Volume V Issue X

01

Necessary & Sufficient Conditions for Consistency of Conditional Parameter XML Tree Expressions to their Resizable Hadoop Cluster Complexity

Ravi (Ravinder) Prakash G · October 2016 · pp. 01-19

We develop a novel technique for resizable Hadoop cluster’s lower bounds, *bipartite matching rectangular array of conditional parameter XML tree expressions.* Specifically, fix an arbitrary hybrid kernel function f:{0,1}n ->{0,1} and let Af be the rectangular array of conditional parameter XML tree expressions whose columns are each an application of f to some subset of the variables x1, x2,... x4n. We prove that Af has bounded-capacity resizable Hadoop cluster’s complexity omega(d) , where d is the approximate degree of f . This finding remains valid in the MapReduce programming model, regardless of prior measurement. In particular, it gives a new and simple proof of lower bounds for robustness and other symmetric conjunctive predicates. We further characterize the discrepancy, approximate PageRank, and approximate trace distance norm of Af in terms of well-studied analytic properties of f, broadly generalizing several findings on small-bias resizable Hadoop cluster and agnostic inference. The method of this paper has also enabled important progress in multi-cloud resizable Hadoop cluster’s complexity..

**[1]. Ravi (Ravinder) Prakash G, What is a Geometric Spanner of Resizable Hadoop Channel for Homogeneous Lower Bounds? International Journal of Latest Technology in Engineering, Management & Applied Science, Volume V, Issue VIII, Pages 08-27, August 2016, ISSN 2278- 2540. [2]. Greenlaw, R. and Kantabutra. 2008. On the parallel complexity of hierarchical clustering and CC-complete problems. Complexity 14, 18- 28. (doi:10.1002/cplx.20238) [3]. Ravi (Ravinder) Prakash G, Necessary & Sufficient Conditions for Wavelet Filter Convergence of Lower Bounds to their Resizable Hadoop Channels, International Journal of Research and Scientific Innovation, Volume III, Issue IX, Pages: 12-30, September 2016, ISSN 2321–2705. [4]. Amazon Elastic MapReduce. https://aws.amazon.com/elasticmapreduce/ [5]. J. Lu, T.W. Ling, Z. Bao, and C. Wang, “Extended XML Tree Pattern Matching: Theories and Algorithms,” IEEE Trans. Knowledge and Data Eng., vol. 23, no. 3, pp. 402-416, Mar. 2011. [6]. N. Ailon, B. Chazelle, S. Comandur, D. Liu. 2007. Estimating the Distance to a Monotone Function. Random Structures and Algorithms 31, 371-383. (doi:10.1002/rsa.20167) [7]. A. Gavish, Abraham Lempel. 1996. Match-length functions for data compression. IEEE Transactions on Information Theory 42, 1375-1380. (doi:10.1109/18.532879) [8]. Ravi (Ravinder) Prakash G, Necessary & Sufficient Conditions for Recursive Image Filter Convergence of Hadoop Channels to their Homogeneous Lower Bound, International Journal of Research and Scientific Innovation, Volume III, Issue 9, Pages 34-52, September 2016, ISSN 2321–2705. [9]. Ping Wah Wong. 1997. Rate distortion efficiency of subband coding with crossband prediction. IEEE Transactions on Information Theory 43, 352-356. (doi:10.1109/18.567761) [10]. A. Lafourcade, Alexander Vardy. 1996. Optimal sectionalization of a trellis. IEEE Transactions on Information Theory 42, 689-703. (doi: 10.1109/18.490504) [11]. T.M. Cover. 1998. Comments on Broadcast Channels. 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IEEE Transactions on Information Theory 40, 696-705. (doi:10.1109/18.335881) [17]. Donald Miner, Adam Shook, 2013, "MapReduce Design Patterns" O’Reilly Media, Inc.: 978-1-449-32717-0. [18]. Rudolf F. Ahlswede, Zhen Zhang. 1994. On multiuser write-efficient memories. IEEE Transactions on Information Theory 40, 674-686. (doi:10.1109/18.335880) [19]. B. Chazelle. 2000. The Discrepancy Method: Randomness and Complexity. Cambridge University Press. 978-0-521-77093-9. [20]. B. Chazelle, A. Lvov. 2001. A Trace Bound for the Hereditary Discrepancy. Discrete Computational. Geom. 26, 221-231. (doi:10.1007/s00454-001-0030-2) [21]. B. Chazelle, A. Lvov. 2001. The Discrepancy of Boxes in Higher Dimension. Discrete Computational. Geom. 25, 519-524. (doi:10.1007/s00454-001-0014-2) [22]. B. Chazelle, J. Matoušek, M. Sharir. 1995. An Elementary Approach to Lower Bounds in Geometric Discrepancy. Discrete Comput. Geom. 13, 363-381. (doi:10.1007/BF02574050) [23]. E. Arikan. 1994. An upper bound on the zero-error list-coding capacity. IEEE Transactions on Information Theory 40, 1237-1240. (doi:10.1109/18.335947) [24]. B. Chazelle, H. Edelsbrunner, L.J. Guibas, M. Sharir. 1991. A Singly Exponential Stratification Scheme for Real Semi-Algebraic Varieties and Its Applications. Theoretical Computer Science 84, 77-105. (doi:10.1016/0304-3975(91)90261-Y) [25]. B. Kimelfeld and Y. Sagiv, “Twig Patterns: From XML Trees to Graphs,” Proc. Ninth Int’l Workshop Web and Databases (WebDB ’06), 2006. [26]. B. Chazelle. 1999. Discrepancy Bounds for Geometric Set Systems with Square Incidence Matrices. Advances in Discrete and Computational Geometry, Contemporary Mathematics AMS 223, 103-107. [27]. B. Chazelle. 2004. The Discrepancy Method in Computational Geometry. Handbook of Discrete and Computational Geometry, CRC Press 44, 983-996. [28]. Fadika, Z.; Govindaraju, M. 2010. LEMO-MR: Low Overhead and Elastic MapReduce Implementation Optimized for Memory and CPUIntensive Applications. IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), 1-8. (doi:10.1109/CloudCom.2010.45) [29]. Fadika, Z.; Govindaraju, M. 2011. DELMA: Dynamically Elastic MapReduce Framework for CPU-Intensive Applications. 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 454-463. (doi: 10.1109/CCGrid.2011.71) [30]. Iordache, A.; Morin, C.; Parlavantzas, N.; Feller, E.; Riteau, P. 2013. Resilin: Elastic MapReduce over Multiple Clouds. 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 261-268. (doi:10.1109/CCGrid.2013.48) [31]. XiaoyongXu; Maolin Tang. 2013. A comparative study of the semielastic and fully-elastic mapreduce models. IEEE International Conference on Granular Computing (GrC), 380-385. (doi:10.1109/GrC.2013.6740440) [32]. Wei Xiang Goh; Kian-Lee Tan. 2014. Elastic MapReduce Execution. 14th IEEE/ACM, International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 216-225. (doi:10.1109/CCGrid.2014.14) [33]. B. Chazelle, W. Mulzer. 2011. Computing Hereditary Convex Structures. Discrete Comput. Geom. 45, 796-823. (doi:10.1007/s00454- 011-9346-8) [34]. B. Chazelle, H. Edelsbrunner, M. Grigni, L.J. Guibas, M. Sharir, E. Welzl. 1995. Improved Bounds on Weak ε-Nets for Convex Sets. Discrete Comput. Geom. 13, 1-15. (doi:10.1007/BF02574025) [35]. David P. Williamson, David B. Shmoys. 2011. The Design of Approximation Algorithms.Cambridge University Press, 978-0-521- 19527-0. [36]. Oded Goldreich. 2008. Computational Complexity: A Conceptual Perspective.Cambridge University Press, 978-0-521-88473-0. [37]. Sanjeev Arora, Boaz Barak. 2009. Computational Complexity: A Modern Approach.Cambridge University Press, 978-0-521-42426-4. [38]. Dimitri P. Bertsekas, Convex Optimization Algorithms, Athena Scientific, Hardcover Edition ISBN: 1-886529-28-0, 978-1-886529-28- 1, Publication: February, 2015, 576 pages. [39]. Ravi (Ravinder) Prakash G, Necessary & Sufficient Conditions for Consistency of Bipartite Matching Polyhedral Path Expressions to their Resizable Hadoop Cluster Complexity, International Journal of Latest Technology in Engineering, Management & Applied Science, Volume V, Issue IX, Pages: 07-25, September 2016. ISSN 2278-2540. [40]. Marouane Hachicha and Jerome Darmont, "A Survey of XML Tree Patterns", IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. , pp. 29-46, Jan. 2013, doi:10.1109/TKDE.2011.209. [41]. Kevin P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press. [42]. Koller and Nir Friedman. 2009. Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press**

02

Performance Evaluation of Effluent Treatment Plant for Pigment Industry

Ami N Dave, Satyam L Panchal, Jignesh K Patel · October 2016 · pp. 20-23

Pigment industry is among the most polluting chemical industries. Pigment industry is one of the major industries causing water pollution. The pigment industries waste water not only contains high level of suspended solids and total solids but also contains pH, COD, BOD, acidity, alkalinity, color. The study reveals the performance, evaluation and operational aspects of effluent treatment plant and its treatability rather than the contamination status of real property. Samples are collected from Oil & Grease trap, Equalization & Neutralization tank, Primary sedimentation tank, Activated sludge unit and Carbon absorption to evaluate the performance of Effluent Treatment Plant. The results revealed that the treated effluent shows most of the parameters are within permissible limits of central pollution control board, India and based on the site visits, discussion with operation peoples, treatment system, existing effluent discharge, results of sample analyzed and found that effluent treatment plant of pigment industry are under performance satisfactory.

03

Investigation on Peek and Polyurethane for Strength Used in Articulating Surface of Knee Implants

Venkatesh N, Dr. H. G. Hanumantharaju, Hemanth B · October 2016 · pp. 24-29

Today in Medical field biomaterial’s are in use for the replacement of artificial heart, skin, lever, bone etc in human body. In the advanced material engineering field, mainly mechanical parameters increase the efficiency of the system. Due to fast growing in population and more usage of automobiles leading to increase in accidents in daily life. Men/women met with such type of accidents which may cause fracture of bone. The largest and heaviest joint in the human body is the knee joint which includes femur, tibia, pattelar and articulating surface. Currently polyethylene is the widely used material for articulating surface of knee implants in total knee replacement. Failures of the implants after the implantation within prescribed time is mainly due to its low tensile, compressive, shear strengths, and also poor load bearing properties of Polyethylene material. Among all available biocompatible materials PEEK and Polyurethane is selected which are biocompatible, economical, low density, easily manufacturable and readily available. Polyethylene is considered as reference for the present investigation to compare the strength of the selected materials. In the investigation basic mechanical test’s viz. tension, compression, shear tests were conducted according to ASTM standards to record the mechanical properties which help to decide their strength parameter for the articulating surface.

04

Males, Media and Metrosexuality: An Exploratory Study of Persuasion

Jaiman Preet Kaur, Dr. Jagmeet Bawa · October 2016 · pp. 30-38

The level of metrosexuality among males has increased significantly over the last number of years as males are now showing more of an interest in fashion, appearance and beauty enhancement products. They are increasingly becoming passionate consumers of once taboo goods and services such as grooming products, cosmetics, salon services and fashion goods. This consumer, labeled as the metrosexual, is alleged to indicate a departure from conventional notions of masculinity. Advertisers are taking notice by increasingly promoting goods and services to target this lucrative consumer segment. This paper aims to explore and analyze the impact of media (advertisements basically) on the identity of metrosexual men whose certain characteristics are different from conservative males. It is an exploration of the relationship between ads of transforming conservative, old-fashioned males into appealing and more confident ones and the resulting anxiety levels. All the statistical analysis is carried out with the help of SPSS and to find out significant difference between the opinions of different demographic groups, Frequency distribution with tables and charts, Cross-tables and One-way ANOVA (Analysis of Variance) is used. This contrastive study will provide understanding and insight not only on the influence of advertising but also on the new gender of modern men who are different from the conventional ones. The present study conducted during the year 2015, will also provide marketers and advertisers with the tools to effectively target this important segment i.e. men.

Jaiman Preet Kaur

Research Scholar, Department of IRC-UHVE, IKG Punjab Technical University, Kapurthala, Punjab, India

Dr. Jagmeet Bawa

Joint-Director, Department of IRC-UHVE, IKG Punjab Technical University, Kapurthala, Punjab, India, , , **[1]. Simpson (1994), Male Impersonators: Men performing masculinity, London, Castell. [2]. Conseur (2004). Factors Influencing the Emergence of the Metrosexual, University of Georgia Theses and dissertations, Source: https://purl.galileo.usg.edu/uga\_etd/conseur\_amanda\_a\_200405\_m s [3]. Janowska (2008). Metrosexual men’s shopping habits –study of the modern men’s clothing brand selection, Bachelor Thesis FE3933, source: https://www.divaportal. org/smash/get/diva2:206468/FULLTEXT01.pdf [4]. Lertwannawit et. al. (2010). Metrosexual Identification: Gender Identity and Beauty Related Behaviors, International Business and Economics Research Journal, Vol. 9 No..11, pp 85-90. [5]. Hall and Gough (2011). Magazine and reader constructions of ‘metrosexuality’ and masculinity: A membership categorisation analysis, Journal of Gender Studies Vol. 20, No.01, pp 67-86. [6]. Piayura (2013). Metrosexual men in Thai Classical Literature, International journal of social science and humanity, May, Vol. 3, No. 3, pp- 218-221. [7]. Harris (2013). An Investigation into the Social Acceptance into the Irish Society, thesis submitted in National College of Ireland in fulfillment of M.Sc in Marketing. Source: https://trap.ncirl.ie/855/1/fionaharris.pdf.**, , **Jaiman Preet Kaur, Dr. Jagmeet Bawa "Males, Media and Metrosexuality: An Exploratory Study of Persuasion" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.5 issue 10, pp.30-38 2016**, , **For Full Text Click here**[![pdf download](images/pdf.png)](https://ijltemas.in/DigitalLibrary/Vol.5Issue10/30-38.pdf "Males, Media and Metrosexuality: An Exploratory, Study of Persuasion") **Share on Social media** , , , , , ## [Empirical Study on the Dimensions of Employee Empowerment](https://ijltemas.in/DigitalLibrary/Vol.5Issue10/39-45.pdf)

K. Arun Prasad

- **October 2016**

Page No.: 39-45

- [Abstract](#tab-21), - [Authors](#tab-22), - [References](#tab-23), - [Cite](#tab-24), - [Full Text & Share](#tab-25), , **I. INTRODUCTION Employee empowerment is giving employees a certain degree of autonomy and responsibility for decisionmaking regarding their specific organizational tasks. It allows decisions to be made at the lower levels of an organization where employees have a unique view of the issues and problems facing the organization at a certain level. Employee empowerment is creating a working environment where an employee is allowed to make his own decision in specific work related situations. Employee empowerment results in increased employee satisfaction, increased productivity and increased customer satisfaction. Organizations today understand that in a knowledgedriven economy, speed in taking decisions, efficient methods of functioning and innovative ideas help them gain an edge over competitors. It is with this view point that organizations are giving attention to employee empowerment. The important factors that drive organizations towards employee empowerment are to encourage creativity and innovation, increase productivity, align goals of employees with those of the organization, to help in employee retention.**

Page(s): 39-45

- **Date of Publication: 06 November 2016**, , , , , - **K. Arun Prasad**Assistant Professor, Department of Management Studies, Saranathan College of Engineering, Tiruchirappalli, Tamil Nadu, India

05

Drive Safe: An Intelligent System for Monitoring Stress and Pain from Drivers’ Facial Expressions

R. Manoharan, S. Chandrakala and W. Khan · October 2016 · pp. 46-51

Stress and abnormal pain experienced by drivers during driving is one of the major causes of road accidents. Most of the existing systems focus on drivers being drowsy and monitoring fatigue. In this paper, an effective intelligent system for monitoring drivers’ stress and pain from facial expressions is proposed. A novel method of detecting stress as well as pain from facial expressions is proposed by combining a CK data set and Pain dataset. Initially, AAM (Active Appearance Models) features are tracked from the face; using these features, the Euclidian distance between the normal face and the emotional face are calculated and normalized. From the normalized values, the facial expression is detected via trained models. It has been observed from the results of the experiment that the developed system works very well on simulated data. The proposed system will be implemented on a mobile platform soon and will be proposed for android automobiles.

R. Manoharan

Rajalakhsmi Engineering College, Chennai, Tamil Nadu, India

S. Chandrakala

Sri Krishna College of Engineering & Technology, Coimbatore, Tamil Nadu, India

W. Khan

Bournemouth University,United Kingdom, , , **[1]. “Crashes vs. congestion, what is the cost to society?” American Automobile Association (AAA), Heathrow, FL, USA, 2011. [2]. Doshi and M.M. Trivedi, “On the roles of eye gaze and head dynamics in predicting driver’s intent to change lanes,” IEEE Trans. Intell. Transp.Syst., vol. 10, no. 3, pp. 453–465, Sep. 2009. [3]. Amditis, M. Bimpas, G. Thomaidis, M. Tsogas, M. Netto, S.Mammar, A. Beutner, N. Möhler, T.Wirthgen, S. Zipser, A. Etemad, M. Da Lio, and R. Cicilloni, “A situation-adaptive lanekeeping support system: Overview of the SAFELANE approach,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 617–629, Sep. 2010. [4]. K. Murata, E. Fujita, S. Kojima, S. Maeda, Y. Ogura, T. Kamei, T. Tsuji, S. Kaneko, M. Yoshizumi, and N. Suzuki, “Noninvasive biological sensor system for detection of drunk driving,” IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 1, pp. 19–25, Jan. 2011. [5]. L.-W. Zhu, Z.-Y. Zhang, Z.-J. Bao, and Y. Sun, “Study on the drink driving behavior of drivers in Beijing based on the theory of plan behavior,” in Proc. LEITS, 2010, pp. 1–5. [6]. D. Jiangpeng, T. Jin, B. Xiaole, S. Zhaohui, and X. Dong, “Mobile phone based drunk driving detection,” in 4th Int. Conf. Pervasive Health, Munich, Germany, 2010, pp. 1–8. [7]. Y.C. Liu and C.H. Ho, “The effects of different breath alcohol concentration and post alcohol upon drivers driving performance,” in Proc. IEEE IEEM, 2007, pp. 505–509. [8]. Y. Dong, Z. Hu, K. Uchimura and N. Murayama, “Driver inattention monitoring system for intelligent vehicles: A review,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 596–614, 2011. [9]. Nass, I.-M. Jonsson, H. Harris, B. Reaves, J. Endo, S. Brave and L. Takayama, “Improving Automotive Safety by Pairing Driver Emotion and Car Voice Emotion,” in Intl. Conf. on HCI, 2005. [10]. E. Sariyanidi, H. Gunes, and A. Cavallaro, "Automatic analysis of facial affect: A survey of registration, representation and recognition", IEEE Transactions On Pattern Analysis And Machine Intelligence, August 2013. [11]. Conf. Ocular Measures of Driver Alertness, Washington, DC, Apr.26–27,1999. [12]. J. Healy and R. Picard, “Detecting Stress During Real-World Driving Tasks Using Physiological Sensors,” IEEE Transactions on Intelegent Transportation Systems, vol. 6, no. 3, 2005. [13]. H. Yoshida, H. Kataoka, M. Yasuda, A. Saijo and M. Osumi, “Development of a skin temperature measuring system for noncontact stress evaluation,” in the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1998, vol. 2, pp. 940–943. [14]. Manstetten, M. Rimini-Doering, U. Landsatter, T. Altmueller and M. Mahler, “Monitoring driver drowsiness and stress in a driving simulator,” in First International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 2001, pp. 58–63. [15]. W. Liao, W. Zhang, Z. Zhu and Q. Ji, “A real-time human stress monitoring system using dynamic Bayesian network,” in IEEE Conference on Computer Vision and Pattern Recognition - Workshops, 2005, pp. 70–70. [16]. Kolli, A. Fasih, F. Al Machot and K. Kyamakya, “Non-intrusive car driver’s emotion recognition using thermal camera,” in Joint 3rd Int’l Workshop on Nonlinear Dynamics and Synchronization (INDS), 2011, pp. 1–5. [17]. R. Manoharan and S. Chandrakala, “Android OpenCV based effective driver fatigue and distraction monitoring system”, IEEE ICCCRT, 2015. [18]. T. Cootes, G. Edwards and C. Taylor. “Active Appearance Models”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6):681–685, 2001. [19]. P. Ekman, “Universal and Cultural Differences in Facial Expressions of Emotion,” J. Cole ed, Nebraska Symposium on Motivation, vol. 19, pp. 207–282, 1972. [20]. H. Gao, A. Y¨uce and J.-P. Thiran, "Detecting Emotional Stress From Facial Expressions For Driving Safety", ICIP, 2012. [21]. J.F. Cohn, J. Saragih, T. Kanade, Z. Ambadar, I. Matthews and P. Lucey, "The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression", Proc. of IEEE workshop on CVPR, San Francisco, USA, 2010. [22]. P. Lucey, J.F. Cohn, K.M. Prkachin, P.E. Solomon and I. Matthews, "PAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive Database", submitted to the IEEE International Conference on Automatic Face and Gesture Recognition (FG2011), Santa Barbara, USA, 2011. [23]. P. Ekman, W. Friesen and J. Hager, The Facial Action Coding System, 2nd ed., London: Weidenfeld and Nicolson, 2002. [24]. Z. Ambadar, J.W. Schooler and J. Cohn, “Deciphering the enigmatic face: The importance of facial dynamics in interpreting subtle facial expressions,” Psychological Science, vol. 16, no. 5, pp. 403–410, 2005. [25]. R. Rosenthal and N. Ambady, “Thin slices of expressive behavior as predictors of interpersonal consequences: A meta–analysis,” Psychological Bulletin, vol. 11, no. 2, pp. 256–274, 1992. [26]. P. Ekman, W. Friesen and J. Hager, “The Facial Action Coding System”, 2nd ed., London: Weidenfeld and Nicolson, 2002. [27]. J. Cohn and K.L. Schmidt, “The timing of facial motion in posed and spontaneous smiles,” Int’l J. of Wavelets, Multiresolution and Information Processing, vol. 02, no. 02, pp. 121–132, 2004. [28]. P. Lucey, J. Cohn, I. Matthews, S. Lucey, S. Sridharan, J. Howlett and K. Prkachin, “Automatically detecting pain in video through facial action units,” IEEE Trans. Systems, Man and Cybernetics – Part B, vol. 41, no. 3, pp. 664–674, 2011. [29]. S. Eleftheriadis, O. Rudovic and M. Pantic, "Discriminative Shared Gaussian Processes for Multiview and View-Invariant Facial Expression Recognition", IEEE Transactions On Image Processing, Vol. 24, No. 1, January 2015. [30]. S.L. Happy and A. Routray, "Automatic Facial Expression Recognition Using Features of Salient Facial Patches", IEEE Transactions On Affective Computing, March 2015. [31]. F.Gosselin, R.P. Rainville, C. Blais and D. Fiset, "Efficient information for recognizing pain in facial expressions", Europian Journal of Pain, 2015.**, , **R. Manoharan, S. Chandrakala and W. Khan, "Drive Safe: An Intelligent System for Monitoring Stress and Pain from Drivers’ Facial Expressions" International Journal of Latest Technology in Engineering,Management & Applied Science-IJLTEMAS vol.5 issue 10, pp.46-51 2016**, , **For Full Text Click here**[![pdf download](images/pdf.png)](https://ijltemas.in/DigitalLibrary/Vol.5Issue10/46-51.pdf "Drive Safe: An Intelligent System for Monitoring, Stress and Pain from Drivers’ Facial Expressions") **Share on Social media** , , , , , ## [Use of Renewable Energy in Exploration and Production of Hydrocarbons](https://ijltemas.in/DigitalLibrary/Vol.5Issue10/52-54.pdf)

M.S.N Prasad Reddy, T.N.H Kartheek

- **October 2016**

Page No.: 52-54

- [Abstract](#tab-31), - [Authors](#tab-32), - [References](#tab-33), - [Cite](#tab-34), - [Full Text & Share](#tab-35), , **Hydrocarbons are used to generate energy. Most of the day to day products used by human beings are also derived from hydrocarbons. The use of hydrocarbons for generating energy will leave more global warming gases into the atmosphere. It is better to replace the hydrocarbon usage with renewable energy resource to generate energy. But we cannot stop producing hydrocarbons due to the usage of their derived products in day to day life. The replacement of hydrocarbons with non renewable sources of energy can be started from the point of exploration and production of hydrocarbons itself. In this paper we have designed various concepts for compensating the use of hydrocarbons with renewable energy resources to generate energy for the exploration and production of hydrocarbons both in onshore and offshore.**

Page(s): 52-54

- **Date of Publication: 06 November 2016**, , , , , - **M.S.N Prasad Reddy**Department of Petroleum Technology, Aditya Engineering College, India

T.N.H Kartheek

Department of Petroleum Technology, Aditya Engineering College, India

06

Diagnosis of Phyto Pathology in Pomegranate Plant Diseases using Fuzzy-C-Means Algorithm

E. Rajeswari Devi, M. Munisankar · October 2016 · pp. 55-59

In many states pomegranate is a fruit in the market one of the most profits gaining fruit. The plants are affected by various diseases it destroy the entire crop and it tends very less product yield. The neural network technique and an image processing to deal with the main issues of phyto- pathology. Due to the bacteria, fungus, and the climatic condition the pomegranate fruit and leaves are also infected . Due to these bacteria the Fruit root, Bacterial Blight, Leaf spot and Fruit Spot diseases would segmentation. The features are extracted by using GLCM method, and the features are given to the artificial neural network the results would be accurate and the overall accuracy is up to 90%.

**[1]. Simon Haykins, “An Introduction to Artificial Neural Networks”, Pearson Publications, 2005. [2]. Gonzalviz R. C., Woods R. E.,“Digital Image Processing”, Pearson Publications, 2009. [3]. Shivram Dubey and Anand Singh Jalal, “ Detection and classification of apple fruit diseases using complete local binary patterns”, Third International Conference on Computer and Communication Technology, vol.13(no.8):pages.346-351, August 2012. [4]. Tejal Deshpande, Sharmila Sengupta, and K.S.Raghuvanshi, “Grading identification of disease in pomegranate leaf and fruit”, International Journal of Computer Science and Information Technologies, Vol. 5(3): pages.4638-4645, August 2014. [5]. DaeGwan Kim, Thomas F. Burks, and Duke M. Bulanon Jianwei Qin, “Classification of grapefruit peel diseases using color texture feature analysis”, International Journal on Agriculture and Biological Engineering, pages 41-50, Sept 2009 [6]. Dhanashree Gadkari, “Image quality analysis using glcm”, Thesis, University of Central Florida , Orlando, Florida, 2000. [7]. Monika Jhuria, Ashwani Kumar, and Rushikesh Borse, “ Image processing for smart farming:detection of disease and fruit grading”, Proceedings of 2013 IEEE Second International Conference on Image information Processing, pages 521-526, 2013. [8]. K. M. Rao, Deputy Director, NRSA Hyderabad, “Overview of image processing”, Readings in Image processing. [9]. MacQueen, “Some methods for classification and analysis of multivariate observations”, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol.1: pages. 287-291, Dec 1967. [10]. P. Mohanaiah, P. Sathyanarayana, and L. GuruKumar,“Image texturefeature extraction using glcm approach”, International Journal of Scientific and Research Publications, vol. 3(5), May 2013. [11]. Pradnya Ravindra Narvekar, Mahesh Manik Kumbhar, and S. N. Patil, “Grape leaf diseases detection analysis using sgdm matrix method”, International Journal of Innovative Research in Computer and Communication Engineering, pages pages.287-291, March 2014. [12]. Timo Ojala, Matti Pietikainen, and Topi Maenpaa,“Multiresolution grayscale and rotation invariant texture classification with local binary pattern”, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol.24(7): pages.971-987, 2002. [13]. V.T.Jadhav, “ Vision-2025”, National Research Centre on Pomegranate (Indian Cuncil of Agricultural Research), August 2007.**

07

Static and Fatigue Analysis of a Steam Turbine Blade

Prabhunandan G S, H V Byregowda · October 2016 · pp. 60-62

Steam turbine is one of the most important prime movers for generating electricity. This falls under the category of power producing turbo-machines. Single unit of steam turbine can develop power ranging from 1 MW to 1000 MW. The purpose of turbine technology is to extract the maximum quantity of energy from the working fluid, to convert it into useful work with maximum efficiency, by means of a plant having maximum reliability, minimum cost, minimum supervision and minimum starting time. This present work explores the finite element analysis of a steam turbine blade using ANSYS software. Life cycle assessment of steam turbines is essential to improve their design and maintenance plans, since they should operate more than 20 years with minimum interruptions and without failures. Important element of this turbine is the blades and rotor due to their size, mass and cyclic stresses with relatively high frequencies and amplitude. To cope up with this we are using titanium alloy as a material. Different types of loads acting on steam turbine blade and consequential stresses develops in blade are studied. Fatigue stresses are developed on the steam turbine blade due to change in steam speed. The maximum steam speed range (from cut-in to cut-out steam speed) is considered for design of blade as well as predicting the fatigue life of the blade.

08

Indian Multiplex: Drivers for Growth in Tier-2 and Tier-3 Cities

Sudheer Kumar J S, Dr. A. Sathish Babu · October 2016 · pp. 63-65

Being India is one of the highest spending and fastest growing advertising market globally, has a wide scope for expansion of multiplexes. The Indian market is currently dominated by the four major players in multiplexes. A multiplex is a multi-screen entertainment complex projecting different films in same time under one roof with other supporting businesses such as food courts, shopping, video games, etc., Indian cities are classified based on the size of population, income levels, infrastructure in X, Y, Z categories. There is more room space for development of multiplexes in tier-2 and tier-3 cities as people from different streams with capability in spending amount for recreation are ready to enjoy the experience of multiplexes. There are key benefits to look for tier- 2 and tier-3 cities in business development and drivers for growth are discussed in this paper.

09

A Novel Technique for Fusion of IR and CCD Image Using Sift and WPT Algorithm

Mohammed Fazal Elahi, Md Aftab Alam, Aarif Miyan · October 2016 · pp. 66-72

In this paper, we present a methodology for multispectral image registration. In the proposed method, both the input images are subjected to feature extraction using scale invariant fourier transform (SIFT). Image registration is done for one of the input image using affine transform. Image fusion of charge coupled device (CCD) image and Infrared image has become one of the most promising area of research in the field of multispectral image processing for both security as well as application specific requirement.One of the major applications of this idea is in the security domain such as border areas, Antipoaching purposes. This approach is formulated using wavelet packet transformation (WPT), such that we estimate the parameters of a geometric transformation that aligns multispectral images. The basic concept is to create composite images and then match them by WPT Method. The goal of registration is to establish the correspondence between two images and determine a geometric transform that aligns one with the other which performs better quality prediction accuracy. Since EO and IR sensors are operated at different frequency bands, their images have different gray level characteristics. CCD camera captures visible spectrum of wavelength 300-600 nano meter and IR camera (Blind Image) captures long wavelength IR of 8-14 micro-meter..

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10

Use of Agile Methodology for Mobile Applications

Vaishnavi Patil, Sanjana Panicker, Maitreyi KV · October 2016 · pp. 73-77

With the ever increasing popularity and demand for mobile applications, there has been a significant increase in the number of mobile application development projects. Highly volatile requirements of mobile applications require adaptive software development methods. The Agile approach is seen as a natural fit for mobile application and there is a need to explore various Agile methodologies for the development of mobile applications. This paper evaluates how adopting the Agile approach improves the development of mobile applications and if they can be used in order to provide more tailor-made process improvements within an organization. A case study on the music streaming application 'Spotify' shows how the agile methodology was implemented to deliver a commercially successful mobile based application. The findings of the study show the Agile methods have the potential to help deliver enhanced speed and quality for mobile application development.

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SN	Title	Authors	Page No.
1	Necessary & Sufficient Conditions for Consistency of Conditional Parameter XML Tree Expressions to their Resizable Hadoop Cluster Complexity	Ravi (Ravinder) Prakash G	01-19
2	Performance Evaluation of Effluent Treatment Plant for Pigment Industry	Ami N Dave, Satyam L Panchal, Jignesh K Patel	20-23
3	Investigation on Peek and Polyurethane for Strength Used in Articulating Surface of Knee Implants	Venkatesh N, Dr. H. G. Hanumantharaju, Hemanth B	24-29
4	Males, Media and Metrosexuality: An Exploratory Study of Persuasion	Jaiman Preet Kaur, Dr. Jagmeet Bawa	30-38
5	Drive Safe: An Intelligent System for Monitoring Stress and Pain from Drivers’ Facial Expressions	R. Manoharan, S. Chandrakala and W. Khan	46-51
6	Diagnosis of Phyto Pathology in Pomegranate Plant Diseases using Fuzzy-C-Means Algorithm	E. Rajeswari Devi, M. Munisankar	55-59
7	Static and Fatigue Analysis of a Steam Turbine Blade	Prabhunandan G S, H V Byregowda	60-62
8	Indian Multiplex: Drivers for Growth in Tier-2 and Tier-3 Cities	Sudheer Kumar J S, Dr. A. Sathish Babu	63-65
9	A Novel Technique for Fusion of IR and CCD Image Using Sift and WPT Algorithm	Mohammed Fazal Elahi, Md Aftab Alam, Aarif Miyan	66-72
10	Use of Agile Methodology for Mobile Applications	Vaishnavi Patil, Sanjana Panicker, Maitreyi KV	73-77