Informatics Publishing Limited

K. S. Vijay Selvaraj ¹, A. Bharathi , V. Swaminathan ²

Affiliations
1 Coconut Research Station (T.N.A.U.) Veppankulam T. N., IN
2 Agricultural College and Research institute, Madhurai T. N., IN

Abstract

The experiment was conducted to evolve things related to post harvest physiology mechanism of cut flowers at crop physiology laboratory at Department of Crop Physiology, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai. This experiment comprised of chemicals viz., silver nitrate, calcium chloride, ascorbic acid, aluminium sulphate, sodium benzoate, sodium thio sulphate, cobalt sulphate, salicylic acid and coconut water with sucrose of two level concentrations 3 per cent and 5 per cent with the aim to increase the vase life of tuberose spikes. This was laid in Completely Randomized Design with three replications. Silver nitrate at 50 ppm+ 5% sucrose showed higher fresh weight of cut spikes over 50 per cent of coconut water + 3% level of sucrose, silver nitrate at 50ppm+ 5% sucrose showed higher per cent of opened florets over control. Silver nitrate at 50ppm+ 5% sucrose showed longest vase life of cut spikes of 18 days when compared to control.

Keywords

Sucrose, Post Harvest Keeping, Cut Flowers, Tuberose Spikes

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S. Gokila ¹, K. Ananda Kumar ², A. Bharathi ³

Affiliations
1 Bharathiar University, Coimbatore - 641 046, Tamil Nadu, IN
2 Department of MCA, Bannariamman Institute of Technology, Erode - 638401, Tamil Nadu, IN
3 Department of IT, Bannariamman Institute of Technology, Erode - 638401, Tamil Nadu, IN

Abstract

Objectives: Four seasons of Indian weather are interdependent. Prediction of seasonal weather supports many fields to work successfully. The objective of proposed model to work on Weather Pattern identification in Initial phase of prediction which has to include unequal weight of attributes. Methods: The projected space clustering model is suitable to handle the non-sequence patterns of data set. The existing projected space clustering eliminates the least weighted attribute. The framework suggested in this paper incorporate modified projected space cluster which work on complete set of attributes to form pattern wise clusters which is dynamic in number for each season. Next part of framework is seasonal weather prediction using ANN, works on dynamic set of clusters. Findings: Dynamic nature of clusters formed in modified projected space clustering completely eliminates the error rate arise because of fixed number of cluster. The extreme events patterns formed as a separate clusters are not eliminated as outline. The result of these clusters gives the study report of each season, like the changes of climate pattern, the frequency of extreme event and weather prediction of next season. Application/ Improvement: The modified projected space clustering work well on unequal complete set of attributes to form a cluster of different pattern. For each duration numbers of clusters are dynamic based on the pattern variation in climate data.

Keywords

Climate, Data Mining, Dynamic Clustering, Forecasting, Projected Space, Weather, Weather Season

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D. Saranya ¹, A. Bharathi ²

Affiliations
1 Coimbatore Institute of Information Technology, TamilNadu, IN
2 Bannari Amman Institute of Technology, TamilNadu, IN

Abstract

Machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances, which is used to solve classification problems in many applications. this work perform the function by using OneR, Feature Selection, Attribute Oriented Induction (AOI). Concept hierarchies can be used to reduce the data collecting and replacing low-level concepts by higher level concepts. A new attribute induction paradigm and as improving from current attribute oriented induction. A novel star schema attribute induction will be examined with current attribute oriented induction based on characteristic rule and using cross over and mutation with concept hierarchy. Experimental result shows proposed method has high accuracy with less execution time using UCI repository datasets.

Keywords

Attribute Oriented Induction, Feature Selection, Concept Hierarchy, Multi Level Mining, Support Vector Machine.

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D. Saranya ¹, A. Bharathi ²

Affiliations
1 Coimbatore Institute of Information Technology, Tamil Nadu, IN
2 Bannari Amman Institute of Technology, Tamil Nadu, IN

Abstract

Machine learning deals with programs that learn from experience, i.e. programs that improve or adapt their performance on a certain task or group of tasks over time. The algorithm used for classification is OneR, Naive Bayes and C4.5 algorithm. This work use OneR, it is a simple classification algorithm that generates a one-level decision tree. OneR is able to infer typically simple, yet accurate, classification rules from a set of instances. This paper present Attribute Oriented Induction (AOI) has concept hierarchy as an advantage where concept hierarchy as a background knowledge which can be provided by knowledge engineers or domain experts. The experimental result shows that the proposed method of OneR with Attribute Oriented Induction program provides an accurate result by using UCI repository datasets.

Keywords

One Rule, Attribute Oriented Induction, Machine Learning Algorithm, Naive Bayes Algorithm.

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A. Bharathi ¹, A. M. Natarajan ²

Affiliations
1 Anna University, Coimbatore, IN
2 Bannari Amman Institute of Technology, Sathyamangalam, IN

Abstract

Clustering is one of the major tasks in data mining Clustering algorithms are based on a criterion that maximizes inter cluster distance and minimize intra cluster distance. In higher dimensional feature spaces, the performance and efficiency deteriorates to a greater extent. Large dimensions confuse the clustering algorithms and it is difficult to group similar data points becomes almost the same and is usually called as the “dimensionality curse” problem. These algorithms find a subset of dimensions by removing irrelevant and redundant dimensions on which clustering is performed. Dimensionality reduction technique such as Principal Component Analysis (PCA) is used for feature reduction. If different subsets of the points cluster well on different subspaces of the feature space, a global dimensionality reduction will fail. To overcome these problems, recent directions in research proposed to compute subspace cluster. The algorithms have two common limitations. First, they usually have problems with subspace clusters of different dimensionality. Second, they often fail to discover clusters of different shape and dimensionalities. The goal of this project is to develop new efficient and effective methods for high dimensional clustering.

Keywords

Data Mining, High Dimensional Clustering, Distance Measure.

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A. Punitha ¹, A. Bharathi ¹, D. Sarala Devi ²

Affiliations
1 Regional Research Station (Tamil Nadu Agricultural University), Paiyur, Krishnagiri (T.N.), IN
2 Horticultural College & Research Institute (Tamil Nadu Agricultural University), Periyakulam (T.N.), IN

Abstract

Evaluation of 41 diverse genotypes of cucumber was carried out in a randomized block design. Among the 41 genotypes studied, Mahalanobis D² analysis confined the presence of wide genetic diversity through the formation of seven clusters. The clustering pattern showed the lack of parallelism between geographic and genetic diversities. Among the clusters, intercrossing the genotypes in the cluster I, II, IV and V had high mean values for many characters studied is likely to result in an enlargement of spectrum of variability facilitating the selection for higher yield. The ranking D² values revealed that tender fruit yield per vine, tender fruit girth, tender fruit weight and number of tender fruit per vine contributed high genetic divergence. Hence, these characters could respond favourably for phenotypic selection.

Keywords

Cucumber, Genetic Diversity, Intra and Inter Cluster.

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C. Thiyagarajan ¹, K. Anandha Kumar ², A. Bharathi ³

Affiliations
1 Bharathiar University, Coimbatore – 641046, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam – 638401, Tamil Nadu, IN
3 Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam – 638401, Tamil Nadu, IN

Abstract

Objectives: To design a classifier for the detection of Diabetes Mellitus with optimal cost and precise performance. Method and Analysis: The diagnosis and interpretation of the diabetes data is must because major problem occurs due to this data maintenance. Several researches are made with machine learning but still needs improvements. In this paper a new method is evaluated as hybrid Extreme Learning Machine (HELM) with African Buffalo Optimization (ABO). Findings: ELM is used to select the input data because of fast learning speed. Optimization technique is used for searching and classifying the good diabetic data. The ABO is a population based algorithm in which individual buffalos work together to identify the diabetics data by updating fitness value for best output solution. The proposed HELM technique is successfully implemented for diagnosing diabetes disease. By using this machine learning algorithm, the classification accuracy is achieved for classifying the diabetes patients by using much of the data set for training and few data sets for testing. In order to improve the quality as well as accuracy there is a need for algorithm. The combination of ELM-ABO classifier is applied in training dataset taken from PRIMA Indian dataset for classification and the experimental results are compared with SVM and other ELM classifiers applied on the same database. Improvement: It is observed that the HELM method obtained high accuracy in classification with less execution time along with performance evaluation of parameters such as recall, precision and F-Measure.

Keywords

African Buffalo Optimization (ABO), Diabetes Mellitus (DM), Extreme Learning Machine (ELM), Machine Learning Algorithms, Optimization

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Vivek Deshmukh ¹, Sumeet Prabakar Mankar ¹, C. Muthukumar ¹, P. Divahar ¹, A. Bharathi ¹, Helen Baby Thomas ¹, Ashish Rajurkar ¹, Reena Sellamuthu ¹, R. Poornima ¹, S. Senthivel ², R. Chandra Babu ¹

Affiliations
1 Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore 641 003, IN
2 Agricultural Research Station, Tamil Nadu Agricultural University, Paramakudi 623 707, IN

Abstract

Drought is the most predominant constraint to rainfed rice production. Identifying molecular markers associated with drought resistance traits and deploying them in marker-assisted breeding will hasten the development of drought-resilient cultivars. A total of 49 diverse rice accessions, including traditional landraces, were evaluated for plant production and ischolar_main traits under natural drought stress in rainfed target populations of environment (TPE) in six successive field trials from 2010 to 2015. Significant variation for phenology, plant production and ischolar_main traits under drought was noticed among the accessions. Genotyping of the rice accessions using 599 polymorphic simple sequence repeat (SSR) markers showed considerable variation among them. STRUCTURE analysis grouped the 49 accessions into three subpopulations. Similarly, three clusters were observed in Neighbor joining tree created using Nei’s genetic distance. The subpopulation POP1 consisted mostly of landraces, while subpopulation POP3 consisted of advanced breeding lines and POP2 accessions from all groups. Genome-wide association mapping detected 61 markers consistently associated in two or more trials with phenology, plant production and ischolar_main traits under drought in TPE. The markers PSM52 (Chr 3), RM6909 (Chr 4), RM242 (Chr9) and RM444 (Chr 9) were consistently associated with grain yield and ischolar_main traits under drought. The markers PSM127 (Chr 3) and PSM133 (Chr 4) were consistently associated with yield, plant height and spikelet fertility. These markers with pleiotropic and consistent associations with yield and secondary traits under drought in TPE may be robust candidates for marker-assisted breeding for drought resistance in rice.

Keywords

Association Mapping, Drought Resistance, Molecular Markers, Rice.

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