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Document data Extraction with Optical Character Recognition 14

Document Data Extraction with Optical Character Recognition

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14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sanghvi, Vidit Ukani, Vijay Patel, Raj Sharma, Priyanka Shah, Pooja Computer Science and Engineering Department Institute of Technology Nirma University Gujarat Ahmedabad India Department of Machine Learning Samyak Infotech Private Limited Ahmedabad India

The modern solutions for fetching content from PDF documents or images includes fetching document’s multiple features like processing using Natural Language processing (NLP), border-less table contents, processing it’s stacked lines, fetching document layout information and working on region-based features from documents. There are new challenges like fetching a table’s content where the table is included in borders of other content. In this paper, we have used LayoutLM for parsing the content and RetinaNet for table detection in a particular document. The pretrained models were trained on very less data-set to decide for large scale use. We also have discussed architecture of LayoutLM and tesseract that we used in building the tool with discussing output of some other approaches. © Grenze Scientific Society, 2023.

关键词： Optical character recognition

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Event-Driven FaaS Workflows for Enabling IoT data processing at the Cloud Edge Continuum 16

Event-Driven FaaS Workflows for Enabling IoT Data Processing...

引用

16th IEEE/ACM international conference on Utility and Cloud computing (UCC) / 10th IEEE/ACM international conference on Big data computing, Applications and Technologies (BDCAT)

作者： Sicari, Christian Balouek, Daniel Villiari, Massimo Parashar, Manish Univ Messina Messina Italy INRIA IMT Atlantique CNRS LS2NUMR 6004 F-44307 Nantes France Univ Utah SCI Inst Salt Lake City UT USA

ISBN: (纸本)9798400702341

Continuum computing encompasses the integration of diverse infrastructures, including cloud, edge, and fog, to facilitate seamless migration of applications based on their specific needs, ensuring optimal satisfaction of their requirements. The primary obstacles in this particular context mostly pertain to the incapacity to promptly respond to changes in the environment or the quality of service (QoS) constraints of the application, as well as the incapability to maintain an application in a stateless manner, hence impeding its relocation without the risk of data loss. The objective of this research is to tackle the aforementioned issues through the introduction of a framework based on Function-as-a-Service (FaaS) and event-driven architecture. This framework enables the decomposition, localization, and relocation of applications inside a Continuum infrastructure, facilitated by a rule engine that is both system and data-aware.

关键词： Faas event-driven serverless workflows cloud-edge continuum

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A Distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems 48

A Distributed Adaptive Algorithm for Non-Smooth Spatial Filt...

引用

48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Hovine, Charles Bertrand, Alexander Leuven Belgium Signal Processing and Data Analytics Stadius Center for Dynamical Systems Leuven Belgium Leuven Belgium

ISBN: (纸本)9781728163277

computing the optimal solution to a spatial filtering problems in a Wireless Sensor Network can incur large bandwidth and computational requirements if an approach relying on data centralization is used. The so-called distributed adaptive signal fusion (DASF) algorithm solves this problem by having the nodes collaboratively solve low-dimensional versions of the original optimization problem, relying solely on the exchange of compressed views of the sensor data between the nodes. However, the DASF algorithm has only been shown to converge for filtering problems that can be expressed as smooth optimization problems. In this paper, we explore an extension of the DASF algorithm to a family of non-smooth spatial filtering problems, allowing the addition of non-smooth regularizers to the optimization problem, which could for example be used to perform node selection, and eliminate nodes not contributing to the filter objective, therefore further reducing communication costs. We provide a convergence proof of the non-smooth DASF algorithm and validate its convergence via simulations in both a static and adaptive setting. © 2023 IEEE.

关键词： Adaptive algorithms

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Scalable Machine Learning: Development of Efficient Clustering algorithms for High-Volume Temporal data Analysis 2

Scalable Machine Learning: Development of Efficient Clusteri...

引用

2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Taneja, Madhur Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350369960

In the era of big data, the analysis of large-scale time series data has gained significant importance across various applications such as finance and healthcare. However, traditional clustering approaches, often encounter accuracy and scalability issues when applied to extensive data sets. This paper introduces innovative clustering method specifically tailored for time series data to enter these challenges. These methods leverage advanced data structures for computational efficiency, optimal distance measurements to improve, improve accuracy, and parallel computing framework for enhanced Scalability. Rig testing involving millions of time series occurrences from real world. data sets was conducted to evaluate the effectiveness of the proposed methods. The results indicate that these novel clustering algorithms can deliver high-quality outcomes even when handling massive data sets. The exhibit verity in accommodating different time series lengths and resilience in handling noise. These characteristics contribute to effective and scalable time series clustering, entering the proposed approaches suitable for real-time analytics in data intensive environments. Consequently, this research facilitates the extraction of actionable insights in real time, offering valuable contributions for both academia and industry practitioners. Additionally, it broadens the spectrum of tools available for the analysis and interpretation of large-scale time series data. © 2023 IEEE.

关键词： Distance Metrics Large-Scale data Analysis Parallel computing Scalability Time Series Clustering

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An Automatic Scoring System for Wushu Movements based on Real-time Action Image Recognition and Matching Algorithm 3

An Automatic Scoring System for Wushu Movements based on Rea...

引用

3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Deng, Li Liu, Hengru Polus International College SiChuan ChengDu610103 China Chengdu Sport University Martial Arts Institute Sichuan chengdu610041 China

ISBN: (纸本)9798350322842

Existing engineering practices show that it is a complex task to require automatic detection and matching of corresponding features, usually without any prior knowledge of the scene and image. As the detailed application, this research study considered the scheme of automatic scoring system for Wushu movements based on real-time action image recognition and matching algorithm. Starting from the review of the state-of-the-art study results, the questions for the efficient image recognition is firstly presented. Then, efficient effective moment invariants are designed to serve as the pre-processing of the images and the quadtree method is used for the modelling. The new roles and methods are designed to conduct the task of the efficient image recognition. Furthermore, the SIFT based matching algorithm is designed. Combining these 2 algorithms, the efficient scoring system is implemented and comparison experiment is conducted. After testing, it is reflected that the proposed model has the better recognition accuracy and the scoring performance is good. © 2023 IEEE.

关键词： Image recognition

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data Security Patterns for Critical Big data Systems 6

Data Security Patterns for Critical Big Data Systems

引用

6th IEEE international conference on Cloud computing and Artificial Intelligence: Technologies and Applications, CloudTech 2023

作者： Majdoubi, Chaymae El Mendili, Saida Gahi, Youssef National School of Applied Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9798350303063

With the words' current growth in technologies and digitalization, protecting data in massive data-driven systems has become a new challenge to overcome. Therefore, many measures, algorithms, and protocols are used and still under development to minimize risks of data loss, manipulation, sniffing, or spying... In order to gain more in terms of security, privacy, and integrity levels. As important as this issue is, combined efforts from different disciplines (Network Security, Security Operations Center Analysis (SOC Analysis), Machine Learning (ML), and Deep Learning (DL) algorithms) are necessary to enhance data protection. In this paper, we will expose some natural threats of Big data, based on its definition, ethics, and suggestions to avoid these threats, or at least limit their effects. We will also show works related to big data security based on ML and shed light on Artificial Neural Networks (ANN) applications for data classification and threat detection. Then focus on Convolutional Neural Networks (CNN) to optimize parameters. We will try to find solutions for CNN limitations of hyperparameters tuning and rotating image feature extraction. Furthermore, we will discuss state of art results to understand the matter in a deeper sense and expose high-accuracy solutions with improvement path suggestions. Our work is going to come up with some natural threats, compare some existing methods, expose, and discuss results, and then suggest optimal ways to help increase data security, especially through improving CNN limitations. © 2023 IEEE.

关键词： Big data

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iQuantum: A Case for Modeling and Simulation of Quantum computing Environments

iQuantum: A Case for Modeling and Simulation of Quantum Comp...

引用

2nd IEEE international conference on Quantum Software (IEEE QSW)

作者： Nguyen, Hoa T. Usman, Muhammad Buyya, Rajkumar Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst CLOUDS Lab Parkville Vic 3052 Australia Univ Melbourne Sch Phys Parkville Vic 3052 Australia CSIRO Data61 Clayton Vic 3168 Australia

ISBN: (纸本)9798350304794

Today's quantum computers are primarily accessible through the cloud and are expected to be deployed in edge networks in the near future. With the rapid advancement and proliferation of quantum computing research worldwide, there has been a considerable increase in demand for using cloud-based quantum computation resources. This demand has highlighted the need for designing efficient and adaptable resource management strategies and service models for quantum computing. However, the limited quantity, quality, and accessibility of quantum resources pose significant challenges to practical research in quantum software and systems. To address these challenges, we propose iQuantum, a first-of-its-kind simulation toolkit that can model quantum computing environments for prototyping and evaluating system design and scheduling algorithms. This paper presents the quantum computing system model, architectural design, proof-of-concept implementation, potential use cases, and future development of iQuantum. Our proposed iQuantum simulator is anticipated to boost research in quantum software and systems, particularly in the creation and evaluation of policies and algorithms for resource management, job scheduling, and hybrid quantum-classical task orchestration in quantum computing environments integrating edge and cloud resources.

关键词： quantum computing quantum cloud modeling simulation hybrid quantum computing quantum job scheduling

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Precise processing of Satellite Space-time data and Dynamic Warning Algorithm for Slope 8

Precise Processing of Satellite Space-time Data and Dynamic ...

引用

8th international conference on Intelligent computing and Signal processing, ICSP 2023

作者： Li, Yao Huang, ZhangQin Gao, Han Huang, Ling Beijing University of Technology Beijing Engineering Research Center for IoT Software and System Beijing China

ISBN: (纸本)9798350302455

Deformation monitoring data can most intuitively reflect the current movement process of the landslide and is likewise the most essential and effective warning feature of the landslide warning model. To solve the current problem that it is difficult to identify the uniform deformation phase and adjust it dynamically in time, which leads to the high false alarm and leakage rate of the early warning algorithm, we adopt the Pauta criterion and the Lagrange interpolation method fused with the improved adaptive Kalman filter to improve the accuracy of the original monitoring data, propose a real-time determination method of uniform deformation phase based on statistical analysis, and construct a process-oriented dynamic early warning algorithm by examining the normal distribution of the second-order derivative values of the cumulative displacement curve and the changing trend of the linear regression coefficient of determination. The experimental results show that the research method can effectively reduce the abnormal fluctuation interference of data, accurately discriminate the uniform deformation phase, improve the early warning accuracy of the creep tangent angle warning model, and has excellent application value for the subsequent realization of systematic and automated real-time monitoring and warning. © 2023 IEEE.

关键词： Landslides

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Improved NSGA-II based process dynamic multi-objective algorithm

Improved NSGA-II based process dynamic multi-objective algor...

引用

2023 IEEE international conference on Control, Electronics and Computer Technology, ICCECT 2023

作者： Zhang, Yaze North China University of Technology Beijing Key Laboratory on Integration and Analysis of Large-Cale Stream Data Beijing China

ISBN: (纸本)9798350399028

The NSGA-II-T algorithm is proposed to solve the scheduling problem with dynamic changes in process data processing in the service Internet, modelling the problem as a dynamic multi-objective optimisation problem. In the event of regular changes in load, the solution in the next environment is predicted by the transformer model and added to the original population;in the event of changes caused by physical factors such as node downtime, the population is added randomly. The algorithm uses different means of adding populations to address the lack of diversity that can occur in static multi-objective algorithms when the environment changes. Experiments conducted on the WorkflowSim platform show that the NSGA-II-T algorithm outperforms other classical algorithms in all performance metrics when the environment changes significantly, demonstrating the effectiveness of the algorithm. © 2023 IEEE.

关键词： Multiobjective optimization

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Improved K-Nearest Neighbor Missing data Classification Based on Interval Value Imputation 2

Improved K-Nearest Neighbor Missing Data Classification Base...

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2nd IEEE international conference on Electrical Engineering, Big data and algorithms, EEBDA 2023

作者： Zhang, Ziyang Tang, Can Shandong University of Finance and Economics School of Mathematics and Statistics Jinan China Beijing University of Posts and Telecommunications International School Beijing China

ISBN: (纸本)9781665462532

In reality, when processing data sets for classification, there are often missing data sets, which brings inconvenience to the classification work. To this end, this paper proposes a method to impute incomplete data based on the interval value of K nearest neighbors. The method uses the Euclidean distance between the incomplete data and the complete data to find the K closest complete data to the incomplete data, so that the nearest neighbor can be constructed according to the corresponding attribute value of the complete data to the missing attribute value of the incomplete data. interval. Next, the dataset is constructed into an interval-valued dataset. Based on the interval-valued distance algorithm, the incomplete data can be classified by the K-nearest neighbor algorithm. The experimental results show that the improved K-nearest neighbor algorithm based on interval value imputation is more efficient than the traditional 0-value imputation, median imputation and mean imputation K-nearest neighbor algorithm under certain circumstances. © 2023 IEEE.

关键词： Classification (of information)

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