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Muralidharan, R.
- Genotoxic Effects of Carbendezim (fungicide) on the Root Apicalmeristems of Allium cepa L.
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Affiliations
1 Department of Botany, D. G. Vaishnav College (Autonomous), Arumbakkam, Chennai- 600 106, Tamil Nadu, IN
1 Department of Botany, D. G. Vaishnav College (Autonomous), Arumbakkam, Chennai- 600 106, Tamil Nadu, IN
Source
Research Journal of Pharmacognosy and Phytochemistry, Vol 7, No 1 (2015), Pagination: 29-33Abstract
Higher plants are recognized as excellent genetic models to detect environmental mutagens, and are therefore, frequently used in monitoring studies. The genotoxic potential of carbendezim (fungicide) was investigated by using chromosome aberration in Allium cepa L. ischolar_main tip cells. In this study, the effects of Carbendezim, a systemic fungicide were investigated in the mitotic cell division in onion (Allium cepa L.) ischolar_main tip cells during germination. Allium cepa L. ischolar_mains were treated with 1g/L, 2g/L and 3g/L concentrations of Carbendezim and distilled water as control at 6 hours, 12 hours and 18 hours duration. All the concentrations used, caused several abnormalities in mitotic cell divisions and the Mitotic Index in the onion ischolar_main tip cells decreased when the concentrations of Carbendezim solution is increased.The total percentage of aberrations generally increased in a dose and time dependent manner.Keywords
Genotoxic, Carbendezim, Allium cepa L., Mitotic Index.
- Comparative Analysis of Various Classification and Clustering Algorithms for Heart Disease Prediction System
Abstract Views :164 |
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Authors
Affiliations
1 Department of Computer Science, Rathinam College of Arts & Science (Autonomous), Coimbatore-641021, Tamil Nadu, IN
1 Department of Computer Science, Rathinam College of Arts & Science (Autonomous), Coimbatore-641021, Tamil Nadu, IN
Source
Biometrics and Bioinformatics, Vol 10, No 4 (2018), Pagination: 74-77Abstract
Medical science industry has huge amount of data, but unfortunately most of this data is not mined to find out hidden information in data. Advanced data mining techniques can be used to discover hidden pattern in data. These techniques will be useful for medical practitioners to take effective decision. In this research paper, data mining classification techniques RIPPER classifier, Decision tree, Artificial Neural Network (ANN), Naive Bayes, Support Vector Machine (SVM), are analyzed on heart disease dataset. Performance of these techniques is compared through sensitivity, specificity, Accuracy, true positive Rate and False positive Rate. This analysis shows that out of these five classification techniques methods SVM predicts highest accuracy, Specificity, Sensitivity. And it’s also gives the better result on True positive Rate is high False and positive Rate is low.
References
- HianChyeKoh and Gerald Tan, ―Data Mining Applications in Healthcare‖, journal of Healthcare Information Management – Vol 19, No 2.
- JayanthiRanjan, ―Applications of data mining techniques in pharmaceutical industry‖, Journal of Theoretical and Applied Technology, (2007).
- M.Durairaj, K.Meena, ―A Hybrid Prediction System Using Rough Sets and Artificial Neural Networks‖, International Journal Of Innovative Technology & Creative Engineering (ISSN: 2045-8711) VOL.1 NO.7 JULY 2011.
- K. Srinivas , B. Kavitha Rani and Dr. A. Govrdhan, ―Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks‖ International Journal on Computer Science and Engineering (2010).
- ShwetaKharya, ―Using Data Mining Techniques ForDiagnosis And Prognosis Of Cancer Disease‖, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012.
- EliasLemuye, ―Hiv Status Predictive Modeling Using Data Mining Technology.
- Arvind Sharma and P.C. Gupta ―Predicting the Number of Blood Donors through their Age and Blood Group by using Data Mining Tool‖ International Journal of Communication and Computer Technologies Volume 01 – No.6, Issue: 02 September 2012.
- J.-C. Hsieh and M.-W. Hsu, “A cloud computing based 12-lead ECG telemedicine service,” BMC Med. Informat. Decision Making, vol. 12, no. 77, pp. 1–12, 2012.
- Mai Shouman, Tim Turner, and Rob Stocker “Using Data Mining Technique in Heart Disease Diagnosis and Treatment”, 2012 JapanEgypt Conference on Electronics, Communications and Computers ISSN:978-1-4673-0484-9/ IEEE,pp.173-177,2012. 3
- Dhanashree S. Medhekar, Mayur P. Bote,Shruti D. Deshmukh “HeartDisease Prediction System using Naïve Bayes”, International Journal of Enhanced Research In ScienceTechnology&Engineering,Vol.2,ISSN:2319-7463,pp.1-5,2013
- VikasChaurasia and Saurabh Pal “Data Mining Approach to Detect Heart Dieses”, International Journal of Advanced Computer Science and Information Technology (IJACSIT) Vol. 2, No. 4,ISSN: 2296-1739,pp.56-66,2013.
- Kala John Kappiarukudil and ManeeshaVinodini Ramesh “Real-Time Monitoring and Detection of ‘‘Heart Attack’’ Using Wireless Sensor Networks”, 2010 Fourth International Conference on Sensor Technologies and Applications ISSN:978-0-7695-40962/10IEEE,2010.
- Mai Shouman, Tim Turner, and Rob Stocker “Using Data Mining Technique in Heart Disease Diagnosis and Treatment”, 2012 Japan-Egypt Conference on Electronics, Communications and Computers 978-1-4673-0484-9/12@ 2012 IEEE.pp.173-177
- Dhanashree S. Medhekar, Mayur P. Bote,Shruti D. Deshmukh “HeartDisease Prediction System using Naïve Bayes”, International Journal of Enhanced Research In ScienceTechnology&Engineering, ISSN NO: 2319-7463 VOL. 2 ISSUE 3, MARCH.-2013.pp.1-5
- Vikas Chaurasia and Saurabh Pal “Data Mining Approach to Detect Heart Dieses”, International Journal of Advanced Computer Science and Information Technology (IJACSIT) Vol. 2, No. 4, 2013, Page: 56-66, ISSN: 2296-1739.
- Vikas Chaurasia and Saurabh Pal “Early Prediction of Heart Diseases Using Data Mining Techniques”, Caribbean Journal of Science and Technology, 2013, ISSN 0799-3757, Vol.1, pp.208-217.
- Mrs.G.Subbalakshmi “Decision Support in Heart Disease Prediction System usingNaive Bayes”, Indian Journal of Computer Science and Engineering (IJCSE) ISSN: 0976-5166, Vol. 2 No. 2 Apr-May 2011, pp.170-176.
- Deepali Chandna“Diagnosis of Heart Disease Using Data Mining Algorithm”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2), 2014, pp.1678-1680.
- Kala John Kappiarukudil and Maneesha Vinodini Ramesh “Real- Time Monitoring and Detection of ‘‘Heart Attack’’ Using Wireless Sensor Networks”, 2010 Fourth International Conference on Sensor Technologies and Applications 978-0- 7695-4096-2/10@ 2010 IEEE.
- Assorted Database Query Methodology in DB2 Universal Data Joiner
Abstract Views :181 |
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Authors
Affiliations
1 Department of Computer Science, Rathinam College of Arts and Science, Coimbatore, Tamil Nadu, IN
1 Department of Computer Science, Rathinam College of Arts and Science, Coimbatore, Tamil Nadu, IN
Source
Software Engineering, Vol 10, No 4 (2018), Pagination: 66-68Abstract
DataJoiner (DJ) is an assorted database system that provides a single database image of multiple databases. It provides transparent access to tables at remote databases through user defined aliases (nicknames) that can be accessed as if they were local tables. DJ is also a fully functional relational database system. A couple of salient features of the DataJoiner query optimizer are:
- A query submitted to DataJoiner is optimized using a cost model that takes into account the remote optimizer’s capabilities in addition to the remote query processing capabilities and
- If a remote database system lacks some functionality (eg: arrangement), DataJoiner compensates for it. In this paper, we present the design of the DataJoiner query optimizer.
- Adaptive Education System Analysis Using Machine Learning Techniques
Abstract Views :172 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Rathinam College of Arts and Science (Autonomous), Coimbatore, IN
1 Department of Computer Science, Rathinam College of Arts and Science (Autonomous), Coimbatore, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 7 (2018), Pagination: 162-165Abstract
Educational Data Mining is the field of study concerned with mining educational data to find out interesting patterns and knowledge in educational organizations to analyse and study educational data for student’s improvement. This study explores multiple factors theoretically assumed to affect students’ performance in Machine Learning education, and finds a qualitative model which best classifies and predicts the students’ performance based on related personal and social factors. A student data from a community college database has been taken and various classification approaches have been performed and a comparative analysis has been done.
Keywords
Education, Students, Machine Learning, Data Mining.References
- . Ally, M. (2004). Theory and Practice of Online Learning, chapter Foundations of Educational Theory for Online Learning, pages 3–32.
- . Anderson, C. R. (2002). A Machine Learning Approach to Web Personalization. PhD thesis, University of Washington.
- . Anderson, Terry (2004). Theory and Practice of Online Learning, chapter Toward a Theory of Online Learning, pages 33–60.
- . ATutor (2010). ATutor Learning Content Management System. http://www.atutor.ca.
- . Baker, R. S. (2010). International Encyclopedia of Education, chapter Data Mining for Education. Elsevier, 3 edition.
- . Baker, R. S. and Yacef, K. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1).
- . Blackboard Inc. (2010). Blackboard International. http://www.blackboard.com.
- . Brusilovsky, P. (2007). Adaptive Navigation Support. In The Adaptive Web, Lecture Notes in Computer Science, pages 263–290. SpringerVerlag Berlin Heidelberg.
- . Brusilovsky, P. and Maybury, M. T. (2002). From Adaptive Hypermedia to the Adaptive Web. Communications of the ACM, 45(5).
- . Corral, G., Armengol, E., Fornells, A., and Golobardes, E. (2007). Data Security Analysis Using Unsupervised Learning and Explanations. In Innovations in Hybrid Intelligent Systems, Advances in Soft Computing, pages 112–119. Springer-Verlag Berlin Heidelberg.
- . Das, A. S., Datar, M., Garg, A., and Rajaram, S. (2007). Google News Personalization: Scalable Online Collaborative Filtering. In Proceedings of the 16th International Conference on World Wide Web, pages 271– 280.
- . Divotkey, R., Mu¨hlbacher, J., Reisinger, S., and Remplbauer, D. (2002). The WeLearn Distance Teaching Framework. In Open and Distance Learning in Europe and Beyond Rethinking International Co-operation; Proceedings of the 2002 EDEN Conference, pages 308–314.
- . Fahy, P. J. (2004). Theory and Practice of Online Learning, chapter Media Characteristics and Online Learning Technology, pages 137–174.
- . Greer, J., & Brooks, C. (2006). What to Adapt, and How? Personal communication, May 16, 2006.
- . Heisele, B. (2003). Visual Object Recognition with Supervised Learning. IEEE Intelligent Systems, 18(3).
- . Zancanaro, M., Kuflik, T., Boger, Z., Goren-Bar, D., and Goldwasser, D. (2007). Analyzing Museum Visitor’s Behavior Patterns. In User Modeling 2007, Lecture Notes in Computer Science, pages 238–246.
- Springer-Verlag Berlin Heidelberg.
- . Sonali Agarwal, G. N. Pandey, and M. D. Tiwari: Data Mining in Education: Data Classification and Decision Tree Approach, International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 2, No. 2, April 2012
- . Amjad Abu Saa : Educational Data Mining & Students Performance Prediction, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 5, 2016
- . Golriz Amooee, Behrouz Minaei-Bidgoli, Malihe Bagheri-Dehnavi : A Comparison Between Data Mining Prediction Algorithms for Fault Detection, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No 3, November 2011, ISSN (Online): 1694-0814
- . R.Muralidharan, A.Uthiramoorthy : Object Recognition from an Image through Features Extracted from Segmented Image, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 12, December 2014
- . J.Thirumaran, A.Uthiramoorthy :E-LEARNING CONTENT DEVELOPMENT A ROLE IN VIRTUAL CLASS ROOM, Scholarly Research journal for Humanity science & English Language, Oct-Nov 2015, Vol 2/12, Page - 2977-2184