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6th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA-2017

作者： Pham, Long Hoang Phan, Hung Ngoc Le, Duong Hai Viet-Uyen Ha, Synh School of Computer Science and Engineering International University Vietnam National University HCMC Block 6 Linh Trung Thu Duc District Ho Chi Minh City Viet Nam

ISBN: (纸本)9789811075650

Shadow is one of the common parts in the natural scenes and has become an important topic in the field of computer vision. In many vision-based traffic surveillance systems, shadows interfere with fundamental tasks such as vehicle detection, classification, and tracking. thus, it is necessary to suppress the effect of shadows. A difficult part of the shadow removal problem is how to accurately detect and remove shadow regions and recover the boundaries of the vehicles, while still achieving real-time processing performance. Many powerful methods have been proposed to solve this dilemma;however, instabilities at the boundaries of moving vehicles are still challenges. In this paper, an improved algorithm to remove shadow regions, and quickly recovering the boundaries of moving vehicles is presented in a detailed manner. the proposed method applies edge information with background subtraction to handle daytime traffic scenes. Our approach has demonstrated more accurate results than previous approaches regardless of lighting luminance levels or shadow orientations. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Edge detection

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A new streaming learning for stream chunk data classification based on incremental learning and adaptive boosting algorithm 13th

A new streaming learning for stream chunk data classificatio...

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13th international conference on Computing and Information technology, IC2IT 2017

作者： Claypo, Niphat Hanskunatai, Anantaporn Jaiyen, Saichon Department of Computer Science King Mongkut’s Institute of Technology Ladkrabang Ladkrabang Bangkok Thailand

ISBN: (纸本)9783319606620

Currently, stream data classification is a challenge task to discover new useful knowledge from massive and dynamic data in big data era. this paper proposes a streaming learning method based on the incremental learning using a new adaptive boosting algorithm for stream data. the proposed adaptive boosting consists of a new method for updating distribution weight and the new weight voting. this learning method concentrates on learning from sequential chunks of data stream. the distribution weight updating method uses error of previous hypothesis to update the weight. the learning method uses only one data chunk to create a new hypothesis at a time and after learning, the learned data chunk can be thrown away and can learn the new data chunk without using the previous learned data. the experimental results show that the accuracy of the proposed method is higher than other methods in all datasets. © Springer international Publishing AG 2018.

关键词： Classification (of information)

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Comparative study of computational time that HOG-based features used for vehicle detection 13th

Comparative study of computational time that HOG-based featu...

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13th international conference on Computing and Information technology, IC2IT 2017

作者： Laopracha, Natthariya Sunat, Khamron Department of Computer Science Faculty of Science Khon Kaen University Khon Kaen Thailand

ISBN: (纸本)9783319606620

HOG produces a number of redundant and long features so that they affect to the detection rate and computational time. this paper studied the processes that HOG-based features were generated, selected, and used in vehicle detection and find one that takes the shortest time. there were five combinations of feature extractors and classifiers. Time spent in HV step, accuracy of detection and the false positive rate are considered together for making decision of which combination is the best. the experiments were conducted on GIT dataset. the experimental results showed that process which VHOG preceded ELM provided a little less accurate than HOG preceded SVM did. However, it did not only take shortest time in HV step but also provided the lowest false positive rate. therefore, VHOG preceded ELM should be selected as a method for vehicle detection. © Springer international Publishing AG 2018.

关键词： Support vector machines

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On the way to perfect steganography 9th

On the way to perfect steganography

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9th international conference on Image Processing and Communications, IP and C 2017

作者： Jakubski, Artur Bobulski, Janusz Institute of Computer and Information Sciences Czestochowa University of Technology ul. J.H. Dabrowskiego 73 Czestochowa42-201 Poland

ISBN: (纸本)9783319687193

the content of our paper presents the ways of hiding information with the usage of a digital pictures. A branch of science that deals with hiding messages in the wider media is called steganography. Encrypting files with sensitive data usually do not hide the existence and transmission of data. It seems therefore that a significant complement or alternative for cryptographic methods is the use of steganographic methods. Due to the rapid expansion of Internet and the associated increase in data exchange appears to be a subject of interest in this field. In the paper we present our own algorithms of hiding data in pictures, their implementations and analysis. In this paper we emphasize high quality, undetectability of the methods used. We define perfect steganography and we propose solutions to achieve this goal. the proposed solution can be used to transmit secret information and to find use in business, diplomacy, and other areas of human activity. © 2018, Springer international Publishing AG.

关键词： Steganography

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Old-age health risk prediction and maintenance via IoT devices and artificial neural network 6th

Old-age health risk prediction and maintenance via IoT devic...

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6th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA-2017

作者： Prajapati, Dayashankar Bhargavi, K. Department of Computer Science and Engineering Siddaganga Institute of Technology TumkurKarnataka572103 India

ISBN: (纸本)9789811075650

IoT is the collaboration of physical devices, electronics, software, sensors, actuators, and network connectivity which helps in collecting and exchanging of data. It is a smart technology which cooperates with the environment by the sensors and actuators which are found very useful in monitoring the health of the people. It holds different features such as diagnosis, signal analysis, drug development, medical image analysis, and radiology. these features of IoT devices are used for monitoring the health of old-age citizen as they are highly influenced by several diseases which require continuous monitoring and treatment. In this paper, a novel IoT and neural network-based old-age health risk prediction framework is proposed, and the performance of the proposed framework is found to be good with respect to parameters like time consumption, monitoring efficiency, and cost incurred. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Health risks

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Emerging technologies and opportunities for innovation in financial data analytics: A Perspective 6th

Emerging technologies and opportunities for innovation in fi...

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6th international conference on Big Data Analytics, BDA 2018

作者： Mondal, Anirban Singh, Atul Department of Computer Science Ashoka University SonepatHaryana India BAI7 IIM Bangalore Bengaluru India

ISBN: (纸本)9783030047795

Several key transformations in the macro-environment coupled with recent advances in technology have opened up tremendous opportunities for innovation in the financial services industry. We discuss the implications and ramifications of these macro-environmental trends for data science research. Moreover, we describe novel and innovative IT-enabled applications, use-cases and techniques in retail financial services as well as in financial investment services. Furthermore, this paper identifies the research challenges that need to be addressed for realizing the full potential of innovation in financial services. Examples of such research challenges include context-aware analytics over uncertain and imprecise data, data reasoning and semantics, cognitive and behavioural analytics, design of user-friendly interfaces for improved expressiveness in querying financial service providers, personalization based on fine-grained user preferences and financial Big Data processing on Cloud-based infrastructure. Additionally, we discuss new and exciting opportunities for innovation in financial services by leveraging the new and emerging financial technologies as well as Big Data technologies. © Springer Nature Switzerland AG 2018.

关键词： Data Analytics

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Application of TF-IDF feature for categorizing documents of online Bangla web text corpus 1

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6th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA-2017

作者： Dhar, Ankita Dash, Niladri Sekhar Roy, Kaushik Department of Computer Science West Bengal State University Kolkata India Linguistic Research Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9789811075667

ISBN: (纸本)9789811075650

this paper explores the use of standard features as well as machine learning approaches for categorizing Bangla text documents of online Web corpus. the TF-IDF feature with dimensionality reduction technique (40% of TF) is used here for bringing in precision in the whole process of lexical matching for identification of domain category or class of a piece of text document. this approach stands on the generic observation that text categorization or text classification is a task of automatically sorting out a set of text documents into some predefined sets of text categories. Although an ample range of methods have been applied on English texts for categorization, limited studies are carried out on Indian language texts including that of Bangla. Hence, an attempt is made here to analyze the level of efficiency of the categorization method mentioned above for Bangla text documents. For verification and validation, Bangla text documents that are obtained from various online Web sources are normalized and used as inputs for the experiment. the experimental results show that the feature extraction method along with LIBLINEAR classification model can generate quite satisfactory performance by attaining good results in terms of high-dimensional feature sets and relatively noisy document feature vectors. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Inverse problems

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Proceeding of the 13th international Bologna conference on magnetic resonance in porous media (MRPM13)

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MICROPOROUS AND MESOPOROUS MATERIALS 2018年 269卷 1-2页

作者： Bortolotti, Villiam Fantazzini, Paola Arns, Christoph Galvosas, Petrik Univ Bologna Alma Mater Studiorum Dept Civil Chem Environm & Mat Engn Bologna Italy Univ Bologna Alma Mater Studiorum Dept Phys & Astron Bologna Italy Univ New South Wales Sch Petr Engn Kensington NSW Australia Victoria Univ Wellington Sch Chem & Phys Sci MacDiarmid Inst Adv Mat & Nanotechnol Wellington New Zealand

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A computational intelligent learning architecture in cloud environment 6th

A computational intelligent learning architecture in cloud e...

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6th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA-2017

作者： Paul, Mridul Das, Ajanta Department of Computer Science & Engineering Birla Institute of Technology Mersa Ranchi India

ISBN: (纸本)9789811075650

Being a paradigm shift, cloud computing provides various utility services through virtualization of different computing resources. With fast, progressive, and simultaneous advancement of technology in distributed environment, provisioning of learning and teaching services in the cloud is a novel arena to virtually teach common individual across the globe. In order to monitor the accessing services to satisfy those individual consumer or society, negotiating and signing service level agreement (SLA) is essential. Hence, the objective of the paper is not only to provisioning e-Learning services in cloud, but also to enlist the functional parameters related to the services in SLA for monitoring purposes. this paper proposes a computational intelligent service provisioning and monitoring architecture along with the functionality and role of each component. this paper also presents the instantiation and evaluation of the proposed computational intelligent e-Learning architecture using the public cloud provider, Google App Engine, and monitors the quality of service. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Cloud computing

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Knowledge engineering for distributed case-based reasoning systems 1st

Knowledge engineering for distributed case-based reasoning s...

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1st Workshop on Knowledge Engineering and Software Engineering, KESE 2005, Co-Located with 28th German conference on Artificial Intelligence, KI-2005

作者： Bach, Kerstin Department of Computer and Information Science Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9783319641607

this chapter describes how to identify and collect human knowledge and transform it into machine readable and actionable knowledge. We will focus on the knowledge acquisition for distributed case-based reasoning systems. Case-based reasoning (CBR) is a well-known methodology for implementing knowledge-intensive decision support systems (Aamodt, Plaza, Artif Intell Commun, 7(1):39–59, 1994) [1] and has been applied in a broad range of applications. It captures experiences in the form of problem and solution pairs, which are recalled when similar problems reoccur. In order to create a CBR system the initial knowledge has to be identified and captured. In this chapter, we will summarise the knowledge acquisition method presented by Bach, Knowledge acquisition for case-based reasoning systems. Ph.D. thesis, University of Hildesheim, München (2012) [2] and give an running example within the travel medicine domain utilising the open source tool for developing CBR systems, myCBR. © 2018, Springer international Publishing AG.

关键词： Case based reasoning

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