F-KCW-Department Publications
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Item SURVEY ON SWARM SEARCH FEATURE SELECTION FOR BIG DATA STREAM MINING.(International Journal of Computational Intelligence Research, 2017-01) S, Meera; B, Rosiline JeethaBig 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.Item 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, MeeraMedical 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.Item COMPARATIVE STUDY ON SWARM SEARCH FEATURE SELECTION FOR BIG DATA STREAM MINING(PSGR Krishnammal College for Women, 2017-02-04) S, MeeraIn the modern world there is huge development in the field of networking technology which handles huge data at a time. This data can be structured, semi structured or unstructured. To perform efficient mining of valuable information from such type of data the big data technology is gaining importance nowadays. Data mining application is been used in public and private sectors of industry because of its advantage over conventional networking technology to analyze large real time data. Data mining mainly relies on 3 V’s namely, Volume, Varity and Velocity of processing data. Volume refers to the huge amount of data it collects, Velocity refers to the speed at which it process the data and Variety defines that multi-dimensional data which can be numbers, dates, strings, geospatial data, 3D data, audio files, video files, social files, etc. These data which is stored in big data will be from different source at different rate and of different type; hence it will not be synchronized. This is one of the biggest challenges in working with big data. Second challenge is related to mining the valuable and relevant information from such data adhering to 3rd V i.e. Velocity. Speed is highly important as it is associated with cost of processing. On the other hand, mining through the high dimensional data the search space from which an optimal feature subset is determined and it is enhanced in size, guiding to a difficult stipulate in computation. With respect to handle the troubles, the research work is generally based on the high-dimensionality and streaming structure of data feeds in big data, a new inconsequential feature selection methodology that can be used to identify the feature selection methods in the big data. Some of the research work illustrates the different kinds of optimization methods for data stream mining would lead to tremendous changes in big data. This research work is focused on discussing various research methods that focus on finding the efficient feature selection methods which is used to avoid main challenges and produce optimal solutions. The previous methods are described with their advantages and disadvantages, consequently that the additional research works can be focused more. The tentative experiments were on the entire research works in Mat lab simulation surroundings and it is differentiated with everyone to identify the good methodologies beneath the different performance measures.Item AN ANN APPROACH FOR CLASSIFICATION OF BRAIN TUMOR USING IMAGE PROCESSING TECHNIQUES IN MRI IMAGES(INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY, 2015-01) A, Sindhu; S, MeeraMedical 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.Item A SURVEY ON DETECTING BRAIN TUMOR MRI IMAGES USING IMAGE PROCESSING TECHNIQUES(International Journal of Innovative Research in Computer and Communication Engineering, 2015-01) A, Sindhu; S, MeeraMedical 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.