International Journals

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/157

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Author: search.filters.author.S, MeeraStart date: 2010

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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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    A SURVEY OF PARALLEL SOCIAL SPIDER OPTIMIZATION ALGORITHM BASED ON SWARM INTELLIGENCE FOR HIGH DIMENSIONAL DATASETS
    (International Journal of Computational Intelligence Research, 2017-11-09) B, Shanmugapriya; S, Meera
    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.
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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.
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    AN ANN APPROACH FOR CLASSIFICATION OF BRAIN TUMOR USING IMAGE PROCESSING TECHNIQUES IN MRI IMAGES
    (International Journal Of Engineering Sciences & Research Technology, 2015-12) A, Sindhu; S, Meera
    A brain tumor is defined as the growth of abnormal cells in the tissues of the brain. Brain tumors can be benign (noncancerous) or malignant (cancerous). MRI represents an interesting approach for the anatomical assessment of brain tumors since it provides superior soft tissue contrast and high-resolution information. MRI scan images are taken for this project to process further. This work proposed artificial neural network approach namely Back propagation network (BP-ANN).Image segmentation is done by using region growing algorithm which is used to detect the tumor present in the brain MRI images. GLCM is used for extracting the brain features in the images. This system proposed two modes namely training and testing phase which is used to classify the output.
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    A SURVEY ON DETECTING BRAIN TUMOR IN MRI IMAGES USING IMAGE PROCESSING TECHNIQUES
    (International Journal of Innovative Research in Computer and Communication Engineering, 2015-01) A, Sindhu; S, Meera
    Medical Image Processing is the fast growing and challenging field now a days. Medical Image techniques are used for Medical diagnosis. Brain tumor is a serious life threatening disease. Detecting Brain tumor using Image Processing techniques involves four stages namely Image Pre-Processing, Image segmentation, Feature Extraction, and Classification. Image processing and neural network techniques are used to improve the performance of detecting and classifying brain tumor in MRI images. In this survey various Image processing techniques are reviewed particularly for Brain tumor detection in magnetic resonance imaging. More than twenty five research papers of image processing techniques are clearly reviewed.