A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Parimala, M.
- Asynchronous Periodic Pattern Mining for Cyclic and Incremental Sequential Time Stamp
Authors
1 M. Kumarasamy College of Engineering, Karur, IN
2 Selvam College of Technology, Namakkal, IN
3 K. S. Rangasamy College of Technology, Tiruchengode, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 3 (2010), Pagination: 33-36Abstract
Mining of periodic patterns in time-series databases is an interesting data mining problem. It can be envisioned as a tool for forecasting and prediction of the future behavior of time-series data. Most researches focused on mining synchronous periodic patterns, but in practice some periodic patterns can not be recognized because of presence of random noisy and disturbance in large datasets. To increase the efficiency of mining Asynchronous periodic patterns on large datasets, our proposal work move in the direction of finding all maximal complex patterns in a single step algorithm using a single dataset scan without mining single event and multiple events patterns explicitly. The asynchronous periodic patterns are mined using depth first search technique. Three parameters are employed to specify the minimum number of repetitions required for a valid segment of non disrupted pattern occurrences, the maximum allowed disturbance between two successive valid segments, and the total repetitions required for a valid sequence.
To find multiple periods based partial periodic patterns, looping over single period using the hit set based approach is one of the naive methods. The algorithm is to directly apply the max-pattern hit set method to each period and the sequence. The proposal evaluates another method for multiple periods using shared mining. This method is similar to the max-pattern single period mining algorithm. During the first scan of the sequence for all periods, the frequent pattern and candidate max-pattern are generated. During the second scan the hit sets of all the periods are generated as in the second scan of max-pattern hit set method. The problem of max-sub pattern tree construction and derivation of frequent patterns from the max-sub pattern tree is also discussed.
The proposal of our work integrates the cyclic partial periodic patterns and incremental patterns with adaptive thresholds to produce interactive mining of partial periodic patterns. The maximum pattern search space and the execution overhead for the proposal work are analyzed with synthetic and real data sets from UCI Repository. In addition it work in the direction of merge mining a generalization of incremental mining which discover patterns of two or more databases that are mined independently of each other. An improved version of merge mining algorithm is planned to build for asynchronous partial periodic patterns in time-series databases.Keywords
Asynchronous, Cyclic, Incremental Pattern Mining, Periodic Pattern Mining, Sequential.- A Study of Image Processing in Agriculture
Authors
1 Department of Mathematics, BIT, Sathyamangalam, IN
Source
International Journal of Advanced Networking and Applications, Vol 9, No 1 (2017), Pagination: 3311-3315Abstract
Agriculture is the backbone of human sustenance on this world. Now a days with growing population we need the productivity of the agriculture to be increased a lot to meet the demands. In olden days they used natural methods to increase the productivity, such as using the cow dung as a fertilizer in the fields. That resulted increase in the productivity enough to meet the requirements of the population. But later people started thinking of earning more profits by getting more outcome. So, there came a revolution called "Green Revolution". In this paper we implemented image processing using MATLAB to detect the weed areas in an image we took from the fields.Keywords
Image Processing, Agriculture, Image Segmentation, Classification, Plant Diseases.References
- Hetzroni , Hossein Nejati, Zohreh Azimifar, Mohsen Zamani; “Using Fast Fourier Transform for weed detection in corn fields”; IEEE; 2008.
- Pydipati, Xavier P. Burgos-Artizzu, Angela Ribeiro, Maria Guijarro, Gonzalo Pajares; “Real- time image processing for crop/weed discrimination in maize fields”; Elsevier; 2010.[4]
- Huang, Grianggai Samseemoung, Peeyush Soni, Hemantha P. W. Jayasuriya, Vilas M. Salokhe; “Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soyabean plantation”; Springer; 2012.[4]
- Pugoy and Mariano,G. Jones, Æ Ch. Ge´e, Æ F. Truchetet; “Modeling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance”; Springer; 2008.[4]
- Anup Vibhute, S K Bodhe; “Applications of Image Processing in Agriculture: A survey; International Journal of Computer Applications”; 2012.[4]
- Alasdair McAndrew; “Introduction to digital image processing with MATLAB”; Course Technology; 2004.[1]
- S. Annadurai and Shanmugalakshmi, Fundamentals of Digital image processing India Pearson Education, 2007 , pp.310.
- Zhang, Pengyun and L.jigang, “Computer assistance image processing spores counting, “ in 2009 proc. Int.Asia Conf. on informatics in Control, Automation and robotics, pp.203-206.
- Xu, C.C Yang, S.O. Prasher, J.a.Landry, H.S. Ramaswamy, and A. Ditommaso, ”Application of artificial neurol networks in image precongation and classification of crop and weeds,”Canadian Agricultural Engineeering, vol. 42,no. 3, pp. 147-152, 2000
- Hairuddin ,S.Phadikar and J.Sil, “Rice disease indentification using pattern recongnition techniques,” in Proc. 11th Int. Conf. on computer and information technology, 2008, Khulna, Bangladesh, pp. 420-423
- R. Sathiskumar, Dr B Nagarajan, R.Karthigamani, Dr M.Gunasekaran “Region based object extraction using ANFIS combined with support vector machines” in 2017 Asia Life sciences.
- P. Saravanamoorthi “ Modified Bee Colony optimization for the selection of different combinations of food sources”, International Arab Journal of Information Technology, Vol. 13, No. 6, 2016.
- Prakash, K & Nagarajan, B 2014, ‘A Mathematical Based Approach For Vehicle Object Classification’, International Journal of Research in Computer Applications and Robotics, vol.2, no.7, pp.58-64.
- Sanyal, Abak, T, Barış, U & Sankur, B 1997, ‘The Performance of Thresholding Algorithms for Optical Character Recognition’, Int. Conf. on Document Analysis and Recognition: ICDAR’97, Ulm., Germany, pp.697-700.
- Kai , Kurniawati Ackley, D & Littman, M 1992, ‘Interactions between learning and evolution’, In C. G. Langton, C. Taylor, J. D. Farmer, and S.Rasmussen, eds., Artificial Life II. Addison−Wesly