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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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Rewritable Triple-Mode Light-Emitting Display

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Nano-Micro Letters 2025年第8期 179-196页

作者： Seokyeong Lee Jong Woong Park Jihye Jang Jin Woo Oh Gwanho Kim Jioh Yoo Jong Gun Jung Hyowon Han Wei Jiang Chang Eun Lee Jungwon Yoon Kaiying Zhao Cheolmin Park Department of Materials Science and Engineering Yonsei University Post-Silicon Semiconductor Institute Korea Institute of Science and Technology

Despite great progress in developing mode-selective light emission technologies based on self-emitting materials, few rewritable displays with modeselective multiple light emissions have been demonstrated. Herein, we present a rewritable triple-mode light-emitting display enabled by stimuli-interactive fluorescence(FL), room-temperature phosphorescence(RTP), and electroluminescence(EL). The display comprises coplanar electrodes separated by a gap,a polymer composite with FL inorganic phosphors(EL/FL layer), and a polymer composite with solvent-responsive RTP additives(RTP layer). Upon 254 nm UV exposure, a dual-mode emission of RTP and FL occurs from the RTP and EL/FL layers, respectively. When a polar liquid, besides water, is applied on the display and an AC field is applied between the coplanar electrodes, EL from the EL/FL layer is triggered, and the display operates in a triple mode. Interestingly, when water is applied to the display, the RTP mode is deactivated, rendering the display to operate in a dual mode of FL and EL. By manipulating the evaporation of the applied polar liquids and water, the mode-selective light emission of FL, RTP, and EL is rewritable in the triple-mode display. Additionally, a high-security full-color information encryption display is demonstrated, wherein the information of digital numbers, letters, and Morse code encoded in one optical mode is only deciphered when properly matched with that encoded in the other two modes. Thus, this article outlines a strategy to fulfill the substantial demand for high-security personalized information based on room-temperature multi-light-emitting displays.

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

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computers, Materials & Continua 2023年第4期75卷 891-909页

作者： Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this ***-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.

关键词： Pneumonia prediction distributed deep learning data parallel model ensemble deep learning class imbalance skewed data

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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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Efficient Gait Analysis Using Deep Learning Techniques

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computers, Materials & Continua 2023年第3期74卷 6229-6249页

作者： K.M.Monica R.Parvathi School of Computer Science and Engineering Vellore Institute of TechnologyChennai600117India

Human Activity Recognition(HAR)has always been a difficult task to *** is mainly used in security surveillance,human-computer interaction,and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things(IoT).Human Activity Recognition data can be recorded with the help of sensors,images,or *** daily routine-based human activities such as walking,standing,sitting,etc.,could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network(2D CNN)MODEL,Long Short Term Memory(LSTM)Model,Bidirectional long short-term memory(Bi-LSTM)are used for the *** has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate,with almost all activities accurately getting recognized over 90%of the ***,because all the examples are generated from only 20 s of data,these actions can be recognised *** from classification,the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy(CP)for the evaluation of chosen Spatio-temporal features based on 3D foot ***-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 *** Motion Capture(OMC)equipment was used as the referral method to assess the functionality and quality of the foot-worn *** average accuracy±precision for stride length,cadence,and step length was 3.5±4.3,4.1±3.8,and 0.6±2.7 cm *** cadence,stride length,swing,and step length,people with CP had considerably high inter-stride ***-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.

关键词： Human activity recognition neural networks gait analysis wearable sensors kinetic analysis cerebral palsy

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Study Influencing Factors of Maternal Health and the Role of Internet of Things (IoT) to Improve Maternal Care

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SN computer science 2024年第6期5卷 778页

作者： Rani, Deepa Kumar, Rajeev Chauhan, Naveen Department of Computer Science and Engineering National Institute of Technology

Maternal health during pregnancy is influenced by various factors that significantly impact pregnancy outcomes. This paper aims to highlight these critical factors, promote awareness, and advocate proactive self-care among women. By analyzing various works, a taxonomy of influencing factors is proposed to provide an understanding of their effects on maternal and fetal health. The taxonomy reveals multiple factors, including individual characteristics, social determinants, and environmental factors, that significantly affect maternal and fetal health. Therefore, by thoroughly reviewing reputed articles in the field of IoT technology in maternal care, a list of sensors and devices capable of capturing health-related parameters and various applications of IoT in maternal care is presented. IoT technology and promoting self-care can enhance maternal care and improve outcomes for both mother and fetus. Therefore, this research emphasizes the factors influencing pregnancy and the benefits of the adoption of IoT technology to enable a patient-centric approach for prompt detection of potential risks in maternal care. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： IoT Maternal care Remote monitoring Risk monitoring Self-care

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Modified MMS: Minimization Approach for Model Subset Selection

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computers, Materials & Continua 2023年第10期77卷 733-756页

作者： C.Rajathi P.Rukmani School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Considering the recent developments in the digital environment,ensuring a higher level of security for networking systems is *** security approaches are being constantly developed to protect against evolving *** ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior *** research work aimed to create a more diverse and effective ensemble *** this end,selected six classification models,Logistic Regression(LR),Naive Bayes(NB),K-Nearest Neighbor(KNN),Decision Tree(DT),Support Vector Machine(SVM),and Random Forest(RF)from existing study to run as independent *** the individual models were trained,a Correlation-Based Diversity Matrix(CDM)was created by determining their *** models for the ensemble were chosen by the proposed Modified Minimization Approach for Model Subset Selection(Modified-MMS)from Lower triangular-CDM(L-CDM)as *** proposed algorithm performance was assessed using the Network Security Laboratory—Knowledge Discovery in Databases(NSL-KDD)dataset,and several performance metrics,including accuracy,precision,recall,and *** selecting a diverse set of models,the proposed system enhances the performance of an ensemble by reducing overfitting and increasing prediction *** proposed work achieved an impressive accuracy of 99.26%,using only two classification models in an ensemble,which surpasses the performance of a larger ensemble that employs six classification models.

关键词： Ensemble learning intrusion detection minimization model diversity

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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

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IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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MOHSQO: Multi objective harmony search algorithm based QoS optimization in delay tolerant networks

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Multimedia Tools and Applications 2025年 1-26页

作者： Sonkar, Nidhi Pandey, Sudhakar Kumar, Sanjay Department of Computer Science and Engineering National Institute of Technology Warangal Hanamkonda India Department of Information Technology National Institute of Technology Raipur Raipur India

Delay Tolerant Networks (DTNs) have the ability to make communication possible without end-to-end connectivity using store-carry-forward technique. Efficient data dissemination in DTNs is very challenging problem due to compromising in Quality-of-Service (QoS) metrics such as delivery ratio, end-to-end delay and overhead ratio. That means, if an algorithm is design to find highest delivery ratio, then the algorithm does not bother about delay and overhead ratio. Researchers tried to improve the performance of network but, none of them had given satisfactory results to make a balance between all QoS metrics. In this paper, we have proposed a novel approach to optimize the performance of the network using Multi objective harmony search algorithm by creating a balance between all QoS metrics. Harmony search algorithm considers number of QoS parameters simultaneously. We have used characteristics of the node (Speed, residual energy, buffer capacity and encounters) to select the optimum forwarder node so that we can balance the performance metrics for network optimization. We have used ONE (Opportunistic Network Environment) simulator to simulate the proposed method and compared with exiting techniques. By analyzing the results, we can say that proposed method has the optimum performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Delay tolerant networks

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