4.Conference Paper (09)
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Item IDENTIFICATION OF SUBGROUPS IN A DIRECTED SOCIAL NETWORK USING EDGE BETWEENNESS AND RANDOM WALKS(Springer Link, 2018) Sathiyakumari, K; Vijaya, M SSocial networks have obtained masses hobby recently, largely because of the success of online social networking Web sites and media sharing sites. In such networks, rigorous and complex interactions occur among several unique entities, leading to huge information networks with first rate commercial enterprise ability. Network detection is an unmanaged getting to know challenge that determines the community groups based on common place hobbies, career, modules, and their hierarchical agency, the usage of the records encoded in the graph topology. Locating groups from social network is a tough mission because of its topology and overlapping of various communities. In this research, edge betweenness modularity and random walks is used for detecting groups in networks with node attributes. The twitter data of the famous cricket player is used here and network of friends and followers is analyzed using two algorithms based on edge betweenness and random walks. Also the strength of extracted communities is evaluated using on modularity score and the experiment results confirmed that the cricket player’s network is dense.Item PREDICTING BINDING AFFINITY BASED ON DOCKING MEASURES FOR SPINOCEREBELLAR ATAXIA: A STUDY(Springer Link, 2018) Asha, P R; Vijaya, M SAn obsessive stipulation impairs the regular function or structure of an organ in humans. Spinocerebellar ataxia disorder is a hereditary genetic disorder which is originated by the massive number of sequence variants found in large sets of genes. The mutation in the genes causes many of these disorders. There are certainly no effective drugs to treat those disorders. There are many types of spinocerebellar ataxia, and a better knowledge is required to forecast binding affinity. Binding affinity is crucial to screen the drugs for spinocerebellar ataxia disorder. Accurate identification of binding affinities is a profoundly demanding task. To overcome this issue, a new approach is to be designed in identifying the binding affinity effectively. Due to rapid growth of biological data, there is an increase in the processing time and cost efficiency. This paves the way for challenges in computing. The purpose of machine learning is to excavate beneficial knowledge in distinct to corpus of information and data by constructing effective feasible designs. In this paper, a preface to spinocerebellar ataxia, conventional and innovative strategies involved in predicting binding affinity are discussed.Item DECISION TREE BASED MODEL FOR THE CLASSIFICATION OF PATHOGENIC GENE SEQUENCES CAUSING ASD(Springer Link, 2018-08-21) Pream Sudha, V; Vijaya, M SPathogenic gene identification is an important research problem in biomedical domain. The genetic cause of ASD, which is a multifaceted developmental disability is hard to research. Hence, there is a critical need for inventive approaches to further portray the genetic basis of ASD which will enable better filtering and specific therapies. This paper adopts machine learning techniques to classify gene sequences which are the significant drivers of syndromic and asyndromic ASD. The synthetic dataset with 150 sequences of six different categories of genes were prepared and coding measures of gene sequences were taken as attributes for gene identification. Pattern learning algorithms like support vector machine, decision tree and Multiplayer perceptron were used to train the model. The model was evaluated using 10 fold cross validation and the results are reported. The study reveals that Decision trees outperform other classifiers with an accuracy of 97.33%Item BINDING AFFINITY PREDICTION MODELS FOR SPINOCEREBELLAR ATAXIA USING SUPERVISED LEARNING(Springer Link, 2018-08-21) Asha, P R; Vijaya, M SSpinocerebellar Ataxia (SCA) is an inherited disorder flow in the family, even when one parent is affected. Disorder arises mainly due to mutations in the gene, which affects the gray matter in the brain and causes neuron degeneration. There are certain types of SCA that are caused by repeat mutation in the gene, which produces differences in the formation of protein sequence and structures. Binding affinity is essential to know how tightly the ligand binds to the protein. In this work, the binding affinity prediction model is built using machine learning. To build the model, features like Binding energy, IC50, Torsional energy and surface area for both ligand and protein are extracted from Auto dock, auto dock vina and PYmol from the complex. A total of 17 structures and 18 drugs were used for building the model. This paper proposes a predictive model using applied mathematics, machine learning regression techniques like rectilinear regression, Artificial neural network (ANN) and Random Forest (RF). Experimental results show that the model built using Random Forest outperforms in predicting the binding affinity.Item ENHANCED ANTIMICROBIAL ACTIVITY OF ALOE VERA BLENDED ZINC OXIDE NANOPARTICLES IN PVA MATRIX(Elsevier, 2018) Sharmila, Chandran; Jincy Chemmanda, Sunny; Selvi, Chandran; Chandrashekar, BellanAloe vera, a traditional medicinal plant belongs to Liliaceae family that has been adored for centuries as a remedy for skin infections & cuts. The active ingredients such as berberine and gallic acid present in Aloe vera plays a major role in making the plant a therapeutic agent. In order to increase the efficiency of the plant, ZnO is blended with Aloe vera gel. The Aloe vera blended ZnO nanoparticles was then encapsulated using Polyvinyl Alcohol (PVA) polymer and were subjected to different characterization techniques. The cell viability of Aloe vera blended ZnO nanoparticles in PVA matrix was evaluated and in vitro studies against wound creating pathogens were carried out.Item INDUCED FERROMAGNETISM IN FE DOPED CEO2 NANOPARTICLES(Elsevier, 2018) Shanmuga Sundari, S; Sugan, S; Pabitha, GCeria / CeO2 / Cerium nanoparticles are fluorite-structured rare earth oxide. Cerium oxide is an important material which finds applications as polishing agents, sunscreens, solid electrolytes, and automotive exhaust catalysts. Nano structured CeO2 is very attractive due to its improvements in the redox properties, transport properties and surface to volume ratio with respect to bulk materials. In the present work, CeO2 nanoparticles and Fe doped CeO2 (CeO2 + x mole% of Fe, where x= 1, 2, 3, 4 and 5) have been prepared by co-precipitation method. All the prepared samples were characterized for its structural, optical and magnetic properties by XRD, FTIR, SEM, UV-Vis and VSM analysis respectively. The absence of secondary peaks and cubic fluorite structures were confirmed by XRD and a complete solid solution was achieved between Fe and Ce in all the prepared samples. Uniform spherical like structures was observed from SEM micrographs. Scherrer’s formula was used to calculate the crystallite size and there is no much variation by the incorporation of Fe in CeO2 matrix. UV-Vis spectral analysis was carried out using DRS method and the absorption coefficient, direct band gap was calculated for all the prepared CeO2 nanoparticles. The pristine CeO2 is diamagnetic in nature. The doping of Fe in CeO2 induces ferromagnetism in CeO2 and the hysteresis area increases till 3 mol% of Fe and decreasing for further increase in concentration. Hence to induce a ferromagnetic property in CeO2 nanoparticles 3 mol% of Fe is optimum.Item INFLUENCE OF SOIL FUNGI ON CORROSION OF MILD STEEL PLATES(NACE - International Corrosion Conference Series, 2018-04-15) Dharani, R; Deepalakshmi, R; Padma Devi, S N; Nithya Meenakshi, S; Nalini, DMetal corrosion is an electrochemical reaction between the environment and a metal, in which microbes are thought to play a very important role. These microorganisms do not only cause corrosion, but they can also inhibit or protect against corrosion. Fungi are the most dessicant – resistant microorganisms and are ubiquitous in atmospheric environments. About five fungal organisms were isolated using Starkey media from the soil of corroded pipeline tank. The influence of these fungal isolates on both rusted and non – rusted mild steel plates were studied for a period of 25 days. Among the five fungal isolates, Non – rusted Isolate (NR) – 1 and Rusted Isolate (R) – 3 showed minimum corrosion reaction on mild steel plates, based on the results of weight loss and dissolved iron content. The results revealed that the two isolates showed minimum rate of corrosion on mild steel plates due to the passive mechanism of microbes upon the plates. Therefore the above isolates (NR -1 and R- 3) was identified using molecular markers and it was found to be Aspergillus flavus and Alternaria alternata respectively.Item SECURITY OF HEALTHCARE MONITORING SYSTEM USING EHIP-HOP METHOD(IEEE Xplore, 2018-02) Sindhuja, L SIn the recent years, the mobile WSN pave attention of the researchers due to the mobility of the nodes in the network. The significant applications of mobile WSN [4] includes military, healthcare, robotics, vehicle monitoring and monitoring of environment and industries. Out of which, healthcare monitoring system has been paid attention because of tremendous growth in technology. Hence, security is the major challenge as the health care monitoring system is susceptible to various attacks. One such attack is the node replication attack which compromises the integrity and confidentiality of the data. In order to overcome the above problem, Artificial Immune System based detection method namely EHIP- HOP method is applied to the HCMS architecture. The proposed security architecture is capable of providing robust system with low cost.Item COMPLEMENTARY PLACE TRANSFORMATION IN PETRI NETS(IEEE Xplore, 2018-12-13) Kavitha, K; Babitha, T; Praveena, VNow-a-days in engineering practice, the framework advancement has by and large been confronting some real needs - the need to grow progressively complex frameworks, the need to evaluate the framework's operational dangers and the need cost focused answer for accomplish these prerequisites. In need to this another model called Petri nets is presented. A Petri net is a conceptual, formal model of data stream. As a graphical and scientific apparatus, it gives a uniform situation to displaying, formal examination, and outline of discrete occasion frameworks. The principle target of this paper is to present the major ideas of Petri nets which work in the regions of demonstrating and investigation of mechanical kinds of frameworks and the idea of corresponding spot changes.