S. No

Volume-3 Issue-5, March 2015, ISSN:  2319–6378 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Beniwal Ashok, M.P Sharma,Vinod Kumar

Paper Title:

An Analysis of Land use/land Cover Change Detection Using Geospatial Technology-A Case Study of Rewari District, Haryana, India

Abstract:  Land resources are finite and therefore their conservation, development and management plays the determining role in their sustaining use. The land resources shall be treated as single, renewable and intergenerational entity and they have to be viewed as prime national resources for all societal, economical and political considerations. Preparation of land use/Land cover maps for development plan aims at optimal, eco-friendly, viable & integrated land use planning. As a pre-requisite of any planning and implementation of land resources development action plan with continuous and periodic assessment of status of land use/Land cover both at regional and national level. The objective of study is to generate change analysis of land use/land cover pattern of the years 2005-2006 and 2011-2012 of the study area. This analysis was carried out through geospatial technology using IRS-Resourcesat-2 (LISS-III) data. The study shows that increase in area under built up land category is the expense of agricultural and wasteland land use classes. The study concluded that area under urban infrastructure is increasing at fast pace and which is eating up fertile agriculture land of area. Keywords:

Keywords: Remote sensing and GIS, Resourcesat-2, LISSIII, land use/land cover.

References:
1.    Harmsen K., Nidhumolu B. N. (2002). “Land use planning for sustainable development in South Asia: An example from India,” IAPRS & SIS, Vol. 34, Part 7, “Resource and Environment Monitoring,” Hyderabad, India. PP 1467-1473
2.    Anderson James R. (1971). “Land use classification scheme used in selected recent geographic applications of remote sensing”, Photographic Engineering and remote sensing (PE & RS), vol. 37, No. 4, PP 379-387.
3.    Panigarhi S., Ray S S., Sood A., Patel L.B., Sharma P.K. and Parihar J.S. (2004). “Analysis of cropping pattern changes in Bhathinda District, Punjab” Journal of ISRS, Vol. 32, No.2. PP 209-16.
4.    Minakshi, Sharma P. K., Kaur A. and Shalley V. (2005). “Satellite based study of land transformation of Ludhiana district, Punjab,” Journal of ISRS, Vol.33, No1, PP 181-186
5.    Ray S.S., Sood A., Das G., Panigrahy S., Sharma P. K.and Parihar J.S.(2005). “Use of GIS and remote sensing for crop diversification- A case study of Punjab state”, Journal of ISRS, Vol.33, No1, PP181-186
6.    Sharma, M.P, Archana, Prawasi, Ravindra, Hooda, R.S., (2013): Land use/land over change detection in National Capital Region (NCR) Delhi:
7.    A case study of Gurgaon District.

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2.

Authors:

D. Suresh, S. Manikandan

Paper Title:

Enterprise Resource Planning: Firms are Trying to Extend the System to Frond-End and M-Commerce

Abstract: The growth of global enterprises and the expansion of technical and managerial knowledge are hallmarks of the twenty-first century organization. A wave of interest in improving the integration of information or data across the functional areas of a business has surfaced during the past few years. More specifically, business transactions that occur in one functional area would, indeed, affect transaction in other functional areas. Not surprisingly, the most effective managerial decisions can be made when information and data from all areas of business are available in real time, and that all parties having access to the information are participants in the decision made. A Software which enables this integration of information is the Enterprise Resource Planning (ERP) software system. The core issues and concepts underlying idea behind Enterprise Resource Planning is its ability to bind the entire enterprise in a tight web of information system. This software application leaves no function of an organization untouched. The thrust of this paper is to provide information about the ABC’s of ERP, ERP implementation life cycle, the seven mantra’s for ERP implementation, and the rebirth of ERP extending its footprints to Supply Chain Management (SCM), Customer Relationship Management (CRM) and Collaborative Management.

Keywords:  Enterprise resource planning ERP, ERP II, Cost of ERP, e-commerce.

References:
16.1 Text Books:
[1]           Alexis Leon, 1998,  “Enterprise Resource Planning”
[2]           Alexis Leon, 1998,  “ERP Demystified”
[3]           Parag Diwan,  R.K.Suri, 2000, “Enterprise Resource Planning”, IT Encyclopaedia.com
16.2 Journals:
[1]           Gopinath.S  ,Ramesh Kannan.M  and Ramajeyam.L , “Computerized Critical Path Segmental Scheduling for Construction Projects”, pp 60-66, Proceedings of second International Conference on Advances in Industrial Engineering Applications (ICAIEA2014), Anna University,January 6-8,2014
[2]           Kathleen M.Utecht, Randall B.Hayes, and Patrick A.Okonkwo, “Enterprise Resource Planning and SAP R/3 Functionality”, pp.210-214, 1st International conference of Logistics and Supply Chain Management6-8th August 2001, PSG College Of Technology, Coimbatore  
[3]           R.B.Hayes, P.A.Okonkwo, S.Palaniswami, and K.M.Utecht, “Enterprise Resource Planning Implementation in Institutions Of Higher Learning”, pp.226-233,  From 1st International conference of Logistics and Supply Chain Management 6-8th August 2001, PSG College Of Technology, Coimbatore.
[4]           A.K. Bharadwaj, “Evaluating an ERP Project”, vol 82, Nov 2001,Institute Of Engineers.
[5]           Shelley singh, “Rebirth Of ERP”, pp.46-48,Business World 5, Nov 20001
[6]           Raju Bist, “ A Laboratory Of Ideas “,pp.46-48, MIS South Asia, May 2000
[7]           Chris Bell, “Unlocking ERP Benefits”,pp.64-71, MIS South Asia, Sept 2000.
[8]           Zubin Sethna. “Changing With the Times”, pp.36-39, MIS South Asia, March 2000
[9]           Louis Fernandes, “Sing Glass”,pp.41-42, MIS South Asia, June 2000.
[10]         Vinod Kumar Garg & Dr.Kamal Bharani,”An Empirical Method For Estimation Of Efforts For ERP Implementation”, pp.10-14, Industrial Engineering Journal, vol xxx No.11.
[11]         John Ribeiro, “ERP Counseling from Maini”, pp.6, Information Systems Computerworld Feb 2000.

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3.

Authors:

P. Swapnil, Devesh Narayan, Sipi Dubey

Paper Title:

An Approach for Human Identification by Ear Biometric System

Abstract:  Ear biometric for identification of human is quite complex task. It’s use either uni-modal or multi-modal approach in order to authenticate a person. A uni-modal biometric system involves a single source of biometric to identify a person. This paper is based on uni-modal approach with ear as a biometric trait for recognizing a person. In this paper, an experimental approach is used for identifying a person based by their ear structure. As researchers suggest that ear is a physiological biometric that is quite reliable source of identifying human, we here, present the work related to this field of ear biometrics. Firstly, we will discuss about identification process of our ear biometric system that includes pre-processing, edge detection using canny edge detection method and then extracting farthest boundary points that form a maxline. Now this maxline is divided into multiple points to extract geometrical intersecting points on the outer edge. Then finally template matching is done for identification.

Keywords:  Biometrics, ear biometrics, canny edge detection, maxline and template matching.

References:
1.       Human Computer Interpreting with Biometric Recognition System, Gajanan Pandurangrao, Khetri, Satish L.Padme, Dinesh Chandra Jain, IJARCSSE, Volume 2, Issue 12, December 2012.
2.       A Review on Biometrics and Ear Recognition Techniques, Sukhdeep Singh, Dr. Sunil Kumar Singla, Volume 3, Issue 6, June 2013, ISSN: 2277 128X
3.       Automated Biometric Verification: A Survey on Multimodal Biometrics, Rupali L. Telgad, Almas M. N. Siddiqui,Dr. Prapti D. Deshmukh, International Journal of Computer Science and Business Informatics, ISSN: 1694-2108, Vol. 6, No. 1. October 2013.
4.       A Review Paper on Ear Biometrics: Models, Algorithms and Methods, Khamiss Masaud, S.Algabary, Khairuddin Omar, Md. Jan Nordin, Siti Norul Huda Sheikh Abdullah, Aust. J. Basic & Appl. Sci. 7(1): 411-421,2013.
5.       A New Force Field Transform for Ear and Face Recognition, David J. Hurley, Mark S. Nixon and John N. Carter, IEEE, 2000.
6.       Contour Matching for 3D Ear Recognition, Hui Chen and Bir Bhanu, Proceedings of the Seventh IEEE Workshop on Applications of Computer Vision (WACV/MOTION’05) 0-7695-2271-8/05, IEEE.
7.       Human Ear Recognition in 3D, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 29, No. 4, April 2007.
8.       Ear Recognition Using Wavelets, M. Ali1, M. Y. Javed and A. Basit, Proceedings of Image and Vision Computing New Zealand 2007, pp. 83–86, Hamilton, New Zealand, December 2007.
9.       Robust Multi-biometric Recognition Using Face and Ear Images”, Nazmeen Bibi Boodoo, R K Subramanian, IJCSIS, Vol. 6, No. 2, 2009.
10.    The Human Identification System Using Multiple Geometrical Feature Extraction of Ear –An Innovative Approach, Jitendra B. Jawale, Dr. SMT. Anjali S. Bhalchandra, IJETAE, ISSN 2250-2459, Volume 2, Issue 3, March 2012.
11.    Ear Localization from Side Face Images using Distance Transform and Template Matching, Surya Prakash, Umarani Jayaraman and Phalguni Gupta, Proceedings of IEEE Int'l Workshop on Image Processing Theory, Tools and Applications, IPTA 2008, Sousse, Tunisia, pp. 1-8, Nov 2008.
12.    Ear Recognition for Automated Human Identification, Singh Amarendra and Verma Nupur, Research Journal of Engineering Science, ISSN 2278 – 9472, Vol. 1(5), 44-46, November (2012)
13.    Ear Biometrics: A Survey of Detection, Feature Extraction and Recognition Methods, Anika Pflug, Christoph Busch, , IET Biometrics, July 2012.
14.    On ear-based human identification in the mid-wave infrared spectrum, 2013, Ayman Abaza, Thirimachos Bourlai, Image and Vision Computing 31 (2013) 640–648, Elsevier.
15.    A Book on Biometric Technology Application Manual,(2008), National Biometric Security Project(NBSP).

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4.

Authors:

Kalesh M. Karun, Binu V. S, Kala M. Karun, Keerthana Prasad, Nair N. S, K. Manjunatha Prasad, K. M. Girisha

Paper Title:

Review on Image Segmentation Methods in cDNA Microarray Experiments and a Novel Algorithm for Segmentation

Abstract: Microarray experiments are used to measure gene expression levels of thousands of genes at a time. The image analysis has an important role in the microarray data analysis and has potential impact on the identification of differentially expressed genes. Segmentation is one of the important processes in image analysis. The current paper attempts to provide an overview of commonly used segmentation methods in microarray image analysis like fixed circle segmentation, adaptive circle segmentation, the adaptive shape segmentation, histogram-based method and machine learning algorithms. We estimated intensity ratios of selected spots from an image file downloaded from the Gene Expression Omnibus (GEO) database based on the above segmentation methods. It was observed that all these methods give almost similar estimates of intensity ratio value. We are also proposing a new algorithm to identify the spot radius for the adaptive circle segmentation, instead of manual fixing of the radius. 

Keywords:  Microarray, Image analysis, Segmentation, Intensity ratio.

References:
1.       V. S. Binu, N. S. Nair, K. M. Prasad, and K. M. Kalesh, “Estimation of uncertainty associated with intensity ratio in cDNA microarray experiments”, Research & Reviews: A Journal of Statistics, 1:24-33,2012.
2.       R. Steve, A. M. Lisa, and R. R. Roslin, Microarray Technology in Practice, Academic press, UK, 2009.
3.       Carl F.E., Daniel R.E., David J.W., Eugene T., Ronald G.S, William F.B et.al. 1997. Heller Electric field directed nucleic acid hybridization on microchips. Nucleic Acids Research, 25:4907–4914.
4.       Lehmussola, P. Ruusuvuori, and O. Yli-Harja, “Evaluating the performance of microarray segmentation algorithms”, Bioinformatic, 22:2910–2917, 2006.
5.       A.A. Ahmed, M. Vias, N. G. Iyer, C. Caldas, and J. D. Brenton, “Microarray segmentation methods significantly influence data precision”, Nucleic Acids Res. 32, e50, 2004.
6.       Antti L., Pekka R. and Olli Y.H. 2006. Evaluating the performance of microarray segmentation algorithms. Bioinformatics, 22: 2910–2917.
7.       Themis P.E., Athanasios P. and Dimitrios I.F. 2009. Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications. IGI Global, USA.
8.       Ernst W. and John M. 2004. Statistics for Microarrays-Design, Analysis and Inference. John Wiley & Sons Ltd. England.
9.       Li Q., Luis R., Adnan A. and Alioune N. 2005. Spot Detection and Image Segmentation in DNA Microarray Data. Appl Bioinformatics, 4:1-11.
10.    Buhler J., Ideker T. and Haynor D. 2000. Dapple: Improved Techniques for Finding Spots on DNA Microarray Data. Journal of Computational and Graphical Statistics, 11:108-136.
11.    Volkan U. and Dhsan O.B. 2010. Microarray image segmentation using clustering methods. Mathematical and Computational Applications, 15:240-247.
12.    Lakshmana P.M., Keshav R. and Srinivasu P.N. 2013. A Comparative Analysis of Clustering based Segmentation Algorithms in Microarray Images. International Journal of Emerging Science and Engineering (IJESE), 1:2319–6378
13.    Nagarajan R. 2003. Intensity-Based Segmentation of Microarray Images. IEEE Trans. On Medical Imaging, 22:882–889.
14.    H. Laurie, “MicroArray Genome Imaging & Clustering (MAGIC) Tool”, Davidson College, Available: http://www.bio.davidson.edu/projects/magic/magic.html
15.    P. Exarchos, A. Papadopoulos, and D. I. Fotiadis Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, Mar. 2009 (ISBN 978-1-60566-314-2): IGI Global.

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5.

Authors:

Faten Z. Mahmoud, Fouz M. Omar, Mohamed M. Mahmoud, Atef A. T. Ramadan

Paper Title:

Synthesis and Characterization of Palladium (II) Complexes with Hydrazo-β-diketone Ligands

Abstract: Phenyl hydrazo-β-diketone ligands were prepared and their complexes with Pd(II) were studied. The β-diketone moieties included acetyl acetone (AA), benzoyl acetone (BA) and dibenzoyl methane (DBM), while the aniline part included 2- mezoxy-, 2-chloro-, 3-nitro-, 3-bromo- and 4-flouro- moieties. The stability constants and protonation constants of Pd(II) complexes and the ligand were studied in 75% (v/v) dioxanewater spectrophotometrically. Solid complexes of Pd(II) where established by elemental analysis, IR spectra, mass spectra and 1H-NMR methods. A linear relationship exists between the chemical shift of hydrazo proton and the protonation constants of the ligand, in addition to the linear relation obtained between the stability constants and the ligand basicties (∑pKH ).

Keywords:  hydrazo-β-diketone ligand, potentiometric and spectrophotometric studies of Pd(II)-PHBDH complexes, preparation of solid complex.

References:
1.          Tammam G. H., Alnaimi I. S. and Ramadan A. T.; Oriental J. Chem., 1, 4, 1988.
2.          Abdel-Moez M. S., Ramadan A. T. and Tammam G. H.; J. As. Chem. Soc., 105, 1, 1992.
3.          Ramadan A. T., Seada M. H. and Rizkalla E. N.; Talanta, 245, 30, 1983.
4.          Ramadan A. T., Seada M. H., El-Behairy M. A., Ismail A. I. and Mahmoud M. M.; Anal. Lett. , 745, 26, 1993.
5.          Briegleb C.; “Electron Donor-Acceptor Complex”, Springer Veleg, Berlin 1961.
6.          Nach C. P.; J. Phys. Chem., 950, 64, 1960.
7.          Irving H. and Rossotti H.; Acta Chem. Scand., 72, 10, 1956.
8.          Groy H. B. and Bullhauseu C. J.; Am. Chem. Soc., 260, 85, 1963.
9.          Mehta R. K. and Saighiz V. C.; Nature Forsh. 304, 276, 1972.
10.       Ozha D. D. and Mehta R. K.; J. Ind. Chem. Soc., 370, 52, 1975.

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