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Latha, M.
- Polymorphism Based Maintenance Prediction Scheme Using Design Complexity Metrics
Abstract Views :131 |
PDF Views:1
Authors
M. Latha
1,
K. Duraiswamy
2
Affiliations
1 Department of Computer Science, Sri Sarada College for Women, Salem-16, IN
2 K.S.R. College of Engineering, K.S.R. Kalvi Nagar, Tiruchengode, IN
1 Department of Computer Science, Sri Sarada College for Women, Salem-16, IN
2 K.S.R. College of Engineering, K.S.R. Kalvi Nagar, Tiruchengode, IN
Source
Software Engineering, Vol 4, No 3 (2012), Pagination: 107-113Abstract
The Object-Oriented paradigm has become increasingly popular in recent years. Researchers agree that, although maintenance may turn out to be easier for Object-Oriented systems, it is unlikely that the maintenance burden will completely disappear.One approach to controlling software maintenance costs is the utilization of software metrics during the development phase, to help identify potential problem areas. Many new metrics have been proposed for Object-Oriented systems.
The purpose of this work is to apply three existing Object-Oriented design complexity metrics and, specifically, to assess their ability to predict maintenance time for the polymorphism process.This research reports the results of validating three metrics,Interaction Level (IL), Interface Size (IS), and Operation Argument Complexity (OAC).
This system is designed to estimate maintenance time for the software's that are developed by using java. A controlled experiment was conducted to investigate the effect of design complexity (as measured by the above metrics) on maintenance time. Each of the three metrics by itself was found to be useful in the experiment in predicting maintenance performance.
Keywords
Interface Size Interaction Level Maintenance Time, Operation Argument Complexity.
- Inheritance Based Maintenance Prediction Scheme Using Design Complexity Metrics
Abstract Views :132 |
PDF Views:2
Authors
M. Latha
1,
K. Duraiswamy
2
Affiliations
1 Department of Computer Science, Sri Sarada College for Women, Salem-16, IN
2 K.S.R College of Engineering, K.S.R Kalvi Nagar, Tiruchengode, IN
1 Department of Computer Science, Sri Sarada College for Women, Salem-16, IN
2 K.S.R College of Engineering, K.S.R Kalvi Nagar, Tiruchengode, IN
Source
Software Engineering, Vol 4, No 1 (2012), Pagination: 35-41Abstract
The Object-Oriented paradigm has become increasingly popular in recent years. Researchers agree that, although maintenance may turn out to be easier for Object-Oriented systems, it is unlikely that the maintenance burden will completely disappear. One approach to controlling software maintenance costs is the utilization of software metrics during the development phase, to help identify potential problem areas. Many new metrics have been proposed for Object-Oriented systems.
The purpose of this work is to apply three existing Object-Oriented design complexity metrics and, specifically, to assess their ability to predict maintenance time for the inheritance process. This research reports the results of validating three metrics, Interaction Level (IL), Interface Size (IS), and Operation Argument Complexity (OAC).
This system is designed to estimate maintenance time for the software's that are developed by using java. A controlled experiment was conducted to investigate the effect of design complexity (as measured by the above metrics) on maintenance time. Each of the three metrics by itself was found to be useful in the experiment in predicting maintenance performance.
Keywords
Interface Size Interaction Level Maintenance Time, Operation Argument Complexity.- Reverse Engineering Based Maintenance Prediction Scheme Using Design Complexity Metrics
Abstract Views :132 |
PDF Views:2
Authors
M. Latha
1,
K. Duraiswamy
2
Affiliations
1 Sri College for Women, IN
2 K.S.R. College of Engineering & Tech, IN
1 Sri College for Women, IN
2 K.S.R. College of Engineering & Tech, IN
Source
Software Engineering, Vol 3, No 5 (2011), Pagination: 188-193Abstract
The Object-Oriented paradigm has become increasingly popular in recent years. Researchers agree that, although maintenance may turn out to be easier for Object-Oriented systems, it is unlikely that the maintenance burden will completely disappear. One approach to controlling software maintenance costs is the utilization of software metrics during the development phase, to help identify potential problem areas. Many new metrics have been proposed for Object-Oriented systems. The purpose of this work is to apply three existing Object-Oriented design complexity metrics and, specifically, to assess their ability to predict maintenance time for the reverse engineering process. This research reports the results of validating three metrics, Interaction Level (IL), Interface Size (IS), and Operation Argument Complexity (OAC). This system is designed to estimate maintenance time for the softwares that are developed by using java. A controlled experiment was conducted to investigate the effect of design complexity (as measured by the above metrics) on maintenance time. Each of the three metrics by itself was found to be useful in the experiment in predicting maintenance performance.Keywords
Interaction Level, Interface Size, Maintenance Time, Operation Argument Complexity.- Bayesian Multiple Deferred Sampling Plan BMDS (0,1) with Weighted Poisson Model Using Golub’s Minimum Risks Method
Abstract Views :232 |
PDF Views:0
Authors
K. Subbiah
1,
M. Latha
2
Affiliations
1 Department of Statistics, Government Arts College, Udumalpet (T.N.), IN
2 Kamarajar Government Arts College, Surandai (T.N.), IN
1 Department of Statistics, Government Arts College, Udumalpet (T.N.), IN
2 Kamarajar Government Arts College, Surandai (T.N.), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 9, No 1 (2018), Pagination: 18-24Abstract
Acceptance sampling plans by attributes involve sampling from the weighted poisson distribution and the non-conforming process of average fraction, following a gamma distribution are considered in this article. Our work presents a new procedure for the selection of bayesian multiple deferred state sampling plan (BMDSP) through average probability of acceptance (APA) with weighted poisson distribution (WPD) as a base line distribution and reduced risk. In constructing sample plan, we propose a procedure for constructing a bayesian MDSP using WPD and developed a technique to determine the parameters of the plan by ensuring a specific required protection to both producers and consumers. The performance power of the weighted poisson BMDSP is also discussed by determining the operating characteristic (OC) curve. which are developing under the producer’s and consumer’s risk for specified acceptable and limiting quality levels, a gamma prior distribution is baseline distribution. The procedure is given for BMDSP with the weighted poisson distribution for given (μ1,1-α) and (μ2,β).Keywords
Bayesian MDS-1 (0, 1), Weighted Poisson Distribution, Minimum Risks Plan, Acceptable Quality Level (AQL), Limiting Quality Level (LQL).References
- Dodge, H. F. (1955). Chain sampling inspection plan, Industrial Quality Control,11: 10-13.
- Golub, A. (1953). Designing single sampling inspection plans when the sample is fixed. J. American Stat. Assoc., 48: 278-288.
- Hald, A. (1981). Statistical theory of sampling inspection by attributes (Newyork, Academic Press).
- Latha, M. and Subbiah, K. (2014). Bayesian multiple deferred state (BMDS-1) sampling plan with weighted poisson distribution, Internat. J. Emerging Trends Engg. & Develop., 4 (3) : 275-282.
- Latha, M. and Subbiah, K. (2015). Selection of bayesian multiple deferred state (BMDS-1) sampling plan based on quality regions, Internat. J. Recent Scientific Res., 6 (4): 275-282.
- Soundararajan, V. (1981). Sampling inspection plans when the sample is fixed, J. Madras Univ. Sect. B 44 : 9-10.
- Soundararajan, V. and Govindaraju, K. (1983). A note on designing single sampling plans, IAPQR Transactions, 18 : 37-41.
- Soundararajan, V. and Vijayaraghavan, R.(1990). Construction and selection of multiple dependent (deferred) state sampling plan, J. Appl. Stat.,17 (3) : 397-409.
- Varest, R. (1982). A procedure of construct multiple deferred state sampling plans, Methods Operation Res., 37: 477-485.
- Wortham, A.W. and Baker, R.C. (1976). Multiple deferred dtate sampling inspection, Internat. J. Prod. Res., 14 (6) : 719-731.
- Detection of Septoria Spot on Blueberry Leaf Images using SVM Classifier
Abstract Views :182 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Sri Sarada College for Women, IN
1 Department of Computer Science, Sri Sarada College for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 4 (2019), Pagination: 2015-2019Abstract
Identification and classification of the plant leaf is efficient way to preventing loss occurred in agricultural field. The Septoria leaf spot is mainly affect the leaves which caused by a fungus, flu, bacteria. The Production of blueberry fruit is decreasing due to the disease affected on its stem and leaf. Small brown spots are frequently visible on blueberry leaves at specific period in the year. The spots, generally surrounded by bright yellow halos, start on the lower leaves and slowly appear on upper leaves over time. Image processing technology has been proved to be an efficient analysis to identify and detect the disease on a leaf. This proposed paper intends to focus to detect and classify a Septoria leaf spot on blueberry using Image Processing techniques such as, k-means clustering (k-nearest neighbor) for Segmentation, Gray-Level Co-occurrence Matrix for feature extraction and Support Vector Machine classifier to detect the leaf stage whether it is affected by Septoria spot or not. Totally 13 features have been extracted from each Blueberry leaf images where dataset of 40 images were taken for training and testing process partially and obtained the accuracy level was 96.77% using F-measure.Keywords
Septoria Leaf Spot, K-means Clustering, Segmentation, Feature Extraction, GLCM, SVM.References
- S.R. Dubey and A.S. Jalal, “Detection and Classification of Apple Fruit Disease using Complete Local Binary Patterens”, Proceedings of 3rd International Conference on Computer and Communication Technology, pp. 1-5, 2012.
- Abraham Gastelum Barrios, Rafael A. Borquez-Lopez, Enrique Rico-Garcia, Manuel Toledano-Ayala and Genaro M. Soto-Zarazua, “Tomato Quality Evaluation with Image Processing: A Review”, African Journal of Agricultural Research, Vol. 6, No. 14, pp. 1-5, 2011.
- M.K. Chetia and S. Chetia, “Detection and Classification of Plant Leaf Diseases in Image Processing using Matlab”, International Journal of Life Sciences Research, Vol. 5, No. 4, pp. 120-124, 2017.
- V.M. Gupta, “Plant Leaf Disease Analysis using Image Processing Techniques with Modified SVM-Classifier”, Indian Journal of Endocrinology and Metabolism, Vol. 5, No. 1, pp. 1-8, 2017.
- Usama Mokhtar, Nashwa El Bendary, Aboul Ella Hassenian, E. Emary, Mahmoud A. Mahmoud, Hesham Hefny and Mohamed F. Tolba, “SVM-based Detection of Tomato Leaves Diseases”, Proceedings of International Conference on Advances in Intelligent Systems and Computing, pp. 641-652, 2015.
- Niket Amoda, Bharath Jadhav and Smeetha Nailkwadi, “Detection and Classification of Plant Disease by Image Processing”, International Journal of Innovative Science, Engineering and Technology, Vol. 1, No. 2, pp. 70-74, 2014.
- P. Padol and A.A. Yadhav, “SVM Classifier based Grape Leaf Disease Detection”, Proceedings of International Conference on Advances in Signal Processing, pp. 1-10, 2016.
- V. Pooja, R. Das and V. Kanchana, “Identification of Plant Leaf Disease using Image Processing Techniques”, Proceedings of International Conference on Technological Innovations in ICT for Agriculture and Rural Development, pp. 1-5, 2017.
- Sandesh Raut and Amit Fuldsunge, “Plant Disease Detection in Image Processing using Matlab”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, No. 6, pp. 10373-10381, 2017.
- Shivaputra S.Panchal and R. Sonar, “Pomegranate Leaf Disease Detection using Support Vector Machine”, International Journal of Engineering and Computer Science, Vol. 5, No. 6, pp. 1-4, 2016.
- S.R. Dubey and A.S. Jalal, “Detection and Classification of Apple Fruit Diseases using Complete Local Binary Patterns”, Proceedings of 3rd International Conference on Computer and Communication Technology, pp. 1-6, 2012.
- R. Swarna Lakshmi, B. Kanchanadevi, “A Review on Fruit Grading Systems for Quality Inspection”, International Journal of Computer Science and Mobile Computing, Vol. 3, No. 7, pp. 12-18, 2014.
- Dameshwari Sahu and Chitesh Dewangan, “Identification and Classification of Mango Fruits using Image Processing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 2, No. 2, pp. 203-210, 2016.