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Toolkit for Web Development Based on Web Based Information System

Journal of Physics: Conference Series 2019年第1期1362卷

作者： S.Muthamil Selvan Ogiboina Purna Sai Krishna Siginamsetty Raviteja (Assistant Professor (O.G)) COMPUTER SCIENCE & ENGINEERING SRM INSTITUTE OF SCIENCE & TECHNOLOGY CHENNAI INDIA COMPUTER SCIENCE & ENGINEERING SRM INSTITUTE OF SCIENCE & TECHNOLOGY CHENNAI INDIA

this paper intends to exhibit the social and authoritative parts of electronic data frameworks. Presently we are in the 21st century and two decades will be finished soon. A century ago has been considered as the ascending of Information and correspondence innovation. These days there is expanding advancement of online data frameworks for social and hierarchical divisions Electronic data frameworks in light of web innovations which shares distinctive kinds of Internet convention and assumes an essential part to gather, putting away, gathering and imparting information from various sources and spread data initially. The speed and productivity of electronic data framework which incorporates both neighborhood and worldwide systems, databases and diverse sorts of program for data preparing makes social and hierarchical life less demanding for individuals. The online applications keep up activities of administration. Such applications incorporates quality administration forms, life cycle of authoritative objective, actualizing and sharing creation plan This electronic innovation affects social connection, social conduct and different association issues.

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VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

arXiv

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arXiv 2020年

作者： Sekuboyina, Anjany Kumar Husseini, Malek E. Bayat, Amirhossein Löffler, Maximilian Liebl, Hans Li, Hongwei Tetteh, Giles Kukačka, Jan Payer, Christian Štern, Darko Urschler, Martin Chen, Maodong Cheng, Dalong Lessmann, Nikolas Hu, Yujin Wang, Tianfu Yang, Dong Xu, Daguang Ambellan, Felix Amiranashvili, Tamaz Ehlke, Moritz Lamecker, Hans Lehnert, Sebastian Lirio, Marilia de Olaguer, Nicolás Pérez Ramm, Heiko Sahu, Manish Tack, Alexander Zachow, Stefan Jiang, Tao Ma, Xinjun Angerman, Christoph Wang, Xin Brown, Kevin Kirszenberg, Alexandre Puybareau, Élodie Chen, Di Bai, Yiwei Rapazzo, Brandon H. Yeah, Timyoas Zhang, Amber Xu, Shangliang Hou, Feng He, Zhiqiang Zeng, Chan Xiangshang, Zheng Liming, Xu Netherton, Tucker J. Mumme, Raymond P. Court, Laurence E. Huang, Zixun He, Chenhang Wang, Li-Wen Ling, Sai Ho Huynh, Lê Duy Boutry, Nicolas Jakubicek, Roman Chmelik, Jiri Mulay, Supriti Sivaprakasam, Mohanasankar Paetzold, Johannes C. Shit, Suprosanna Ezhov, Ivan Wiestler, Benedikt Glocker, Ben Valentinitsch, Alexander Rempfler, Markus Menze, Björn H. Kirschke, Jan S. Department of Informatics Technical University of Munich Germany Munich School of BioEngineering Technical University of Munich Germany Department of Neuroradiology Klinikum Rechts der Isar Germany Department for Quantitative Biomedicine University of Zurich Switzerland Friedrich Miescher Institute for Biomedical Engineering Switzerland Institute of Biological and Medical Imaging Helmholtz Zentrum München Germany Department of Computing Imperial College London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Austria Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria School of Computer Science The University of Auckland New Zealand Computer Vision Group iFLYTEK Research South China China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Shenzhen Research Institute of Big Data China School of Biomedical Engineering Health Science Center Shenzhen University China NVIDIA Corporation United States Zuse Institute Berlin Germany 1000shapes GmbH Berlin Germany Damo Academy Alibaba Group China Department of Mathematics University of Innsbruck Austria Department of Electronic Engineering Fudan University China New York University United States France Deep Reasoning AI Inc United States Technical University of Munich Germany East China Normal University China Chinese Academy of Sciences China Lenovo Group China Ping An Technologies China College of Computer Science and Technology Zhejiang University China Real Doctor AI Research Centre Zhejiang University China The University of Texas MD Anderson Cancer Center United States Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Department of Computing The Hong Kong Polytechnic University China The School of Biomedical Engineering University of Technology Sydney Australia Department of Biomedical Engineering Brno University of Technology Czech Republic India

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://***/nqjyw/, https://***/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://***/anjany/verse. © 2020, CC BY-NC-ND.

关键词： computerized tomography

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Facial expression recognition based on feature fusion and sparse representation 2017

Facial expression recognition based on feature fusion and sp...

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9th International Conference on Machine Learning and Computing, ICMLC 2017

作者： Fu, Xiao-Feng Fu, Ke-Bo Zhang, Yu Institute of Graphics and Image School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781450348171

Aiming at the limitation of single feature classification, this paper proposes a facial expression recognition algorithm based on feature fusion and sparse representation residual fusion. The proposed method is composed of three components. Firstly, a feature fusion method based on local binary quantization (LBP) and local phase quantization (LPQ) is proposed. The LBP/LPQ features are an expression feature with image space and frequency domain. Second, Curvelet transform is used to extract the global features of facial expression, which is a very effective multi-scale, band-pass and direction analysis tool. Finally, the LBP/LPQ features and the Curvelet feature are classified by the sparse representation based classification (SRC) with stronger discrimination and robustness respectively. According to the sparse residuals of LBP/LPQ+SRC and Curvelet+SRC, a classification method of residual fusion is proposed. Experiments on JAFFE database and Cohn-Kanade database show that the proposed method effectively improves the ability of facial expression recognition and achieves a high recognition rate. © 2017 ACM.

关键词： Face recognition

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Privacy preserving query model using inverse laplacian differential technique

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Journal of Physics: Conference Series 2019年第1期1228卷

作者： K Sandhya Rani Kundra J Hyma P V G D Prasad Reddy K Venkata Rao Assistant Professor Department of Information Technology GVPCOE (A) Vishakhapatnam. Associate Professor Department of computer science engineering ANITS Vishakhapatnam. Professor Department of Computer Science & Systems Engineering Andhra University Vishakhapatnam.

Digitalization of data has proven its competent edge in the present digital era, leading to aggregation of mass amounts of data. This segregated data is made expendable by discovering interesting patterns made possible by 'Data Mining'. Setting apart its perks, it possesses a discernible intimidation of outpouring personal information of data owners to anonymous resources. As always privacy is a prime issue of concern, On the other hand trading absolute privacy for loss of certainty of a query may not produce efficient patterns like in in-depth analysis of the source data sets. To maintain a balance among the above mentioned areas of interest 'Privacy Preserving Data Mining (PPDM) using various privacy techniques is employed. The proposed work potentiates the working of privacy protection technique namely differential privacy using privacy parameter ε with heterogeneous adaptable method. In addition, this framework provides a basis for working with the inverse laplacian data distortion for better privacy protection in an interactive query model.

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Rock glacier monitoring using aerial photographs: Conventional vs. UAV-based mapping - A comparative study

Rock glacier monitoring using aerial photographs: Convention...

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2018 ISPRS Technical Commission I Midterm Symposium on Innovative Sensing - From Sensors to Methods and Applications

作者： Kaufmann, V. Seier, G. Sulzer, W. Wecht, M. Liu, Q. Lauk, G. Maurer, M. Institute of Geodesy Graz University of Technology Steyrergasse 30 Graz8010 Austria Department of Geography and Regional Science University of Graz Heinrichstraße 36 Graz8010 Austria Institute of Applied Geosciences Graz University of Technology Steyrergasse 30 Graz8010 Austria Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria

Rock glaciers are creep phenomena of mountain permafrost. Typically, these landforms look like lava flows from a bird's eye view. Active rock glaciers move downslope with flow velocities in the range of few centimeters to several meters per year. Thus, large masses of rock and ice can be gradually transported down-valley. In this paper we present a comparative study analyzing surface change for Tschadinhorn rock glacier, a relatively fast moving rock glacier located in the Hohe Tauern Range of the Austrian Alps. Aerial photographs (1954-2017) of both metric (conventional) and non-metric (UAV-based) aerial surveys were compared to derive multi-annual to annual flow vector fields and surface height change. For each time interval given we computed a single representative value for flow velocity and, if applicable, also for area-wide surface height change, i.e. volume change. The velocity graph obtained represents the temporal evolution of the kinematics of the rock glacier with good discrimination. Volume change was difficult to quantify since temporal changes were rather small and close to insignificance. The precision and accuracy of the results obtained were numerically quantified. Our study showed that for the Tschadinhorn rock glacier UAV-based aerial surveys can substitute conventional aerial surveys as carried out by national mapping agencies, such as the Austrian Federal Office of Metrology and Surveying (BEV). Thus, UAV-based aerial surveys can help to bridge the data gap between regular aerial surveys. The high accuracy of the UAV-derived results would even allow intra-annual change detection of flow velocity. © Authors 2018. CC BY 4.0 License.

关键词： Unmanned aerial vehicles (UAV)

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Simulation of Transient Liquid Phase Bonding Process and the Influence of Interlayer Thickness

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IOP Conference Series: Materials science and Engineering 2020年第1期923卷

作者： B. Benita D.S. Samuvel Prem Kumar R. Pravin N.Samuel Dinesh Hynes J.Angela Jennifa Sujana Assistant Professor Department of Computer Science & Engineering Francis Xavier Engineering College Tirunelveli-627002 Asst.Professor Department of Mechanical Engineering JP College of Engineering Tenkasi Under Graduate Student Department of Mechanical Engineering Mepco Schlenk Engineering College (Autonomous) Sivakasi Independent Researcher & Former Part Time Lecturer Department of Computer Science & Engineering Government College of Engineering Tirunelveli-627007 Associate Professor Department of Information Technology Mepco Schlenk Engineering College (Autonomous) Sivakasi

Ceramic/metal joints are possible with silid state joining techniques. Transient liquid phase bonding is a solid state joining process which is choice process. In this paper, numerical simulation of TLP bonding of ceramic/metal is done by Finite Element simulation. The finite element analysis of the TLP process provides the temperature and residual stress distribution during the bonding. Aluminium sheet as interlayer having high wettability is used. Transient thermal analysis is performed and discussed.

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Study of the Management of Tickets in IT Administration

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IOP Conference Series: Materials science and Engineering 2019年第1期590卷

作者： Aditya Bhowmik Samrat Paul D. Usha Nandini S. Prince Mary U.G Student Department of Computer Science & Engineering Sathyabama Institute of Science and Technology Chennai India Assistant Professor Department of Computer Science & Engineering Sathyabama Institute of Science and Technology Chennai India

IT Services are very much crucial for the smooth functioning of the system. And so is their administration. IT Services should be monitored at periodically for maintaining an efficient and productive system at an IT Workplace. Errors or Incident are bound to occur, which are then converted into 'Tickets'. Every ticket has a general format in which they are created. Tickets are then sent to the Admin side for the further resolution procedures. Classification of tickets is an instrumental step in which the emerged tickets from a monitoring system are classified using context basis. After this, all the tickets have a correct hierarchy, they are then sent to separate ticket resolution teams for the incident resolution.

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Visualization and Classification of Diseases using Deep Learning Based Convolution Neural Network

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IOP Conference Series: Materials science and Engineering 2020年第1期937卷

作者： D.A Janeera P. Amudhavalli P Sherubha S.P Sasirekha P. Anantha Christu Raj R Sitharthan Assistant Professor Sri Krishna College of Engineering and Technology Coimbatore Assistant Professor Department of Computer Applications B S AbdurRahman Crescent Institute of Science and Technology Chennai Independent Researcher Coimbatore ORCID: 0000-0002-5945-2402 Research Scholar Karpagam Academy of Higher Education Coimbatore Assistant Professor Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore School of Electrical Engineering Vellore Institute of Technology Vellore

In the contemporary phase of big data, data visualization is one of the challenging segment of the discovery process. For a classifier, the primary goal is to identify the hidden levels of big data. The performance of classifiers depends on the feature space, number of classes and size of the data. To improve the reliability, efficiency and accuracy of the classifiers, new algorithms are required for analysis. This paper enables classification and visualization of information on diseases using a Deep Learning based Convolution Neural Network classifier. For feature selection and handling massive data in analysis of multivariate data is performed using particle swarm optimization (PSO) and principal component analysis (PCA) techniques. Real-world datasets are utilized for demonstration of the proposed learning algorithm. Deep learning classifiers are scientifically higher and offers better performance when compared to other classifiers according to the comparative study.

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Approaches For Automation In Assisting Evaluator For Grading Of Answer Scripts: A Survey

Approaches For Automation In Assisting Evaluator For Grading...

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International Conference on Computing, Communication & Automation (ICCCA)

作者： Rudragouda G. Patil Syed Zakir Ali Faculty School of Computer Engineering and Technology MIT Academy of Engineering Pune Alandi(D.) INDIA Professor Computer Science Department SECAB Institute of Engineering and Technology VTU Belagavi Vijayapur INDIA

ISBN: (纸本)9781538669488

Student who possess several qualities of intelligence and critical thinking skills are believed to think independently. To develop overall skills for a student, it is required that a student demonstrate his knowledge, skills, and abilities in a various ways i.e., through multiple choice questions, fill the gaps, problem solving, short and long answers. Assessment of short and long/essay answers is hectic and tedious tasks for several teachers. Learning Management Systems provide an opportunity to learn online. To develop critical thinking in an online environment, it is necessary to conduct exams and assessment online. Most online LMS tools support assessment through multiple choice questions, but these tools test only higher order thinking skills. Many researchers have tried to provide a system to automatically assess short and long/essay answers using several different approaches. The objective of survey is to highlight problems associated with the evaluator, discuss difficulties and limitations of online tools. In this literature, the methodologies used to develop various models of short answer grading given by several authors and their results are discussed. This paper highlights drawbacks/difficulties with automatic short answers grading. Authors tried to discuss the scope for researchers in this field of automated grading using machine learning algorithms. Authors recommend researchers to conduct an experiment in hybrid and feedback supported systems for grading answers.

关键词： Semantics Tools Education Computational modeling Machine learning Natural language processing

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Credit Card Transaction Based on Face Recognition technology

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Journal of Physics: Conference Series 2019年第1期1362卷

作者： Dhikhi, T Rana, Ajay Thakur, Anurag Kapoor, Karan Assistant Professor SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

This paper proposes a method for credit card transaction system which will make use of face recognition and face detection technology, using Haar Cascade and GLCM algorithm. The main problem faced by credit card users is attack to lot of privacy issues such as credit card. This generally happens when users give their credit card number to unknown people or when the card is lost. So, we are proposing a system that will reduce the risk of credit card frauds. The system we are proposing will match the image of user’s face with dataset of respective user. A database will be maintained for authentication purpose. If the image matches, that means user is genuine and he will be allowed to proceed otherwise, the user will be denied to do the transaction.

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