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Priyanga, M.
- The Multiple Time Series Clinical Data Processing with Modified Artificial Bee Colony Algorithm and Artificial Neural Network
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Authors
Affiliations
1 Department of Computer Science and Engineering, Hindusthan Institute of Technology,Coimbatore, Tamilnadu, IN
1 Department of Computer Science and Engineering, Hindusthan Institute of Technology,Coimbatore, Tamilnadu, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 5 (2016), Pagination: 1-12Abstract
Objectives: The main objective of this research is to discover patient acuity or severity of illness for immediate practical use for clinicians by evaluating the use of multivariate time series modelling along with multiple models.
Methods/Statistical analysis: As large-scale multivariate time series data become increasingly common in application domains, such as health care and traffic analysis, researchers are challenged to build efficient tools to analyze it and provide useful insights. In many situations, analyzing a time-series in isolation is reasonable. And also this scenario is used to increase the prediction accuracy and reducing the time complexity using optimization algorithm.
Findings: The various research works has been analyzed and evaluated. From the analysis, the multiple measurements support vector machine (MMSVM), multiple measurements random forest regression (MMRF) and improved particle swarm optimization (IPSO) algorithm, modified artificial bee colony algorithm (MABCA) to solve the multiple time series problems by maximizing the optimal feature information which found to be superior for higher performance in terms of accuracy, precision and recall. The proposed MABCA with transductive support vector machine (TSVM) and artificial neural network (ANN) is used to improve the classification performance.
Application/Improvements: The findings of this work prove that the graph search based method provides better result than other approaches.
Keywords
Data Mining, Multiple Measurements, Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Modified Artificial Bee Colony Algorithm (MABCA) and Artificial Neural Network (ANN).Full Text
References
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- S. P. Gardner. Ontologies and Semantic Data Integration. Drug Discovery Today, 2005; 10(14), pp.1001-1007.
- S. Gupta, D. Kumar, A. Sharma. Data Mining Classification Techniques Applied For Breast Cancer Diagnosis And Prognosis. 2011; 11(7), pp.125-135.
- J. Han, M Kamber. Data Mining: Concepts and Techniques. 2nd ed. San Francisco, CA, USA: Morgan Kaufmann. 2006; 20(12), pp.325-365.
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- Y. Sam Sung Zhao, Li, Peng Sun. A Fast Filtering Schema for Large Database Cleansing, 2002; 26(8), pp.520-530.
- J. Soni, D. Ansari, S. Sharma. Predictive Data Mining for Medical Diagnosis:An Overview of Heart Disease Prediction, 2011; 47(2), pp.640-650.
- M. Stacey,C. McGregor. ‘Temporal abstraction in intelligent clinical data analysis: A survey,’ Artificial Intelligence in Medicine, 2007; 39(1), pp. 1-24.
- R. Saranya, K. Balamurugan,M. Karuppasamy. Artificial Bee Colony Algorithm Based Congestion Management in Restructured Power System. Indian Journal of Science and Technology, 2015; 8(S7), 171-178.
- A. Dhivya, S. N. Sivanandan. Hybrid Fuzzy Jordan Network for Robust and Efficient Intrusion Detection System. Indian Journal of Science and Technology, 2015; 8(34), pp. 1-10.
- LBP-Top Descriptor for Detecting Interesting Events in Crowded Environments
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Authors
Affiliations
1 Dept of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, IN
1 Dept of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 5 (2016), Pagination: 1-8Abstract
Objectives: To detect interesting events in crowded environments by introducing Local Binary Patterns from Three Orthogonal Planes (LBP-TOP).The mission of automatically detecting frames with anomalous or interesting events from long duration video sequences has become the research interest in the last decade.
Methods/Statistical analysis: The existing system introduced a Swarm Intelligence based approach for Detecting Interesting Events in Crowded Environments. In this system both appearance and motion are measured to detect the anomalies. The Histograms of Oriented Gradients (HOG) is used for capture the appearance information and Histograms of Oriented Swarms (HOS) is used for capture the frame dynamics. Both are combined to form a new descriptor that effectively characterizes each scene. However it does not considered dynamic texture to achieve high accuracy. To solve this problem the proposed system introduced histogram of LBP-TOP to represent dynamic texture.
Findings: In a time window of each frame average triplets of HOG, HOS and LBP-TOP are consecutively computed. Then, these features are passed as an input to classifier. Here proximal support machine is used for classification. Proximal Support Vector Machine is based on Support Vector Machine, it is simpler and faster than traditional Support Vector Machines algorithm, which is especially suitable for large amounts of data or image classification and operations.
Improvements/Applications: The experimental results show that the proposed system achieves better performance compared with existing system.
Keywords
Histograms of Oriented Gradients, Histograms of Oriented Swarms and Texture.Full Text
References
- Anil M. Cheriyadat and Richard J. Radke. Detecting Dominant Motions in Dense Crowds. IEEE Journal of special topics in signal processing. 2010; 2(4), 568-581.
- VagiaKaltsa, Alexia Briassouli, IoannisKompatsiaris, Michael G. Strintzis, Swarm-based motion features for anomaly detection in crowds, 2014 IEEE International Conference on Image Processing (ICIP), 2014; 2353-2357.
- Ravi Ranjan, G. Sahoo, A New clustering approach for anomaly intrusion detection. International Journal of Data Mining & Knowledge Management Process (IJDKP) March 2014, 4(2), 29-38.
- Amin Karami, ManelGuerrero-Zapata, A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks Neurocomputing, Elsevier 2014, 149, Part C, pp. 1253-1269.
- Ibrahim Aljarah, Simone A. Ludwig, MapReduce Intrusion Detection System based on a Particle Swarm Optimization Clustering Algorithm, IEEE Congress on Evolutionary Computation: Cansun, Mexico. 2013; June 20-23, 955-962.
- SeyedMojtab, HosseiniBamakan, BehnamAmiric, MahboubehMirzabagheri,Yong Shia , A New Intrusion Detection Approach using PSO based MultipleCriteria Linear Programming, 3rd Conference on Information Technology and Quantitative Management (ITQM 2015). 2015; 55, 231-237.
- FangjunKuang,Siyang Zhang, Zhong Jin, WeihongXu. A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection.2015; 19(5), 1187-1199.
- Saber M. Elsayed, Ruhul A. Sarker, EfrénMezura-Montes. Self-adaptive mix of particle swarm methodologies forconstrained optimization, Elsevier, 2015; 277, 216-233.
- K. K. Vardhini, T. Sitamahalakshmi. A Review on Nature-based Swarm Intelligence Optimization Techniques and its Current Research Directions. Indian Journal of Science and Technology, 2016; 9(10), 1-13.
- A. A. Ayodele, K. A. Charles. Improved Constrained Portfolio Selection Model using Particle Swarm Optimization. Indian Journal of Science and Technology, 2015; 8(1), 1-8.
- Improved and Ensemble Methods for Time Series Classification with Cote
Abstract Views :222 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore-641028, Tamil Nadu, IN
2 Hindusthan Institute of Technology, Coimbatore-641028, Tamil Nadu, IN
1 Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore-641028, Tamil Nadu, IN
2 Hindusthan Institute of Technology, Coimbatore-641028, Tamil Nadu, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 6 (2016), Pagination: 1-9Abstract
Background/Objectives: To classify the time series data efficiently by introducing Collective of Transformation-Based Ensembles method (COTE).
Methods/Statistical analysis: In existing scenario, the method is introduced named as COTE. It is mainly used for increasing the classification accuracy than preceding research. Another algorithm is named as Time series classification (TSC) which is used for transformation process which is based on comparative features. COTE contains classifiers constructed in the time, frequency, change, and shapelet transformation domains combined in alternative ensemble structures. However it has issue with transformation process and hence accuracy of the classification is reduced significantly. To avoid this issue introduced the concept called as run length transformation to improve the classification accuracy higher than existing system.
Findings: The run length algorithm is improved along with genetic approach to produce the optimal features. In this scenario, the measures are considered as similarity coefficient, likelihood ratio and dynamic time warping (DTW). Based on the modified k- nearest neighbor distance concept the speed is increased and classification accuracy is improved prominently.
Improvements/Applications: From the experimental result we can conclude that our proposed scenario yields better classification performance rather than existing scenario.
Keywords
Collective of Transformation-Based Ensembles Method, K Nearest Neighbor, Periodogram Transformation, Heterogeneous Ensemble, Elastic Ensemble.Full Text
References
- Ye Lexiang, Eamonn Keogh. Time series shapelets: a new primitive for data mining, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2009.
- Lin Jessica, Rohan Khade, Yuan Li. Rotation-invariant similarity in time series using bag-of-patterns representation, Journal of Intelligent Information Systems. 2012, 39(2), 287-315.
- Mueen Abdullah, Eamonn Keogh, and Neal Young. Logical-shapelets: an expressive primitive for time series classification, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2011.
- Ye Lexiang, Eamonn Keogh. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification, Data mining and knowledge discovery. 2011, 22(1-2), 149-182.
- Lines Jason, Luke M. Davis, Jon Hills, Anthony Bagnall. A shapelet transform for time series classification, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2012.
- Baydogan, Mustafa Gokce, George Runger, Eugene Tuv, A bag-of-features framework to classify time series, Pattern Analysis and Machine Intelligence, IEEE Transactions.2013, 35(11), 2796-2802.
- Houtao Deng, George Runger, Eugene Tuv, Martyanov Vladimir. A time series forest for classification and feature extraction, Information Sciences. 2013, 239, 142-153.
- Bagnall Anthony, Gareth Janacek. A Run Length Transformation for Discriminating Between Auto Regressive Time Series. Journal of Classification 2014, 31(2), 154-178.
- R. Agrawal. Design and Development of Data Classification Methodology for Uncertain Data. Indian Journal of Science and Technology, 2016; 9(3), 1-12.
- S. U. Maheswari, R. Ramakrishnan. Sports Video Classification using Multi Scale Framework and Nearest Neighbor Classifier. Indian Journal of Science and Technology, 2015, 8(6), 529-535.
- Opportunities and Constraints in Organic Rice Marketing-A Study in Sirkazhi Block of Nagapattinam District
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Authors
Affiliations
1 Department of Agricultural Economics, Annamalai University, Annamalainagar, Chidambaram (T.N.), IN
2 Department of Agricultural Economics, Annamalai University, Annamalainagar, Chidambaram (T.N.), IN
1 Department of Agricultural Economics, Annamalai University, Annamalainagar, Chidambaram (T.N.), IN
2 Department of Agricultural Economics, Annamalai University, Annamalainagar, Chidambaram (T.N.), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 1 (2017), Pagination: 37-42Abstract
India is a large country with inherent geographic, ecological and cultural diversity and providing safe food to the nation is a challenging task. Production and marketing of organic food material is one of the way outs for providing safe food to the people. So, in order to analyse the opportunities and constraints in organic rice marketing, a study was conducted in Sirkazhi block of Nagapattinam district, taking a sample size of 60. Major findings emanated from the study which is based on the analysis on the constraints faced by the organic rice growers revealed that the prime constraint was the "risk of low yield" for the initial two years of transition from conventional to organic farming. The study indicated that 76 per cent of the farmers had awareness regarding certification and out of which only 64 per cent materialized the certification process. Reasons attributed for not growing certified organic rice showed that the "purpose for own family consumption" was the prime reason. Marketing channel study revealed that the farmers preferred to market organic rice through contract farming with CIKS. Awareness on consumption of organic rice showed that 60 per cent of the consumers having awareness on organic rice, out of which only 40 per cent of them were turned into organic rice consumers. Consumption pattern of organic rice revealed that out of the total consumers of organic products, 65 per cent of them were rice consumers. Factors determining consumers preference for organic rice exposed that "health" is the main attribute preferred by the consumers for shifting to organic rice consumption. Among the various options ensuring consumer credibility on organic rice purchase "buying organic rice from specific identified organic farmer" was considered as the best option, Marketing through small organic rice co-operative was the main suggestion given by the farmers to popularize organic rice consumption. Traders had awareness regarding organic rice but out of the total trader's only 20 per cent really marketing organic rice. The price factor appeared to be the key motivating factor for organic rice production, the healthy and safety factors were appeared to be the key motivating factors in the consumption or purchase of organic rice and unavailability of organic rice was found to be the major constraint in organic rice marketing.Keywords
Opportunities, Constraints, Organic Rice, Marketing.
References
- Gowri, M. Uma (2015). Rice marketing - A macro and micro analysis. Internat. Res. J. Agric. Eco. & Stat., 6 (1) : 210-217.
- Neesan, R. and Keing, T. (2004). Improving yields of organic rice, in IREC Farmers Newsletter, 60th Anniversary Ed.No.167, IREC.
- Nguyen, Cong Thanh and Singh, Baldeo (2006). Constraints faced by the farmers in rice production and export” Cuu Long Delta Rice Research Institute,Head of the Division of Agricultural Extension, IARI, New Delhi, India Omonrice 14 97-110.
- Ramesh, P. and Panwar, N.R., Singh, A.B., Ramana, S., Yadav, Sushil Kumar, Srivastava, Rahul and Rao, A. Subha (2010). Status of organic farming in india.Curr. Sci., 98 (9) : 1190 : 194. Proceedings of the International Conference of Agri Business and Food Industry in Developing Countries: Challenges and Oppartunities organized by Indian Institute of Management, Lucknow, 1-6.
- Velusamy, T. (2008). An economic analysis of paddy production under organic farming system in Nagapattinam District” M.Sc. (Ag.) Thesis, Annamalai University, Chidambaram, p.18.
- An Economic Analysis of Challenges and Feasibility of Poultry Industry in Tamilnadu
Abstract Views :194 |
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Authors
Affiliations
1 Department of Agricultural Economics, Annamalai University, Chidambaram, (T.N.), IN
1 Department of Agricultural Economics, Annamalai University, Chidambaram, (T.N.), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 2 (2017), Pagination: 264-270Abstract
The Indian poultry industry has been on the continuous growth trajectory in the recent past aided by different contributing factors of national developmental regulations, emerging organized retail industry, governments including export supported by availability of funds for new projects as well as for easy financing for perspective poultry farmers. In South India, Tamil Nadu state is leading in broiler integration in the country which has Coimbatore as a major poultry pocket. In Namakkal district, while the demand for egg and chicken meat is increasingly commendably, poultry farmers here are forced to restrict their expansion processes owing to escalating land and construction costs over the last five years in Namakkal Zone. However, the various challenges, threats and weakness of the sector including: diseases, high feed cost, poor marketing infrastructure, regional imbalances in production, among others should be diligently addressed and the strengths of the sector well exploited so as to harness the opportunities. Otherwise, the poultry sector offers a bright future. A future right in our hands to shape.Keywords
Poultry, Poultry Industry, Marketing, Challenges.References
- Iisa, Augustine and Shukla, Ruchira (2015). An analysis of opportunities and challenges in poultry sector in global and Indian perspectives. IJMSS, 3 (1) ISSN: 2321-1784 Impact Factor- 3.259. 4 Iss.
- Mohanraj, R. and Manivannan, L. (2012). A study on economic profile and financial decision of poultry farm owner’s with reference to Namakkal district, Tamil Nadu. IJMT, 2 (5), International J. Mktg. & Technol. http://www.ijmra.us.
- Paramasivan, C. (2011). Economics of poultry industries in Namakkal (T.N.). Internat. J. Commerce & Business Mgmt., 4 (1) : 60-64.
- Vijayakumar, E.P. and Ramamoorthy,V. (2012). A study on problems of practicing poultry farming in Namakkal district. IDR, 10 (1): 45-53| ISSN: 0972-9437|.
- Agricultural and Processed Food Products Export Development Authority (APEDA) (2014). Ministry of Commerce and Industry, Government of India. Retrieved March 26, 2014 from:http://www.apeda.gov.in/apedawebsite/SubHead_Products/Poultry_Products.
- CLFMA of India www.clfmaofindia.org/.
- Department of Animal Husbandry and Veterinary Services, Chennai, www.tanuvas.tn.nic.in/.
- FAOSTAT 2013.
- ICAR-directorate of poultry research http://www.icar.org.in/
- Poultry. In Wikipedia. Retrieved, March 10, from http://en.wikipedia.org/wiki/Poultry.
- WATT Executive Guide to World Poultry Trends (2012). Retrieved April 11, 2014, from http://www.vniipp.ru/EG2012.pdf).
- WATT Executive Guide to World Poultry Trends (2013) Retrieved April 11, 2014 from www.poultrytrends.com.
- www.indexmundi.com/commodities/
- www.rncos.com/Market.../Indian-Poultry-Industry-Outlook-to-2017-IM621.html.
- Efficiency of Water Market and Intersectoral Water Transfer-A Macro Review and Micro Analysis
Abstract Views :181 |
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Authors
Affiliations
1 Department of Agriculture Economics, Annamalai University, Chidambaram (T.N.), IN
2 Department of Agricultural Economics, Tamil Nadu Agricultural University, Coimbatore (T.N.), IN
3 Department of Agricultural Economics, Annamalai University, Chidambaram (T.N.), IN
1 Department of Agriculture Economics, Annamalai University, Chidambaram (T.N.), IN
2 Department of Agricultural Economics, Tamil Nadu Agricultural University, Coimbatore (T.N.), IN
3 Department of Agricultural Economics, Annamalai University, Chidambaram (T.N.), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 9, No 1 (2018), Pagination: 224-231Abstract
Where water is scarce but demand is growing, water markets offer an opportunity to increase economic efficiency by enabling the reallocation of water among users and sectors. While buyers and sellers willingly enter into such transactions, indirect impacts on agricultural communities can be devastating, as intersectoral transfers may substantially alter the nature of the community’s underlying economy. Hence, attempt should be made for the gradual conversion of existing informal water markets into formal market. The emergence of formal markets though considered advantageous in economic perspective the other dimensions like social, cultural, institutional and legal should be bestowed due attention owing to their importance in human life.Keywords
Efficiency of Water, Water Market, Intersectoral Water, Macro, Micro Review.References
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