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Garg, Sonu Bala
- Effective Implementation of E-Governance in Technical Institutions in India using ICT to Make them World Class
Abstract Views :90 |
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Authors
Affiliations
1 Baba Hira Singh Bhattal Institute of Engineering & Technology, Lehragaga, Punjab, IN
1 Baba Hira Singh Bhattal Institute of Engineering & Technology, Lehragaga, Punjab, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 4 (2011), Pagination: 535-543Abstract
E-governance or electronic governance is the delivery of government services and information to the public using electronic means for efficient, speedy and transparent process. Technical education as a sector has remained relatively untouched by e-governance. This is most distressing, considering that technical&vocational education is the cornerstone of our efforts to build up the technical manpower for rapid industrial & economic growth of the nation. E-governance can facilitate in improving transparency, providing speedy information, dissemination, improving administrative efficiency and public services in all the aspects of technical education&its effective implementation can act as one of the key ICT initiatives to help India become a provider of world-class technical education. The paper in hand discusses various aspects of implementation of E-governance in Technical&vocational education institutes in India.Keywords
E-Governance, Technical Institutes, Integrated Institute Management Expert Systems.Full Text
- An Investigation for Detection of Breast Cancer using Data Mining Classification Techniques
Abstract Views :186 |
PDF Views:0
Authors
Affiliations
1 IKG Punjab Technical University, Jalandhar, Punjab, IN
2 Beant College of Engineering and Technology, Gurdaspur, Punjab, IN
3 Radiant Institute of Engineering and Technology, Abohar, Punjab, IN
1 IKG Punjab Technical University, Jalandhar, Punjab, IN
2 Beant College of Engineering and Technology, Gurdaspur, Punjab, IN
3 Radiant Institute of Engineering and Technology, Abohar, Punjab, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 26 (2017), Pagination: 153-165Abstract
Breast cancer is one of the curses for women. Breast cancer caused deaths. It is the second most common cause. 1 in 28 women develop breast cancer during her lifetime in India. Urban/Rural ratio in a lifetime of women for the risk of developing breast cancer is 60:22. High risk group in India has the average age of 43-46 years whereas the same in the west is 53-57 years. The main objective of this paper is to investigate the performance of different classification techniques. Here, the breast cancer data available from the Wisconsin dataset from UCI machine learning is analyzed. In this experiment, Comparison of three different classification techniques have been done in Weka software and comparison results shows that Sequential Minimal Optimisation (SMO) has higher prediction accuracy i.e. 95.8512 % than methods Instance based K-Nearest neighbours classifier ( IBK) and Best First (BF) Tree method.Keywords
Breast Cancer, Data Mining, Data Mining Classification Techniques.Full Text
References
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- Vikas Chaurasia, Saurabh Pal, “Novel approach for breast cancer detection using data mining techniques”, International Journal of Innovative Research in Computer and Communication Engineering, (2014) Vol. 2, Issue 1, pp. 2456-2465.
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- Gouda I. Salama, M.B. Abdelhalim, Magdy Abd-elghany Zeid “Breast cancer Diagnosis on three different datasets using Multi-Classifiers”, International Journal of Computer and information Technology, (2012) Vol. 1, Issue 1,pp.36-43.
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- Rajni Bedi and Ajay Shiv Sharma “Classification Algorithms for Prediction of Lumbar Spine Pathologies”, IEEE International Conference on advanced informatics for computing research, (2017) pp. 42–50.
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- An Approach for Diabetes Detection using Data Mining Classification Techniques
Abstract Views :142 |
PDF Views:0
Authors
Affiliations
1 IKG Punjab Technical University, Jalandhar, Punjab, IN
2 Beant College of Engineering and Technology, Gurdaspur, Punjab, IN
3 PEC University of Technology, Chandigarh, IN
1 IKG Punjab Technical University, Jalandhar, Punjab, IN
2 Beant College of Engineering and Technology, Gurdaspur, Punjab, IN
3 PEC University of Technology, Chandigarh, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 26 (2017), Pagination: 202-218Abstract
Disease diagnose by expert systems, is one of the areas where tools of data mining are establishing successful results. The aim of this paper is to discover solutions for diagnosing the disease by analyzing the patterns found in the data through techniques of data mining like classification analysis. Classification is a common technique used in data mining that utilizes a set of pre-classified examples for developing a model that can help in classifying the population of records at enormous amount. There are various techniques of classification that are used for analysis of biomedical data. These include Naive Bayes, Bayes Net, J48, SMO, and Random Forest. In this paper, the comparison of different classification algorithms using Weka has been shown. Also these techniques are used to find out which algorithm is most suitable. The best algorithm based on the Cross validation is SMO classifier with an accuracy of 77.34 % and has the lowest average error at 22.65 % compared to others. The best algorithm based on the Percentage split, Decision Table classifier with accuracy of 81.99 % and has the lowest average error at 18.00 % compared to others.Keywords
Data Mining, Bioinformatics, Data Mining Techniques, Weka, Diabetes.Full Text
References
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- Aher Sunita B, L.M.R.J LOBO, “Data Mining in Educational System using Weka,” International Conference on Emerging Technology Trends (2011) pp.20-25.
- Gangwar Vivek, Singh Yogendra, Ghose Udayan, “Data mining of Biological Data in Bioinformatics using Transcription ,translation Algorithm and Pattern Matching of Protein Sequences”, International Journal of Advanced Research In Computer Science (2012) Vol.3, No.3, pp.479-482.
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- Lyer Aiswarya , Jeyalatha S, Sumbaly Ronak, “Diagnosis of diabetes using classification mining techniques”, International Journal of Data Mining & Knowledge Management Process (2015) Vol.5, No.1, pp.1-14.
- Bedi Rajni, Sharma Ajay Shiv, “Classification Algorithms for Prediction of Lumbar Spine Pathologies”, Springer, ICAICR (2017), pp. 42-50.
- Saini Nisha, Monica, Kumar Vijay, Kumbhar S, “Churn Prediction in Telecommunication Using Classification Techniques Based on Data Mining: A Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, (2015) Vol. 5, No. 3.
- Salama I Gouda, Abdelhalim M. B, Zeid Magdy Abd-elghany “ Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers”, International journal of Computer and Information Technology (2012), Vol.1, No.1, pp.36-43.
- Amin Md. Nurul, Habib Md. Ahsan, “Comparison of Different Classification Techniques Using WEKA for Hematological Data”, American Journal of Engineering Research (2015) Vol. 4, No. 3, pp. 55-61.