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Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8639-8658页

作者： Karthika, K. Rangasamy, Devi Priya Anna University Chennai India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time. © 2024 – IOS Press. All rights reserved.

关键词： Sensitive data

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Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-6页

作者： Panigrahi, Bhawani Sankar Kanna, R. Kishore Das, Pragyan Paramita Sahoo, Susanta Kumar Dutta, Tanusree Department of Computer Science & Engineering GITAM Institute of Technology GITAM University Vishakhapatnam India Chennai India Department of Computer Science & Engineering Silicon Institute of Technology Odisha Bhubaneswar India Odisha Sarang India Hyderabad India

INTRODUCTION: Cloud computing, a still emerging technology, allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centers, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption. METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment. RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts. CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies. © (2024) Panigrahi, B.S. et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Machine learning

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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Thoracic-net: Explainable artificial intelligence (XAI) based few shots learning feature fusion technique for multi-classifying thoracic diseases using medical imaging

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Multimedia Tools and Applications 2025年第9期84卷 5397-5433页

作者： Bhosale, Yogesh H. Patnaik, K. Sridhar Zanwar, S.R. Singh, Saket Kr. Singh, Vandana Shinde, U.B. Department of Computer Science & Engineering Birla Institute of Technology Ranchi Mesra India India India India

Background & Need: The early detection of thoracic diseases and COVID-19 (coronavirus disease) significantly limits propagation and increases therapeutic outcomes. This article focuses on swiftly distinguishing COVID-19 patients with 10 chronic thoracic illnesses from healthy examples. The death rates of COVID-19-confirmed patients are rising due to chronic thoracic illnesses. Method: To identify thoracic illnesses (Consolidation, Tuberculosis, Edema, Fibrosis, Hernia, Mass, Nodule, Plural-thickening, Pneumonia, Healthy) from X-ray images with COVID-19, we provide an ensemble-feature-fusion (FFT) deep learning (DL) model. 14,400 chest X-ray images (CXRI) of COVID-19 and other thoracic illnesses were obtained from five public sources and applied UNet-based data augmentation. High-quality images were intended to be provided under the CXR standard. To provide model parameters and feature extractors, four deep convolutional neural networks (CNNs) with a proprietary CapsNet as the backbone were employed. To generate the ensemble-fusion classifiers, we suggested five additional USweA (Unified Stacking weighted Averaging)-based comparative ensemble models as an alternative to depending solely on the findings of the single base model. Additionally, USweA enhanced the models' performance and reduced the base error-rate. USweA models were knowledgeable of the principles of multiple DL evaluations on distinct labels. Results: The results demonstrated that the feature-fusion strategy performed better than the standalone DL models in terms of overall classification effectiveness. According to study results, Thoracic-Net significantly improves COVID-19 context recognition for thoracic infections. It achieves superior results to existing CNNs, with a 99.75% accuracy, 97.89% precision, 98.69% recall, 98.27% F1-score, shallow 28 CXR zero-one loss, 99.27% ROC-AUC-score, 1.45% error rate, 0.9838 MCC, (0.98001, 0.99076) 95% CI, and 5.708 s to test individual CXR. This suggested USweA m

关键词： COVID-19

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Automatic Query Generation Based on Adaptive Naked Mole-Rate Algorithm

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Multimedia Tools and Applications 2025年第16期84卷 16249-16280页

作者： Kinikar, Mayura Saleena, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Pune India

In the growing information retrieval (IR) world, selecting suitable keywords and generating queries is important for effective retrieval. Modern database applications need a sophisticated interface for automatically updating the connections between users and databases. Most database applications are intelligent;however, some may be complex to understand when generating queries for effective retrieval. Therefore, this paper develops a new Adaptive Naked Mole-Rate algorithm (ANMR) for an automatic query generation (AQG) based IR system. The query generation approach primarily generates the query with expanded keywords to enhance IR. Modified sealion optimization (MSO) is applied to select several features. The selected features are combined in the feature fusion process using the Fuzzy based Search and Rescue approach (FSR). Similarity matching is performed using the hybrid cosine and Jaccard similarity measures. At last, the ranking process is performed using the Dynamic Global Local Attention Network based on Capsules (DGLANC). The developed AQG-ANMR system improves the performance of the entire information retrieval system. Then, to analyze the performance, the proposed approach is implemented in the python platform and evaluated in terms of accuracy, recall, precision, and F1-Score by employing TREC-3 and CISI datasets. Besides, the performance of the proposed approach is compared with that of state-of-the-art approaches. Finally, the simulated results clearly showed that the proposed approach outperformed the state-of-the-art approaches better. The maximum accuracy attained by the proposed approach is 97.2% with AQG-ANMR and 94.69% without AQG-ANMR for the TREC 3 dataset. In the same way, using the CISI dataset, the proposed approach reached a maximum accuracy of 98.6% with AQG-ANMR and 95.8% without AQG-ANMR, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Information retrieval

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Bidirectional Legendre memory unit: bidirectional memory for person authentication combining voice and online signature

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Neural Computing and Applications 2025年第3期37卷 1541-1563页

作者： Kumar, Rohitesh Ghosh, Rajib Department of Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Patna800005 India

With the increasing popularity of smart portable electronic gadgets, voice-based online person verification systems have become prevalent. However, these systems are susceptible to attacks where illegitimate individuals exploit the recorded voices of legitimate users, leading to false confirmations—spoofing attacks. To overcome this limitation, this article presents an innovative solution by combining speech and online handwritten signatures to mitigate the risks associated with spoofing attacks in voice-based authentication systems because a person has to be present in front of the system to produce an online handwritten signature. To accomplish this objective, this work proposes a novel bidirectional Legendre memory unit (BLMU), a type of recurrent neural network (RNN), for person authentication (verification) and recognition. The Legendre memory unit (LMU) is an innovative memory cell for RNNs that efficiently retains temporal/non-temporal sequential information over a long period with minimal resources. It achieves information orthogonalization by solving coupled ordinary differential equations (ODEs) and leveraging Legendre polynomials, ensuring effective data representation. The proposed framework for person authentication and recognition comprises seven convolution layers, four BLMU layers, two dense layers, and one output layer. The performance of the proposed BLMU-based deep learning framework has been evaluated on a self-generated/private dataset of combined feature matrix of voice signals and online handwritten signatures in the Devanagari script. To assess performance, experiments have also been conducted using various RNN architectures, such as LSTM, BLSTM, and ordinary differential equation recurrent neural network (ODE-RNN), to have a performance comparison with the proposed BLMU-based deep learning (DL) framework. The results demonstrate the superiority of the proposed BLMU-based DL framework in enhancing the accuracy of person verification systems,

关键词： Recurrent neural networks

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Automated breast nuclei feature extraction for segmentation in histopathology images using Deep-CNN-based gaussian mixture model and color optimization technique

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Multimedia Tools and Applications 2025年 1-27页

作者： Murmu, Anita Kumar, Piyush Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Hematoxylin and eosin (H&E) staining are the key sources for identifying breast cancer patterns with different colors and shapes of nuclei cells for segmenting histopathology nucleus images. In nucleus cells, the color categorization of hematoxylin is stained blue, whereas cytoplasmic cells of eosin are stained pink. This color dissimilarity in the nucleus cells indicates different structures of the cellular and cancer-specific cells, which need to detect on time is a challenge for accurate diagnosis. To overcome this issue, a Deep Convolution Neural Network (Deep-CNN) based Unet framework with Transfer Learning (TL) is proposed for segmenting histopathology breast cancer images. Specifically, the Deep-CNN framework has been used to extract features automatically from input images, and select the features by using the TL technique for minimizing the target tasks to train it faster. Furthermore, a deep convolutional-based Gaussian Mixture Model (GMM) is used to normalize the shapes and merge the nucleus cell pixels for color optimization. The proposed method has been tested on Kaggle’s 2018 Data science Blow histopathology breast image dataset. The experimental results of the proposed scheme have been evaluated by performance evaluation matrices, namely accuracy, Jaccard coefficient, Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and F1-score. The proposed approach outperforms compared with different state-of-the-art models on the same dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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SMART DRIVING LICENSING SYSTEM AND AUTHORIZATION OF AUTONOMOUS VEHICLE INTEGRATING WIRELESS SENSOR NETWORKS AND IoT

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 69-85页

作者： Santhakumar, D. Sudhan, M.B. Prasanna, R. Manikandan, S.P. Prathaban, Banu Priya Kanna, Rajesh R. Saravanan, G. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Ramapuram India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sree Sastha Institute of Engineering and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Tamil Nadu Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This article presents an effective smart Driving Licensing System (SDLS), a digital framework designed to regulate the existing licensing system and authorize licensing for autonomous vehicles. Through a comprehensive assessment process, the system assesses the drivers and the autonomous vehicle’s ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. SDLS integrates high-level security through facial and fingerprint verification for identity verification to preserve licensing data. Hacking and unauthorized access to autonomous vehicles is prevented by robust cybersecurity protocols. The key benefits of SDLS include automated data collection, integration of wireless sensor networks (WSN) and Internet of Things (IoT), smart application processing, real-time license checks, cybersecurity measures, automated testing, and digital certification. The proposed system offers a comprehensive approach to safely integrating autonomous vehicles into our transportation ecosystem. The autonomous vehicles are integrated with antennas at rooftops or into bumpers, depending upon the vehicle design which can be connected with the SDLS for signal communication. Furthermore, the SDLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2024 by Begell House, Inc.

关键词： Autonomous vehicles

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De novo design of polymer electrolytes using GPT-based and diffusion-based generative models

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npj Computational Materials 2024年第1期10卷 119-131页

作者： Zhenze Yang Weike Ye Xiangyun Lei Daniel Schweigert Ha-Kyung Kwon Arash Khajeh Toyota Research Institute Los AltosCAUSA Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMAUSA

Solid polymer electrolytes offer promising advancements for next-generation batteries,boasting superior safety,enhanced specific energy,and extended lifespans over liquid ***,low ionic conductivity and the vast polymer space hinder *** study leverages generative AI for de novo polymer electrolyte design,comparing GPT-based and diffusion-based models with extensive hyperparameter *** evaluate these models using various metrics and full-atom molecular dynamics *** 46 candidates tested,17 exhibit superior ionic conductivity,surpassing existing polymers in our database,with some doubling the conductivity ***,by adopting pretraining and fine-tuning methodologies,we significantly enhance our generative models,achieving quicker convergence,better performance with limited data,and greater *** method efficiently generates a large number of novel,diverse,and valid polymers,with a high likelihood of synthesizability,enabling the identification of promising candidates with markedly improved efficiency and effectiveness for practical applications.

关键词： polymer diffusion conductivity

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Verification of news and certificate, and plagiarism detector

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Multimedia Tools and Applications 2025年 1-28页

作者： Jain, Pushpak Anand, Dewansh Rajni Behal, Vansh Kapoor, Vipul Sharma, Neha Jindal, Neeru Department of Electronics and Communication Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Patiala India

Fake news, Fake certification, and Plagiarism are the most common issues arising these days. During this COVID-19 situation, there are a lot of rumors and fake news spreading and some of us are using fake certification to avoid vaccination and plagiarism became the most common topic during online studies. It is found that several apps and websites are present for the detection of false news and plagiarism. However, there is no single website for the detection of fake certificates. Moreover, nothing is made that consists of all these features in one platform. So, this proposed work develops a website for detecting fake news, Fake certificate, and plagiarism detector. The proposed method uses Natural Language Processing techniques for detecting NPC (news, plagiarism, and certificate). The proposed work uses image processing with some Python libraries like OpenCV in all the detections and that makes it quite interesting. For detecting NPC (news, plagiarism, and certificate) the algorithm is built using a k-mean model, logistic regression, and neural network. The proposed work uses the datasets that are trained using the count vectorizer method for the detection and to get its accuracy which will be tested using machine learning algorithms. The proposed method has shown an accuracy rate of 94.28% on Logistics Regression in comparison to existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fake news Machine learning Natural language processing Plagiarism Social media platform

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