International Journal of Innovative Technology and Exploring Engineering(TM)
Exploring Innovation| ISSN:2278-3075(Online)| Reg. No.:65021/BPL/CE/12| Published by BEIESP| Impact Factor:4.66
Author Guidelines
Publication Fee
Privacy Policy
Associated Journals
Frequently Asked Questions
Contact Us
Volume-6 Issue 9: Published on April 10, 2017
Volume-6 Issue 9: Published on April 10, 2017

 Download Abstract Book

S. No

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

Page No.



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.


1.       Nagataki, S., Fujiwara, H, “Self compacting property of highly-flowable concrete”American Concrete Institute, SP 154, pp 301 - 314.
2.       Naik , T.R Singh S, “ Influence of Fly ash on setting and hardening characteristics of concrete systems” Materials Journal , Vol.94, Issue 5, pp. 355 - 360

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

7.       Hajime Okamura , Masahiro Ouchi ,’’ Self compacting concrete “  Journal of Advanced concrete Technology Vol. 1, 2003 , pp 5 - 15 

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.

11.    Surabhi , C.S , Mini Soman , Syam Prakash . V. “Influence of Lime stone Powder on properties of Self  compacting concrete “10th National conference on Technological Trends (NCTT09) 6 -7 Nov 2009.

12.    Suraj N. Shah , Shweta  S. Sutar , Yogesh Bhagwat , “ Application of Industrial Waste in the manufacturing of  self compacting concrete “ Government college of Engineering , Karad. 

13.    Guneyisi E., Ozturan T., Gesoglu M. A study on reinforcement corrosion and related properties of plain and blended cement concretes under different curing conditions, Cement and Concrete composites Vol.No.27, Istanbul, Turkey, 2005.

14.    Soleymani H., Mohamed E. Ismail. Comparing corrosion measurement methods to assess the corrosion  activity of laboratory OPC and HPC concrete specimens, Cement and Concrete Research, Vol.No.34.

15.    Cabrera, J.G. Deterioration of concrete due to Reinforcement Steel Corrosion, Cement and Concrete Composites, Vol.No.18, 1996.

16.    Standard test method for half cell potentials of uncoated reinforcing steel in concrete ASTM C876-91, (Reapproved 1999).

17.    WWW.





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.


1.       Alexander Osherenko, “Opinion Mining and Lexical Affect Sensing,” Augsburg . Germany, 2010.
2.       Tran Thi Minh Duc, "The principles of psychology," Education Publishing House, Hanoi, 1996.

3.       S. Aman and S. Szpakowicz, “Using roget's thesaurus for fine-grained emotion recognition,” in Proceedings of the Third International Joint Conference on Natural Language Processing, 2008,  pp. 296-302.

4.       B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: sentiment classification using machine learning techniques,” Proceedings of the Conference on Empirical methods in natural language processing, 2002.

5.       J. Martineau and T. Finin, “Delta tfidf: An improved feature space for sentiment analysis,” in Proceedings of the AAAI Internatonal Conference on Weblogs and SocialMedia, 2009.

6.       S. M. Kim, A. Valitutti, and R. A. Calvo, “Evaluation of unsupervised emotion models to textual affect recognition,” Proceedings of the NAACL HLT Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 2010,  pp. 62-70.

7.       Z. Kozareva, B. Navarro, S. V¡zquez, and A. Montoyo, “Uazbsa: a headline emotion classification through web information,” in Proceedings of the 4th International Workshop on Semantic Evaluations, 2007,  pp. 334-337.

8.       Agrawal and A. An, “Unsupervised Emotion Detection from Text using Semantic and Syntactic Relations”, Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI'12), Macau, China, 2012

9.       Olveres, M. Billinghurst, J. Savage, and A. Holden, “ Intelligent, expressive avatars,” Proceedings of the First Workshop on Embodied Conversational Characters, 1998.

10.    Shiv Naresh Shivhare and Prof. Saritha Khethawat, “EMOTION DETECTION FROM TEXT,” Department of CSE and IT, Maulana Azad National Institute of Technology . Bhopal . Madhya Pradesh . India, 2012.

11.    Strapparava and R. Mihalcea, “Learning to identify emotions in text,” in Proceedings of the ACM symposium on Applied computing, 2008, pp. 1556-1560.

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

13.    System for Sentiment Analysis in Twitter Messages,” Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 568–572, Atlanta, Georgia, June 14-15, 2013, pp.568-572.

14.    S. Al Masum, H. Prendinger, and M. Ishizuka, “Emotion sensitive news agent: An approach towards user centric emotion sensing from the news,” Web Intelligence, IEEE/WIC/ACM International Conference, 2007, pp. 614 -620.Meena and T. V. Prabhakar, “Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis,” Proceedings of the 29th European Conference on IR Research, 2007, pp. 573-580.

15.    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.

16.    H. Liu, H. Lieberman, and T. Selker, “A model of textual affect sensing using real-world knowledge,” Proceedings of the 8th International Conference on Intelligent User Interfaces, 2003, pp. 125-132.



19.    Strapparava  Carlo  and  Valitutti,  A.  2004.  Wordnetaffect:  an  affective  extension  of  wordnet,  In  4th  International  Conference on  Language  Resources  and Evaluation, pp. 1083-1086.


21.    Meena and T. V. Prabhakar, “Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis,” Proceedings of the 29th European Conference on IR Research, 2007, pp. 573-580.

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.

23.    H. Liu, H. Lieberman, and T. Selker, “A model of textual affect sensing using real-world knowledge,” Proceedings of the 8th International Conference on Intelligent User Interfaces, 2003, pp. 125-132.



26.    Strapparava  Carlo  and  Valitutti,  A.  2004.  Wordnetaffect:  an  affective  extension  of  wordnet,  In  4th  International  Conference on  Language  Resources  and Evaluation, pp. 1083-1086.





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 


1.       Amornsinlaphachai.P. (2016). Efficiency of data mining models to predict academic performance and a cooperative learning model.2016 IEEE 8th International Conference on Knowledge and Smart Technology (KST)
2.       Buniyamin, N., Mat, U. bin, & Arshad, P. M. (2015). Educational data mining for prediction and classification of engineering student’s achievement. 2015 IEEE 7th International Conference on Engineering Education (ICEED), 49–53.

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 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

12.    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.

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.

17.    Lee, G., and Chen, Y.C. (2012), “Protecting sensitive knowledge in association pattern mining”, John Wiley & Sons, Inc .2, pp.60-68.,DOI:10.1002/widm.50.
18.    Calders, T., and Pechenizkiy, M. (2012), “Introduction to the special section on Educational Data Mining”, SIGKDD Explorations.Vol.13, No.2, pp.3-6.
19.    Huebner, R.(2012), “A.Educational data-mining research”, Research in Higher Edu. Journal, pp.1-13.

20.    Vandamme, J.P. et al. (2007), “Predicting academic performance by Data Mining methods”, Taylor and Francis group Journal Education Economics.Vol.15, No.4, pp.405-419.

21.    Mansmann, S.and Scholl, H. (2007), “Decesion Support System for managing Educational Capacity Utilization”,IEEE Transaction on Education, Vol.50,No.2,pp.143-150,DOI: 10.1109/TE.2007.893175

22.    Romero,C. et al.,(2008), “Data Mining in course management systems: Moodle case study and tutorial”, Computer and Education, Elsevier publication. Vol. 51, No. 1,pp.368-384.

23.    Delavari,N. et al.(2008), “Data Mining Application in Higher Learning Institutions”,Journal on Informatics in Education.Vol.7,No.1,pp.31-54.

24.    Perera,D. et al.(2009), “Clustering and sequential pattern mining of online collaborative learning data”, IEEE Transactions on Knowledge and Data Engineering,Vol.21, No.6,pp.759-772.

25.    Zurada, al.(2009), “Building Virtual Community in Computational Intelligence and Machine Learning”, IEEE Computational Intelligence Mazazine.pp.43-54,.DOI: 10.1109 / MCI. 2008. 9309 86.

26.    Baker, R.S.J.D.,and Yacef, K.(2009), “The state of Educational Data Mining in 2009:A review and future vision” Journal of Educational Data Mining, Vol.1,No. 1,pp.3-17.

27.    Chrysostomu K. el al.(2009), “Investigation of users’ preference in interactive multimedia learning systems: a data mining approach”,Taylor and Francis online journal
Interactive learning environments. Vol. 17,No. 2.

28.    Romero, C., and Ventura, S. (2010), “ Educational Data Mining: A review of the state of the Art”, IEEE Trans.on on Sys. Man and Cyber.-Part C: Appl. and rev., Vol.40, No.6, pp. 601-618.

29.    Al-shargabi, A.A. and Nusari, A. N (2010), “Discovering Vital Patterns From UST Students Data by Applying Data Mining Techniques”, in Proc. Int. Conf. On Computer and Automation Engineering, China: IEEE, 2010, 2,547-551.DOI:10.1109/ICCAE.2010.5451653.

30.    Jafar, M.J., (2010), “A Tool based approach to teaching Data Mining Methods”, Journal of Information Technology Education: Innovations in Practice..Vol.9, pp. 1-24.

31.    Zhang al. (2010), “Using data mining to improve student retention in HE: a case study”, in Proc.12th Int. Conf. on Enterprise Information Systems, Volume 1: Databases and Information Systems Integration. Portugal, pp.190-197.

32.    Dalip, D.H., Gonclaves, M. A. (2011), “Automatic Assessment of Document Quality in Web collaborative Digital Libraries”, ACM Journal of Data and Information Quality.Vol.2,No.3, pp.14.DOI 10.1145/2063504.2063507

33.    Baradwaj,B.K., and Pal,S.(2011), “Mining Student Data to Analyze Students’ Performance” International Journal of advanced Computer Science and applications.2,6.

34.    Wang and Liao.(2011), “Data Mining for adaptive learning in a TESL based e-learning system”, in Elsevier journal Expert systems with applications,Vol.38,No.6,pp.6480

35.    Alberg, D., Last, M., and Kandel, A (2012), “Knowledge discovery in data streams with regression tree methods” John Wiley & Sons, Inc,2,pp.69-78,DOI:10.1002/widm.51




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.