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Secure data authentication and access control protocol for industrial healthcare system

Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4853-4864页

作者： Gupta, Daya Sagar Mazumdar, Nabajyoti Nag, Amitava Singh, Jyoti Prakash Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais UP Amethi229304 India Department of Information Technology Indian Institute of Information Technology Allahabad Prayagraj India Department of Computer Science and Engineering Central Institute of Technology Kokrajhar BTR Kokrajhar India Department of Computer Science and Engineering National Institute of Technology Patna Patna India

Because of recent COVID-19 epidemic, the Internet-of-Medical-Things (IoMT) has acquired a significant impetus to diagnose patients remotely, regulate medical equipment, and track quarantined patients via smart electronic devices installed at the patient’s end. Nevertheless, the IoMT confronts various security and privacy issues, such as entity authentication, confidentiality, and integrity of health-related data, among others, rendering this technology vulnerable to different attacks. To address these concerns, a number of security procedures based on traditional cryptographic approaches, such as discrete logarithm and integer factorization problems, have been developed. All of these protocols, however, are vulnerable to quantum attacks. This paper, in this context, presents a data authentication and access control protocol for IoMT systems that can withstand quantum attacks. A comprehensive formal security assessment demonstrates that the proposed algorithm can endure both current and future threats. In terms of data computing, transmission, and key storage overheads, it also surpasses other related techniques. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Authentication

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AniSan@NLU of Devanagari Script Languages 2025: Optimizing Language Identification with Ensemble Learning 1

AniSan@NLU of Devanagari Script Languages 2025: Optimizing L...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Shanto, Anik Mahmud Priya, Sanjida Jamal Arefin, Mohammad Shamsul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891762015

Identifying languages written in Devanagari script, including Hindi, Marathi, Nepali, Bhojpuri, and Sanskrit, is essential in multilingual contexts but challenging due to the high overlap between these languages. To address this, a shared task on "Devanagari Script Language Identification" has been organized, with a dataset available for subtask A to test language identification models. This paper introduces an ensemble-based approach that combines mBERT, XLM-R, and IndicBERT models through majority voting to improve language identification accuracy across these languages. Our ensemble model has achieved an impressive accuracy of 99.68%, outperforming individual models by capturing a broader range of language features and reducing model biases that often arise from closely related linguistic patterns. Additionally, we have fine-tuned other transformer models as part of a comparative analysis, providing further validation of the ensemble’s effectiveness. The results highlight the ensemble model’s ability in distinguishing similar languages within the Devanagari script, offering a promising approach for accurate language identification in complex multilingual contexts. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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Enhancing Interest Message Forwarding in NDN-based VANETs using Black Hole Optimization 2

Enhancing Interest Message Forwarding in NDN-based VANETs us...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Devi, S. Padma Dhanalakshmi, K. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350373110

In Intelligent Transportation Systems (ITS) and smart cities, Vehicular Ad hoc Networks (VANETs) are vital but face challenges due to their dynamic topology, making traditional IP-based content retrieval impractical. Named Data Networking (NDN) offers an alternative by using content names for retrieval, though locating content-holding nodes efficiently in NDN-based VANETs remains difficult. This paper introduces an optimized method using Black Hole optimization to enhance interest message forwarding in NDN-based VANETs, aiming to improve packet delivery ratios and reduce latency. Simulation studies comparing proposed method with classic NDN and Particle Swarm Optimization NDN (PSO-NDN) show that proposed approach significantly outperforms these methods, achieving higher packet delivery ratios and lower latency. This research advances efficient communication strategies in NDN-based VANETs, crucial for robust ITS in smart cities, demonstrating the potential of Black Hole optimization to enhance VANET performance and support smart city infrastructure. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Human Hand Gesture Classification Based on Surface Electromyography Signals by Using a Vector–Kernel Convolutional Takagi–Sugeno–Kang Neuro-fuzzy Classifier

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Sensors and Materials 2024年第2-3期36卷 623-637页

作者： Lin, Cheng-Jian Lin, Chun-Jung Lin, Xin-Wei Department of Computer Science & Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan

Muscle–computer interfaces are devices that can identify the meaning of human bioelectrical signals, such as surface electromyography (sEMG) signals. sEMG signals can be obtained from arm-worn sensors and can be used to classify human gestures. In this paper, we propose a vector–kernel convolutional Takagi–Sugeno–Kang (TSK)-type neuro-fuzzy classifier (VK-CTNFC) to recognize human gestures represented by sEMG signals. First, vector–kernel convolution is used to extract the features of sEMG signals;this modification reduces the model parameters by half and increases the classification accuracy compared with those achieved with a conventional convolutional kernel. Second, the global average pooling method is used instead of the flattening method to improve feature fusion performance. Finally, a TSK-type neuro-fuzzy network is used for gesture classification. The publicly available dataset Ninapro DB1 was used in experiments for verifying the performance of the proposed VK-CTNFC. Data preprocessing was performed by wavelet denoising to smooth the sEMG waveform, and fast Fourier transform was used to convert time-domain sEMG signals into frequency-domain signals. Finally, the processed sEMG signals were input into the VK-CTNFC for training. The experimental results indicate that the proposed VK-CTNFC has an average accuracy of 87.18% and outperforms other reported methods. © MYU K.K.

关键词： Fast Fourier transforms

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Enhancing Indian sign language recognition through data augmentation and visual transformer

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Neural Computing and Applications 2024年第24期36卷 15103-15116页

作者： Singla, Venus Bawa, Seema Singh, Jasmeet Department of Computer Science Thapar Institute of Engineering and Technology Adarsh Nagar Punjab Patiala147004 India

This paper introduces a novel approach to Indian Sign Language Recognition (ISLR) by integrating Keras, Visual Transformers (ViT), and sophisticated data augmentation techniques. Our methodology emphasizes the development of a Vision Transformer model trained on a comprehensive dataset, enhanced with both image data and keypoint information, leveraging the capabilities of the Mediapipe library. To improve the model’s ability to generalize, we applied advanced augmentation strategies, including ImageDataGenerator, among others. Through extensive experimentation, involving rigorous hyperparameter optimization across numerous epochs, we sought to determine the most effective model configuration. The results of our validation process revealed a promising evaluation loss of 0.2941 and an impressive accuracy rate of 97.52%. This integration of data augmentation techniques with ViT transformers establishes a groundbreaking framework in the field of ISLR, underscoring its significant potential for practical implementation and marking a substantial progression in the technology for sign language recognition. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image enhancement

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An Optimised Task Scheduling of Remote Sensing Data Processing for Smart Patient Health Monitoring

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 542-553页

作者： Ravikishan, Surapaneni Reddy, B. Eswar Rao, K. V. Sambasiva Department of Computer Science and Engineering JNTUA Anantapur 515002 India Department of Computer Science and Engineering NRI Institute of Technology Vijayawada 521212 India

In healthcare, remote sensing technologies are popular for smart patient health monitoring. Real-time health assessment and early intervention are possible using remote sensing data from wearable sensors and imaging equipment. However, processing and analysing huge remote sensing data are complex. Task scheduling improves processing workflow for accurate and fast health monitoring. This research offered the optimised task scheduling of remote sensing data processing for smart patient health monitoring, to describe the processing activities such as data preparation, task scheduling algorithms, and decision-making involved in data analysis from remote sensing. We used datasets associated with Internet of Things (IoT) devices that patients wear as sensors on their wrists. We used min–max normalisation to standardise the data’s scale after preprocessing the data and choose the task scheduling algorithm methods to distribute work among resources effectively. We proposed the multi-objective dwarf mongoose optimised with Deep Q Network (MODMO-DQN), which aims to address the task scheduling issue for remote sensing data processing in the Internet of Things by monitoring vital sign data of faraway patients. Next, to optimise the e-health services, an optimisation module built on top of MODMO-DQN is developed. The experiment compares the variation in completion time in the remote sensing data process. The results showed that the proposed perform to compare the existing methods. This will make it possible to evaluate the suggested works in terms of throughput, latency, energy usage, and task scheduling efficiency. The performance analysis shows that the recommended method is successful and may be a feasible and efficient solution to track patient vital sign data in Internet of Things-based e-Health. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Cloud E-health Min–max normalisation Multi-objective dwarf mongoose optimised with Deep Q Network (MODMO-DQN) Remote sensing Smart healthcare Task scheduling algorithm

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A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

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computer Systems science & engineering 2023年第3期44卷 1921-1938页

作者： Anas Basalamah Mahedi Hasan Shovan Bhowmik Shaikh Akib Shahriyar Department of Computer Engineering Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science and Engineering Khulna University of Engineering&TechnologyKhulna9203Bangladesh

The recognition of pathological voice is considered a difficult task for speech ***,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this *** have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia *** ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected *** K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML *** to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component Analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia.

关键词： Dimensionality reduction dysphonia detection linear discriminant analysis logistic regression speech feature extraction support vector machine

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Focusing on your subject:Deep subject-aware image composition recommendation networks

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Computational Visual Media 2023年第1期9卷 87-107页

作者： Guo-Ye Yang Wen-Yang Zhou Yun Cai Song-Hai Zhang Fang-Lue Zhang BNRist Department of Computer Science and TechnologyTsinghua UniversityBeiing 100084China School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand

Photo composition is one of the most important factors in the aesthetics of *** a popular application,composition recommendation for a photo focusing on a specific subject has been ignored by recent deep-learning-based composition recommendation *** this paper,we propose a subject-aware image composition recommendation method,SAC-Net,which takes an RGB image and a binary subject window mask as input,and returns good compositions as crops containing the *** model first determines candidate scores for all possible coarse cropping *** crops with high candidate scores are selected and further refined by regressing their corner points to generate the output recommended cropping *** final scores of the refined crops are predicted by a final score regression *** existing methods that need to preset several cropping windows,our network is able to automatically regress cropping windows with arbitrary aspect ratios and *** propose novel stability losses for maximizing smoothness when changing cropping windows along with view *** results show that our method outperforms state-of-the-art methods not only on the subject-aware image composition recommendation task,but also for general purpose composition *** also have designed a multistage labeling scheme so that a large amount of ranked pairs can be produced *** use this scheme to propose the first subject-aware composition dataset SACD,which contains 2777 images,and more than 5 million composition ranked *** SACD dataset is publicly available at https://***/SACD/.

关键词： subject-aware image composition image cropping deep learning recommendation

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An Ophthalmic Evaluation of Central Serous Chorioretinopathy

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computer Systems science & engineering 2023年第1期44卷 613-628页

作者： L.K.Shoba P.Mohan Kumar Department of Information Technology Jeppiaar Engineering CollegeChennai600119India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal *** this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application *** approach,which was focused on image quality,improves medical image *** enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be *** total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

关键词： OCT CSCR macula segmentation boosted anisotropic diffusion with unsharp maskingfilter two class support vector machine classifier and shallow neural network with powell-beale classifier

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Stacking Ensemble Framework for Hate Speech Detection in Bangla Videos

Stacking Ensemble Framework for Hate Speech Detection in Ban...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Mim, Shamrin Jahan Mahmud, Tanjim Ali, Md Hasan Aziz, Mohammad Tarek Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

The proliferation of internet usage and social media platforms has significantly enhanced the ability of individuals to express their opinions on various topics. However, this freedom of expression sometimes morphs into a vehicle for disseminating hate speech, leading to increased incidents of cyberbullying, violations, and conflicts. Particularly on video-sharing websites, which have become a prominent stage for such activities due to their widespread use and the dynamic nature of video content. This study aims to address the issue of hate speech in video content by developing a robust method for detecting hate speech in Bangla language videos. The focus is on the spoken content within these videos, which is a primary vector for the transmission of harmful messages. We constructed a comprehensive dataset by extracting and converting audio from a collection of videos into text. Utilizing this dataset, we applied machine learning techniques and deep learning models to analyze and classify the content. Specifically, our approach involves a stacking ensemble model that combines the strengths of Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) with the analytical capabilities of customized feature extraction applied to Multinomial Naive Bayes and Random Forest classifiers serving as a meta-model. The proposed stacking ensemble model demonstrates a high level of efficacy, achieving an accuracy rate of 96% in detecting hate speech within the tested video content. This performance indicates a significant advancement over existing methods, underlining the effectiveness of our hybrid, multi-model approach. © 2024 IEEE.

关键词： Convolutional neural networks

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