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Kamal, T. S.
- Role of Fuzzy Logic in Information Routing-A Review
Abstract Views :187 |
PDF Views:2
Authors
Affiliations
1 Punjab Technical University, Jalandhar, Punjab, IN
2 Department of CSE, National Institute of Technology, Jalandhar, Punjab, IN
3 Dept of Electronics and Communication Engineering, Doaba College of Engineering and Technology, Kharar, Punjab, IN
1 Punjab Technical University, Jalandhar, Punjab, IN
2 Department of CSE, National Institute of Technology, Jalandhar, Punjab, IN
3 Dept of Electronics and Communication Engineering, Doaba College of Engineering and Technology, Kharar, Punjab, IN
Source
Networking and Communication Engineering, Vol 4, No 3 (2012), Pagination: 131-134Abstract
The routing is restricted by a multiple constraints such as node buffer capacities, residual link capacities, and the number ofhops on the path that often makes the routing problem intractable. These multiple constraints have very diverse effects on delay, delay jitter, loss ratio, bandwidth, and so on especially in distributed applications such as Internet phone and distributed games. Secondly, any future integrated services network is likely to carry both QoS and best effort traffic, which makes the issue of performance optimization complicated. Thirdly, the network state changes dynamically due to transient load fluctuation, connections in and out, link up and down and thus, the growing network size makes it increasingly difficult to gather up-to-date state information in such dynamic environments. The mathematical forms including these factors together becom extremely complex to derive and difficult to work with. This is one area where Adaptive fuzzy mechanism is beneficial.
Keywords
Fuzzy Logic Control, Fuzzy Algorithms, Routing Algorithms.
- Square Patch Antenna with Fractal DGS for Band Notch Function
Abstract Views :180 |
PDF Views:0
Authors
Affiliations
1 Electronics Engineering, IKG PTU, Kapurthala – 144603, Punjab, IN
2 Department of ECE, RIET, Abohar – 152116 , Punjab, IN
3 Department of ECE, SLIET, Longowal – 148106, Punjab, IN
1 Electronics Engineering, IKG PTU, Kapurthala – 144603, Punjab, IN
2 Department of ECE, RIET, Abohar – 152116 , Punjab, IN
3 Department of ECE, SLIET, Longowal – 148106, Punjab, IN
Source
Indian Journal of Science and Technology, Vol 10, No 31 (2017), Pagination:Abstract
In this article a simple and small size wideband square patch antenna has been examined for band notch characteristics. In order to broadening the band width of the simple patch antenna, the role of partial ground has been critically studied and analyzed. The presented methodology reveals that the implementation of meander line fractal shaped defected ground structure not only augment the band width of the antenna but also provide a notched band at 12.14 GHz required to reject the interference for existing wireless communication. A prototype of the proposed antenna has been fabricated to validate the simulated results. The measured and simulated results are in good agreement.Keywords
Band Notch, DGS, Meander Line, Microstrip Patch AntennaFull Text
- An Investigation for Detection of Breast Cancer using Data Mining Classification Techniques
Abstract Views :189 |
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
- Vikas Chaurasia, Saurabh Pal “Data Mining Techniques: To Predict and Resolve Breast Cancer Survivability”, International Journal of Computer Science and Mobile Computing, (2014) Vol. 3, Issue 1, pp. 10 – 22.
- 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.
- Sushil Kumar.R. Kalmegh, “Analysis of Weka data mining algorithm REPTree,Simple Cart and Random Tree for Classification of Indian News”, International Journal of Innovative Science, Engineering & Technology, (2015) Vol. 2, Issue 2.
- 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.
- Sushil Kumar. R. Kalmegh, “Successful Assessment of Categorization of Indian News Using JRip and Nnge Algorithm”, International Journal of Emerging Technology and Advanced engineering, (2014) Vol. 4, Issue 12, pp. 395-402.
- Shelly Gupta, Dharminder Kumar and Anand Sharma “Performance analysis of various data mining classification techniques on health care data”, International Journal of Computer Science & Information Technology, (2011) Vol. 3, Issue 4.
- V.Vaithiyanathan, K.Rajeswari, Rashmi Phalnikar and Swati Tonge “Improved Apriori algorithm based on Selection Criterion”, IEEE International Conference on Computational Intelligence and Computing Research (2012).
- M. Halkidi, “Quality assessment and uncertainty handling in data mining process,” in Proc, EDBT Conference, Konstanz, Germany (2000).
- Zhou, Z.H., “Three perspectives of data mining”, Artificial Intelligence, (2003) Vol. 143, Issue 1, pp.139-146.
- Tan AC, Gilbert D. “Ensemble machine learning on gene expression data for cancer classification”, Appl Bioinformatics (2003).
- 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.
- D. Wolpert and W. Macready, No Free Lunch Theorems for Search, Santa Fe Institute, (1995) Technical report No., No. SFI-TR-95-02-010.
- Haijian Shi. “Best-first decision tree learning”, Master’s thesis, University of Waikato, Hamilton,NZ, (2007) COMP594.New Zealand. Retrieved from http://hdl.handle.net/10289/2317
- UCI Machine Learning Repository [online]. Available http://archive.ics.uci.edu/ml/datasets.html
- An Approach for Diabetes Detection using Data Mining Classification Techniques
Abstract Views :146 |
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
- David Satish Kumar , Amr T.M Saeb, Khalid AI Rubeaan , “Comparative Analysis of Data Mining Tools and Classification Techniques using Weka in Medical Bioinformatics”, Computer Engineering and Intelligent Systems (2013) Vol. 4, No.3, pp. 28-38.
- 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.
- Mohammed J. Zaki, George Karypis, Jiong Yang, “Data Mining in Bioinformatics”, Algorithms for molecular biology (2007), Vol.2, No.4.
- Han J , “How can data mining help bio-data analysis”, 2nd International Conference on Data Mining in Bioinformatics (2002) Springer-Verlag, pp. 1-2.
- 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.