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data Masking for Chinese Electronic Medical Records with Named Entity Recognition

Intelligent Automation & Soft Computing 2023年第6期36卷 3657-3673页

作者： Tianyu He Xiaolong Xu Zhichen Hu Qingzhan Zhao Jianguo Dai Fei Dai School of Computer Science Nanjing University of Information Science and TechnologyNanjing21000China Geospatial Information Engineering Research Center Xinjiang Production and Construction CorpsShihezi832003China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming650224China

With the rapid development of information technology,the electronifi-cation of medical records has gradually become a *** China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical *** medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical ***,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open ***,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this ***,the text is vectorized to satisfy the required format of the model ***,since the input sentences may have a long or short length and the relationship between sentences in context is not *** this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is ***,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and *** addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this *** experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.

关键词： Named entity recognition Chinese electronic medical records data masking principal component analysis regular expression

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Real Time Fruit Recognition and Calories Estimation Using CNN and Model Deployment on Amazon's AWS 3

Real Time Fruit Recognition and Calories Estimation Using CN...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Roy, Vikas Kumar Roy, Ganpati Kumar Thakur, Vasu Baliyan, Nikhil Goyal, Nupur Chauhan, Yash Ruprecht Karl University of Heidelberg Department of Data and Computer Science Germany Sharda University Department of Computer Science and Engineering Greater Noida India RIT Roorkee Department of Computer Science and Engineering Uttarakhand Roorkee India Graphic Era Deemed to Be University Department of Mathematics Dehradun India Graphic Era Deemed to Be University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350303360

To carry out vigorous and effective fruit recognition in farming is difficult since there are a number of variations. Lately, various deep-learning approaches presented an outstanding result in numerous visual-guided agricultural usage. These approaches need labelling on training data, which is a tedious work. This study presented a customized five layers CNN architecture and various pretrained deep learning algorithms i.e., VGG-16, DenseNet-121 and InceptionV3are used to recognise the fruit and their calories for helping farmers, patients and peoples to estimate the fruit calories for their health and in farming. The model is trained and validated on integrated datasets which is downloaded from Kaggle and IEEE dataport having 10 classes of different fruits. After extensive experiments, highest accuracy obtained was 99.60% by five layers of CNN and proposed deep learning model has been deployed on Amazon AWS EC2 instance using WinSCP, PuTTY and PuTTYgen. Flask (Python Framework), HTML, CSS and JavaScript is used for website development and connected to its parent website which help users to recognize the fruit and their calories. © 2023 IEEE.

关键词： Fruits

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A Systematic Survey of Automatic Detection of Lung Diseases from Chest X-Ray Images: COVID-19, Pneumonia, and Tuberculosis

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SN computer science 2024年第2期5卷 229页

作者： Koyyada, Shiva Prasad Singh, Thipendra P. School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun 248007 India Data Sciences uGDXIT (Formerly INSOFE) Telangana Hyderabad 500082 India School of Computer Science Engineering and Technology Bennett University NCR-Delhi Greater Noida 201310 India

Chest X-ray images (CXR) can convey a great deal about a patient’s condition;hence, the standard chest radiograph should be reconsidered. Interpretation of radiographs is challenging and requires skilled people to determine lung disease without false positives and negatives. A detailed investigation addressing lung diseases COVID-19, Pneumonia, and Tuberculosis is presented here with the goal of assisting investigators in constructing models that automatically identify lung diseases. This paper is presented in three folds. The first is an exploration of how research has progressed from classic feature engineering approaches to deep learning methods;the second is how these are used to identify the listed diseases using radiology images such as Chest X-rays (CXRs);and the third is the future path way of research to detect these diseases. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Chest X-ray image Convolutional neural networks COVID-19 Pneumonia Transfer learning Tuberculosis

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： data integrity

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An Optimized Hybrid Deep Learning and IoT-Enabled Health System in Retina Image Analysis for Blindness Detection 2

An Optimized Hybrid Deep Learning and IoT-Enabled Health Sys...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Yamini, J. Kanimozhi, T. Jenefa, A. Bagyalakshmi, K. Aarthi, K. Dhivya, P. Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Machine learning Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Ppg Institute of Technology Department of Computer Science and Engineering Coimbatore India Sns College of Technology Department of Mathematics Coimbatore India Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350368413

Early signs of blind identification and retina disorder in retina image has become prominent. Deep learning techniques are used to improve the accuracy in detecting pathological conditions from the retina images. However, the models have some challenges such as suboptimal feature extraction and inefficient filtering methods. Most of the traditional methods often result in overfitting and under fitting, thus decreasing the generalizability and robustness of the model. The proposed hybrid approach combines bilateral filtering with Convolutional Neural Networks (CNN). In this approach, bilateral filtering acts as a pre-processing technique to enhance image quality by reducing noise and preserving edges for the correct extraction of features. The CNN is applied for feature extraction and it is used to self-learn complex patterns from filtered images. The proposed approach gives an accuracy of 95.65% with a loss of 0.18 without overfitting and underfitting. © 2024 IEEE.

关键词： Convolutional neural networks

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RLDR: Reinforcement Learning-Based Fast data Recovery in Cloud-of-Clouds Storage Systems

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IEEE Transactions on Cloud Computing 2025年第2期13卷 526-543页

作者： Shen, Jiajie Wu, Bochun Wang, Maoyi Zou, Sai Cui, Laizhong Ni, Wei Fudan University Shanghai200433 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Shenzhen University College of Computer Science and Software Engieering Shenzhen518060 China Commonwealth Scientific and Industrial Research Organization Data61 EastwoodNSW2122 Australia

Cloud-of-clouds storage systems are widely used in online applications, where user data are encrypted, encoded, and stored in multiple clouds. When some cloud nodes fail, the storage systems can reconstruct the lost data and store it in the substitute nodes. It is a challenge to reduce the latency of data recovery to ensure data reliability. In this paper, we adopt a Reinforcement Learning-based data Recovery (RLDR) approach to reduce the regeneration time. By employing the Monte-Carlo method, our approach can construct the tree-topology-based regeneration process, a.k.a. regeneration tree, to effectively reduce the regeneration time. Through rigorous analysis, we apply the information flow graph to optimize the inter-cloud traffic for a given regeneration tree. To verify the merit of RLDR, We conduct extensive experiments on real-world traces. Experiments demonstrate that RLDR can significantly accelerate the regeneration process. Specifically, RLDR can reduce the regeneration time by up to 92% and increase the throughput by up to twelve-fold, compared to the prior art. © 2013 IEEE.

关键词： Cloud storage

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Machine Learning Challenges of E-government Models of Cloud Computing in Developing Countries 18

Machine Learning Challenges of E-government Models of Cloud ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Tamilarasi, R. Kumar, P. N. Santhosh Ghantasala, G. S. Pradeep Rao, D. Nageswara Bathla, Priyanka Gupta, Gaurav Sonia Alliance University Department of Computer Science and Engineering Bengaluru India Hyderabad India Alliance University Department of Computer Science and Engineering Bangalore India Chandigarh University Punjab India Shoolini University Solan India Shoolini University Yogananda School of AI Computer and Data Sciences Solan India

ISBN: (纸本)9789380544519

Cloud computing usage in electronic government (e-government) is developing across nations and presents a range of difficulties. Infrastructure problems, such as unreliable internet connections and insufficient devices, make it difficult to integrate cloud solutions efficiently. Concerns about data security and privacy arise when private information about citizens is stored in the cloud and could be hacked or accessed without permission. Economic limits and resource constraints could prevent cloud infrastructure installation, thereby reducing e-government service scalability and efficiency. Compliance with various regulations adds complexity to the legal and governmental aspects of managing cloud data. A scientific investigation reveals that while cloud computing-driven e-government services offer numerous advantages, factors such as IT infrastructure, internet accessibility, and confidence in emerging technologies could pose barriers to their adoption. Also, recommend trust-focused cloud computing solutions for e-government services (CCSE-GS). Finally, this study focuses on the number of obstacles that developing countries must overcome to fully realize the potential of cloud computing for e-government services. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Challenges about e-government Cloud Computing E-Government models IT infrastructure

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An Analytical Review of Large Language Models Leveraging KDGI Fine-Tuning,Quantum Embedding’s,and Multimodal Architectures

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computers, Materials & Continua 2025年第6期83卷 4031-4059页

作者： Uddagiri Sirisha Chanumolu Kiran Kumar Revathi Durgam Poluru Eswaraiah G Muni Nagamani Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering(Data Science) AVN Institute of Engineering and TechnologyHyderabad501510India Department of Computer Science and Engineering(Data Science) Vignan’s Institute of Management and Technology forWomenHyderabad501301India Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and TechnologyVijayawada520008India

A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across *** studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and *** gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment *** this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly *** propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal *** methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance *** for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are *** investigation’s scope,mad,and methods are described,but the primary results are *** work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy *** medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote ***-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple *** discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain *** framework helps academics and practitioners identify,adapt,and innovate LLMs for different *** work

关键词： Large languagemodels quantum embeddings fine-tuning techniques multimodal architectures ethical AI scenarios

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Improving Event Coreference Resolution Using Prompt Learning and U-shaped Module 4

Improving Event Coreference Resolution Using Prompt Learning...

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4th International Conference on computer science and Blockchain, CCSB 2024

作者： Zhang, Mengyao Long, Jun Huang, Wenti School of Software Xinjiang University Urumqi China Institute of Big Data Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China

ISBN: (纸本)9798350356502

Event Coreference Resolution (ECR) focuses on clustering event mentions that allude to identical actual events. Previous research primarily focuses on encoding event mentions without incorporating human interpretations, making it difficult to integrate human-summarized rules for guiding the ECR task. Furthermore, the semantic relations between event mentions have not been thoroughly explored, which impacts coreference judgment. To address these issues, we first utilize the powerful Longformer to acquire event representations at the document level. In addition, we introduce a prompt learning template to better emphasize event types and parameters, enhancing the interaction between event modeling and coreference judgment. Ultimately, we create matching vectors for event mentions using tensor matching and incorporate a U-shaped module to effectively identify the semantic relationships among these mentions, which enhances coreference discrimination. DPTU surpasses other ECR models on the KBP2017 dataset in cause of some experiments. © 2024 IEEE.

关键词： Semantics

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