of algorithmic based on dataset characteristics and clustering objectives. The framework designed to support such widely used algorithms such as KM, DBSCAN and Hierarchical Clustering, offers visual and quantitative analysis for performance comparison. This platform was designed to make it easier for users to try out various algorithms and different parameter settings and thus, help them to choose the best clustering methods for a range of data types. This work impacts that greater understanding of how effective clustering is based on different data structures and application contexts by providing a useful framework for data driven research and analysis. © 2025 IEEE.

关键词： clustering algorithms DBSCAN Hierarchical Clustering K-Means Web-based framework

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Comparative Analysis of Handwritten Digit Recognition using MLP, CNN, LeNet-5 Model's

Comparative Analysis of Handwritten Digit Recognition using ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Mughesh Kumar, N.R. Joshi, Siddharth Chanduri, Pranava Preethivardhan Sivakumar, S. Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

The analysis of three distinct neural network architectures for Handwritten Digit Recognition (HDR): Multi-Layer Perceptron (MLP), Simple Convolutional Neural Network (CNN), and LeNet-5. The MNIST dataset, an internationally renowned benchmark made up of 70,000 grayscale photos of handwritten numbers, is used to assess the models. To alleviate overfitting and improve model resilience, the data is pre-processed and split into training and validation sets using K-Fold cross-validation. The MLP architecture employs a fully connected layer (FCL) structure with three layers, achieving a training accuracy of 87.12% and a validation accuracy of 95.78% in 115 seconds of training time. The Simple CNN consists of two convolutional layers (CL) and two fully connected layers, yielding, after 395 seconds of training, a validation accuracy of 99.06% and a training accuracy of 99.76%. LeNet-5, with two convolutional layers and three fully connected layers, takes 168 seconds to train and achieves 99.38% training accuracy and 98.89% validation accuracy. Performance evaluation reveals that while the Simple CNN exhibits the highest accuracy (99.15%) on the test set, LeNet-5 offers a more balanced trade-off between training time and accuracy. This work underscores the varying capabilities of MLP, Simple CNN, and LeNet-5 architectures in HDR tasks, providing insights for selecting the appropriate model based on specific application requirements. © 2024 IEEE.

关键词： Convolution

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A Survey of Non-Volatile Main Memory File Systems

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Journal of computer science & technology 2023年第2期38卷 348-372页

作者：王盈贾文庆蒋德钧熊劲 State Key Laboratory of Processors Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China Research Center for Advanced Computer Systems Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Non-volatile memories(NVMs)provide lower latency and higher bandwidth than block ***,NVMs are byte-addressable and provide persistence that can be used as memory-level storage devices(non-volatile main memory,NVMM).These features change storage hierarchy and allow CPU to access persistent data using load/store ***,we can directly build a file system on ***,traditional file systems are designed based on slow block *** use a deep and complex software stack to optimize file system *** design results in software overhead being the dominant factor affecting NVMM file ***,scalability,crash consistency,data protection,and cross-media storage should be reconsidered in NVMM file *** survey existing work on optimizing NVMM file ***,we analyze the problems when directly using traditional file systems on NVMM,including heavy software overhead,limited scalability,inappropriate consistency guarantee techniques,***,we summarize the technique of 30 typical NVMM file systems and analyze their advantages and ***,we provide a few suggestions for designing a high-performance NVMM file system based on real hardware Optane DC persistent memory ***,we suggest applying various techniques to reduce software overheads,improving the scalability of virtual file system(VFS),adopting highly-concurrent data structures(e.g.,lock and index),using memory protection keys(MPK)for data protection,and carefully designing data placement/migration for cross-media file system.

关键词： non-volatile main memory(NVMM) file system performance scalability crash consistency data protection crossmeida

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Interactive tool for visualizing the comprehensive performance of evolutionary multi-objective algorithms applied to problems with two or three objectives

Interactive tool for visualizing the comprehensive performan...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Tomczyk, Michal Kadziński, Milosz Poznan University of Technology Institute of Computing Science Poznań Poland

ISBN: (纸本)9798400704956

The performance of evolutionary algorithms for multi-objective optimization is typically assessed by considering only the final populations. This is troublesome for the following reasons. First, it ignores most solutions constructed throughout the evolution, thus not portraying overall progression. Second, evolutionary methods are susceptible to randomness. Therefore, the results obtained from a single test run are unreliable. Third, these methods are complex algorithms that can be evaluated from many perspectives, not just by assessing the qualities of the final solutions they present. Overcoming these issues motivated the development of our novel visualization tool that accounts for robustness in assessment. It supports both 2D and 3D visualization, and, in the case of the latter, it introduces interactivity, allowing the inspection of presented results in a manner most suited to the user. The developed software is an inherent part of this paper and can be downloaded and re-used by researchers to foster their research. © 2024 held by the owner/author(s).

关键词： evolutionary multi-objective optimization

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Improving Early Detection of Lung Disorders: A Multi-head Self-Attention CNN-BiLSTM Model

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 595-607页

作者： Indumathi, V. Siva, R. School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India

Respiratory system diseases are a leading cause of increased mortality, morbidity, and disability rates globally. Lung disorders occur due to constant exposure of the lungs to harmful agents present in the ambient air. Early diagnosis is the only prevention measure to avoid the risk of health issues. In recent times, numerous technologies exist to treat pulmonary disease in the early stages. However, evaluating pulmonary microstructural variations is considered a challenging task in routine clinical practice. In this paper, we propose a novel automated pulmonary disease prediction system using the convolutional neural network-based bidirectional neural network (CNN-BiLSTM) with multi-head self-attention layer approach to address the above-mentioned issue. We implemented this novel approach on four medical image datasets, namely, chest X-ray 14, contrast-enhanced CT (CECT), PET/CT, and the Japanese Society of Radiological technology (JSRT) databases. The proposed approach predicts diseased instances from the image datasets and classifies them as normal cases and predicted diseases of multiple classes, which include pneumonia, lung cancer, COVID-19, asthma, etc. To attain high classification accuracy, we preprocess the datasets using normalization and standardization pipelines. The existing methods such as DLSA, VDSNet, SLIC, and DNN models offer accuracy of 92.5%, 93.7%, 93.2%, 93.5%, and 91.8%, 93.8%, 94%, and 91% on the chest X-ray and JSRT datasets, respectively. The proposed CNN-BiLSTM with multi-head self-attention layer approach offers an accuracy of 94.9%, 97.8%, 97.9%, and 94.6% in the chest X-ray, PET/CT, CECT, and JSRT datasets, respectively. © The Institution of Engineers (India) 2024.

关键词： Pulmonary diseases

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Improving the spectral purity of single photons by a single-interferometer-coupled microring

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Chinese Physics B 2022年第3期31卷 311-315页

作者： Yang Wang Pingyu Zhu Shichuan Xue Yingwen Liu Junjie Wu Xuejun Yang Ping Xu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410013China

We experimentally engineer a high-spectral-purity single-photon source using a single-interferometer-coupled silicon microring. By the reconfiguration of the interferometer, different coupling conditions can be obtained, corresponding to different quality factors for the pump and signal/idler. The ratio between the quality factor of the pump and signal/idler ranges from 0.29 to 2.57. By constructing the signal–idler joint spectral intensity, we intuitively demonstrate the spectral correlation of the signal and idler. As the ratio between the quality factor of the pump and signal/idler increases, the spectral correlation of the signal and idler decreases, i.e., the spectral purity of the signal/idler photons increases. Furthermore,time-integrated second-order correlation of the signal photons is measured, giving a value up to 94.95 ± 3.46%. Such high-spectral-purity photons will improve the visibility of quantum interference and facilitate the development of on-chip quantum information processing.

关键词： spectral-purity microring asymmetric Mach–Zehnder interferometer

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering

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Research in Astronomy and Astrophysics 2023年第5期23卷 52-65页

作者： Hai-Feng Yang Xiao-Na Yin Jiang-Hui Cai Yu-Qing Yang A-Li Luo Zhong-Rui Bai Li-Chan Zhou Xu-Jun Zhao Ya-Ling Xun Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology North University of ChinaTaiyuan 030051China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China

Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) has completed the observation of nearly 20 million celestial objects,including a class of spectra labeled “Unknown.” Besides low signal-to-noise ratio,these spectra often show some anomalous features that do not work well with current *** this paper,a total of 637,889 “Unknown” spectra from LAMOST DR5 are selected,and an unsupervised-based analytical framework of “Unknown” spectra named SA-Frame(Spectra Analysis-Frame) is provided to explore their origins from different *** SA-Frame is composed of three parts:NAPC-Spec clustering,characterization and origin ***,NAPC-Spec(Nonparametric density clustering algorithm for spectra) characterizes different features in the “unknown” spectrum by adjusting the influence space and divergence distance to minimize the effects of noise and high dimensionality,resulting in 13 ***,characteristic extraction and representation of clustering results are carried out based on spectral lines and continuum,where these 13 types are characterized as regular spectra with low S/Ns,splicing problems,suspected galactic emission signals,contamination from city light and un-gregarious type ***,a preliminary analysis of their origins is made from the characteristics of the observational targets,contamination from the sky,and the working status of the *** results would be valuable for improving the overall data quality of large-scale spectral surveys.

关键词： methods data analysis-surveys-techniques spectroscopic-site testing-methods analytical

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