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Volume-6 Issue-9, April 2017, ISSN:  2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

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R. Rukmini, K.V. Ramana, V. Giridhar Kumar

Paper Title:

Experimental Studies on the Prediction of Corrosion Levels in Reinforced TMT bars in SCC Exposed to Marine Environment

Abstract: Reinforced concrete structures have good potential to be durable and capable of withstanding adverse environmental conditions. Failures in R.C.C structures do still occur as a result of premature reinforcement corrosion. Corrosion of steel has been recognized as one of the major durability problems in R.C.C structures. Damage due to corrosion of steel bars considerably reduces the strength, serviceability and life of structural components. Inspection and continuous monitoring techniques are necessarily to be carried out to assess the steel corrosion in buildings and bridge components in order to ensure their safety, durability for longer time. These techniques are essentially required for easy maintenance and repairs of the structural components also. Few investigations were carried out to study the corrosion levels in reinforced steel bars exposed to marine environment. Very few investigations were carried out so far to predict the corrosion levels in SCC exposed to salts and chemical environments. The present paper outlines the investigations carried out to predict the corrosion levels in TMT bars in Normal Conventional Concrete (NVC) and Self compacting concrete (SCC) exposed to marine environment . It also shows the severity of concrete exposure condition on the progressive corrosion in TMT bars when immersed in salt solution.

Reinforcement corrosion, Self Compacting Concrete (SCC), De-ionized water, Reinforced Thermo Mechanically Treated (TMT) bars, marine environment, Potential difference, Saturated Calomel Electrode (SCE), Open Circuit Potential (OCP) method.


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3.       N. Bouzoubaa  M. Lachemi, “ Self compacting concrete incorporating high volumes of Class F Fly ash          Preliminary results” Cement and Concrete research, 31, 2001, pp 413 -420.

4.       Nan Su , Kung- Chung Hsu , His - Wen  Chai  “ A simple mix design method for self compacting concrete“ Cement and Concrete Research , 31 , 2001 , pp  1799 - 1807.

5.       Bertil Persson ,  “ A Comparison between mechanical properties of  self compacting concrete and the corresponding properties of normal concrete “ Cement and Concrete Research , 31 , 2001 , pp 193 -198.

6.       Subramanian S. Chattopadhyay, “Experiments for mix proportioning of self compacting concrete “Indian Concrete Journal, January, Vol. pp 13 - 20

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8.       Parathiba Aggrawal , Aggrawal and Surinder M. Gupta “ Self compacting concrete - Procedure for Mix Design “ Leonardo Electronic Journal of  Practices and Technologies , Issue 12 , 2008 , pp 15 - 24.

9.       S.Girish, R.V Ranganath and Jagadish Vengala “Influence of powder and paste on flow properties of SCC” Construction and Building Materials, 24, 2010 , pp 2481 - 2488.

10.    Mayur  B. Vanjare , Shriram H. Mahure,  “ Experimental Investigation on self compacting concrete using Glass Powder “ , International Journal of Engineering Research and Applications ( IJERA) ISSN: 2248 - 9622  www. ijera .com Vol. 2 , Issue 3 , May - June 2012, pp 1488 - 1492.

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Phan Thi Ha, Phuong Nguyen

Paper Title:

Emotion Detection and Recognition from Vietnamese Text

Abstract: the areas of Emotion Detection and Recognition from text have become increasingly interested in finding and exploiting information about people. Various problems have been identified such as product evaluations, emotional recognition and emotional findings in the text. In this paper, we present the application of Support Vector Machine (SVM) to detect emotional states in the Vietnamese sentences. The results of our experiments on datasets extracted from Vietnamese novels show that our proposed SVM classification method has higher accuracy than unsupervised learning methods. 

emotion detection, emotion classification, emotions, natural language processing, learning support vector machine.


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12.    Pedro P. Balage Filho and Thiago A. S. Pardo, “NILC USP: A Hybrid

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22.    Neviarouskaya, H. Prendinger, and M. Ishizuka, “Recognition of affect, judgment, and appreciation in text,” Proceedings ofthe 23rd International Conference on Computational Linguistics, ser.COLING '10, 2010, pp. 806-814.

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Ashna Sethi, Charanjit Singh

Paper Title:

A Survey on Educational Data Mining-Prediction and Classification

Abstract: Educational Data Mining (EDM) is an upcoming field examining and exploring data in educational context by implementing different Data Mining (DM) techniques/tools. It provides knowledge of teaching and learning as a process for effective education planning. In this survey work focuses on highlighting Techniques and educational Outcomes. In this paper, Various DM techniques are discussed and comparison of classifiers is made. A general Methodology for classification and Prediction is mentioned.

 Educational Data Mining (EDM), EDM Components, DM Methods, Education Planning 


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3.       Abaidullah, A. M., Ahmed, N., & Ali, E. (2014). Identifying Hidden Patterns in Students’ Feedback through Cluster Analysis. International Journal of Computer Theory and Engineering, 7(1), 16–20

4.       Devasia. T, T P.V, Hegde. V .(2016). Prediction of Students Performance using Educational Data Mining.2016 IEEE International Conference on Data Mining and Advanced Computing (SAPIENCE)

5.       Borah.M.D et.al(2012). Application of knowledge based decision technique to predict student enrollment decision. IEEE 2011 International Conference on Recent Trends in Information Systems (ReTIS).

6.       Banumathi and A. Pethalakshmi, "A novel approach for upgrading Indian education by using data mining techniques," in Technology Enhanced Education (ICTEE), 2012 IEEE International Conference on, 2012, pp. 1-5.

7.       Shahiria.A.M, Husaina.W, Rashida.N.A.(2015) A Review on Predicting Student’s Performance using Data Mining Techniques. 3rd International Conference on Information Systems

8.       Shaukat.K, Nawaz.I , Aslam.S, Zaheer.S,  Shaukat.U. (2017). Student's performance in the context of data mining. 19th International Conference on Multi-Topic Conference (INMIC)

9.       Gobert, J. D., Kim, Y. J., Sao Pedro, M. A., Kennedy, M., & Betts, C. G. (2015). Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld. Thinking Skills and Creativity, 18(September 2016), 81–90.

10.    F. Yi and Z. Chunyuan, "Improving the Quality of Graduate Education by Association Rules Analysis," in Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on, 2008, pp. 570-573.

11.    Mayilvaganan, M., &Kalpanadevi, D. (2015). Cognitive Skill Analysis for Students through Problem Solving Based on Data Mining Techniques. Procedia Computer Science, 47, 62–75

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13.    Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers and Education, 61(1), 133–145.

14.    Kaur, P., Singh, M., &Josan, G. S. (2015). Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector. Procedia Computer Science, 57, 500–508.

15.    Alshareef, A., Ahmida, S., Bakar, A. A., Hamdan, A. R., &Alweshah, M. (2015). Mining survey data on university students to determine trends in the selection of majors. Proceedings of the 2015 Science and Information Conference, SAI 2015, 586–590.

16.    La Red Martínez, D. L., &Podestá Gómez, C. E. (2014). Contributions from Data Mining to Study Academic Performance of Students of a Tertiary Institute. American Journal of Educational Research, 2(9), 713–726.

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Rupali N. Patil, Mansi M. Kambli

Paper Title:

Contour Analysis Based Gesture Control PC Operation

Abstract:  There are a lot of home appliances and personal computers around us. However, few of the user interfaces are designed for user basic access. In this study, as an interface focusing on the ease of use, we develop a system to control personal computer by applying the natural behavior of human. This paper introduces a system that allows a user to carry out computer operation using a web camera. This system consist of four stages viz image acquisition, image pre-processing, feature extraction and gesture recognition. In the first stage, the input image is capture with the help of a camera. In the second phase, the skin color of hand region is distinguished using HSV color space and morphological operations like erosion, dilation, smoothing and thresholding. In Feature extraction stage, contours of hand image are identified. Lastly, Gesture recognition stage contains recognized hand gestures suing contour analysis. The Open CV is used to perform our research.

Hand Gesture recognition, contour analysis, HSV color space, skin detection, Open CV.


1.       Ruchi Manish Gurav, Premanand K. Kadbe “Real time Finger Tracking and Contour Detection for Gesture Recognition using Open CV” , 2015 International Conference on Industrial Instrumentation and Control (ICIC) College of Engineering Pune, India. May 28-30, 2015.
2.       Kaoru Yamagishi, Lei Jing*, Zixue Cheng*, “A System for Controlling Personal Computers by Hand Gestures using a Wireless Sensor Device”, 978-1-4799-4476-7/14/$31.00 ©2014 IEEE.

3.       Jayesh S. Sonkusare, Nilkanth. B. Chopade, Ravindra Sor, Sunil.L. Tade,” A Review on Hand Gesture Recognition System”, 2015 International Conference on Computing Communication Control and Automation.

4.       Hsiang-Yueh. Lai , Han-Jheng. Lai, “Real-Time Dynamic Hand Gesture Recognition”, 2014 International Symposium on Computer, Consumer and Control.

5.       Feng-Sheng Chen, Chih-Ming Fu, Chung-Lin Huang, “Hand gesture recognition using a real-time tracking method and hidden Markov model”, F.-S. Chen et al. / Image and Vision Computing 21 (2003) 745–758.

6.       Thittaporn Ganokratanaa and Suree Pumrin ,” The Vision-Based Hand Gesture Recognition Using Blob Analysis”, 78-1-5090-5210-3/17/$31.00 ©2017 IEEE.