Department of Computer Science (PG)

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Subject: search.filters.subject.Classification

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    A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS APPLIED TO PREDICTIVE DIABETES DATA
    (CiiT International Journal of Data Mining and Knowledge Engineering, 2009-11-25) K, Sathiyakumari; V, Pream Sudha
    Healthcare industry encompasses abundant data, which is increasing everyday. Conversely, tools for analyzing these records are incredibly less. Machine learning provides a lot of techniques for solving diagnostic problems in a variety of medical domains. Intelligent systems are able to learn from machine learning methods, when they are provided with a set of clinical cases as training set. This paper aims at a comparative study of widely used supervised classification algorithms – Naïve Bayes, Multi Layer Perceptrons, Logistic Model Trees, and Nearest Neighbor with Generalized Exemplars applied to predictive diabetes dataset. The machine learning algorithms used in this study are chosen for their representability and diversity. They are evaluated on the basis of their accuracy, learning time and error rates.
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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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    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.
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    COMPARATIVE STUDY ON SWARM SEARCH FEATURE SELECTION FOR BIG DATA STREAM MINING
    (PSGR Krishnammal College for Women, 2017-02-04) S, Meera
    In 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.
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    ANALYZING DATA MINING ALGORITHMS USING DERMATOLOGY DATASET
    (Department of Computer Science at Nehru Arts and Science, 2010-02-06) Radha N; Rubya T
    Machine Learning plays a major role in several applications. Machine learning algorithms can be used to classify the data with more accuracy. In this paper, Dermatology Dataset is used and model created using Weka and performance is compared among various classifiers
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    CLASSIFICATION OF USER OPINIONS FROM TWEETS USING MACHINE LEARNING TECHNIQUES
    (International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), 2013-07) Poongodi S; Radha N
    Online Social Network is a standard platform for collaboration, communication where people are connected to each other for sharing their opinion. In general, opinions can be articulated about anything like products, surveys, topics, individuals, organizations and events. There are two main types of textual information in web like facts and opinions. Facts can be expressed in defined terms by the user implicitly. To mine opinion, from the user defined facts is intellectually very demanding. User opinion is valuable data, which can be used for marketing research in business during decision making process. So opinion mining and classification plays a vital role in predicting what people think about products. In this work, basic Natural Language Processing (NLP) techniques and hash tag segments, emoticons are used for classification. The performance comparison of Support Vector Machine (SVM), Naïve bayes (NB) and Multilayer Perceptron (MLP) are done using weka. It is observed that the MLP gives better accuracy to classify the opinion from tweets
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    AN OVERVIEW OF TECHNIQUES USED FOR EXTRACTING KEYWORDS FROM DOCUMENTS
    (International Journal of Computer Trends and Technology (IJCTT), 2013-07) Menaka S; Radha N
    Keywords are a set of major words in a document that give high-level description of the content for readers. Keywords are useful for scanning large documents in a short time. Extracting keywords manually are very difficult and time-consuming process. Therefore, there is in need for process to extract keywords from documents automatically. Keyword extraction is a process in which a set of words are selected that gives the meaning of the whole document. This paper presents an overview of techniques used for keyword extraction.