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The possessions about projection poses a challenge toward recognized semantics dynamic text theories, due this apparent nothing-compositional character. Projected elements are consequently characteristically analyzed because individual different from and independent of asserted content. Now above persons utilize dynamic text during actual life for communication with multiple chatting reasons. Dynamic texts be too uses within social posts, news titles, proceedings, investigate queries, tweets, conversation, key statements, and dynamic text sympathetic be a puzzling procedure within thoughts deals among top secret messages. Because dynamic text has additional than multiple sense, they be demanding toward appreciate because they be deafening with ambiguous. The expression be able to be some solitary or dynamic-statement. Semantics study be needed toward appreciate the dynamic text correctly. Goal for instance distribute talking classification, concept labeling along with segmentation be used for semantics analysis. Behavior dynamic text uses during actual life data. The prototype organization be constructed along with used to identify the dynamic text. These systems distribute the semantics information as of information base along with set of written statements to be automatically harvest. Now, we suggest such united, compositional semantics psychiatry about asserted as well as projected contented. Our analysis capture the similarity with difference among presupposition, anaphora, conventional implicatures with assertion lying on the origin of data structure, We celebrate our psychiatry during an addition about dynamic semantic framework about Discourse Representation Theory (DRT)—called Projective DRT (PDRT)—so as to employ projection attributes toward imprison the data structural in addition to compositional properties about PDRT facet about semantics contented; dissimilar constellation about such attributes imprison the difference among the dissimilar type about projected in addition to asserted satisfied inside a uni- dimension about connotation`s well as this semantics interpretation. We quarrel that this paves method intended for a additional listening carefully study about data-structural co-occurrence network along with phrase withdrawal presentation to superior recognize for dynamic text aspects about significance.

Dynamic Text, dynamic Semantics, Text segmentation, PDRT

[1]. Abrusán, M. & K. Szendroi (2013), ‘experimenting with thekinof France: Topics, verifiability and definite descriptions’. Semantics and Pragmatics 6: 1–43.
[2]. Amaral, P., Roberts, C. & E. A. Smith (2007), ‘Review of “The Logic of Conventional Implicatures” by Chris Potts’. Linguistics and Philosophy 30: 707–49.
[3]. AnderBois, S., Brasoveanu, A. & R. Henderson (2010), ‘Crossing the appositive/at-issue
[4]. Arnold, J. E., Kaiser, E., Kahn, J. M. & L. K. Kim (2013), ‘Information structure: linguistic, cognitive, and processing approaches’. Wiley Interdisciplinary Reviews: Cognitive Science 4: 403–13.
[5]. Asher, N. (2008), ‘Troubles on the right frontier’.In A. Benz and P. Kühnlein (eds.) Con-straints in Discourse, Pragmatics & Beyond New Series, vol. 172.Benjamins.Amsterdam.29–52.
[6]. Asher, N. & A. Lascarides (1998), ‘The semantics and pragmatics of presupposition’. Journal of Semantics 15: 239–99.
[7]. Asher, N. & A. Lascarides (2003), Logics of Conversation.Cambridge University Press.
[8]. Basile, V. & J. Bos (2013), ‘Aligning formal meaning representations with surface strings for wide-coverage text generation’.In Proceedings of the 14th European Workshop on Natural Language Generation, Sofia, Bulgaria.Association for Computational Linguistics.1–9.
[9]. Basile, V., Bos, J., Evang, K. & N. J. Venhuizen (2012), ‘Developing a large semantically annotated corpus’. In N. Calzolari, K. Choukri, T. Declerck, M. U. Doáan, B. Maegaard, J. Mariani, A. Moreno, J. Odijkand and S. Piperidis (eds).Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey.European Language Resources Association (ELRA).3196–200.
[10]. Beaver, D. I. (2002), Presupposition projection in DRT: A critical assessment. In D. I. Beaver, L. D. C. Martínez, B. Z. Clark, and S. Kaufmann (eds.) The Construction of Meaning.CSLI Publications.Stanford.California23–43.
[11]. Beaver, D.I. & B. Geurts (2011), ‘Presupposition’. In E. N. Zalta, (ed.) The Stanford Encyclopedia of Philosophy.Metaphysics Research Lab, CSLI, Stanford University, summer 2011 edition.
[12]. Blackburn, P., Bos, J., Kohlhase, M. & H. De Nivelle (2001),‘Inference and computational semantics’. Studies in Linguistics and Philosophy 11–28.
[13]. Bos, J. (2003), ‘Implementing the binding and accommodation theory for anaphora resolution and presupposition projection’. Computational Linguistics 29: 179–210.
[14]. Bos, J., Basile, V., Evang, K., Venhuizen, N. J. & J. Bjerva (2017),‘The Groningen Meaning Bank’. In N. Ide and J. Pustejovsky(eds.) Handbook of Linguistic Annotation.Springer.Netherlands, Dordrecht.463–96.
[15]. Bresnan, J., Cueni, A., Nikitina, T. & R. H. Baayen (2007), ‘Predicting the dative alternation’. In G. Bouma, I. Krämer and J. Zwarts (eds.), Cognitive Foundations of Interpretation. Royal Netherlands Academy of Arts and Sciences.69–94.
[16]. Chemla, E. & L. Bott (2013), ‘processing presuppositions: Dynamic semantics vs. pragmatic enrichment’. Language and Cognitive Processes 28: 241–60.
[17]. Del Gobbo, F. (2003), Appositives at the Interface.Ph.D.thesis, University of California, Irvine, CA.
[18]. Geurts, B. (1999), Presuppositions and Pronouns.Elsevier.
[19]. Geurts, B. (2010), ‘Specific indefinites, presupposition and scope’. In R. Bäuerle, U. Reyle and T. E. Zimmermann (eds.), Presuppositions and Discourse: Essays Offered to Hans Kamp.Emerald. Bingley.
[20]. Geurts, B. & E. Maier (2003), Layered DRT. Ms. University of Nijmegen.
[21]. Geurts, B. & E. Maier (2013), ‘Layered Discourse Representation Theory’. In A. Capone, F. L. Piparo & M. Carapezza (eds.), Perspectives on Linguistic Pragmatics. Springer International Publishing. 311–27.
[22]. Heringa, H. (2012), Appositional Constructions.Ph.D.thesis, University of Groningen, Gronin-gen, Netherlands.
[23]. Horn, L. R. (2007), ‘Toward a fregean pragmatics: Voraussetzung, Nebengedanke, Andeutung’. In I. Kecskes and L. R. Horn (eds.), Explorations in Pragmatics: Linguistic, Cognitive and Intercultural Aspects. Mouton de Gruyter. Berlin. 39–69
[24]. Hunter, J. (2010), Presuppositional Indexicals. Ph.D. thesis, University of Texas, Austin, TX.
[25]. Hunter, J. (2013), ‘Presuppositional indexicals’. Journal of Semantics 30: 381–421.
[26]. Hunter, J. & M. Abrusán (2017), Rhetorical Structure and QUDs. Springer International Publishing, Cham. 41–57.
[27]. Hunter, J. & N. Asher (2005), ‘A presuppositional account of indexicals’. In P. Dekker and M.Franke (eds.), Proceedings of the Fifteenth Amsterdam Colloquium, vol. 201. 119–24.
[28]. Kamp, H., van Genabith, J. & U. Reyle(2011), ‘Discourse Representation Theory’. In D.M. Gabbay and F. Guenthner (eds.), Handbook of Philosophical Logic, vol. 15. Springer.Netherlands. 125–394.
[29]. Koev, T. (2014), ‘Two puzzles about appositives: Projection and perspective shift’. In U. Etxeberria, A. Fal˘au¸s,˘ A. Irurtzun and B. Leferman (eds.), Proceedings of Sinn und Bedeutung 18, Bayonne and Vitoria-Gasteiz. 217–234.
[30]. Krahmer, E. (1998), Presupposition and Anaphora. CSLI Publications. Stanford, CA.
[31]. Kripke, S. A. (2009), ‘Presupposition and anaphora: Remarks on the formulation of the projection problem’. Linguistic Inquiry 40: 367–86.
[32]. Krahmer, E. (1998), Presupposition and Anaphora.CSLI Publications. Stanford, CA.
[33]. Kripke, S. A. (2009), ‘Presupposition and anaphora: Remarks on the formulation of the projection problem’. Linguistic Inquiry 40: 367–86.
[34]. Nouwen, R. (2007), ‘On appositives and dynamic binding’. Research on Language & Computa-tion 5: 87–102.
[35]. Potts, C. (2003), The Logic of Conventional Implicatures. Ph.D. thesis, University of California.
[36]. Potts, C. (2005), The Logic of Conventional Implicatures. Oxford University Press, USA.
[37]. Potts, C. (2015), ‘Presupposition and implicature’. In S. Lappin and C. Fox (eds.), The Handbook of Contemporary Semantic Theory, vol. 2. John Wiley & Sons. Oxford. 168–202.
[38]. Reyle, U. & A. Riester (2016), ‘Joint information structure and discourse structure analysis in an underspecified drt framework’. In J. Hunter, M. Simons and M. Stone (eds.), JerSem: The 20th Workshop on the Semantics and Pragmatics of Dialogue. New Brunswick, NJ. 15–24.
[39]. Riester, A. (2016), ‘Constructing QUD trees’. In K. von Heusinger, E. Onea and M. Zimmermann (eds.) Questions in Discourse, CRiSPI Series. Brill. Leiden. Forthcoming.
[40]. Rosenbach, A. (2014), ‘English genitive variation–the state of the art’. English Language and Linguistics, 18: 215–62.
[41]. Schlenker, P. (2011), ‘DRT with local contexts’. Natural Language Semantics 19: 373–92.
[42]. Schlenker, P. (2013), Supplements without bidimensionalism. Unpublished manuscript, Institut Jean-Nicod and New York University.
[43]. Simons, M., Tonhauser, J., Beaver, D. & C. Roberts (2010), ‘What projects and why’.In N. Li and D. Lutz (eds.) Proceedings of SALT, vol. 20. CLC Publications.309–27.
[44]. Syrett, K. & T. Koev (2014), ‘Experimental evidence for the truth conditional contribution and shifting information status of appositives’. Journal of Semantics 32: 525–77.
[45]. Tonhauser, J.,Beaver, D., Roberts, C. & M. Simons (2013), ‘Toward a taxonomy of projective content’. Language 89: 66–109.
[46]. Van Leusen, N. (2004), ‘Incompatibility in context: A diagnosis of correction’. Journal ofSemantics 21: 415–41.
[47]. Van Leusen, N. (2007), Description Grammar for Discourse.Ph.D.thesis, Radboud Universiteit Nijmegen.
[48]. Van Leusen, N. & R. Muskens (2003), ‘Construction by description in discourse representation’.In J. Peregrin (eds.), Meaning, the Dynamic Turn. Elsevier. 34–67.
[49]. Venhuizen, N. & H. Brouwer (2014), ‘PDRT-SANDBOX: An implementation of Projective Discourse Representation Theory’. In V. Rieser and P. Muller (eds.), Proceedings of the 18th Workshop on the Semantics and Pragmatics of Dialogue (DialWatt - SemDial 2014), Edinburgh. 249–51.
[50]. Venhuizen, N. J. (2015), Projection in Discourse: A Data-driven Formal Semantic Analysis. Ph.D.thesis, University of Groningen.
[51]. Venhuizen, N. J., Bos, J. & H. Brouwer (2013), ‘Parsimonious semantic representations with projection pointers’. In K. Erk and A. Koller (eds.), Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers, Potsdam. Germany. Association for Computational Linguistics. 252–63.
[52]. Venhuizen, N. J., Bos, J., Hendriks, P. & H. Brouwer (2014), ‘How and why conventional implicatures project’. In T. Snider, S. D’Antonio and M. Wiegand (eds.), Semantics and Linguistic Theory (SALT). volume 24. New York, USA. LSA and CLC Publications. 63–83.
[53]. P. Li, H. Wang, K. Q. Zhu, Z. Wang, and X. Wu, “Computing term similarity by large probabilistic ISA Knowledge,” in Proc. 22nd ACM Int. Conf. Inform. #38; Manage., 2013, pp. 1401–1410
[54]. W. Hua, Z. Wang, H. Wang, K. Zheng, and X. Zhou, “Short text understanding through lexical-semantic analysis,” in ICDE, pp. 495–506, 2015.
[55]. Zheng Yu, Haixun Wang, Xuemin Lin, Senior Member, IEEE, and Min Wang, “Understanding Short Texts through Semantic Enrichment and Hashing”. VOL. 28, NO. 2, FEBRUARY 2016
[56]. Wen Hua, Zhongyuan Wang, Haixun Wang, Member, IEEE, Kai Zheng, Member, IEEE, and Xiao Fang Zhou, Senior Member, IEEE,“Understand Short Texts by Harvesting and Analyzing Semantic Knowledge”,VOL.29,NO.3,MARCH2017 (57)S.Jarkad,J.E.Nalavade,“ApproachfoBgDataMininginHadoopFramework:A Overview,”IJIRSET,Jan 2017.
[57]. Y. Xun, J. Zhang, and X. Qin, “FiDoop: Parallel Mining of Frequent Itemsets Using Mapreduce,” IEEE Trans. Systems, Man, and Cybernetics, vol.46, no.3, Mar. 2016.
[58]. C. V. Suneel, K. Prasanna, M. R. Kumar, “Frequent Data Partitioning using Parallel Mining Itemset and Mapreduce”,IJSRCSEIT, Vol.2,Issue 4,2017, pg.641-644.

Sachin Kumar Pandey, Prabhat Pandey, "Analytical Study of Semantics Dynamic Text with Data Structure," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.443-459, 2018.

Geographical data related to image processing, environmental monitoring and urban planning are beings collected and stored in various databases. Processing and managing these voluminous multidimensional data have become an important requirement and gained the researchers attention. For any application dealing with the multidimensional data analysis, efficient and effective data processing techniques are required to produce best results from these geographical data sets. The processing of these datasets in timed manner using appropriate techniques is the ultimate requirement while dealing with multidimensional data. There are number of optimization methods available but the Nature-inspired algorithms are among the most powerful algorithms for optimization. We proposed Multi Objective Algorithm (MOA) which is the combination of Dragon Fly (DF) Optimization and Cuckoo Search (CS) Algorithm for Visualization & Data Analysis of Geospatial database. We have compared the various parameters from MOA algorithm with the existing K-nearest neighbors (KNN) algorithm. Results indicate that the MOA algorithm is producing better output in term of classification compare to existing algorithm. Finally, the proposed algorithm provides a framework where image classification and interpretation can be possible for various types of GIS images.

Data Analysis, Cuckoo Search, Dragon Fly Optimization, Optimization, Levy flight, Visualization, K-Nearest Neighbour

[1] F.S. Gharehchopogh, I. Maleki, S.R. Khaze, “A New Optimization Method for Dynamic Travelling Salesman Problem with Hybrid Ant Colony Optimization Algorithm and Particle Swarm Optimization”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 2, Issue 2, pp 352-358, February 2013.
[2] Xin-She Yang, Suash Deb, “Cuckoo Search via L ́evy Flights”, IEEE: pp 210-214, 2009.
[3] Ghodrati, A. and Lotfi, S. “A hybrid CS/PSO algorithm for global optimization”, in Intelligent Information and Database Systems, Lecture Notes in Computer Science, Vol. 7198, pp 89–98, Springer 2012.
[4] Pinar Civicioglu and Erkan Besdok, “A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms”, © Springer Science+Business Media B.V.: pp 315-346, 2011.
[5] Ahmed, H. and Abdelhafid, H. “Cuckoo search optimization for linear antenna arrays synthesis”, Serbian Journal of Electrical Engineering, October, Vol. 10, No. 3, pp 371–380, 2013.
[6] Bhargava, V., Fateen, S. and Bonilla-Petriciolet, A. “Cuckoo search: a new nature-inspired optimization method for phase equilibrium calculations”, Fluid Phase Equilibria, Vol. 337, pp191–200, 2013.
[7] Cui Z, Alignment particle swarm optimization. In: Cognitive informatics, 2009. ICCI’09. 8th IEEE international conference on, pp 497–501, 2009.
[8] Reynolds CW, Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput Gr 21: pp25–34, 1987.
[9] Russell RW, May ML, Soltesz KL, Fitzpatrick JW Massive swarm migrations of dragonflies (Odonata) in eastern North America. Am Midl Nat 140: pp 325–342, 1998.
[10] J. Monteil, A. Beghdadi, New interpretation and improvement of the nonlinear anisotropic diffusion for image enhancement, IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9) pp 940–946, 1999.
[11] K.Krissian, S. Aja-Fernandez, Noise-driven anisotropic diffusion filtering of MRI, IEEE Transactions on Image Processing, pp 2265–2274, 2009.

Sanjay Srivas, P. G. Khot, "Analysis & Visualization of Multidimensional GIS Images Using Multi Objective Algorithm (MOA)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.460-464, 2018.

In this digital era, with the advancements of technology a major role is being played by Information and Communication Technology in agriculture. Especially the issues related to agriculture such as real-time crop detection and monitoring, leaf identification is still a challenging task for the researchers and practitioners. Automatic detection of the crop type and its growth by analysing the colour and size of the leaves helps the farmers to take immediate advice from the botanical domain expert. The work in this paper deals with study and implementation of texture based classification and annotation of groundnut crop leaves using machine learning algorithms like HAAR, HOG and LBP. A set of trained and untrained images are employed in this task. Experiments are conducted using the cascade trainer tool in MATLAB 2016 by varying several parameters and selecting regions-of-interest on the crop for training. Later, the impact of each of the parameters on the above algorithms are recorded and well described in this paper. Furthermore, from the perspective of number of objects detected, it is noticed that LBP has yielded better results than HAAR and HOG.

Computer vision, ICT, leaf identification, HAAR, HOG, LBP, machine learning

[1] Brosnan, T. and Sun, D.W.,. “Inspection and grading of agricultural and food products by computer vision systems—a review. “ Computers and electronics in agriculture, 36(2), pp.193-213, 2002.
[2] Bourgeois, R.. “ A Preliminary Assessment of the Potential Role of Information and Communication Technology in Support of Poverty Alleviation Policies for Rural Populations”--AGRI-ICT Project Report (No. 32660), 2004.
[3] Kidd, P.T.,. “The role of the internet of things in enabling sustainable agriculture in Europe.” International Journal of RF Technologies, 3(1), pp.67-83, 2012.
[4] Zacepins, A., Brusbardis, V., Meitalovs, J. and Stalidzans, E., “Challenges in the development of Precision Beekeeping.” Biosystems Engineering, 130, pp.60-71., 2015.
[5] Rosenzweig, C. and Parry, M.L.,. “Potential impact of climate change on world food supply.“ Nature, 367(6459), pp.133-138, 1994.
[6] Mall, R.K., Singh, R., Gupta, A., Srinivasan, G. and Rathore, L.S., “Impact of climate change on Indian agriculture: a review.” Climatic Change, 78(2-4), pp.445-478, 2006.
[7] Meera, S.N., Balaji, V., Muthuraman, P., Sailaja, B. and Dixit, S., “Changing roles of agricultural extension: harnessing information and communication technology (ICT) for adapting to stresses envisaged under climate change.” In Crop Stress and its Management: Perspectives and Strategies (pp. 585-605). Springer Netherlands, 2012.
[8] McBratney, A., Whelan, B., Ancev, T. and Bouma, J.,. “Future directions of precision agriculture.” Precision agriculture,”6(1), pp.7-23, 2005.
[9] Dong, X., Vuran, M.C. and Irmak, S., “Autonomous precision agriculture through integration of wireless underground sensor networks with center pivot irrigation systems.” Ad Hoc Networks, 11(7), pp.1975-1987, 2013.
[10] Rajkumar, R.R., Lee, I., Sha, L. and Stankovic, J.,.”Cyber-physical systems: the next computing revolution.” In Proceedings of the 47th Design Automation Conference (pp. 731-736).ACM, 2010.
[11] Chen, N., Zhang, X. and Wang, C., “Integrated open geospatial web service enabled cyber-physical information infrastructure for precision agriculture monitoring.” Computers and Electronics in Agriculture, 111, pp.78-91, 2015.
[12] Gómez-Candón, D., De Castro, A.I. and López-Granados, F., “Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat.” Precision Agriculture, 15(1), pp.44-56, 2014.
[13] Rad, C.R., Hancu, O., Takacs, I.A. and Olteanu, G.,. “Smart monitoring of potato crop: a cyber-physical system architecture model in the field of precision agriculture. “ Agriculture and Agricultural Science Procedia, 6, pp.73-79, 2015.
[14] Khosla, R.,. “Precision agriculture: challenges and opportunities in a flat world.” In 19th World Congress of Soil Science, Soil Solutions for a Changing World, Brisbane (pp. 1-6), 2010.
[15] Sammouda, R., Adgaba, N., Touir, A. and Al-Ghamdi, A.,. “Agriculture satellite image segmentation using a modified artificial Hopfield neural network.” Computers in Human Behavior, 30, pp.436-441, 2014.
[16] Farooque, A.A., Quang, T., Zaman, Q.U., Groulx, D., Schumann, A.W. and Chang, Y.K.,. “Development of a predictive model for wild blueberry fruit losses during harvesting using artificial neural network.” Applied Engineering in Agriculture, 32(6), 725-738. doi:10.13031/aea.32.10872, 2016.
[17]Jones, A., Ali, U. and Egerstedt, M., “ April.Optimal Pesticide Scheduling in Precision Agriculture.” In ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) (pp. 1-8).IEEE., 2016.
[18] Peters, R.,. “Nine billion and beyond: from farm to fork [Agriculture Big Data]. “ Engineering & Technology, 11(4), pp.74-74, 2016.
[19] Nguyen, T., Hefenbrock, D., Oberg, J., Kastner, R. and Baden, S.,. “A software-based dynamic-warp scheduling approach for load-balancing the Viola–Jones face detection algorithm on GPUs.” Journal of Parallel and Distributed Computing, 73(5), pp.677-685, 2013.
[20] Feng, K.P. and Yuan, F.,” December. Static hand gesture recognition based on HOG characters and support vector machines. “In Instrumentation and Measurement, Sensor Network and Automation (IMSNA), 2nd In1ernational Symposium on (pp. 936-938). IEEE, 2013.
[21] Dubey, S.R. and Jalal, A.S., “November. Detection and classification of apple fruit diseases using complete local binary patterns.” In Computer and Communication Technology (ICCCT), Third International Conference on (pp. 346-351). IEEE., 2012.
[22] Harini, D.N.D. and Bhaskari, D.L., 2011. “Identification of leaf diseases in tomato plant based on wavelets and PCA.” In 2011 World Congress on Information and Communication Technologies (pp. 978-1), 2011.
[23]Liu, C., Yuen, J. and Torralba, A “SIFT Flow: Dense Correspondence Across Scenes and Its Applications.” In Dense Image Correspondences for Computer Vision, Springer International Publishing,., (pp. 15-49), 2016.
[24] Yao, S., Pan, S., Wang, T., Zheng, C., Shen, W. and Chong, Y., “A new pedestrian detection method based on combined HOG and LSS features.” Neurocomputing, 151, pp.1006-1014, 2015.
[25] Li, J. and Zhang, Y., “Learning surf cascade for fast and accurate object detection.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3468-3475), 2013.
[26] Xiao, Xue-Yang, et al. "HOG-based approach for leaf classification." Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. Springer Berlin Heidelberg,. 149-155, 2010.
[27] Arafat, S.Y., Saghir, M.I., Ishtiaq, M. and Bashir, U., July. “Comparison of techniques for leaf classification.” In Digital Information and Communication Technology and its Applications (DICTAP), 2016 Sixth International Conference on (pp. 136-141). IEEE, 2016.

Ch. Pavan Sathish, D. Lalitha Bhaskari, "An Experimental Analysis on Texture Based classification Using Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.465-474, 2018.

We’re in the midst of a digital revolution in which technology is moving forward at exponential speed. Robotic Process Automation and Artificial intelligence is gaining in acquisition. Robotic process automation is a key component of the digital revolution, driving much of the long-tail process automation that were previously impossible to achieve. Robotics is a field of engineering that deal with design and application of robots and the use of computer for their manipulation and processing. Robots are used in industries for speeding up the manufacturing process. Automation and Robotics Engineering is the use of control systems and information technologies to reduce the need for human work in the production of goods and services. The Robotic process automation technology is based on perception of software robot. RPA can be used to automate workflow, infrastructure, back office process which are labor intensive. Basically involving RPA has eliminated lots of manual effort which helps business to keep pace with current technology trends. In this paper I will explain how Robotic automation process will help to save manual exertion and time.

Robotic Process Automation, Business Process,Service Automation,Software Robot

[1] M. Lacity, ,S.Solomon,A. Yan, , and L. Willcocks, "Business Process Outsourcing Studies: A Critical Review andResearch Directions," Journal of Information Technology, Vol.26, issue 4, pp.221-258.
[2] Y.A.Kumar, G. Raghavendra,” The Pioneers of Robotic Process Automation (RPA) Software”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology , Vol 3 , Issue 4 , PP. 96-100 ISSN : 2456-3307.
[3] S. Aguirre and A. Rodriguez ,“Automation of a Business Process Using Robotic Process Automation RPA”, Springer International Publishing AG pp. 1–7, 2017.
[4] P. Esko, K. Henje, A. Aleksandre,” How To Choose Between Robotic Process Automation And Back-End System Automation?”, Twenty-Sixth European Conference on Information Systems (ECIS2018),PP 2-15
[5] A. Asatiani, and E. Penttinen, “Turning Robotic Process Automation into Commercial Success - Case OpusCapita”, Journal of Information Technology Teaching Cases: PP .1–8, 2016.
[6] B. Bygstad, “Generative innovation: a comparison of lightweight and heavyweight IT”, Journal of Information Technology Vol.32,Issue 4 , PP 180–193, 2016.
[7] N. Sabooniha, D. Toohey, and K. Lee, “An evaluation of hospital information systems integration approaches”, In Proceedings of the International Conference on Advances in Computing Communications and Informatics. 2012.
[8] N. Serrano, J. Hernantes, and Gallardo, G. “Service-oriented architecture and legacy systems”, IEEE Software Vol. 31,Issue. 5 ,PP 15–19, 2014.
[9] M. Barrett, E. Oborn, W. J. Orlikowski, and J. Yates, “Reconfiguring Boundary Relations: Robotic Innovations in Pharmacy Work ,” Organ. Sci., vol. 23, pp. 1448– 1466, 2012.
[10] M. C. Lacity, and L. P. Willcocks, (2016)” A new approach to automating services”. MIT Sloan Management Review, Fall. ISSN 1532-9194
[11] H. P. Fung, “Criteria, use cases and effects of information technology process automation”(ITPA). Adv. Robot. Autom. 3, 1–11 (2014).
[12] S. Malve, P. Sharma, “Article Investigation of Manual and Automation Testing using Assorted Approaches” International Journal of Scientific Research in Computer Sciences and Engineering Issue. 2, pp.81-87, Apr-2017,(ISSN: 2320-7639) .
[13] N.S. Lele ,"Image Classification Using Convolutional Neural Network"International Journal of Scientific Research in Computer Science and Engineering,Vol.6, Issue.3, pp.22-26 , June (2018)

G. Ghosh, "Automation with RPA (Robotic Process Automation)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.475-477, 2018.

Scheduling algorithms identify which process will be given cpu at a particular point of time There are different scheduling algorithms available each have its own advantages and disadvantages we have to decide which scheduling algorithm is best suited to our current application.We compare scheduling algorithm on following factors: waiting time, response time ,turn around time resource consumption, throughput, fairness, primitiveness, predictability, one to decide which threads are given to resource from moment to moment. Names of commonly used scheduling algorithm are First-Come-First-Served (FCFS), Round Robin (RR), Shortest Job First (SJF), Shortest Remaining Time First (SRTF) ,Priority Based Scheduling, Multi level Queue Scheduling. In those paper we will discuss each algorithm and they will be compared with regards to 7 Parameters.

Scheduling, Priority, Process, Waiting Time, Turn Around time, CPU

[1] Bidgoli .Amir M, Zahra Masoudi Nezad: A new scheduling algorithm design for grid computing tasks, 5th Symposium on Advances in Science and technology, Iran
[2] Cho-Chin Lin and Chun-Wei Shih, An Efficient Scheduling Algorithm for Grid Computing with Periodical Resource Reallocation.
[3] Jain, S., Shukla, D. and Jain, R. Linear Data Model Based Study of Improved Round Robin CPU Scheduling algorithm, International Journal of Advanced Research in Computer and Communication Engineering, 4(6), June 2015, 562-564.
[4] Suranauwarat, S., A CPU Scheduling Algorithm Simulator, IEEE Proceedings (Frontiers in Education Conference - Global Engineering: Knowledge without Borders, Opportunities without Passports, 2007. FIE `07. 37th Annual), 19-24.
[5] Sindhu, M., Rajkamal, R. and Vigneshwaran, P., An Optimum Multilevel CPU Scheduling Algorithm, IEEE (International Conference on Advances in Computer Engineering (ACE)), 2010, 90-94.

Vivek Chaplot, "Study of Comparison of Scheduling Algorithms Based On Priority and Complexity," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.478-481, 2018.

The present study is to examine the potential for water harvesting and conservation of ground water against drought in Moinabad Mandal of Rangareddy district of Telangana (India). The Climate of the Block is characterized by a hot summer and is generally dry except during the South west monsoon season. Water resources development plan needs better understanding about the various natural resources their relations with each other and their relations with livelihood of the stakeholders. In order to optimum utilize the existing natural resources like land, vegetation and water in a selected area, proper scientific surveys should be conducted. These include mapping of soils, hydro-geomorphic features, land use/land cover pattern, surface & ground water potential surveys in addition to gradient of the terrain particularly, slope and aspect which plays a vital role in suggesting various soil and moisture conservation measures. Based on the mapping and analysis (using GIS& RS as a powerful information tools) of these natural resources, water resource development plan has been prepared on the basis of integration of information on Geomorphology, Land use / Land cover, Drainage, Ground water Prospect map and slope. Suitable structures are suggested for surface harvesting / recharge in the study area. Based on this location priority, various water-harvesting structures have been proposed to mitigate the adverse effects of drought conditions which lead to the degradation of natural resources.

Water harvesting, Remote Sensing, GIS, Ground water, Water resources

[1] Proceedings of the IAHS conference on Application of Geographic Information Systems in Hydrology and Water Resources Management held in Vienna, Austria, on April 19-22 1993, pp.93-100. Vienna.
[2] Krishnamurthy, J., Kumar Venkates, N., Jayaraman, V., and Manivel, M. (1996) An approach to demarcate groundwater potential zones through remote sensing and a geographic information system, International Journal of Remote Sensing, 17(10), 1867-1884.
[3] Minor, T., Carter, J., Charley, M., Knowks, B., and Gustafson, P., (1994). The use of GIS and Remote Sensing in Groundwater exploration for developing countries. Proc. of the 10th ERIM thematic conference on geologic remote sensing held in San Antonio, USA on May 9-12 1994 pp.168-179.
[4] Arunbose, S., "Evaluation of Groundwater Condition Using Geo-electrical Soundings inParts of Tiruchendur Taluk, Tamilnadu, India", International Journal of Computer Sciences and engineering, Vol.6, Issue.7, pp.137-144, 2018.
[5] Cholke, S. P., “Applications of Geographical Information System (GIS) in Assessment of Water Balance in Watersheds” International Journal of Computer Sciences and Engineering, Vol.-6, Issue-5, pp. 255-259, May 2018
[6] Proceedings of AWRA symposium on GIS and Water Resources held in Ft.Lauderdale, Florida, USA, 1996. Florida: Ft. Lauderdale. Sander, P (1997) Water-Well Siting in Hard- Rock Areas: Identifying Promising Targets Using a Probabilistic Approach, Hydrogeology Journal, 5(3), 32-43.732
[7] Richards, C.J., Roaza, H.P. and Pratt, T.R. (1996) Applying geographic information systems to groundwater assessments.
[8] Gustafsson, P. (1993) High-resolution satellite imagery and GIS as a dynamic tool in groundwater exploration in a semi-arid area.

Alajangi Simhachalam, P. Kesava Rao, E. Amminedu, A. Jaya Prakash, "Water Resource Development Plan for Rural Development: A Geospatial Solution," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.482-487, 2018.

The organization`s hunger for data insights and the adaptation of the World Wide Web has increased exponentially the generation and collection speed of data. There is a challenge to capture, store and analyze this large set of unstructured data, which have taken the shape of Big Data. In this paper, the definition of Big Data is introduced from different aspects to comprehend its concept. The architecture of Big Data is analyzed to study the processing mechanism of Big Data. The various Big Data technologies like Hadoop, HBase, Map Reduce, Pig, Hive, Sqoop, and Flume are studied and compare based on features supported by them. A comprehensive study of frameworks like Apache Spark, Cloudera, and Hortonworks used for execution of Big Data technologies is done by highlighting their important features. This paper also represents how data related to fields like the Stock market, Agriculture, Medical Health Records, and Internet traffic is stored, processed and analyzed using Big Data technologies and frameworks.

Big Data; Hadoop; MapReduce; HBase; Sqoop; Flume; Apache Spark; Cloudera; Hortonworks

[1] 3pillarglobal.com, How to Analyze Big Data with Hadoop Technologies [Online], Available: http://www.3pillarglobal.com/ and http://www.3pillarglobal.com/insights/analyze-big-data-hadoop-technologies (accessed on 11 April 2018)
[2] Er. Rupinder Kaur, Raghu Garg, Dr Himanshu Aggarwal, Big Data Analytics Framework to Identify Crop Disease and Recommendation a Solution, IEEE, International Conference on Inventive Computation Technologies (ICICT), volume 2, 2016.
[3] Haritha Chennamsetty, Suresh Chalasani, Derek Riley, Predictive Analytics on Electronic Health Records (EHRs) using Hadoop and Hive, IEEE, International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2015.
[4] Abdeltawab M. Hendawi, Fatemah Alali, Xiaoyu Wang, Yunfei Guan, Tianshu Zhou, Xiao Liu, Nada Basit, John A. Stankovic, Hobbits: Hadoop and Hive Based Internet Traffic Analysis, IEEE, International Conference on Big Data (Big Data), 2016.
[5] J. Gantz and D. Reinsel, Extracting value from chaos, in Proc. IDC iView, pp. 1–12, 2011.
[6] J. Manyika et al, Big Data: The Next Frontier for Innovation Competition, and Productivity, San Francisco, CA, USA: McKinsey Global Institute, pp. 1–37, 2011.
[7] M. Cooper and P. Mell (2012), Tackling Big Data [Online], Available: http://csrc.nist.gov/groups/SMA/forum/documents/june2012presentations/fcsm_june2012_cooper_mell.pdf (accessed on 13 May 2018)
[8] G. Blackett (2013), Analytics Network-O.R. Analytics [Online], Available: http://www.theorsociety.com/Pages/SpecialInterest/AnalyticsNetwork_analytics.aspx (accessed on 13 May 2018)
[9] Palanisamy, B. Singh, & Liu, “cost-effective resource provisioning for MapReduce in a cloud,” IEEE Transactions on Parallel and Distributed Systems, pp: 1265-1279, 2015.
[10] Mike Frampton, Mastering Apache Spark (ed.) 2015, Packet publication ltd., U.K.
[11] Cloudera, Cloudera Platform 2018, [Online] http://cloudera.com/ (accessed on 15 January 2018)
[12] Hortonworks, Discussion about Horton Platform working,[Online] http://hortonworks.com/hdp/ (accessed on 15 June 2018)

Mayank Tripathi, A. K. Agarwal, "Comparative Study of Big Data Technologies and Frameworks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.488-495, 2018.

Character recognition creates an increasing demand on various evolving applications areas and methodologies of image processing in order to effectively recognize text of each script. It is considered as common method of digitizing an image in order to gather text of image more efficiently. The stages of character recognition include pre-processing, segmentation, feature extraction, classification and post processing. It has been noticed that there exist numerous languages such as Marathi, Gujarati, Gurumukhi, Arabic, Modi etc. where stages of character recognition lays important role for recognition of text image. Pre-processing and segmentation are considered as two crucial stages of character recognition with the purpose of providing smooth and clean image for further processing. These stages help in enhancing color format, to maintain skew angle of images, compresses size of image, separates line word and characters from images etc. A comparative study of various sub stages of pre-processing and segmentation is performed based on some parameters such as data type, window size, recognition rate, sample size, and so on. These parameters help in improving quality of image in order to achieve successful recognition rate.

Character recognition, binarization, noise reduction, segmentation, compression, normalization

[1] R. Mithe, S. Indalkar and N. Divekar, “Optical Character Recognition”, International Journal of Recent Technology and Engineering, Vol. 2, No.1, pp. 2277-3878, 2013.
[2] R. Palmondon and S. N. Srihari, “Online and Offline Handwritten Recognition: A Comprehensive Survey”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No.1, pp. 63-84, 2000.
[3] M. Kasthuri and V. Shanthi, “Noise Reduction and Pre-processing techniques in Handwritten Character Recognition”, International Journal of Computing Science and Communication Technologies, Vol. 6, No. 2, 2014.
[4] J. Shi, N. Ray and H. Zhang, “Shape Based Local Thresholding for Binarization of Document Images”, Elsevier Journal of Pattern Recognition on Letters, Vol. 33, Issue 1, pp. 24-32, 2012.
[5] T. Sari, L. Souici and M. Selami, “Off-line Handwritten Arabic Character Segmentation Algorithm: ACSA”, IEEE, 2007.
[6] M. S. Das, C. R. K. Reddy, A Govardhan and G. Saikrishna, “Segmentation of Overlapping Text Lines, Characters in Printed Telugu Text Document Images”, International Journal of Engineering Science and Technology, Vol. 2, No.11, pp. 6606-6610, 2010.
[7] P. Stathis, E. Kavallieratou and N. Papamarkos, “An Evaluation Survey of Binarization Algorithms on Historical Documents”, IEEE, 2008.
[8] D. S. Guru, M. Ravikumar and S. Manjunath, “Multiple Skew Estimation in Multilingual Handwritten Documents”, International Journal of Computer Science, Vol. 10, No. 2, pp. 1694-0814, 2013.
[9] P. Poovizhi, “A Study on Preprocessing Techniques for the Character Recognition”, International Journal of Open Information Technologies, Vol. 2, No. 12, pp. 2307-8162, 2014.
[10] N. Attlas and Dr. Sheifali Gupta, “Wavelet Based Techniques for Speckle Noise Reduction in Ultrasound Images”, International. Journal of Engineering Research and Applications, Vol. 4, Issue 2, pp.508-513, 2014.
[11] D.Maheswari and Dr.V.Radha, “Noise Removal in Compound Image using Median Filter”, International Journal on Computer Science and Engineering, Vol. 2, No. 4, pp.1359-1362, 2010.
[12] T. Gebreyohannes and D. Y. Kim, “Adaptive Noise Reduction Scheme for Salt and Pepper”, 2012.
[13] V. Murugan, T. Avudaiappan and Dr. R. Balasubramanian, “A Comparative Analysis of Impulse Noise Removal Techniques on Gray Scale Images”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 7, No.5, pp.239-248, 2014.
[14] C. Mythili and V. Kavitha, “Efficient Technique for Color Image Noise Reduction”, ACM Proceeding on The Research Bulletin of Jordan, Vol. 2, Issue 3, 2011.
[15] Y. Yang and H. Yan, “An Adaptive Logical Method for Binarization of Degraded Document Images”, Elsevier Journal on Pattern Recognition, Vol. 33, Issue 5, pp.787-807, 2000.
[16] Amjad Rehman Khan & Dzulkilfli Muhammad, “A Simple Segmentation Approach for Unconstrained Cursive Handwritten Words in Conjunction with the Neural Network”, International Journal of Image Processing, 2008.
[17] D.N. Basekar, “Special Approach for Recognition of Handwritten MODI Script’s Vowels”, International Journal of Computer Applications, 2012.
[18] H. R. Thaker and C. K. Kumbharana, “Preprocessing and Segregating Offline Gujarati Handwritten Datasheet for Character Recognition”, International Journal of Computer Applications, Vol. 97, No. 18, pp. 0975-8887, 2014.
[19] T. Nawaz, S. A. H. S. Naqvi, H. Rehman and A. Faiz, “Optical Character Recognition System for Urdu Using Pattern Matching Technique”, International Journal of Image Processing, Vol. 3, Issue 3, 2009.
[20] GajananBirajdar and MansiSubhedar, “Use of JPEG Algorithm in Handwritten Devanagari Numeral Recognition”, International Journal of Distributed and Parallel Systems ,Vol. 2, No. 4, 2011.
[21] P. Haffner, L. Bottou, P.G. Howard, and YannLeCun, “DjVu: Analyzing and Compressing Scanned Documents for Internet Distribution”, International Conference Document Analysis and Recognition, 2008.
[22] S. V. Khangar and L.G. Malik, “Compression of Scan Digitized Handwritten Text for Indian Language Document”, Internatioanl Conference on Advances in Computer, Electronics and Electrical Engineering, 2012.
[23] C. L. Liu, M. Koga, H. Sako and H. Fujisawa, “Aspect Ratio Adaptive Normalization for Handwritten Character Recognition”, Springer Conference on Advances in Multimodal Interfaces, 2000.
[24] A. Vinciarelli and J. Luettin, “A New Normalization Technique for Cursive Handwritten Words”, Elsevier Journal on Pattern Recognition Letters, Vol. 22, pp. 1043-1050, 2001.
[25] T.Abu-Ain, S.N. H. Sheikh Abdullah, K. Omar, A. Abu-Ein , B. Bataineh and W. Abu-Ain, “Text Normalization Method for Handwritten Script”, Vol. 7, No. 2, pp. 164-175, 2013.
[26] L. Shah, R. Patel, S.Patel and J. Maniar, “Skew Detection and Correction for Gujarati Printed and Handwritten Character using Linear Regression”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, Issue 1, 2014.
[27] M.L.M Karunanayaka, C.A Marasinghe and N.D Kodikara, “Thresholding, Noise Reduction and Skew correction of Sinhala Handwritten Words”, IAPR Conference on Machine VIsion Applications, May 2005.
[28] Nandini N., Srikanta Murthy K., and G. Hemantha Kumar, “Estimation of Skew Angle in Binary Document Images Using Hough Transform”, World Academy of Science, Engineering and Technology, Vol. 18, pp. 06-23,2008.
[29] G.S Lehal and C. Singh, “A Technique for Segmentation of Gurmukhi Text”, International Journal of Computer Applications, 2002.
[30] R. K. Sharma and A. Singh, “Segmentation of Handwritten Text in Gurmukhi Script”, International Journal of Image Processing, Vol.3, No.2, 2008.
[31] S. Aman and S. Gupta, “An Approach for Recognizing Modi Lipi using Ostu’s Binarization Algorithm and Kohenen Neural Network”, International Journal of Computer Applications, Vol. 111, No. 2, February 2015.
[32] V. Saraf and D.S. Rao, “Devnagari Script Character Recognition Using Genetic Algorithm for Get Better Efficiency”, International Journal of Soft Computing and Engineering, Vol. 2, Issue. 4, 2013.
[33] C. Silva and C. Kariyawasam, “Segmenting Sinhala Handwritten Characters”, International Journal of Conceptions on Computing and Information Technology, Vol. 2, Issue. 4, 2014.
[34] M. Sarfraz, S.N. Nawaz and A. Al-Khuraidly, “Offline Arabic Text Recognition system”, IEEE Internatioanl Conference on geometric Modeling and Graphics, August 2003.
[35] R. J. Rodrigues and A.G.C Thome, “Cursive Character Recognition- A Character Segmentation Method Using Projection Profile-Based Technique”, 2002.
[36] G. S. Rao, M. Imanuddin and B. H. Kumar, “Script Identification of Telugu, English and Hindi Document Image”, International Journal of Advanced Engineering and Global Technology, Vol. 2, No. 2, pp. 2309-4893, 2014.
[37] P.P. Roy, U. Pal and JosepLlados, “Morphology Based Handwritten Line Segmentation Using Foreground and Background Information”, International Journal of Computer Applications, 2011.
[38] S. Osher and R. Fedkiw, “Level Set Methods and Dynamic Implicit Surfaces”, Springer, 2000.
[39] Y. Li, Y. Zheng, and D. Doermann, “Detecting Text Lines in Handwritten Documents,”International Conference on Pattern Recognition, pp. 1030–1033, 2006.
[40] M. Kumar, R. K. Sharma and M. K. Jindal, “Segmentation of Lines and Words in Handwritten Gurmukhi Script Documents”, ACM Proceeding on Intelligent Interactive Technologies and Multimedia, Vol. 1, Allahabad, 2010.
[41] A. Cheung, M. Bennamoun and N.W. Bergmann, “An Arabic Optical Character Recognition System using Recognition-Based Segmentation”, Elsevier, Pattern Recognition, pp. 215-233, 2001.
[42] S. Al-Ma’adeed, D. Elliman, and C. Higgins, “A Data Base for Arabic Handwritten Text Recognition Research”, International Arab Journal of Information Technology, 2004.
[43] G. Bansal, D. Sharma”, “Isolated Handwritten Words Segmentation Techniques in Gurmukhi Script”, International Journal of Computer Applications, Vol. 1, No. 24, pp. 0975 – 8887, 2010.
[44] S. Wshah, Z. Shi and V.Govindaraju, “Segmentation of Arabic Handwriting based on both Contour and Skeleton Segmentation”, International Conference on Document Analysis and Recognition, 2009.

B. Solanki, M. Ingle, "A Comparative Study of Specific Phase based Character Recognition Techniques for Various Scripts," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.496-502, 2018.

Agriculture is the main sector of employment in India. One of the major causes for the continuing downfall in agricultural trends is cultivation of crops that are not suitable with the environmental factors like soil and weather conditions. A recommendation system can provide suggestions for a crop that can be cultivated based on spatial conditions. The research focus on to build a recommendation system that can collect raw data for environmental factors like NDVI, SPI parameters from satellite images. The collected data then will be forwarded where this data is processed. In this paper modified convolutional neural network was proposed which takes spatial features as input and trained by back propogation, this reduce error of prediction as well. Experiment was done real dataset from authentic geo-spatial resources. Results are compared with some previous existing methods and it was obtained that proposed modified CNN model was better on various evaluation parameters.

Crop yield prediction, Data mining, machine learning, Vegetation Index

[1]. Pritam Bose, Nikola K. Kasabov, Fellow, IEEE, Lorenzo Bruzzone, Fellow, IEEE, and Reggio N. Hartono. “Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series”. IEEE Transactions On Geoscience And Remote Sensing 2016.
[2]. Abhishek Pandey and Anu Mishra. “Application of Artificial Neural Networks in Yield Prediction of Potato Crop”. Springer ISSN 1068-3674, Russian Agricultural Sciences, 2017, Vol. 43, No. 3, pp. 266–272.
[3]. E. Manjula, S. Djodiltachoumy “A Model for Prediction of Crop Yield”, International Journal of Computational Intelligence and Informatics, Vol. 6: No. 4, March 2017 ISSN: 2349-6363 298.
[4]. Yuzugullu O., Erten E., Hajnsek I., Rice growth monitoring by means of X-band co-polar SAR: Feature clustering and BBCH scale, IEEE Geo science and Remote Sensing Letters 12(6) (2015), 1218-1222.
[5]. Shastry A., Sanjay H.A., Hegde M., A parameter based ANFIS model for crop yield prediction, IEEE International Conference on Advance Computing (IACC) (2015), 253-257.
[6]. Natarajan R., Subramanian J., Papageorgiou E.I., Hybrid learning of fuzzy cognitive maps for sugarcane yield classification, Computers and Electronics in Agriculture (2016), 147-157.
[7]. R.Kalpana,N.Shanti and S.Arumugam ,“A survey on data mining techniques in Agriculture”,International Journal of advances in Computer Science and Technology, vol. 3, No. 8,426- 431, 2014
[8]. G.N.Fatima,R.Geeta ,“Agriculture crop pattern using data mining techniques”, International Journal of Advanced Research in in Computer Science and Software Engineering, vol. 4, No. 5,781-786 , 2014.
[9]. Mr. Abhishek B. Mankar, Mr. Mayur S. Burange, “Data Mining - An Evolutionary View of Agriculture”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), vol. 3, No. 3, pp. 102-105 , 2014
[10]. S. Dahikar and S. Rode, “Agricultural crop yield prediction using artificial neural network approach”, International Journal of Innovative Research in Electrical, Electronic Instrumentation and Control Engineering, vol. 2, no. 1, pp. 683-686, 2014
[11]. A.A.Raorane and R.V.Kulkarni, “Review-Role of data mining in Agriculture”, International Journal of Computer Science and Information Technology, vol. 4, No. 2, pp. 270-272 , 2013
[12]. Yetheraj .N.J , “Applying data mining techniques in the field of agriculture and allied sciences”, International Journal of Business Intelligent, vol. 1, No. 2, pp. 72-76 , 2012.

Preeti Tiwari, Piyush Shukla, "Crop Yield Prediction by Modified Convolutional Neural Network and Geographical Indexes," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.503-513, 2018.

The study explores the thermal performance of Earth air Heat Exchanger (EAHE) for warming and cooling modes under Indian climatic conditions. A 3-dimensional, computational fluid dynamics (CFD) model is produced in ANSYS FLUENT v15.0 under relentless conditions for various pipe materials and pipe geometries. The pipe geometries considered for the investigation are round; square; triangular and circular-corrugated and the pipe materials considered are Aluminium and Steel. This paper expects to locate the optimal geometry and pipe material to acquire ideal temperature variation for thermal comfort. The effect of ambient temperature, mass stream rate, Reynolds Number, Prandtl number and Nussult number were considered. Results demonstrated that if the length of the pipe increases, the temperature at the outlet diminishes in cooling mode and vice versa. The greatest temperature drop watched is 12.05K and 16.65K during cooling and warming mode respectively for the triangular-corrugated pipe. Moreover, most extreme temperature variation was watched for aluminium pipe material at 2m/s. It can be presumed that corrugated aluminium pipes can be utilized to get ideal temperature drop for better thermal comfort. In addition, as the mass stream rate increases, the temperature variation also increases regardless of the pipe materials and pipe cross-segments.

EAHE, CFD Simulations, Corrugated Geometry, Pipe Materials, Heat Transfer, Temperature Variation

[1] N. L. Panwar, S. C. Kaushik, and S. Kothari, “Role of renewable energy sources in environmental protection : A review,” Renew. Sustain. Energy Rev., vol. 15, no. 3, pp. 1513–1524, 2011.
[2] UNDP, World Energy Assessment. Energy and the challenge of Sustainability. 2000.
[3] R. K. Gera, Y. Parvej, and H. Soni, “Renewable Energy Scenario in India : Opportunities and Challenges,” no. 1, pp. 10–16, 2013.
[4] “Commission Staff Working Paper,” pp. 1–49, 2012.
[5] D. Industries, “In the summer in Tokyo, people take part in Cool Biz but do not feel cool !? 80 % of the foreign workers take part in Cool Biz but complain about not being able to implement it completely. How do the foreign workers battle the summer in Tokyo ?,” pp. 1–10, 2015.
[6] F. O. F. Petroleum, “Functioning of petroleum conservation research association,” vol. 1937, 2016.
[7] C. Peretti, A. Zarrella, M. De Carli, and R. Zecchin, “The design and environmental evaluation of earth-to-air heat exchangers (EAHE).A literature review,” Renew. Sustain. Energy Rev., vol. 28, pp. 107–116, 2013.
[8] L. Ozgener, “A review on the experimental and analytical analysis of earth to air heat exchanger (EAHE) systems in Turkey,” Renew. Sustain. Energy Rev., vol. 15, no. 9, pp. 4483–4490, 2011.
[9] A. D. Singh, “Earth air Tunnels.”
[10] S. Kumar, M. Pandey, and V. Nath, “Ground coupled heat exchangers : A review and applications,” Renew. Sustain. Energy Rev., vol. 47, pp. 83–92, 2015.
[11] M. K. Ghosal, G. N. Tiwari, and N. S. L. Srivastava, “Thermal modelling of a greenhouse with an integrated earth to air heat exchanger : an experimental validation,” vol. 36, pp. 219–227, 2004.
[12] T. S. Bisoniya, A. Kumar, and P. Baredar, “Energy metrics of earth – air heat exchanger system for hot and dry climatic conditions of India,” Energy Build., vol. 86, pp. 214–221, 2015.
[13] J. Sobti and S. K. Singh, “Earth-air heat exchanger as a green retrofit for Chandigarh — a critical review,” Geotherm.Energy, pp. 1–9, 2015.
[14]G. Florides and S. Kalogirou, “Ground heat exchangers-A review of systems, models and applications,” Renew. Energy, vol. 32, no. 15, pp. 2461–2478, 2007.
[15] V. Bansal, R. Misra, G. Das Agrawal, and J. Mathur, “Performance analysis of earth-pipe-air heat exchanger for summer cooling,” Energy Build., vol. 42, no. 5, pp. 645–648, 2010.

Charnveer Singh, Gautam Kocher, Pardeep Singh, "A Computational Study on the Performance of Earth Air Heat Exchanger (EAHE) Using Different Duct Geometries and Material Combinations," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.514-519, 2018.