Browsing by Author "B, Rosiline Jeetha"

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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.
ACCELERATION ARTIFICIAL BEE COLONY OPTIMIZATION-IMPROVED TRANSDUCTIVE SUPPORT VECTOR MACHINE FOR EFFICIENT FEATURE SELECTION IN BIG DATA STREAM MINING.
(Jour of Adv Research in Dynamical & Control Systems, 2017-04) S, Meera; B, Rosiline Jeetha
High dimensional data seen in a practical issue imposes a hurdle for large data analysis. Attribute reduction or feature selection aids the learning algorithm to work with efficiency by eliminating unnecessary and repetitive information in the big data. The existing system like Acceleration Particle Swarm Optimization–Support Vector Machine (APSO-SVM) is proposed in order to deal with the above challenge. But the already existing technique has issues in addition to the pre-processing technique and optimal feature selection for scalable dataset. Therefore the system’s overall performance is decreased significantly. With the aim of eliminating these problems, in the proposed system, Acceleration Artificial Bee Colony –Improved Transductive SVM (AABC-ITSVM) is introduced so as to improve the system performance in a more efficient manner. The proposed system comprises of three important modules like preprocessing, feature selection and classification. The preprocessing is carried out by making use of min-max normalization algorithm that assists in increasing the classification accuracy more. Thereafter the feature selection is carried out by employing AABC optimization algorithm that is utilized for selecting the significant and necessary features from the data that is preprocessed. The selected features are classified by employing ITSVM algorithm. The ITSVMs gets the labeling of the test features, which increases the margin conjoined on the training and the test data. It yields classification results with more accuracy for the datasets specified. The proposed system offers great performance with regard to superior accuracy, recall, sensitivity, specificity, precision, f-measure, gmean, and lesser selected features, time complexity by utilizing the AABCITSVM technique.
CCHS: AN IMPROVED CENTRALIZED CLUSTER HEAD SELECTION IN WIRELESS SENSOR NETWORKS
(Journal of Advanced Research in Dynamical and Control Systems, 2019) R, Kowsalya; B, Rosiline Jeetha
In wireless sensor network, devices or nodes are normally battery powered devices. These nodes have imperfect quantity of primary energy that an enthusiastic at dissimilar rates, depending on the power or energy level. A modified centralized cluster based cluster-head selection is a primary issue in existing representative clustering methods such as M-LEACH and SEDC, cluster heads are selected with a optional likelihood in a distributed manner. So, there are huge deviations of the amount of clusters and size for each cluster at each round throughout network lifetime. In order to conquer issues of difficult cluster-head selection and great energy consumption in Centralized Clustering-Task Scheduling for wireless sensor networks (WSNs), in this paper presents a Modified Centralized Cluster-Head Selection (MCCHS) algorithm based on Simple Energy-efficient Data Collecting (SEDC) protocol was proposed. The proposed system presents a MCCHS algorithm in static manner selection algorithm for cluster head selection technique using NS (Network Simulator) 2.34 Framework. This technique leads to improved CH strength, reduction in the amount of clusters in the network, and improving the energy efficiency.
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
FEATURE SELECTION USING MODIFIED PARALLEL SOCIAL SPIDER OPTIMIZATION ALGORITHM BASED ON SWARM INTELLIGENCE FOR HIGH DIMENSIONAL DATASETS
(Dr. N.G.P Arts and Science College, 2018-08-09) S, Meera; B, Rosiline Jeetha
The Social-Spider Optimization (SSO) is one among the recently developed swarm intelligence. It is impressed from the social behavior of spiders living in huge colonies. The spiders communicate among themselves to find prey and to find a suitable partner for mating. In this manuscript, a parallel version of this algorithm is modified and termed as MPSSO. Social spider optimization algorithm (SSO) was applied to develop a clustering algorithm since it does not favor the premature convergence or damage the exploration–exploitation balance. This scheme requirements a large number of iteration to achieve the desired convergence. So, a new parallel SSO was proposed via making the sequential movements of dominate male, non-dominate male and female spiders to parallel, in this manner decreasing the computational complexity of the algorithm. This approach was based on uniform distribution. The primary objective of the modified parallel SSO (MPSSO) scheme based on beta distribution and natural gradient (NG) local search for improving the performance of PSSO algorithm. The proposed approach is achieved a better exploration/exploitation trade-off while applied to optimization issues in the continuous domain. In this approach, a beta distribution is applied to tune the control parameters and a local search process is improved by the usage of the natural gradient.
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
A NOVEL HYBRID APPROACH ON SECURE DATA TRANSMISSION IN WIRELESS SENSOR NETWORKS
(International Journal of Future Generation Communication and Networking, 2020) R, Kowsalya; B, Rosiline Jeetha
n recent year wireless sensor network plays an important role in day to day life, to achieve the security, cryptography techniques are used. As wireless sensor has the limited memory space and energy consumption to provide security is vital problem. The main aim of this research work is to analysing different cryptographic techniques such as symmetric key cryptography and asymmetric key cryptography and comparing AES, DES, 3DES, RC5 and IDEA encryption techniques. In this paper, a new security symmetric algorithm was proposed to provide high security. It provides cryptographic primary key integrity, confidentiality and authentication. The results show that the proposed hybrid algorithm HSR19 gives efficient performance for communication devices with the parameters in computation time with different file sizes, encryption and decryption speed and energy
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.