Department of Computer Science (PG)

Permanent URI for this communityhttps://dspace.psgrkcw.com/handle/123456789/146

Browse

Has files: YesSubject: search.filters.subject.classification

Search Results

Now showing 1 - 5 of 5
  • No Thumbnail Available
    Item
    ACUTE CYSTITIS AND ACUTE NEPHRITIS PREDICTION USING MACHINE LEARNING TECHNIQUES
    (Global Journal of Computer Science and Technology, 2010-09) Kowsalya R; Sasikala G; Sangeetha Priya J
    Urinary System includes kidneys, bladder, ureters and urethra. This is the major system involves electrolyte balance of the body and filters the blood and excretes the waste products in the form urine. Even the small disturbance in the renal function will step in a disasters manifestation. Among them we are considering the two diseases that affect the system are acute cystitis and acute nephritis. This paper presents the implementation of three supervised learning algorithms, ZeroR, J48 and Naive Bayes in WEKA environment. The classification models were trained using the data collected from 120 patients. The trained models were then used for predicting the acute cystitis or acute nephritis of the patients. The prediction accuracy of the classifiers was evaluated using 10-fold cross validation and the results were compared.
  • No Thumbnail Available
    Item
    PERFORMANCE ANALYSIS OF PREDICTING SURVIVAL RATES IN IMBALANCED HEALTHCARE DATASET.
    (International Journal of Advanced Research in Computer Science, 2018-04) R, Vani
    Predicting Patients health is a critical task in the Healthcare Industry. Healthcare datasets show a high degree of imbalance especially for rare diseases. The current work aims at predicting the post operative survival rate in thoracic surgery datasets. The dataset exhibits data imbalance with around 15% positive cases and remaining 85% negative cases. The commonly applicable machine learning techniques for prediction score poorly in predicting the positive cases in spite of high accuracy of the predictions for the negative cases. We use SMOTE (synthetic minority oversampling technique) to reduce the degree of imbalance and increase the positive samples proportion before the application of the following classifiers: Naive Bayes, Neural Networks, Random Forest, Boosting algorithms - Adaboost, Extreme Gradient boosting and Support Vector Machines and examine the results. The study shows that SVM and Naïve Bayes show significantly better performance on the imbalanced datasets than other models using synthetic datasets than under normal conditions.
  • No Thumbnail Available
    Item
    ACUTE CYSTITIS AND ACUTE NEPHRITIS PREDICTION USING MACHINE LEARNING TECHNIQUES
    (Global Journal of Computer Science and Technology, 2010-09) Kowsalya R; Sasikala G; Sangeetha Priya J
    Urinary System includes kidneys, bladder, ureters and urethra. This is the major system involves electrolyte balance of the body and filters the blood and excretes the waste products in the form urine. Even the small disturbance in the renal function will step in a disasters manifestation. Among them we are considering the two diseases that affect the system are acute cystitis and acute nephritis. This paper presents the implementation of three supervised learning algorithms, ZeroR, J48 and Naive Bayes in WEKA environment. The classification models were trained using the data collected from 120 patients. The trained models were then used for predicting the acute cystitis or acute nephritis of the patients. The prediction accuracy of the classifiers was evaluated using 10-fold cross validation and the results were compared.
  • No Thumbnail Available
    Item
    ENHANCED PARTICLE SWARM OPTIMIZATION WITH GENETIC ALGORITHM AND MODIFIED ARTIFICIAL NEURAL NETWORK FOR EFFICIENT FEATURE SELECTION IN BIG DATA STREAM MINING
    (PSG College of Technology, 2019-01-05) S, Meera; B, Rosiline Jeetha
    In the recent years the volume of data has been growing tremendously with the development in the information technology. Hugh dimensionality would be one of the major challenges faced by people working in research with big data as a high dimensionality that happens while a dataset comprises of a big number of features (autonomous attributes). For resolving this issue, often researchers make use of a feature selection step for identification and removal of irrelevant features (not helpful in the classification of the data) and repetitive features (yield the same information like other features). Acceleration Artificial Bee Colony- Artificial Neural Network (AABC-ANN) has been introduced in the preceding research for handling the feature selection process over the big data. Computational complexity and inaccuracy of dataset remains as a problem for these methods. Enhanced Particle Swarm Optimization with Genetic Algorithm – Modified Artificial Neural Network (EPSOGA -MANN) is proposed in the proposed methodology for avoiding the above mentioned issues. Modules’ including preprocessing, feature selection and classification has been included in this research process. Fuzzy C Means (FCM) denotes the clustering algorithm which is used to handle the noise information efficiently in preprocessing. Issues like missing data, repetitiveness and error data are resolved effectively. Discarding of irrelevant features leads to the significant decrease in size of the structured and semi structured dataset. These features are then considered for the feature selection process. Feature selection process is carried out by means of EPSOGA algorithm optimally in this research. More important and relevant features are selected by EPSOGA optimization algorithm from the as it provides classification results with more accuracy for the huge volume of dataset given. Input, hidden and output layer are the three layers of MANN. It is introduced for improving the time complexity by means of neurons. This proposed methodology is more suitable for handling the big data principles such as volume, velocity and variety. The proposed model provides superior performance as show in experimental results in terms of superior accuracy, recall, precision, f-measure, and lesser time complexity by means of EPSOGA –MANN approach
  • No Thumbnail Available
    Item
    ACCELERATION ARTIFICIAL BEE COLONY OPTIMIZATION-ARTIFICIAL NEURAL NETWORK FOR OPTIMAL FEATURE SELECTION OVER BIG DATA
    (Saveetha Engineering College, Chennai, 2012-09) S, Meera; B, Rosiline Jeetha
    Finding an appropriate set of features from data of high dimensionality for building an accurate classification model is challenging in recent years. In the preceding research, Acceleration Particle Swarm Optimization–Support Vector Machine (APSO-SVM) and Acceleration Artificial Bee Colony –Improved Transductive SVM (AABC-ITSVM) is introduced to handle the feature selection process over big data. However these methods are issues with computational complexity and accuracy of dataset. To avoid the above mentioned issues, in the proposed system, AABC-Artificial Neural Network (ANN) is proposed. This research contains modules are such as preprocessing, feature selection and classification. In preprocessing, k-Nearest Neighbor algorithm is applied which is used to handle the noise data efficiently. The size of the dataset is reduced significantly. Then these features are taken into feature selection process. In this research, AABC algorithm is used for performing feature selection. AABC optimization algorithm is used to select the important and relevant features from the preprocessed data. Then classification is done by using Artificial Neural Network (ANN) and it classifies more accurate classification results for the given large volume of dataset. ANN contains three layers are such as input, hidden and output layer. It is proposed to improve the time complexity by using neurons. The experimental result proves that the proposed system gives superior performance in terms of higher accuracy, recall, precision, f-measure, and lower time complexity by using AABC-ANN approach.