Image Data Classification using Hadoop Based on Semi Supervise Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-47 Number-5
Year of Publication : 2017
Authors : Dr. Pratik Gite, Aditya Acharya, Udit Gupta
  10.14445/22315381/IJETT-V47P244

MLA 

Dr. Pratik Gite, Aditya Acharya, Udit Gupta "Image Data Classification using Hadoop Based on Semi Supervise Algorithm", International Journal of Engineering Trends and Technology (IJETT), V47(5),270-274 May 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
In this paper, an technique is presented for storing and dispensation bulky satellite images by using the Hadoop MapReduce framework and HDFS(Hadoop distributed file system)by incorporate Remote Sensing image processing tools into MapReduce The huge volume of visual data in current years and their require for efficient and efficient processing arouse the exploit of distributed image processing frameworks in image processing area. So that up to the imminent years, numerous algorithms which have been bring in in the field of image processing and pattern recognition should believe the necessities for macro image processing in order to be salutation by the outside world. This paper provides an indication of distributed processing method and the programming models. . To proposed image data classification with hadoop based on semi supervise SVM learning algorithm. The experiment consequence illustrate that the proposed system can attain a enhanced consequence even dealing with big data volume.

 References

[1] Yang Hu, Member, IEEE, Venkat Yashwanth Gunapati, Pei Zhao, Devin Gordon, Nicholas R.Wheeler,,” A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data From Multiple Heterogeneous Photovoltaic Test Sites” IEEE JOURNAL OF PHOTOVOLTAICS, VOL. 7, NO. 1, JANUARY 2017
[2] Swapnil Arsh , Abhishek Bhatt†, Praveen Kumar,” Distributed Image Processing Using Hadoop and HIPI” 2016 Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 21-24, 2016, Jaipur, India.
[3] Abdulrahman Alhamali, Nibal Salha, Raghid Morcel, Mazen Ezzeddine, Omar Hamdan, Haitham Akkary, and Hazem Hajj ,” FPGAAccelerated Hadoop Cluster for Deep Learning Computation” 2015 IEEE 15th International Conference on Data Mining Workshops.
[4] Michael R. Evans, Dev Oliver,XunZhou, and Shashi Shekhar Spatial Big Data: Case Studies on Volume, Velocity, and Variety , in Big Data: Techniques and Technologies in Geoinformatics ,isbn 978-1-46-6586512, CRC Press, 2014.
[5] Almeer, M. H., Cloud hadoop map reduce for remote sensing image analysis,Journal of Emerging Trends in Computing and Information Sciences 3(4): 637-644 ,2012.
[6] K.Bakshi, Considerations for Big Data: Architecture and Approach Aerospace Conference-Big Sky, MT, 3-10 March 2012.
[7] K. Michael, and K. W. Miller, Big Data: New opportunities and New Challenges, IEEE Computer, 46 (6) (2013): 22-24. [8] The Apache Hadoop Project, http://hadoop.apache.org
[9] J. Kelly, Big Data: Hadoop, Business Analytics and Beyond, Wikibon Whitepaper,27thAugust2012, http://wikibon.org/wiki/v/Big Data: Hadoop, Business Analytics and Beyond.
[10]Y.Huetal.,“ComparisonofmulticrystallinesiliconP Vmodules’performanceunderaugmentedsolarirradiati on,”MRSProc.,vol.1493,pp.3–9, 2013.
[11] M. A. Hossain et al., “Microinverter thermal performance in the realworld: Measurements and modeling,” PloS One, vol. 10, no. 7, 2015, Art. no. e0131279.
[12] RCoreTeam,R:ALanguageandEnvironmentforStatisti calComputing. Vienna, Austria: R Found. Statist. Comput., 2016. [Online]. Available: https://www.Rproject. org/.
[13] J. S. Fada et al., “Democratizing an electroluminescence imaging apparatus and analytics project for widespread data acquisition in photovoltaic materials,” Rev. Sci. Instrum., vol. 87, no. 8, 2016, Art. no. 085109.
[14] M. Adhikari et al., “NoSQL databases,” in Handbook of Research on Securing Cloud-Based Databases with Biometric Applications. Hershey, PA, USA: IGI Global, 2014, p. 109.
[15] M. ˇ S´uri et al., “SolarGIS: Solar data and online applications for PV planning and performance assessment,” in Proc. 26th Eur. Photovoltaics Sol. Energy Conf., 2011, pp. 3930–3934.
[16]A.Woyteetal.,“Monitoringofphotovoltaicsystems: Goodpracticesand systematic analysis,” in Proc. 28th Eur. Photovoltaic Sol. Energy Conf., 2013, pp. 3686– 3694.

Keywords
big data, Hadoop, yet another resource negotiator (YARN), parallel processing, remote sensing (RS).