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International Conference and Workshop on Computing and Communication (IEMCON)

作者： Akash Dutta Arunabha Ghosh Department of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9781509009978

An Image is a visual representation of any object, place, person etc. In the field of computer science a Digital Image is a numeric representation of a two-dimensional image. Often images may contain texts, which are a sequence of human-readable characters. Our general conundrum is extraction of texts from an image for processing and editing. In an image texts are defined by set of pixels just like any other object and thus cannot be processed or edited. In this work we have strived to develop a system which can extract texts from a digital image of a Sudoku puzzle, solve the puzzle, and then provide a solution. Our approach is specific but its application are varied.

关键词： Classification algorithms Character recognition Digital images Databases Optical character recognition software computer science

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Learning automata based decision making algorithm for task offloading in mobile cloud

Learning automata based decision making algorithm for task o...

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2016 International Conference on computer, Information and Telecommunication Systems, CITS 2016

作者： Krishna, P. Venkata Misra, Sudip Nagaraju, D. Saritha, V. Obaidat, Mohammad S. Department of Computing Science SPM University Tirupati India School of Information Technology Indian Institute of Technology Kharagpur India School of Computing Science and Engineering VIT University Vellore India Dept. of Computer and Information Science Fordham University FordhamNY United States

ISBN: (纸本)9781509034406

In recent years, mobile cloud computing (MCC) is treated as one of the important enablers of Internet of Things. MCC has evolved from a mix of mobile computing and cloud computing. Most of the researcher works on MCC focus on the reduction of cost of applications in mobile devices by leveraging cloud technology. The limitations of mobile environment such as computation capacity, battery power and limited memory lead to the integration of cloud technology. The performance of the mobile environment improves by the implementation of task offloading using cloud technology. In this paper, a model for task offloading using learning automata based decision making algorithm (LADMA) is proposed. The algorithm considers the completion time and energy consumption of the tasks during the allocation of the tasks to the suitable VMs in the cloud. The proposed model is tested on Amazon EC2 and Android X 86 Platforms. © 2016 IEEE.

关键词： Decision making

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Palm fruit harvester algorithm for elaeis guineensis oil palm fruit grading using UML 6

Palm fruit harvester algorithm for elaeis guineensis oil pal...

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6th IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015

作者： Patkar, Gaurang Anjaneyulu, G.S.G.N. Mouli, P.V.S.S.R. Chandra School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Advanced Sciences Vellore Institute of Technology Vellore India

ISBN: (纸本)9781479978489

This paper intends to solve palm fruit grading using the proposed prototype and Palm fruit ripeness Unified Modeling Language diagrams. In this paper the issues of correct harvesting prediction, categorizing palm fruits and distinguishing Ripe and Overripe fruit is solved suing Unified Modeling Language (UML) diagrams. UML diagrams designed and shown in the paper helped to model and implement the palm fruit harvester algorithm. The proposed algorithm is implemented in Visual C++ and results are tested with the live data collected from field and farmers. This research will resolve the issue of manual grading thereby helping farmers in producing good quality oil production and also will help researchers to use the prototype for implementation. © 2015 IEEE.

关键词： Fruits

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Bigdata Analytics on Diabetic Retinopathy Study (DRS) on Real-time Data Set Identifying Survival Time and Length of Stay

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Procedia computer science 2016年 87卷 227-232页

作者： V. Sujatha S. Prasanna Devi S. Vinu Kiran S. Manivannan Phd Scholar MS University Assistant Professor Vellore Institute of TechnologyVellore Tamil Nadu 632014 India Department of Computer Science & Engineering Apollo Engineering College Chennai 602105 India Deputy Dean Dr.MGR Educational & Research Institute University Chennai 600107 India

In this paper, we had analyzed a large scale Diabetic data sets for several patients to find the length of time taken for treatment for each class of Diabetes and the risk of re-admission of diabetic patients performing Bigdata analytics, the type of diabetes and its outcome which acted as a high risk sample of patient data sets. We have collected and integrated different sources of diabetic information for several patients, from primary and secondary treatment information to administrative information, to analyze novel view of patient care processes such as type of treatments and every patient behaviors on which results multifaceted nature of chronic care that we take into our account to predict the survival factors and length of stay. Nowadays by using electronic medical equipments with high quality and high degree calibrations, we are able to gather large amounts of real-time diabetic data sets. The requires the usage of distributed platforms for making BigData analysis that results on making decisions based on available data and its trends. This type of Bigdata analysis allows geographical and environmental information of patients’ enables the capability of interpreting the ethnicity of data gathered and extract new analysis to identify survival options and treatment timelines (LOS) from them.

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Learning automata based decision making algorithm for task offloading in mobile cloud

Learning automata based decision making algorithm for task o...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： P. Venkata Krishna Sudip Misra D. Nagaraju V. Saritha Mohammad S. Obaidat Department of Computing Science SPM University Tirupati India School of Information Technology Indian Institute of Technology Kharagpur India School of Computing Science and Engineering VIT University Vellore India Dept. of Computer and Information Science Fordham University NY USA

ISBN: (纸本)9781509006915

关键词： Mobile communication Cloud computing Mobile handsets Learning automata Servers Energy consumption Decision making

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A person identification system combining recognition of face and lip-read passwords

A person identification system combining recognition of face...

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International Conference on Computing and Network Communications, CoCoNet 2015

作者： Radha, N. Shahina, A. Nayeemulla Khan, A. Department of Information Technology SSN College of Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9781467373098

This paper presents a person identification system which combines recognition of facial features as well as spoken word using visual features alone. It incorporates a face recognition algorithm to identify the person, followed by spoken word recognition of 'lip-read' password. For face recognition, PCA is used for feature extraction, followed by a KNN based classification on the reduced dimensionality features. Spoken word recognition of passwords is performed using a Visual Lip reading (Visual ASR). The visual features corresponding to the spoken word is extracted using DWT, which are then recognized using a HMM based approaches. Since evidences from face recognition and visual lip reading could be complementary in nature, the scores from the two modalities are combined. Based on the combined evidences, decision making is for person identification is carried out. The performance for face identification is 90% while the accuracy for visual speech recognition is 72%. By combining these evidences, an improved accuracy 98% is achieved. © 2015 IEEE.

关键词： Principal component analysis

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Execution of novel explicit RKARMS(4,4) technique in determining initial configurations of extra-solar protoplanets formed by disk instability

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NRIAG Journal of Astronomy and Geophysics 2016年第1期5卷 1-8页

作者： Gour Chandra Paul Sukumar Senthilkumar Department of Mathematics University of Rajshahi Rajshahi 6205 Bangladesh Universiti Sains Malaysia School of Mathematical Sciences Pulau Pinang 11800 Malaysia Center for Advanced Image and Information Technology Division of Computer Science and Engineering School of Electronics & Information Engineering Chon Buk National University 664-14 1Ga Deok Jin-Dong Jeonju Chon Buk 561-756 South Korea School of Computing Science and Engineering Vellore Institute of Technology University Tamilnadu Vellore 632014 India

Implementation of a novel embedded Runge–Kutta fourth order four stage arithmetic root mean square technique to determine initial configurations of extra-solar protoplanets formed by gravitational instability is the main goal of this present paper. A general mathematical framework for the introduced numerical technique is described in addition to error estimation description. It is noticed that the numerical outputs through the employed novel RKARMS(4,4) method are found to be more effective and efficient in comparison with the results obtained by the classical Runge–Kutta technique.

关键词： Novel RKARMS(4,4) technique Error estimates Conductive-radiative Disk instability Protoplanet

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Adaptive Commodity Suggestion System — Match-BOT

Adaptive Commodity Suggestion System — Match-BOT

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International Conference on Digital Information Processing and Communications (ICDIPC)

作者： Shreyaan Kaushal Jatin Agrawal Taranveer Singh Manav Gulati School of Computer Science and Engineering Vellore Institute of Technology

Potential customers of E-commerce garment businesses purchase clothes based on their personal choice as well as contemporary perception about fashion trends which is highly sublime in nature. This, coupled with the influential influx of cinema, social media and the Internet has led to rapid change in dressing preferences. E-commerce sites have a huge inventory categorized based on different attributes like colour, fabric, fit, product type and vendor. Hence, it is a tedious task for the user to find products coherent with the latest trend and appropriate according to their personal taste and global trends. The recommendation engines of these websites gives recommendations based only on a linear similarity of products, not comprehensively taking into account latest trends in fashion backed by fashion specialists, user preference or wardrobe collections. We propose an Adaptive Commodity Suggestion System called ”Match-BOT” which takes into account the trends obtained through insights provided by Google Trends, individual preferences, and similarity between user preferences. The user preferences are extracted by analyzing user purchase patterns and thus obtaining categorised product attribute preferences in the form of weights. This is an application of Content Based Learning The global trend obtained from Google Trends is then overlapped with the user preference and a user specific suggestion list is generated. The similarity between two user is calculated using Collaborative Filtering and then taken into account by including cross-user suggestions based on the degree of correlation. The output of this process would be in the form of list of suggestions of products which have been customized to society's understanding of fashion and predilections of the user and constantly updated to reflect dynamic global trends. This would result in an unparalleled browsing experience for the user and an efficient sales and successive inventory management systems.

关键词： Market research Cotton Google Correlation History Solids Fabrics

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Learning automaton based context oriented middleware architecture for precision agriculture

Learning automaton based context oriented middleware archite...

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International Conference on computer, Information and Telecommunication Systems, IEEE CITS 2015

作者： Venkata Krishna, P. Sivanesan, S. Misra, Sudip Obaidat, Mohammad S. Department of Computer Science Sri Padmavathi Mahila Visvavidyalayam Tirupati India School of Computing Science and Engineering VIT University Vellore India School of Information Technology Indian Institute of Technology Kharagpur India Dept. of Computer Science and Software Engineering Monmouth University NJ United States

ISBN: (纸本)9781467369459

The continuous alteration of living landscape in earth due to the greatest impact of information technology has converted the whole earth into a small digital village. That means anyone could share information in any form with any other person residing anywhere in the earth. With the availability of information and communication technology like cloud computing, heterogeneous networking, crowd sensing, web services and data mining, anywhere and anytime information sharing is possible, but this will bring out lot of challenges like incompatibility in standards, data portability, data aggregation, data dissemination, differential context and communication overhead. The ICT has changed many aspects of human lifestyle, work places and living spaces. However, there is one sector namely agriculture which has been deprived of the real advantages of ICT. Hence, there exists a digital divide between farming industry and other industries. Farming industry consumes large amount of natural resources such as water, energy and fertilizers that could escalate issues such as global warming, soil degradation and depletion of ground water to the next level. To reduce the global warming effect, farming industry needs to be integrated with relevant technologies. This paper proposes ubiquitous context oriented middleware architecture for precision agriculture (LA-COMPa) to solve major issues such as waste of water, improper application of fertilizer, choice of wrong crops and season, poor yield and lack of marketing. © 2015 IEEE.

关键词： Middleware

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