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Image frame mining using indexing technique 3rd

Image frame mining using indexing technique

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3rd international conference on Computer and Communication Technologies, IC3T 2016

作者： Saravanan, D. IFHE University IBS-Hyderabad HyderabadTelangana India

ISBN: (纸本)9789811032226

data mining is a technique the bring out hidden information effectively from an available data set. Most of this extraction works well when performed for binary and character information. mining information form images is a challenge today for many researchers. Creating of images and videos is easy as it does not require any domain knowledge, but extracting the required knowledge is difficult. For this reason, today video data mining is an interesting area for many researchers. To overcome these problems many researchers are motivated for finding an effective retrieval and indexing technique. This research paper brings a new technique for video content retrieval using hierarchical clustering technique. Objective of this work is to extract image key frames from the trained image set and use this as an image input query. The experiment proved that the proposed technique provided better results than existing video retrieval and indexing technique. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data mining

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Joint Observation of the Galactic Center with MAGIC and CTA-LST-1 38

Joint Observation of the Galactic Center with MAGIC and CTA-...

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38th international Cosmic Ray conference, ICRC 2023

作者： Abe, Shotaro Berti, Alessio Di Pierro, Federico López-Coto, Rubén Moralejo, Abelardo Ohtani, Yoshiki Sitarek, Julian Strzys, Marcel Suda, Yusuke Vovk, Ievgen Abe, H. Abe, K. Abe, S. Abhir, J. Acciari, V.A. Aguasca-Cabot, A. Agudo, I. Alvarez Crespo, N. Aniello, T. Ansoldi, S. Antonelli, L.A. Aramo, C. Arbet-Engels, A. Cornelia, A. Artero, M. Asano, K. Aubert, P. Baack, D. Babić, A. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernardos, M.I. Bernete Medrano, J. Berti, A. Besenrieder, J. Bhattacharjee, P. Biederbeck, N. Bigongiari, C. Biland, A. Bissaldi, E. Blanch, O. Bonnoli, G. Bordas, P. Bošnjak, Ž. Bulgarelli, A. Burelli, I. Burmistrov, L. Buscemi, M. Busetto, G. Campoy-Ordaz, A. Cardillo, M. Caroff, S. Carosi, A. Carosi, R. Carrasco, M.S. Carretero-Castrillo, M. Cassol, F. Castro-Tirado, A.J. Cauz, D. Cerasole, D. Ceribella, G. Chai, Y. Cheng, K. Chiavassa, A. Chikawa, M. Chilingarian, A. Chytka, L. Cifuentes, A. Cikota, S. Colombo, E. Contreras, J.L. Cortina, J. Costantini, H. Covino, S. D’Amico, G. Dalchenko, M. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. de Bony de Lavergne, M. De Lotto, B. De Lucia, M. de Menezes, R. Del Peral, L. Deleglise, G. Delfino, M. Delgado, C. Delgado Mengual, J. della Volpe, D. Dellaiera, M. Del Popolo, A. Depaoli, D. Di Piano, A. Di Pierro, F. Di Pilato, A. Di Tria, R. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Eisenberger, L. Elsässer, D. Emery, G. Escudero, J. Fallah Ramazani, V. Fariña, L. Fattorini, A. Ferrara, G. Ferrarotto, F. Fiasson, A. Foffano, L. Font, L. Freixas Coromina, L. Fröse, S. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Garczarczyk, M. Gasbarra, C. Gasparrini, D. Gasparyan, S. Gaug, M. Geyer, D. Giesbrecht Paiva, J. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Grau, R. Green, J. Green, D. Gunji, S. Günther, P. Hackfeld, J. Hadasch, D. Hahn, A. Institute for Cosmic Ray Research Kashiwa-no-ha 5-1-5 Kashiwa-shi Chiba Japan Max-Planck-Institut für Physik Föhringer Ring 6 München80805 Germany INFN Sezione di Torino Via P. Giuria 1 Torino10125 Italy Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Faculty of Physics and Applied Informatics University of Lodz ul. Pomorska 149-153 Lodz90-236 Poland Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Institute for Cosmic Ray Research University of Tokyo Japan Department of Physics Tokai University Japan ETH Zürich Switzerland Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain EMFTEL department IPARCOS Universidad Complutense de Madrid Spain Italy INFN Sezione di Trieste Università degli Studi di Udine Italy Italy INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy INFN Sezione di Padova Università degli Studi di Padova Italy LAPP Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 Annecy France Department of Physics TU Dortmund University Germany Croatia Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan Universidad del Azuay Ecuador INAF Osservatorio di Astrofisica e Scienza dello spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil CIEMAT Spain INFN Sezione di Bari Politecnico di Bari Italy INAF Osservatorio Astronomico di Brera Italy University of Geneva Département de physique nucléaire et corpusculaire Switzerland INFN Sezione di Catania Italy Departament de Física CERES-IEEC Universitat Autónoma de Barcelona Spain Italy Università di Pisa INFN Pisa Italy Aix Mars

MAGIC is a system of two Imaging Atmospheric Cherenkov Telescopes (IACTs), designed to detect very-high-energy gamma rays, and is operating in stereoscopic mode since 2009 at the Observatorio del Roque de Los Muchachos in La Palma, Spain. In 2018, the prototype IACT of the Large-Sized Telescope (LST-1) for the Cherenkov Telescope Array, a next-generation ground-based gamma-ray observatory, was inaugurated at the same site, at a distance of approximately 100 meters from the MAGIC telescopes. Using joint observations between MAGIC and LST-1, we developed a dedicated analysis pipeline and established the threefold telescope system via software, achieving the highest sensitivity in the northern hemisphere. Based on this enhanced performance, MAGIC and LST-1 have been jointly and regularly observing the Galactic Center, a region of paramount importance and complexity for IACTs. In particular, the gamma-ray emission from the dynamical center of the Milky Way is under debate. Although previous measurements suggested that a supermassive black hole Sagittarius A* plays a primary role, its radiation mechanism remains unclear, mainly due to limited angular resolution and sensitivity. The enhanced sensitivity in our novel approach is thus expected to provide new insights into the question. We here present the current status of the data analysis for the Galactic Center joint MAGIC and LST-1 observations. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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A Model Framework for Enhancing Document Clustering Through Side Information 1st

引用

1st international conference on Soft computing: Theories and Applications, SoCTA 2016

作者： Motwani, Kajal R. Jitkar, B.D. D. Y. Patil College of Engineering & Technology KolhapurMaharashtra India

ISBN: (纸本)9789811056864

mining textual data is the need of this era. In most text mining applications, side information is associated with the text documents. This side information consists of document origin information, links present in document, user-access behavior which can be retrieved from web logs, and different kinds of non-textual attributes. Such side information may include explanatory data which can be beneficial for text clustering. Conventional text clustering methods are available that perform quite well but do not consider such attributes. As side information consists of meaningful data, it can assist in enriching the quality of clusters by incorporating such side information into clustering process. Nevertheless, not all side information is important. Hence it should be incorporated carefully. Herein, an effective clustering technique is proposed which adds only important side information in the clustering process and determines if incorporating side information improves the cluster quality. The clustering technique is extended to classification problem. © 2018, Springer Nature Singapore Pte Ltd.

关键词： data mining

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Learning with Big data Technology: The Future of Education 3rd

Learning with Big Data Technology: The Future of Education

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3rd international Afro-European conference for Industrial Advancement (AECIA)

作者： Birjali, Marouane Beni-Hssane, Abderrahim Erritali, Mohammed Univ Chouaib Doukkali Fac Sci Dept Comp Sci LAROSERI Lab El Jadida Morocco Univ Sultan Moulay Slimane Fac Sci & Technol Dept Comp Sci TIAD Lab Beni Mellal Morocco

ISBN: (纸本)9783319608341;9783319608334

The use of Big data systems in the field of education allows to envisage new approaches and new learning contexts. Indeed, the rapid emergence of the new e-learning platforms have been presented in many interests. However, the quality of the teaching service rendered depends on the capacity of the learning approaches to be provided to learners, content and learning path tailored to their needs. In this paper, we will present how Big data helps to solve education issues through reaching the objective of learning. Then, we will introduce some opportunities of Big data analytics to develop the efficiency and effectiveness of students learning and maximize their knowledge retention. Finally, our research method show that students can generate personalized activities and offer academic advising. Big data can expose the capabilities of learners, predict their future performances and offer assistance for educational organizations to make strategic decisions.

关键词： Big data Education data mining Learning analytics e-Learning

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E-learning: Mode to improve the quality of educational system 1st

E-learning: Mode to improve the quality of educational syste...

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1st international conference on Smart computing and Informatics, SCI 2016

作者： Sheshasaayee, Ananthi Nazreen Bee, M. Quaid-e-Millath Government College for Women ChennaiTamil Nadu India

ISBN: (纸本)9789811055461

data mining plays a very important role in uncovering unseen information from large volumes of data. Educational data mining (EDM) is a specific data mining field applied in discovering invisible knowledge from the educational database. In EDM, e-learning environment is an important field to learn the context of learning environments. The intend of the research paper is to look up into the current trends in the e-learning environment. This paper mainly focuses, in understanding the research done and current trends in e-learning environment. This paper reports, how the previous scholars tackled the earlier/recent trends in e-learning research. This work makes use of Harzing’s Publish software for analyzing and visualizing the conduct level of the research papers. This study utilizes cites space, for analyzing tendency in e-learning research and to summarize the learning model and design for educational technology system appropriately. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data mining

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Predictive classification of ECG parameters using association rule mining

Predictive classification of ECG parameters using associatio...

引用

international conference on Computer, Communication and Computational Sciences, ICCCCS 2016

作者： Tyagi, Kratika Thakur, Sanjeev Department of Computer Science & Engineering ASET Amity University NoidaUttar Pradesh India

ISBN: (纸本)9789811037726

data mining is the procedure of extricating valuable information from the tremendous information stored in the database. Association rule mining is one of the most important and powerful data mining techniques. Association rule mining is normally carried out in two stages: first is to find frequent item set and second is to utilize those item sets to recognize the association rules. In recent medical history of cardiac arrests it has been observed that a huge gap exists in interpreting ECG data among differently skilled doctors. In this paper we will use the principle of meta-analysis and will reduce the gap between the interpretations of different doctors by employing statistical techniques like correlation and multiple linear regression. We would also generate rules using predictive apriori association rule mining among the various attributes of ECG to classify whether a patient requires an ECG before a cardiac arrest or not. The purpose of carrying out this work is to reduce the fatality rate and be able to predict that whether a patient requires ECG before actually facing a cardiac arrest and to minimize the cases of wrong interpretation. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data mining

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A Novel Architecture for k-means Clustering Algorithm 3rd

A Novel Architecture for <i>k</i>-means Clustering Algorithm

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3rd international Afro-European conference for Industrial Advancement (AECIA)

作者： Khawaja, S. G. Khan, Asad Mansoor Akram, M. Usman Khan, Shoab A. Natl Univ Sci & Technol Coll Elect & Mech Engn Islamabad Pakistan

ISBN: (纸本)9783319608341;9783319608334

Technological advancements in todays information age has helped the researchers to capture digital footprints of humans with regards to their daily activities. These logs of information posses valuable information for the data analytics who process it to find hidden pattern and unique behavior. Among the many algorithms k-means clustering is one of the very popular and widely used algorithm in the field of data mining and machine learning. k-means provides natural segments of dataset provided for clustering. It uses proximity to assign data points to a specific cluster, here the criteria of allocation is the minimum distance from the cluster center. Unfortunately, the rate of data growth has not been met by the speed of the algorithms. A number of hardware based solutions have been proposed to increase the processing power of different algorithms. In this paper, we present a novel algorithm for k-mean clustering which exploits the data redundancy occurring in the dataset. The proposed algorithm performs computations for the available unique items in the dataset and uses its frequency to finalize the results. Furthermore, FPGA based hardware architecture for the proposed algorithm is also presented in the paper. The performance of the proposed algorithm and its hardware implementation is evaluated using execution time, speedup and throughput. The proposed architecture provides speedup of 23 times and 2600 times against sequential hardware architecture and software implementation with a very small area requirement.

关键词： data mining

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Proceedings - 13th IEEE international conference on Ubiquitous Intelligence and computing, 13th IEEE international conference on advanced and Trusted computing, 16th IEEE international conference on Scalable computing and Communications, IEEE international conference on Cloud and Big data computing, IEEE international conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016

Proceedings - 13th IEEE International Conference on Ubiquito...

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13th IEEE international conference on Ubiquitous Intelligence and computing, 13th IEEE international conference on advanced and Trusted computing, 16th IEEE international conference on Scalable computing and Communications, IEEE international conference on Cloud and Big data computing, IEEE international conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016

ISBN: (纸本)9781509027705

The proceedings contain 166 papers. The topics discussed include: activity recognition based on the dynamic coordinate transformation of inertial sensor data;plogs: materializing datalog programs with mapreduce for scalable reasoning;evaluating the impact of data transfer time and mobility patterns in opportunistic networks;improving the reliability of pervasive computing applications by continuous checking of sensor readings;a navigation cognitive system driven by hierarchical spiking neural network;heterogeneous data driven manifold regularization model for fingerprint calibration reduction;towards efficient data services in vehicular networks via cooperative infrastructure-to-vehicle and vehicle-to-vehicle communications;dynamic domain model for micro context-aware adaptation of applications;human activity recognition of continuous data using hidden Markov models and the aspect of including discrete data;and build smart campus using human behavioral data.

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Comparative Analysis of Clustering Techniques for Customer Behaviour 1st

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1st international conference on Soft computing: Theories and Applications, SoCTA 2016

作者： Shalini Singh, Deepika Teerthanker Mahaveer University MoradabadUttar Pradesh India

ISBN: (纸本)9789811056987

In real world, customer behaviour is changing and evolving over time. Business in each industry endeavours to expand client esteem and accomplish a high level state of consumer loyalty. Sells group concentrate on open doors for cross-sell and up-sell while client mind concentrates on certain key measurements, for example first call determination, snappy determination to client’s issues, best service levels and quality scores. Customer clustering is utilized to know about behavioural patterns of customers so that industry or organization can make their marketing strategies according to the customers’ preferences and retain them. This paper shows the performance review of clustering data mining techniques to know which technique is more suitable to identify customer profile and patterns for a retail store, to improve better customer satisfaction and retention. © 2018, Springer Nature Singapore Pte Ltd.

关键词： data mining

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A Fast Track Approach Towards Automatic Detection of Gray Zone Activities from Text

A Fast Track Approach Towards Automatic Detection of Gray Zo...

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AHFE international conference on Cross-Cultural Decision Making (CCDM)

作者： Schlachter, Jason Lautenschlager, Jennifer Lockheed Martin Adv Technol Labs Suite 250 1825 Barrett Lakes Blvd NW Kennesaw GA 30144 USA

ISBN: (纸本)9783319607474;9783319607467

Gray Zone Operations (GZ Ops) are increasingly being used by countries to achieve national security objectives that have historically only been achieved through armed conflict. GZ Ops employ actions that are diverse, covert, and adversarial but fall below the threshold required to trigger an aggressive response from major powers. We aim to help analysts with early identification of Gray Zone Operations by automatically detecting taxonomy-backed Gray Zone activities in news stories and text. We map our proven geo-political event extraction and data processing capabilities previously developed under the World-Wide Integrated Crisis Early Warning System (W-ICEWS) program to Gray Zone activities and apply these mappings to text from a corpus of Russian news stories from 2015 to 2016 to generate a set of sentences labeled with specific Gray Zone activities. We evaluate the validity of these matches to determine if the sentences and stories accurately reflect their label.

关键词： Gray zone NLP Text analytics Event coding data mining Taxonomy

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