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A user-adaptive privacy-preserving authentication of IoMT using zero knowledge proofs with ECC

Multimedia Tools and Applications 2025年 1-32页

作者： Misra, Garima Hazela, Bramah Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science & Engineering Pranveer Singh Institute of Technology Utter Pradesh Kanpur India

The Internet of Things (IoT) has developed into a crucial component for meeting the connection needs of the current smart healthcare systems. The Internet of Medical Things (IoMT) consists of medical devices that are connected to patients in order to sense their medical parameters and exchange that information with healthcare professionals so they can deliver remote healthcare services. The need to protect the patient’s private information from being misused makes IoMT security an urgent issue that merits further study. Elliptic Curve cryptography (ECC), a popular substitute for traditional public key encryption like RSA, has several advantages. ECC is a prime contender for implementation on constrained devices with confined memory and speed, as well as wireless communication protocols that use low power like IoMT environment. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the IoMT that is based on elliptic curve encryption based zero-knowledge proof (ZKP) protocols. The proposed mechanism completes in order of milliseconds up to 300 ms for 160 bits. The research will include the comparison between computational delays in traditional protocols and ZKP-ECC protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP-ECC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Authentication Protocol

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Road Surface Analysis through Machine Learning Techniques

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IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 344-353页

作者： Singh, Prabhat Sharma, Shilpi Kamal, Ahmed E. Kumar, Sunil Department of Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India Department of Electrical and Computer Engineering Iowa State University Ames United States Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India

Roads are an important part of transporting goods and products from one place to another. In developing countries, the main challenge is to maintain road conditions regularly. Roads can deteriorate from time to time. Monitoring the conditions of the roads, which may degrade with time, is very difficult, resulting in a delay in transportation and damage to the vehicles moving on the roads. Poor road conditions cause road accidents. A model is being proposed to monitor the conditions of the road surface by smartphone sensors. Accelerometer, gyroscope, and GPS sensors are deployed in the mobile phones, which will help to collect data on the road conditions. After collecting the data about the road conditions, various machine learning approaches, such as supervised, multi-layered, and multiclass, are applied to data filtration. Road conditions are divided into three categories to achieve this methodology: potholes, deep traverse cracks, and smooth roads. This categorization helped in analyzing the road surface condition through smartphone sensors over all three axes instead of taking it over a single axis. Neural networks helped analyze data or road conditions more accurately than Decision Tree and SVM. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Decision trees

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LIFT: Lightweight Incremental and Federated Techniques for Live Memory Forensics and Proactive Malware Detection

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International Journal of Advanced computer science and Applications 2025年第4期16卷 439-448页

作者： Dangi, Sarishma Ghanshala, Kamal Sharma, Sachin Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Amity School of Engineering and Technology Amity University Punjab Mohali India

Live Memory Forensics deals with acquiring and analyzing the volatile memory artefacts to uncover the trace of inmemory malware or fileless malware. Traditional forensics methods operate in a centralized manner leading to a multitude of challenges and severely limiting the possibilities of accurate and timely analysis. In this work, we propose a decentralized approach for conducting live memory forensics across different devices. The proposed federated learning-based live memory forensics model uses FedAvg algorithm in order to make a lightweight, incremental approach to conduct live memory forensics. The study demonstrates the performance of federated learning algorithms in anomaly detection, achieving a maximum accuracy of 92.5% with Clustered Federated Learning (CFL) while maintaining a convergence time of approximately 35 communication rounds. Key features such as CPU usage and network activity contributed over 85% to the detection accuracy, emphasizing their importance in the predictive process. © (2025), (science and Information Organization). All rights reserved.

关键词： Federated learning

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A recover-then-discriminate framework for robust anomaly detection

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science China(Information sciences) 2025年第4期68卷 300-318页

作者： Peng XING Dong ZHANG Jinhui TANG Zechao LI School of Computer Science and Engineering Nanjing University of Science and Technology Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology

Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.

关键词： recovery network HOG prompt discriminative network self-correlation loss anomaly detection

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Yoga pose classification: a CNN and MediaPipe inspired deep learning approach for real-world application

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16551-16562页

作者： Garg, Shubham Saxena, Aman Gupta, Richa Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

Yoga is a centuries-old style of exercise followed by sports personnel, patients, and physiotherapist as their regime. A correct posture and technique are the key points in yoga to reap the maximum benefits. Hence, developing a model to classify yoga postures correctly is a recently emerging research topic. The paper presents a novel architecture that aims to classify various yoga poses. The proposed model estimates and classifies yoga poses into five broad categories with low latency. In the proposed architecture, the images are skeletonized before inputting into the model. The skeletonization process is done using the MediaPipe library for body keypoint detection. The paper compares the performance of various deep learning models with and without skeletonization. Different learning models showed the optimum result with the training of skeletonized images to the network. The comparison is drawn to establish the positive impact of skeletonization on the results obtained by various models. VGG16 achieves the highest validation accuracy on non-skeletonized images (95.6%), followed by InceptionV3, NASNetMobile, YogaConvo2d (proposed model) (89.9%), and lastly, InceptionResNetV2. In contrast, the proposed model YogaConvo2d using skeletonized images reports a validation accuracy of 99.62%, followed by VGG16, InceptionResNetV2, NASNetMobile, and InceptionV3. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： computer vision

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Red deer algorithm to detect the secret key of the monoalphabetic cryptosystem

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Soft Computing 2024年第17-18期28卷 10569-10582页

作者： Jain, Ashish Bansal, Sulabh Das, Nripendra Narayan Gupta, Shyam Sunder Department of Information Technology School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior474005 India

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Tabu search

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Proposed AES-based Obfuscation and Encryption Model for Cloud Data Security 1

Proposed AES-based Obfuscation and Encryption Model for Clou...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Singh, Prabhdeep Singh, Pawan Agarwal, Abhay Kumar Kumar, Anil Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow India Kamla Nehru Institute of Technology Department of Computer Science and Engineering Sultanpur India Amity University Amity School of Engineering and Technology Lucknow India

ISBN: (纸本)9798331529246

Cloud Computing (CC) offers a diverse range of services along with huge data storage across a network. CC has collaborated with varied emerging technologies like IoT because of its numerous advantages. Despite CC's benefits, cloud-based servers are not always trustworthy in real-world scenarios. Users' privacy will be compromised if they send their data straight to cloud platforms since crucial information will be exposed to cloud servers. Thereby, this work develops a data security model in the cloud with two stages, proposed secured data storage and proxy re-encryption. Initially, the data is stored securely in the cloud based on improved AES-based encryption as well as obfuscation-based encryption. Based on the request from the data user, proxy re-encryption is done using Kronecker product-based encryption. The enhanced AES achieved the lowest value of 0.025 for the CCA attack with a key size of 128 bits. Thus, the final encoded key is obtained. © 2025 IEEE.

关键词： Cloud platforms

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An Enhanced Aspect-Based Sentiment Analysis Model Based on RoBERTa For Text Sentiment Analysis

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 193-202页

作者： Chauhan, Amit Sharma, Aman Mohana, Rajni Jaypee University of Information Technology HP Solan173234 India Department of Computer Science Amity School of Engineering and Technology Amity University Punjab Punjab Mohali140306 India

Using an aspect-based sentiment analysis task, sentiment polarity towards specific aspect phrases within the same sentence or document is to be identified. The process of mechanically determining the underlying attitude or opinion indicated in the text is known as sentiment analysis. One of the most important aspects of natural language processing is sentiment analysis. The RoBERTa transformer model was pretrained in a self-supervised manner using a substantial corpus of English data. This means it was pretrained solely with raw texts and an algorithmic process to generate inputs and labels from those texts. No human labelling was involved, allowing it to utilise a vast amount of publicly available data. The authors of this work provide a thorough investigation of aspect-based sentiment analysis with RoBERTa. The RoBERTa model and its salient characteristics are outlined in this work, followed by an analysis of the model's optimisation by the authors for aspect-based sentiment analysis. The authors compare the RoBERTa model with other state-of-the-art models and evaluate its performance on multiple benchmark datasets. Our experimental results show that the RoBERTa model is effective for this important natural language processing task, outperforming competing models on sentiment analysis tasks. Based on the SemEval-2014 variant benchmarking datasets, the restaurant and laptop domains have the highest accuracy, scoring 92.35 % and 82.33 %, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Natural language processing systems

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HViTML: Hybrid vision transformer with machine learning-based classification model for glaucomatous eye

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Multimedia Tools and Applications 2025年 1-24页

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Batra, Devanshu Chaurasia, Brijesh Kumar Department of Information Technology Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Babu Banarasi Das University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

Glaucoma is currently one of the most significant causes of permanent blindness. Fundus imaging is the most popular glaucoma screening method because of the compromises it has to make in terms of portability, size, and cost. In recent years, convolution neural networks (CNNs) have revolutionized computer vision. Convolution is a "local" CNN technique that is only applicable to a small region surrounding an image. Vision Transformers (ViT) use self-attention, which is a "global" activity since it collects information from the entire image. As a result, the ViT can successfully gather distant semantic relevance from an image. This study examined several optimizers, including Adamax, SGD, RMSprop, Adadelta, Adafactor, Nadam, and Adagrad. With 1750 Healthy and Glaucoma images in the IEEE fundus image dataset and 4800 healthy and glaucoma images in the LAG fundus image dataset, we trained and tested the ViT model on these datasets. Additionally, the datasets underwent image scaling, auto-rotation, and auto-contrast adjustment via adaptive equalization during preprocessing. The results demonstrated that preparing the provided dataset with various optimizers improved accuracy and other performance metrics. Additionally, according to the results, the Nadam Optimizer improved accuracy in the adaptive equalized preprocessing of the IEEE dataset by up to 97.8% and in the adaptive equalized preprocessing of the LAG dataset by up to 92%, both of which were followed by auto rotation and image resizing processes. In addition to integrating our vision transformer model with the shift tokenization model, we also combined ViT with a hybrid model that consisted of six different models, including SVM, Gaussian NB, Bernoulli NB, Decision Tree, KNN, and Random Forest, based on which optimizer was the most successful for each dataset. Empirical results show that the SVM Model worked well and improved accuracy by up to 93% with precision of up to 94% in the adaptive equalization preprocess

关键词： Decision trees

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of Electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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