Volume-6 Issue-10

S. No

Volume-6 Issue-10, June 2017, ISSN:  2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

T. H. Patel, V. Venkateshwara Reddy, S. R. Mise

Paper Title:

Impact from Mining & Associated Industrial Activities on Air Quality of Ballari Region

Abstract:  Industrialization and urbanization are the two major causes of air pollution. With the implementation of Sustainable Development concept will ensure development with insignificant impact on Environment and preserve this precious environment for future generation. The impact from mining and associated industrial activities may have impact on Environment if Air Pollution Control measures are not implemented. In this paper efforts have been made to assess the impact on the Air quality from Mining and Associated Industrial activities in Bellary region. Also an attempt has been made to suggest mitigative measures to attenuate Air Quality impacts on environment.

Keywords:  AAQ, NAAQ, Ground Level Concentration (GLC), Mitigative Measures, CPCB, KSPCB

References:
1.       Rama Krishna, Reddy.M.K and Sing.R.N (2005), “Impact of an industrial complex on the ambient air quality: Case study using a dispersion model”, Journals of Atmospheric Environment, No.34, pp 37-46.
2.       APHA (2006). Standard methods for examination of water and wastewater, 21st Edition, American Public Health Association; Washington.
3.       Air (prevention and Control of pollution) Act, 1981, and notifications issued there under, “The Environmental Protection and pollution control Manual”, (2000), Karnataka Law Journal Publications, Bangalore.
4.       Beer Tom (2001), “Air Quality as a Meteorological Hazard”, Journal of Natural Hazards, No.23, pp 157-169.
5.       Bhanarkar.A.D, Gajghate.D.G and Hassan.M.Z (2001), “Air quality management in iron and steel industry”, Journal of Environmental Pollution control, No.5, pp 17-26.
6.       Hand Book on Environmental Legislation & Technology, Karnataka State pollution Control Board, Bangalore 2000,pp 181,185,187,286,296.
7.       Indian Council of Forest Research & education, Dehradun, ‘’Macro level Environment Impact assessment Study report of Bellary District, Karnataka, Vol I, Nov 2011, pp 18-22,36-39,60-69,103-112.
8.       Mackenzie L. Davis, David A. Cornwell (1998), “Introduction to Environmental Engineering”, McGraw- Hill Book Co, Singapore.
9.       M. Mahadeva Swamy, M.G.Yathish (1994) “Air quality modeling for a single point source”, Indian Journal of Environment, Vol 36, No.4, pp 36-43.
10.    Rao.M.N, Rao.H.V.N (1989), “Air Pollution”, Tata McGraw-Hill Publishing Company Limited, New Delhi.
11.    Survey of India, Toposheet no. 52 A/12, First edition (1973), Govt of India, New Delhi.
12.    The Environment (Protection) Rules, 1986 and notifications issued there under, “The Environment Protection and Pollution Control Manual” (2000). pp 109-110, 136-140, Karnataka Law Journal Publications, Bangalore.
13.    Wark Kenneth, Warner F. Cecil (1981), “Air pollution, Its Origin and Control”, II edition, Harper and Row publishers, New York, USA.
14.    Website: www.epa.gov (2005), “Air pollution dispersion models”, United States Environment Protection Agency, USA.

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2.

Authors:

Akanksha Garg, Shiv K. Sahu

Paper Title:

Improve the Efficiency of Image Segmentation Scheme using Swarm Intelligence Techniques

Abstract: Clustering analysis is a primitive exploratory approach in data analysis with little or no prior knowledge. Clustering has been widely used for data analysis and been an active subject in several research fields such as pattern recognition, information retrieval, data mining applications, bioinformatics and many others. This paper presents a particle of swarm optimization with self-optimal clustering (SOC) technique which is an advanced version of improved mountain clustering (IMC) technique. Proposed POS based SOC clustering techniques for large data. We used the POS for the selection of important parameter such as value of centroid and center, this parameter decides the selection of center point of cluster technique. The SOC clustering technique decides the cluster level wise seed and generates cluster according to their features attribute of data. The experiments also revealed the convergence property of the level fitness in Proposed. We compared our Proposed with existing clustering algorithms and shows that the results are improved.

Keywords: Improved Mountain Clustering, elf Optimal Clustering, Particle swarm optimization, K-means, CRM.

References:
1.       Nishchal K. Verma, Abhishek Roy “Self-Optimal Clustering Technique Using Optimized Threshold Function” IEEE SYSTEMS JOURNAL, IEEE 2013. Pp 1-14.
2.       Pavel Berkhin “A Survey of Clustering Data Mining Techniques” Pp 1-59.
3.       K. A. Abdul Nazeer, M. P. Sebastian “Improving the Accuracy and Efficiency of the k-means Clustering Algorithm” WCE 2009. Pp 1-6.
4.       Hae-Sang Park, Chi-Hyuck Jun “A simple and fast algorithm for K-medoids clustering” Expert Systems with Applications, 2009. Pp 3336–3341.
5.       Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, Angela Y. Wu “A local search approximation algorithm for k-means clustering” Elsevier B.V. All rights reserved, 2004. Pp 89-112.
6.       LUO Xin “Chinese Text Classification Based on Particle Swarm Optimization” 4th National Conference on Electrical, Electronics and Computer Engineering,  NCEECE 2015, Pp 53-59.
7.       Ramachandra Rao Kurada, Dr. K Karteeka Pavan,  Dr. AV Dattareya Rao “A Preliminary Survey On Optimized Multiobjective Metaheuristic Methods For Data Clustering Using Evolutionary Approaches” International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 5, October 2013. Pp 58-78.
8.       Nishchal K. Verma, Payal Gupta, Pooja Agrawal and Yan Cui “MRI Brain Image Segmentation for Spotting Tumors Using Improved Mountain Clustering Approach” 2011.
9.       N. K. Verma, P. Gupta, P. Agarwal, M. Hanmandlu, S. Vasikarla, and Y. Cui, “Medical image segmentation using improved mountain clustering approach,” in Proc. 6th Int. Conf. ITNG, Las Vegas, NV, USA, 2009, pp. 1307–1312.
10.    Rui Xu, and Donald Wunsch “Survey of Clustering Algorithms” IEEE Transactions On Neural Networks, VOL. 16, NO. 3, MAY 2005. Pp 645-678.
11.    Yixin Chen, James Z. Wang, and Robert Krovetz “CLUE: Cluster-Based Retrieval of Images by Unsupervised Learning” IEEE transactions on image processing, vol. 14, no. 8, august 2005. Pp 1187-1201.
12.    N. K. Verma, A. Roy, and S. Gupta, “Color segmentation using improved mountain clustering technique version-2,” in Proc. 2nd IEEE Int. Conf. Intell. Human Comput. Interact., Allahabad, India, 2011, Pp 536–542.
13.    Jennifer Erxleben, Kelly Elder and Robert Davis “Comparison of spatial interpolation methods for estimating snow distribution in the Colorado Rocky Mountains” Hydrol. Process. 2002. Pp 3627–3649.
14.    Singh Vijendra, Kelkar Ashwini, Sahoo Laxman, “An effective clustering algorithm for data mining”, Proc. of the 2010 International Conference on Data Storage and Data Engineering, pp.250–253, 2010.
15.    Yu Jin, Qian Feng, Qi Rongbin, “Improvement of stochastic particle swarm optimization by succession strategy”, Communications of the Systemics and Informatics World Network, Vol.3, pp.155–159, 2008.
16.    N.R. Pal, K. Pal, J.C. Bezdek et al., “A possibilistic fuzzy C-Means clustering algorithm”, IEEE Trans. Fuzzy Systems, Vol.13, No.4, pp.517–530, 2005.
17.    Lv Zehua, Jin Hai, Yuan Pingpeng, Zou Deqing, “A fuzzy clustering algorithm for interval-valued data based on Gauss distribution functions”, Acta Electronica Sinica,
Vol.38, No.2, pp.295–300, 2010.
18.    C.L. Sun, J.C. Zeng, J.S. Pan “An improved vector particle swarm optimization for constrained optimization problems”, Information Scieces, Vol. 181,  2011. Pp. 1153–1163.
19.    Mathew, Juby, and R. Vijayakumar. "Scalableparallel clustering approach for large data usinggenetic possibilistic fuzzy c-means algorithm", 2014 IEEE International Conference on Computational Intelligence and Computing Research,2014.
20.    RM Suresh, K Dinakaran, P Valarmathie, “Model based modified k-means clustering for microarray data”, International Conference on Information Management and Engineering, Vol.13, pp 271-273, 2009, IEEE.
21.    C. Escudero "Classification of Gene Expression Profiles: Comparison of k-means and expectation maximization algorithms", IEEE Computer Society, 2008, pp. 831-836.
22.    Manpreet Kaur, Usvir Kaur “ A Survey on Clustering Principles with K-Means clustering Algorithms Using Different Methods in Detail” International Journal of Computer Science and Mobile Computing, Vol-2, 2013. Pp 327-331.
23.    K. Kameshwaran, K. Malarvizhi “Survey on Clustering Techniques in Data Mining" IJCSIT: International Journal of Computer Science and Information Technologies, Vol-5, 2014. Pp 2272-2276.
24.    S. Suganya, Rose Margaret “Image Segmentation Using Two Weighted Variable Fuzzy K Means” International Journal of Computer Applications Technology and Research Volume 2, 2013.  Pp 270-276.
25.    Geng Li, Stephan Gunnemann, Mohammed J. Zaki “Stochastic Subspace Search for Top-K Multi-View Clustering” ACM, 2013. Pp 1-6.
26.    Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst ,Nikolai Nefedov “Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds” 2013. Pp 1-13.
27.    https://archive.ics.uci.edu/ml/datasets.html

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3.

Authors:

Geeta Bhagwan Mehetre, M. B. Kalkumbe

Paper Title:

The Behavioral Modeling Approach for Sarcasm Detection on E-Commerce & OSN

Abstract: Sarcasm transforms the polarity of an apparently positive or negative affirmation into its opposite. We propose a method to construct a sarcastic Twitter message corpus in which the determination of the sarcasm of each message is made by the system. We use this reliable corpus to compare sarcastic statements in Twitter with statements that express positive or negative attitudes without sarcasm. We study the impact of lexical and pragmatic factors on the effectiveness of automatic learning to identify sarcastic utterances and we compare the performance of automatic learning techniques and human judges in this task. Perhaps it is not surprising that neither human judges nor mechanical learning techniques work very well.

Keywords:  Hashtags, Linguistics, Opinion Mining, Sarcasm Detection, Tweets, Open NLP.

References:
1.       Carvalho, P., Sarmento, S., Silva, M. J., and de Oliveira, E. 2009. Clues for detecting irony in user-generated contents: oh...!! it's "so easy" 😉 In Proceeding of the 1st international CIKM workshop on Topicsentiment analysis for mass opinion (TSA '09). ACM, New York, NY, USA, 53-56 
2.       Clark, H. and Gerrig, R. 1984. On the pretence theory of irony. Journal of Experimental Psychology: General, 113:121–126. D.C.
3.       Davidov, D., Tsur, O., and Rappoport, A. 2010. SemiSupervised Recognition of Sarcastic Sentences in Twitter and Amazon, Dmitry Proceeding of Computational Natural Language Learning (ACL-CoNLL).
4.       Derks, D., Bos, A. E. R., and Grumbkow, J. V. 2008. Emoticons and Online Message Interpretation. Soc. Sci. Comput. Rev., 26(3), 379-388.
5.       Gibbs, R. 1986. On the psycholinguistics of sarcasm. Journal of Experimental Psychology: General, 105:3–15.
6.       Gibbs, R. W. and Colston H. L. eds. 2007. Irony in Language and Thought. Routledge (Taylor and Francis), New York.
7.       Kreuz, R. J. and Glucksberg, S. 1989. How to be sarcastic: The echoic reminder theory of verbal irony. Journal of Experimental Psychology: General, 118:374-386.
8.       Kreuz, R. J. and Caucci, G. M. 2007. Lexical influences on the perception of sarcasm. In Proceedings of the Workshop on Computational Approaches to Figurative Language (pp. 1-4). Rochester, New York: Association for Computational. LIWC Inc. 2007.
9.       The LIWC application. Retrieved May 10, 2010, from http://www.liwc.net/liwcdescription.php.
10.    Nigam, K. and Hurst, M. 2006. Towards a Robust Metric of Polarity. In Computing Attitude and Affect in Text: Theory and Applications (pp. 265-279). Retrieved February 22, 2010, from http://dx.doi.org/10.1007/1-4020-4102-0_20.
11.    Pak, A. and Paroubek, P. 2010. Twitter as a Corpus for Sentiment Analysis and Opinion Mining, in 'Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)' , European Language Resources Association (ELRA), Valletta, Malta
12.    Pang, B. and Lee, L. 2008. Opinion Mining and Sentiment Analysis. Now Publishers Inc, July.
13.    Pennebaker, J.W., Francis, M.E., & Booth, R.J. (2001). Linguistic Inquiry and Word Count (LIWC): LIWC2001 (this includes the manual only). Mahwah, NJ: Erlbaum Publishers
14.    Strapparava, C. and Valitutti, A. 2004. Wordnet-affect: an affective extension of wordnet. In Proceedings of the 4th International Conference on Language Resources and Evaluation, Lisbon.
15.    Tepperman, J., Traum, D., and Narayanan, S. 2006. Yeah right: Sarcasm recognition for spoken dialogue systems. In InterSpeech ICSLP, Pittsburgh, PA.
16.    Utsumi, A. 2000. Verbal irony as implicit display of ironic environment: Distinguishing ironic utterances from nonirony. Journal of Pragmatics, 32(12):1777– 1806.

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4.

Authors:

Amaya, Flocerfida L., Briones, Lloyd Alfred, Evardone, Caryl Josef

Paper Title:

Productivity Improvement Through Line Balancing in the Assembly Area of a Lighting Manufacturing Company in the Philippines

Abstract: One of the important aspects of business efficiency is to reduce cycle time and eliminate idle time in the production.  Optimum cycle time can be determined using the line balancing techniques. Line balancing supports optimal layout that helps in reducing processing time by eliminating non value added activities. In a lighting manufacturing company in the Philippines, line balancing is used in the assembly line of 25A – 19A of clear household lamps.  This is used as a production line technique in every station to have an equal amount of workload and equal cycle time to diminish bottlenecks and reduced idle time.  However, the current operation process still cannot meet the standards set by the management. Thus study aims to establish a standard operating procedures for a lighting manufacturing company to achieve a balanced line and improve their rate of efficiency.  Time study was used to identify the average cycle time per process and Westing House System was used to determine the standard process time per workstation.  Eliminating the idle time and minimizing the number of the workstation can make the number of outputs per task or station balanced and increase their rate of efficiency. After using a simulation application to test the proposed solution to the problem, it is recommended that the company should use simplify and combine task elements that can be merged to improve the efficiency rate in the assembly line.

Keywords: Cycle Time, Line Balancing, Productivity Improvement, Time Study, Westing House System

References:
1.        Mahto, D. G., & Kumar, A. (2016, April 28). An empirical investigation of assembly line balancing techniques and optimized implementation approach for efficiency improvements by D. G. Mahto, Anjani Kumar: SSRN. Retrieved August 24, 2016,  from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2770345
2.        Joaquin Bautista, Jordi Pereira (2011) “Procedures for the Time and Space constrained Assembly Line Balancing Problem”. European Journal of  Operational Research Vol.212, pp. 473–481.
3.        Evgeny Gurevsky, Olga Battaïa, Alexandre Dolgui (2013) “Stability measure for a generalized assembly line balancing problem”. Discrete Applied Mathematics Vol.161, pp.377–394.
4.        Amardeep, Rangaswamy, T. M., & Gautham j. (2013, May). Retrieved August 23, 2016, from International Journal of Innovative Research in Science, Engineering and Technology,http://www.ijirset.com/upload/may/55_LINE%20BALANCING.pdf
5.        Neda Manavizadeh, Nilufar-sadat Hosseini, MasoudRabbani, Fariborz Jolai (2013) “A Simulated Annealing algorithm for a mixed model assembly U-line balancing         type-I problem considering human efficiency and Just-In-Time approach”. Computers& Industrial Engineering. Vol.64, pp.669–685
6.        Al-Saleh, K. S. (2011). Productivity improvement of a motor vehicle inspection station using motion and time study techniques. , 23(1), 33–41. doi:http//dx..org/10.1016/j.jksues.2010.01.001
7.        Christian Blum, Cristobal Miralles (2011) “On solving the assembly line worker assignment and balancing problem via beam search”. Computers &Operations ResearchVol.38, pp.328–339.
8.        Xu Weida, Xiao Tianyuan (2011) “Strategic Robust Mixed Model Assembly Line Balancing Based on Scenario Planning”. Tsinghua science and technology Volume 16, Number 3, June 2011 ISSN1007-0214 13/16, 308-314.
9.        Hazır, Ö., Delorme, X., & Dolgui, A. (2015). A review of cost and profit oriented line designand balancing problems and solution approaches. Annual  Reviews in Control, 4014-24. doi:10.1016/j.arcontrol.2015.09.001
10.     Sihombing Haeryip, Kannan Rassiah, Parahsakthi Chidambaram (2011) “Line balancing               analysis of tuner product manufacturing”. International Journal of Engineering         Science and Technology (IJEST),ISSN: 0975-5462, Vol. 3 No. 6 June 20115206- 5214
11.     Rokach, L., & Hutter, D. (2011). Automatic discovery of the root causes for quality drift in high dimensionality manufacturing processes. Journal of Intelligent Manufacturing, 23(5), 1915–1930. Doi: 10.1007/s10845-011-0517-5
12.     ALTEKİN, F. T. (2015, January). Retrieved August 30, 2016, from http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=4E4D672E01DE67C549E326AA29C3209?doi=10.1.1.633.474&rep=rep1&type=pdf
13.     Yazdanparast, V., Hajihosseini, H., & Bahalke, A. (2011). Line balancing, line balancing operations management. Retrieved August 30, 2016, from http://civilserviceindia.com/subject/Management/notes/line-balancing.html
14.     Evangelista,(2016).4-Assembly-Line-Balancing-A-Review-of. . Retrieved      from  https://www.academia.edu/15438726/4-Assembly-Line-Balancing- A-Review-of
15.     Sarin, S. (2016). Allocation of work to the stations of an assembly line with buffers between stations and three general learning patterns. Int. J. Intelligent Systems Technologies and Applications, 4(1 of 2),. Retrieved from
https://www.academia.edu/15486954/Allocation_of_work_to_thestations_of_an_assembly_line_with_buffers_between_stations_and_three_general_learning_patterns
16.     Pekin(2013).Retrieved August 24, 2016, from https://core.ac.uk/download/files/890/35365975.pdf
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19.     Scholl, A., Boysen, N., & Fliedner, M. (2011). The assembly line balancing and scheduling problem with sequence-dependent setup times: Problem extension, model
formulation and efficient heuristics. OR Spectrum, 35(1), 291–320. Doi: 10.1007/s00291011-0265-0

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5.

Authors:

Gaurav Sharma, Surbhi Dhiman

Paper Title:

Multifarious Secured Path for Stable Routing in Mobile Ad Hoc Networks

Abstract: A Mobile Adhoc Network (MANET) is characterized by mobile nodes, multi hop wireless connectivity, infrastructure less environment and dynamic topology. A recent trend in Ad Hoc network routing is the reactive on-demand philosophy where routes are established only when required. Stable and secure routing and power efficiency are the major concerns in this field. This paper is an effort to study security problems associated with MANETS and solutions to achieve more reliable routing. The ad hoc environment is accessible to both legitimate network users and illegitimate attackers. The study will help in making protocol more robust against attacks to achieve stable routing in routing protocols.  

Keywords:  Ad hoc Networks, AODV, security, wireless network, packet delivery

References:
1.        Kush, P. Gupta, R. Kumar, “Performance Comparison of Wireless Routing Protocols”, Journal of CSI, Vol. 35 No.2, 2005.
2.        Kush, P. Gupta, C J Hwang, “Stable and Energy Efficient Routing for Mobile Adhoc Networks”, Information Technology: New Generations, ITNG, Fifth International Conference, LAS VEGAS USA ,2008, Page(s):1028 – 1033.
3.        Bayya Arun et. al., “Security in Ad hoc Networks”, Computer Science Department, University of Kentucky, 2013.
4.        Parkins and E. Royer , “Ad Hoc on demand distance vector routing”, 2nd IEEE workshop on mobile computing , pages 90-100, 1999.
5.        Charles Perkins et. al., “Performance of two on-demand Routing Protocols for Ad-hoc Networks”, IEEE Personal Communications, February 2001, pages 16-28.
6.        B. Johnson and D. A. Maltz, “Dynamic source routing in ad hoc networks”, Kluwer academic publishers, 1996.
7.        Y.C. Hu et. al., “The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks (DSR)”, IETF Draft, April 2003,
8.        B. J. et. al., “Ariadne: A secure on-demand routing protocol for ad-hoc networks”, Proceedings of the Eighth Annual International Conference on Mobile Computing and Networking (MobiCom 2002), Sept. 2002.
9.        Hao Yang et. al., “Security in Mobile Ad Hoc Networks: Challenges and Solutions”, UCLA Computer Science Department, 2004.
10.     Md. Golam Kaosar et. al., “Simulation-Based Comparative Study of On Demand Routing Protocols for MANET”, January 2005.
11.     NIST, Fed. Inf. Proc. Standards, “Secure Hash Standard”, Pub. 180, May 1993.
12.     A.Kush and Sunil Taneja, “ Simulation of MANET schemes“, International Journal of Computing and Business Research, Vol 1, Issue 2, Nov 2010.
13.     Kush and Sunil Taneja, “End to End Delay Analysis of Prominent On-demand Routing Protocols” IJCST, International Journal of Computer Science and Technology, Vol 2 Issue 1 March 2011, pp 42-46.
14.     Kush et. al., “Encryption Scheme for Secure Routing in Ad Hoc Networks”, International Journal of Advancements in Technology, Vol 2, No 1, January 2011, pp22-29.
15.     Nitin Goyal and Alka Gaba, “A review over MANET- Issues and Challenges”, International Journal of Enhanced Research in Management & Computer Applications, ISSN: 2319-7471, Vol. 2, Issue 4, April-2013.
16.     Sanjay K. Dhurandher et. al., “GAODV: A Modified AODV Against Single and Collaborative Black Hole Attacks in MANETs”, 27th IEEE International Conference on Advanced Information Networking and Applications Workshops, Pages 357-362, March 25 - 28, 2013, USA.
17.     H Shokrani and S Jabbehdari, “A survey of ant-based routing algorithms for mobile ad-hoc networks”, IEEE International Conference on Signal Processing, 2009.
18.     Nitin Goyal, Alka Gaba, “A New Approach of Location Aided Routing Protocol Using Minimum Bandwidth in Mobile Ad-Hoc Network”, International Journal of Computer Technology & Applications, Volume 4, Issue 4, pp 653-659, July 2013.
19.     Nitin Goyal, Alka Gaba, “Review over Diverse Location Aided Routing”, Global Journal for Current Engineering Research, Volume 2, Issue 2, pp 141-144, August 2013.
20.     Nitin Goyal, Pratibha Kamboj, “Survey of Various Keys Management Techniques in MANET”, International Journal of Emerging Research in Management & Technology,
Volume 4, Issue 6, pp 176-178, June 2015.
21.     https://en.wikipedia.org/wiki/Digital_signature.
22.     https://en.wikipedia.org/wiki/Replay_attack.

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