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Cryptography with optimal deep learning-based authentication scheme for preserving anonymity in telecare medical information system

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Multimedia Tools and Applications 2025年第10期84卷 8311-8330页

作者： Sheik, Syed Amma Durai, Saleem School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India

The Telecare Medicine Information System (TMIS) revolutionizes healthcare delivery by integrating medical equipment and sensors, facilitating proactive and cost-effective services. Accessible online, TMIS empowers patients and practitioners to manage medical data efficiently, ensuring timely care provision and streamlined record maintenance. Recognizing security challenges inherent in this interconnected healthcare landscape, researchers have devised a two-factor authentication solution, increasingly leveraging password-based authentication via smart cards. This article presents the development of a tailored remote authentication system, the Deep Learning-Based Online Threat Detection with Key Agreement (DLOTDKA-MAP), designed specifically for TMIS. Comprising two critical phases, the DLOTDKA-MAP framework employs an Arithmetic Optimization Algorithm (AOA) and Deep Belief Network (DBN) model in real-time online threat detection, ensuring swift identification and mitigation of potential security threats. The subsequent phase employs Elliptic Curve Cryptography (ECC) with Schnorr's signature to establish secure data transmission among collaborating entities, preemptively addressing security breaches and ensuring mutual authentication. A comprehensive experimental study validates the presented DLOTDKA-MAP technique, showcasing its superiority over existing approaches and underscoring its role in advancing TMIS security infrastructure. Achieving remarkable performance values, includes an accuracy, precision, recall, and F1 score of 98.57%, this research significantly contributes to safeguarding the confidentiality and integrity of medical data within TMIS, particularly in a federated cloud environment. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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A Fusion Model for Personalized Adaptive Multi-Product Recommendation System Using Transfer Learning and Bi-GRU

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computers, Materials & Continua 2024年第12期81卷 4081-4107页

作者： Buchi Reddy Ramakantha Reddy Ramasamy Lokesh Kumar School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user *** address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited ***-GRU captures both spatial and sequential dependencies in user-item *** innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant *** approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item *** model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional *** study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.

关键词： Personalized recommendation systems transfer learning bidirectional gated recurrent units(Bi-GRU) performance metrics adaptive systems product reviews

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Iterative learning algorithm of upper limb rehabilitation robot based on BP neural network 12

Iterative learning algorithm of upper limb rehabilitation ro...

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12th International Conference on Intelligent Control and Information Processing, ICICIP 2024

作者： Pang, Zaixiang Deng, Xiaomeng Gong, Linan Wang, Nan Changchun University of Technology School of Engineering Training Center Changchun China Changchun University of Technology School of Computer Science and Engineering Changchun China Changchun Vocational Institute of Technology School of Mechanical and Electrical Engineering Changchun China Jilin Weimiao Medical Technology Co. Ltd Changchun China

ISBN: (纸本)9798350308020

At present, in the trajectory tracking control of upper limb rehabilitation robot, there are nonlinear and uncertain problems of patients' spasticity disturbance. Therefore, an iterative learning control algorithm based on Back Propagation neural network is proposed in this paper. Based on the repeatability of the rehabilitation robot system, a multi-layer feedforward neural network trained by error reverse propagation algorithm is combined with iterative learning. The algorithm can effectively compensate the effect of spasmodic interference on the system performance and improve the convergence rate of trajectory tracking errors. The structure of the iterative learning control system based on neural network and the iterative learning control algorithm based on Back Propagation neural network are described. The process of network selection and robot trajectory tracking iterative learning control is given. Through simulation experiments, the feasibility and effectiveness of the algorithm are verified, which solves the above problems and meets the control requirements of rehabilitation training. © 2024 IEEE.

关键词： Network layers

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Ancient Character Recognition: A Comprehensive Review

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IEEE Access 2025年 13卷 88847-88857页

作者： Krithiga, R. Varsini, S.R. Joshua, R. Gabriel Om Kumar, C.U. Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

Deep learning-based character recognition of Tamil inscriptions plays a significant role in preserving the ancient Tamil language. The complexity of the task lies in the precise classification of the age-old Tamil letters (Vattezhuthu) into modern-day Tamil letter structures. Various methodologies and pre-processing techniques have been used for denoising the ancient Tamil manuscript to retrieve the Tamil text. Researchers have used various synthesized and scanned images of stone wall inscriptions, palm leaves manuscripts, and offline handwritten characters for their analysis. Over the years, Ancient Tamil scripts have deteriorated with time due to various natural calamities. Strong denoising and feature extraction methods are required to separate the letters accurately to tackle this issue. Techniques such as CNN(OCR), ResNet, SVM, KNN, HorVer method, etc., are utilized to digitize Tamil characters. This technique has successfully converted handwritten characters into digitalized text for multiple languages, including Tamil, Arabic, English, Latin, Chinese, German, etc. Different models have been evaluated based on their segmentation and recognition rates, accuracy, detection rate, precision, and confusion matrix. This paper will concentrate on Ancient Tamil character segmentation and recognition models. Besides, we will give an overview of the different models and datasets available. Lastly, we summarise the key challenges and the future scope related to the topic. © 2013 IEEE.

关键词： computer vision

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Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite Networks

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computers, Materials & Continua 2023年第5期75卷 3903-3922页

作者： Rui Xu Xiaoqiang Di Hao Luo Hui Qi Xiongwen He Wenping Lei School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China School of Information Engineering Henan Institute of Science and TechnologyXinxiang453003China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and TechnologyChangchun130022China Information Center Changchun University of Science and TechnologyChangchun130022China Beijing Institute of Spacecraft System Engineering Beijing100094China Beijing Institute of Space Mechanic and Electricity Beijing100094China

In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite *** up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the ***,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache *** address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is *** two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground *** caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of *** cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached *** the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing *** results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.

关键词： Information centric networking caching strategy two-layer caching model hierarchical division

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Highly efficient 1D p-Te/2D n-Bi_(2)Te_(3) heterojunction self-driven broadband photodetector

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Nano Research 2024年第3期17卷 1864-1874页

作者： Chenchen Zhao Dongbo Wang Jiamu Cao Zhi Zeng Bingke Zhang Jingwen Pan Donghao Liu Sihang Liu Shujie Jiao Tianyuan Chen Gang Liu Xuan Fang Liancheng Zhao Jinzhong Wang School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China School of Astronautics Harbin Institute of TechnologyHarbin 150001China Center for High Pressure Science and Technology Advanced Research Shanghai 201203China State Key Laboratory of High Power Semiconductor Lasers Changchun University of Science and TechnologyChangchun 130022China

Broadband photodetectors with self-driven functions have attracted intensive scientific interest due to their low energy consumption and high optical ***,high-performance broadband self-driven photodetectors are still a significant challenge due to the complex fabrication processes,environmental toxicity,high production costs of traditional 3D semiconductor materials and sharply raised contact resistance,severe interfacial recombination of 2D materials and 2D/3D mixed dimension ***,1D p-Te/2D n-Bi_(2)Te_(3) heterojunctions are constructed by the simple and low-cost hydrothermal method.1D p-Te/2D n-Bi_(2)Te_(3) devices are applied in photoelectrochemical(PEC)photodetectors,with their high performance attributed to the good interfacial contacts reducing interface *** device demonstrated a broad wavelength range(365–850 nm)with an Iph/Idark as high as *** R_(i),D^(*),and external quantum efficiency(EQE)values of the device were as high as 12.07 mA/W,5.87×10^(10) Jones,and 41.05%,respectively,which were significantly better than the performance of the prepared Bi_(2)Te_(3) and Te devices.A comparison of the freshly fabricated device and the device after 30 days showed that 1D p-Te/2D n-Bi_(2)Te_(3) had excellent stability with only 18.08%decay of *** is anticipated that this work will provide new emerging material for future design and preparation of a high-performance self-driven broadband photodetector.

关键词： topological insulating states interfacial recombination self-driven 1D p-Te/2D n-Bi_(2)Te_(3) photodetector

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Intrusion detection system and fuzzy ant colony optimization based secured routing in wireless sensor networks

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Soft Computing 2024年第17-18期28卷 10345-10367页

作者： Subramani, Shalini Selvi, M. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Recent advances in wireless sensor networks (WSNs) have brought the sensor based monitoring developments to the surface in many applications. In such a scenario, the security of communication is a major challenge in the effective delivery of the collected data due to the presence of malicious nodes. Moreover, since security and minimization of energy consumption are critical factors in designing techniques for multi-hop secure routing in wireless sensor networks, it is necessary to address the issues of security in the routing process. Hence, this paper proposes a novel intrusion detection system for enhancing the security and Fuzzy based Ant Colony Optimization based Secured Quality of Service Routing Protocol (F-ACO-SQoSRP) for increasing the security of communication and network performance in WSNs. Using this proposed intrusion detection system (IDS), the proposed model identifies the distinct and malicious behaviours of nodes. Additionally, a clustering algorithm has been proposed in this work, wherein the cluster head selection (CHS) is based on quality of service (QoS) measures and the trust values of nodes are measured using the intrusion detection system results. The proposed ACO based routing framework also predicts the best optimum and secured path to allow for effective communication across each link. The simulation results obtained from this work proved that the proposed secured routing algorithm provides better performance in terms of security using robust trust values, increase in packet delivery ratio and network lifetime reduction in delay and energy consumption when this work is compared with the existing secured routing systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Quality of service

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MILD: A Multimodal Biometric Recognition Framework Integrating Large Foundation Models 18th

MILD: A Multimodal Biometric Recognition Framework Integrati...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Lu, Huimin Zhao, Qingxin Zhang, Zexing Ma, Songzhe Lin, Chenglin School of Computer Science and Engineering Changchun University of Technology Changchun130102 China

ISBN: (纸本)9789819610709

Traditional unimodal biometric recognition technologies, wh-ile widely applied across various fields, still face limitations such as environmental interference, spoofing attacks, and individual differences, leading to insufficient accuracy and reliability. Consequently, multimodal biometric recognition technology enhances recognition performance by integrating multiple biometric features. However, effectively merging the semantic information of different modalities remains a key challenge. This paper proposes a multimodal biometric recognition framework with integrated large models (MILD). The framework incorporates foundational large models for audio, language, and images, and innovatively designs modality adapters and multimodal decoders to address the semantic alignment issue of large models. Additionally, MILD uniquely combines voiceprints, electrocardiograms (ECG), and palm prints to enhance the anti-spoofing performance of biometric recognition. Experimental results validate the effectiveness of the MILD framework in cross-modal feature fusion and accurate recognition, demonstrating the potential of foundational large models in complex scenarios, with the highest cross-dataset recognition accuracy reaching 97.65%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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GAN-Based Federated Adversarial Learning for Enhancing Security Towards Consumer Digital Ecosystems

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1102-1114页

作者： Murmu, Anita Kumar, Piyush Namasudra, Suyel Reddy, M Rajasekhar National Institute of Technology Patna Department of Computer Science and Engineering Patna India Bennett University School of Computer Science Engineering and Technology India National Institute of Technology Department of Computer Science and Engineering Patna India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura India Amrita Vishwa Vidyapeetham Amrita School of Computing Andhra Pradesh India

Federated Adversarial Learning (FAL) maintains the decentralization of adversarial training for data-driven innovations while allowing the collaborative training of a common model to protect privacy facilities. Before sharing with bigger global aggregation, it allows users to change settings locally over many iterations. However, a strong network against attackers in Industry 5.0 towards consumer digital ecosystems is a challenge for adversarial training methodologies. To solve this issue, a novel FAL-based Customized Inequality-Aware Federated Learning (CusIAFL) technique is proposed in this paper for classifying and securing color images. The proposed method reduces the instability brought on by the heterogeneity of the data and optimizes each data sample by understanding the client-label distribution. A unique Pix2Pix Generative Adversarial Network (GAN) algorithm is employed to generate realistic images in the presented research work, while a hybrid approach is used to guarantee consistency in the time series data. This innovative research work is evaluated on various non-medical, consumer electronic, and medical imagery. The experimental results have been evaluated using performance metrics, namely accuracy, entropy, Peak Signal-to-Noise Ratio (PSNR), Hausdorff Distance (HD95), Structural Similarity Index (SSIM), and Mean Square Error (MSE). The results show that the proposed technique outperforms the existing models in terms of security. © 1975-2011 IEEE.

关键词： Generative adversarial networks

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OCRBench: on the hidden mystery of OCR in large multimodal models

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Science China(Information Sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

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