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    FEATURE SELECTION TECHNIQUES FOR THE CLASSIFICATION OF LEAF DISEASES IN TURMERIC
    (International Journal of Computer Trends and Technology (IJCTT), 2017) V, Pream Sudha
    Crop maintenance is one of the crucial factors that determine the quantity and quality of the agricultural products. Protecting crops from plant diseases is an important aspect that increases the profit of the farmer. This study aims at developing a computational model that will facilitate crop production by accurately identifying diseases that affect productivity of turmeric plants. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot, and leaf blotch. This system uses technologies such as feature selection and machine learning techniques for the identification and classification of diseases in turmeric leaf. Principal component analysis, Information gain and Relief-f attribute evaluator methods were investigated in combination with machine learning algorithms like Support Vector Machine, Decision Tree and Naïve Bayes. The performance of the models were evaluated using 10 fold cross validation and the results were reported. Comparatively, the model using SVM applied to features selected using Information gain performed well with an accuracy of 93.75.
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    MISSING VALUE AWARE OPTIMAL FEATURE SELECTION METHOD FOR EFFICIENT BIG DATA MINING PROCESS
    (International Journal of Recent Technology and Engineering (IJRTE), 2019-09) S, Meera; B, Rosiline Jeetha
    Big mining plays a more critical role in the real world environment due to presence of large volume of data with different varieties and type. Handling these data values and predicting the information would be the more difficult task which needs to be concerned more to obtain the useful knowledge. This is achieved in our previous research work by introducing the Enhanced Particle Swarm Optimization with Genetic Algorithm – Modified Artificial Neural Network (EPSOGA -MANN) which can select the optimal features from the big volume of data. However this research work might be reduced in its performance due to presence of missing values in the dataset. And also this method is more complex to perform due to increased computational overhead of ANN algorithm. This is resolved in the proposed research method by introducing the method namely Missing Value concerned Optimal Feature Selection Method (MV-OFSM). In this research method Improved KNN imputation algorithm is introduced to handle the missing values. And then Dynamic clustering method is introduced to cluster the dataset based on closeness measure. Then Anarchies Society Optimization (ASO) based feature selection approach is applied for performing feature selection in the given dataset. Finally a Hybrid ANN-GA classification technique is applied for implementing the classification. The overall performance evaluation of the research method is performed in the matlab simulation environment from which it is proved that the proposed research method leads to provide the better performance than the existing research technique
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    SURVEY ON SWARM SEARCH FEATURE SELECTION FOR BIG DATA STREAM MINING.
    (International Journal of Computational Intelligence Research, 2017-01) S, Meera; B, Rosiline Jeetha
    Big data is the slightly abstract phase which describes the relationship between the data size and data processing speed in the system. The many new information technologies the big data deliver dramatic cost reduction, substantial improvements in the required time to perform the computing task or new product and service offerings. The several complicated specific and engineering problems can be transformed in to optimization problems. Swarm intelligence is a new subfield of computational intelligence (CI) which studies the collective intelligence in a group of simple intelligence. In the swarm intelligence, useful information can be obtained from the competition and cooperation of individuals. In this paper discussed about some of the optimization algorithms based on swarm intelligence such as Ant Colony optimization (ACO), Particle Swarm Algorithm (PSO), Social Spider Optimization (SSO) Algorithm and Parallel Social Spider Optimization (P-SSO) Algorithm. These optimization techniques are based on their merits, demerits and metrics accuracy, sum of intra cluster distance, Recovery Error Etc.