Browsing by Author "Sangeetha, S"

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    A group-key-based sensitive attribute protection in cloud storage using modified random Fibonacci cryptography
    (Springer International Publishing, 2020-06-16) Sumathi, M; Sangeetha, S
    ud computing is an eminent technology for providing a data storage facility with efficient storage, maintenance, management and remote backups. Hence, user data are shifted from customary storage to cloud storage. In this transfer, the sensitive attributes are also shifted to cloud storage with high-end security. Current security techniques are processed with high encryption time and provide identical security of entire data with single key dependent. These processes are taking high computational time and leaks entire information if the key is hacked. The proposed Group Key Based Attribute Encryption using Modified Random Fibonacci Cryptographic (MRFC) technique rectifies these issues. Instead of machine learning technique, data owner preference-based attributes segregation is used to divide an input dataset into sensitive and non-sensitive attribute groups. Based on inter-organization usage and data owner’s willingness, sensitive attribute is divided into ‘n + 1′ subgroups and each subgroup is encrypted by ‘n + 1’ group keys. The encrypted sensitive subgroups are merged with non-sensitive attributes and uploaded into a private cloud. The novelties of this paper are, (1) data owner preferred sensitive attribute classification instead of machine learning algorithms, (2) sensitive attribute encryption instead of entire attributes, (3) To reduce encryption time without compromising data owner privacy, (4) To decrypt and access the required subgroup instead of the entire attribute. Our experimental results show that, the proposed method takes minimal processing time, better classification accuracy and minimal memory space with high security to selected attributes as compared to existing classification and security techniques. Hence, sensitive data security and privacy is achieved with minimal processing cost.
  • No Thumbnail Available
    Item
    A self-learning algorithm for identifying the leverage points in soil data using quantile regression forests
    (Elsevier Inc., 2023-12-14) Rose, Sareena; Nickolas, S; Sunoj, S.M.; Sangeetha, S
    Some unusual combinations of predictor values in multivariate regression often influence tampering with the output, and filtering those observations becomes the trickiest and most challenging task. This concern is prevalent and predominant in ecological domains, especially in soil samples, as the data sets are heteroscedastic and heterogeneous. When there is little domain knowledge on the combinatorial criterion for the leverage points, it is advantageous to derive a labelled framework to differentiate the unusual observations. This study proposes a novel framework by integrating quantiles and proximity matrix of Quantile Regression Forest that builds a framework out of the training data set. Unlike other supervised anomalous detection algorithms, prior knowledge about the samples is not required to train the dataset, as the algorithm works in a self-learning mode. The outcome is two sets of observations: regular and leverage points. When unseen data arrives, the regressors’ proximity to these two observation sets is the demarcation criterion. Three real datasets are used, and the outcome of the proposed approach is verified using Principal Component Analysis, Local Outlier Factor, and Gaussian Mixture Models. The algorithm's results are promising, setting a new trend of using supervised techniques without demanding any prior knowledge of the observations and performing an inlier-based outlier detection technique.
  • No Thumbnail Available
    Item
    Deep neural network and model-based clustering technique for forensic electronic mail author attribution
    (Springer Nature, 2021-02-18) Apoorva, K.A.; Sangeetha, S
    Electronic mail is the primary source of different cyber scams. Identifying the author of electronic mail is essential. It forms significant documentary evidence in the field of digital forensics. This paper presents a model for email author identification (or) attribution by utilizing deep neural networks and model-based clustering techniques. It is perceived that stylometry features in the authorship identification have gained a lot of importance as it enhances the author attribution task's accuracy. The experiments were performed on a publicly available benchmark Enron dataset, considering many authors. The proposed model achieves an accuracy of 94% on five authors, 90% on ten authors, 86% on 25 authors and 75% on the entire dataset for the Deep Neural Network technique, which is a good measure of accuracy on a highly imbalanced data. The second cluster-based technique yielded an excellent 86% accuracy on the entire dataset, considering the authors' number based on their contribution to the aggregate data.
  • No Thumbnail Available
    Item
    Machine learning for high risk pregnancies pre-term birth prediction: A retrospective
    (Science Publishing Corporation Inc, 2018-04-20) Ramla, M; Sangeetha, S; Nickolas, S
    Any birth before 28 weeks of gestation is termed as Pre-Term. This has substantial impact in the emotional reactions of mothers. The post-traumatic stress notably in the mother could be of chronic psychological risk. Moreover, it is to be addressed in the global scenario for sustainable development. Predicting stillbirths is still a distant reality. A plethora of works have been carried out and this paper present the summaries and analysis of current research. The primary focus of the paper is to throw light on the challenging issue of Preterm Birth Prediction. Myriad of machine learning techniques are used by various researchers each with its own estimation accuracy and type of ML model.