Department of Computer Science (PG)

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Author: search.filters.author.R, KowsalyaHas files: Yes

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    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
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    ANTI-DIABETIC POTENTIAL OF INDIAN MEDICINAL PLANTS WITH GARCINIA KOLA AND SYZYGIUM CUMINI
    (A & V Publications, 2021) K J, Sharmila; L, Kanimozhi; Priya, J Shanmuga; G V, Vidhya; Caroline, R Jeba; R, Kowsalya
    Diabetes mellitus is a group of metabolic disease in which a person experiences high blood glucose levels either because the body produces inadequate insulin in the body. Though there are several treatment options available there are limitations such as high costs and side effects, weight gain etc. For this reason, the use of medicinal plants has increased to be used as an anti-diabetic agent with less side-effect and more efficient. In this regard, this study analyzed the anti-diabetic potential of Garcinia kola and Syzygium cumini using alpha amylase inhibition assay and glucose uptake by yeast cells. It was observed that Ethanol extract of Garcinia kola increased anti-diabetic potential compared to Syzygium cumini.
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    WITHDRAWN: CLUSTER BASED DATA-AGGREGATION USING LIGHTWEIGHT CRYPTOGRAPHIC ALGORTIHM FOR WIRELESS SENSOR NETWORKS
    (Science Direct, 2021-02-23) R, Kowsalya; B, Roseline Jeetha
    This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
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    A SURVEY ON DEEP LEARNING TECHNIQUES APPLICATIONS AND CHALLENGES
    (International Journal of Advance Research In Science And Engineering, 2015) V, Pream Sudha; R, Kowsalya
    Deep learning is an emerging research area in machine learning and pattern recognition field. Deep learning refers to machine learning techniques that use supervised or unsupervised strategies to automatically learn hierarchical representations in deep architectures for classification. The objective is to discover more abstract features in the higher levels of the representation, by using neural networks which easily separates the various explanatory factors in the data. In the recent years it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. This paper presents a brief overview of deep learning, techniques, current research efforts and the challenges involved in it
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    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.