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An Efficient Privacy-Preserving Ranked Keyword Search Method

IOP Conference Series: Materials science and Engineering 2021年第1期1084卷

作者： E. Nirmala S. Muthurajkumar D. Subitha Research Scholar Department of Computer Technology Madras Institute of Technology (MIT) Campus Anna University Chennai India Assistant Professor Department of Computer Technology Madras Institute of Technology (MIT) Campus Anna University Chennai India Assistant Professor Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Vel Nagar Chennai

The keyword searching algorithm plays a vital role in cloud computing Architecture. Here, the searching technique is applied as a tool to encrypted data set which is outsourced. This technique works well, by either directed on multi keyword findings with exact match or one keyword search with fuzzy logic with a result of true or false. Though the existing techniques come across sensible difficultyin execution upon the encrypted data set, in the execution of multiple keyword based searching, keyword correction is difficult to achieve. To overcome with this problem, we construct a highly efficient, multiple corrections in keyword searching algorithm using ranking scheme. Multiple corrections like spell checks and errors. In the existing system of Author Wang and others scheme, they were unable to address the aforementioned problems, But in our method of multiple keyword searching technique, the Fuzzy Gramm algorithm helps to detect and handle the spelling errors by making a function call. Additionally the keywords with similar ancestry can be found by making use of stemming procedure. Alongside with thatrank keyword is used to achieve accurate checking with matched text file content. Thus the proposed approach enhances security and reliability to the maximum extent compared to the existing system.

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Comparative Study Of Efficient Data Hiding Techniques

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IOP Conference Series: Materials science and Engineering 2021年第1期1022卷

作者： Lalan kumar Deepanjali Mishra Ayush Kumar Amit Kumar Ashish Bajpai Asst. Professor Dept. of Master of Computer Application G.L. Bajaj Institute of Technology and Management Gr.Noida UP India Scholar Dept. of Master of Computer Application G.L. Bajaj Institute of Technology and Management Gr.Noida UP India Asst.Professor Dept. of Master of Computer Application KIET Group of Institutions Ghaziabad UP India Asst.Professor Dept. of Computer Science KIET Group of Institutions Ghaziabad UP India

Data security is one of the fundamental concerns when it comes to modern networking and information sharing as any breach or data loss can end up causing major losses in terms of credibility, personal data and/or law suites. This paper studies data encryption and decryption techniques using algorithms like DES algorithm, 3 DES algorithm, AES algorithm and RSA algorithm. Cryptography was introduced for data security while transmitting data. However, only cryptography cannot give a robust security approach; as a result, a disorganized message can still be snooped [9]. This leads to a desire for information security. There are several techniques available, among those have taken a few of them and will review them considering cryptography measures. We will analyse the algorithm mentioned above, and try to find out better among them.

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An Experimental Performance Analysis on Robotics Process Automation (RPA) With Open Source OCR Engines: Microsoft Ocr And Google Tesseract OCR

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IOP Conference Series: Materials science and Engineering 2021年第1期1059卷

作者： T Malathi D Selvamuthukumaran C S Diwaan Chandar V Niranjan A K Swashthika Assistant Professor Department of Computer Science & Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu - 638401 Assistant Professor Department of Mechanical Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu - 638401 Second Year Department of Information Science & Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu - 638401 Second Year Department of Computer Technology Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu - 638401

Robotic Process Automation (RPA) plays an important role in the solution for automating human digital interaction in various software domains like IT, Business Strategy and so on. Optical Character Recognition(OCR) superimposes subtitled characters on an *** we use two Open source OCR engines, Google Tesseract OCR - It literally makes use of the open source Tesseract OCR Engine[1]. Microsoft OCR - This is another open source OCR engine accessible in the Robotics Process Automation tool, UiPath[1]. The technique of optical character recognition (OCR) has been used to transform printed text into editable text. In various applications, OCR is a very useful and common technique. OCR precision can be dependent on algorithms for text preprocessing and segmentation. Often because of different scale, design, orientation, complex picture context, etc., it is difficult to retrieve text from the image. We begin this paper with an introduction to the Optical Character Recognition (OCR) process, in this paper, we propose a research analysis to estimate the accuracy of two open source OCR engines with Robotics Process Automation(RPA). These RPA processes are created using the UiPath tool and our results compare the OCR engines performance during the execution with the workflow and save the results to our local storage or on cloud storage if cloud support enabled and here we used local storage for purpose, and the result analysis and error analysis was evaluated by manual process which also included excel formulas to calculate the accuracy of the extracted string with the original string.

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Convex-concave backtracking for inertial bregman proximal gradient algorithms in non-convex optimization

arXiv

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arXiv 2019年

作者： Mukkamala, Mahesh Chandra Ochs, Peter Pock, Thomas Sabach, Shoham Faculty of Mathematics and Computer Science Saarland University Saarbrucken66123 Germany Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Faculty of Industrial Engineering Technion Haifa3200003 Israel

Backtracking line-search is an old yet powerful strategy for finding a better step sizes to be used in proximal gradient algorithms. The main principle is to locally find a simple convex upper bound of the objective function, which in turn controls the step size that is used. In case of inertial proximal gradient algorithms, the situation becomes much more difficult and usually leads to very restrictive rules on the extrapolation parameter. In this paper, we show that the extrapolation parameter can be controlled by locally finding also a simple concave lower bound of the objective function. This gives rise to a double convex-concave backtracking procedure which allows for an adaptive choice of both the step size and extrapolation parameters. We apply this procedure to the class of inertial Bregman proximal gradient methods, and prove that any sequence generated by these algorithms converges globally to a critical point of the function at hand. Numerical experiments on a number of challenging non-convex problems in image processing and machine learning were conducted and show the power of combining inertial step and double backtracking strategy in achieving improved *** Codes 90C25, 26B25, 49M27, 52A41, 65K05 Copyright © 2019, The Authors. All rights reserved.

关键词： Extrapolation

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Secure data sharing for mobile cloud computing using RSA

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IOP Conference Series: Materials science and Engineering 2021年第1期1055卷

作者： M Arumugam S Deepa G Arun P Sathishkumar K Jeevanantham Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Information Technology MP Nachimuthu M Jaganathan Engineering College Chennimalai India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai Erode India

Mobile cloud computing (MCC) refers to an infrastructure in which data processing and storage can take place far from mobile devices. The convergence of compact registration and network distributed computing has created scalable distributed computing. This technology provides consumers a number of points of interest, similar to storage limits, reliability, scalability and access to real-time information. As a result, it is expanding steadily and is undoubtedly organised into a daily day-to-day life. The Cloud servers can be used for the preparation and storage of ***, in the current conditions, the secrecy of photos and information is generally important. In this paper, we concentrate mainly on the safe re-appropriation of photographs.

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Real time Machine Learning Approach for a Smart Door Unlocking Using Face recognition System

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IOP Conference Series: Materials science and Engineering 2021年第1期1130卷

作者： P Rayavel A Sivakumar P V Gopirajan S Surenderanath P Rathnavel Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai-600044 Tamilnadu India Assistant Professor Department of Information Technology Sri Sairam Engineering College Chennai-600044 Tamilnadu India Assistant Profesor Department of Computer Science and Engineering Saveetha Engineering College Chennai-602105 Tamilnadu India Assistant Professor Department of Electrical and Electronics Engioneering Sri Sai Ram Institute of Technology Chennai-600044 Tamilnadu India

This proposed framework goes about as a home security framework for both Person location and gives security to entryway access control by utilizing facial acknowledgment for the home climate. The more peculiar's face is distinguished inside a home climate accomplished by catching a picture from a web camera and handling will be done on caught pictures. The web camera catches the arrangement of pictures when the individual comes before the entryway. The caught picture is analyzed utilizing our face acknowledgment calculation model. In the event that the individual is the approved one, at that point the entryway is opened. Then again, if the individual isn't the approved one. The picture of the individual is shipped off the power in the event that he perceives the individual and wants to open the entryway. He can distantly get to the entryway and permit the individual in.

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Efficiënt load balancing by optimized flexi max-min algorithm

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Journal of Physics: Conference Series 2021年第1期1716卷

作者： P Praveen Kumar V Kanchana Devi Student Master of Computer Applications Vellore Institute of Technology Chennai Campus Tamilnadu India Associate Professor School of Computing Science and Engineering Vellore Institute of Technology Chennai Campus Tamilnadu India

Load Balancing is required to distribute tasks across Virtual Machines on one hand and on the other hand in a cloud Environment. Here, a new task scheduling algorithm: 'Optimized Flexi Max-Min Scheduling Algorithm' is proposed. The algorithm maintains a data structure which is modeled after a Binary Search Tree (BST) for estimations, enhanced searching, task allocation, and migration of tasks. Cloud-Sim is used to model and simulate the cloud computing environment in order to obtain simulated data. The result of this experiment shows that the proposed algorithm outperforms the traditional Max-Min task scheduling algorithms.

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ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data

arXiv

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arXiv 2020年

作者： Wang, Xumeng Xu, Dongshi Chen, Wei Wu, Xiangyang Xia, Jiazhi Xu, Mingliang Chen, Zexian Schreck, Tobias State Key Lab of CAD&CG Zhejiang University China College of Information Engineering China Jiliang University China Institute of Graphics and Image Hangzhou Dianzi University China School of Computer Science and Engineering Central South University China Zhengzhou University China Graz University of Technology Austria

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

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Performance Evaluation with Multivariable and Multiloop control for a class of square MIMO systems

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IOP Conference Series: Materials science and Engineering 2021年第1期1084卷

作者： S Sudhahar C Ganesh Babu D Sharmila Assistant Professor Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Erode Tamilnadu India Professor Professor Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Erode Tamilnadu India Professor Jai Shriram Engineering College Department of Computer Science Engineering Erode Tamilnadu India

Two approaches (centralized and decentralized) for designing a multi-input, multi-output (MIMO) tracing/regulating process are described within article. The vast popular of industrial process control applications are whist focused on multi loop controllers, ignoring the feedback control attainment with advantages of centralized multivariable controllers. Due to their single loop nature, the plant interactions that are merely in use keen on relation in the controller tuning process cannot be suppressed by decentralized controllers. In many situations, therefore it would be beneficial to delimit the detrimental consequences in pairing among inputs and outputs of the closed loop system under certain context. The centralized model predictive controllers (MPC) and decentralized/multi-loop PI controllers are designed. In terms of integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time-weighted Absolute Error (ITAE), the output of both controllers is then compared. The results of the simulation showed that the MIMO MPC is better than the other suggested control schemes. The projected central controllers minimize interactions superior than the multi loop controllers that have recently been published.

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Medical deep learning-A systematic meta-review

arXiv

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arXiv 2020年

作者： Egger, Jan Gsaxner, Christina Pepe, Antonio Pomykala, Kelsey L. Jonske, Frederic Kurz, Manuel Li, Jianning Kleesiek, Jens Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Inffeldgasse 16 Styria Graz8010 Austria Department of Oral & Maxillofacial Surgery Medical University of Graz Auenbruggerplatz 5/1 Styria Graz8036 Austria Computer Algorithms for Medicine Laboratory Styria Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany

Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research effort s. In Q2/2020, the search engine PubMed returned already over 11,000 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though PubMed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys. Copyright © 2020, The Authors. All rights res

关键词： Generative adversarial networks

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