h) 2017-Scopus Open Access (PDF)

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AN INVESTIGATION APPROACH ON THE SEQUESTRATION OF DIVALENT METAL IONS EMPLOYING ANIMAL WASTE
(Oriental Journal of Chemistry, 2017) Gayathri, N S; Muthulakshmi Andal, N; Anuradha, J
The current investigation deals with utilizing Treated Goat Hoof (TGH), a no cost material derived from butcher shop for the removal of Pb(II) and Cd(II) ions from aqueous media. FTIR / SEM analyses are carried out for the functional groups identification and describe the surface morphology of the chosen material respectively. Batch studies are experimented under varied operating factors viz., particle size, dosage, initial concentration, contact time and pH of the medium to assess the sorptive nature of the chosen material. Verification of the experimental data reveal the optimized conditions for the uptake of Pb(II) and Cd(II) by TGH. Langmuir model registered the best linearity amongst the isothermal plots derived for Langmuir, Freundlich and Tempkin models. Experimental results of both the systems: Pb(II) – TGH and Cd(II)- TGH are subjected to Statistical tool analyses using SPSS 20 software for significance and correlation assessment.
TRANSITION METAL COMPLEXES OF 3,5-DIHYDROXY-2-NAPHTHOIC ACID AND ITS NANO METAL OXIDES: SYNTHESIS AND CHARACTERIZATION
(Asian Journal of Chemistry, 2017-06-12) Arunadevi, Natarajan; Kanchana, Ponnuswamy; Kamatchi, Ayyasamy
Synthesis of Cd(II), Cu(II), Mn(II) and Zn(II) complexes are achieved by adding hydrazine hydrate and 3,5-dihydroxy-2-naphthoic acid in the ratio 1:4 to the corresponding metal nitrates. The synthesized complexes are characterized by elemental analysis, IR, UV, TG-DTA and XRD analysis. The nano metal oxides are obtained by decomposing the complexes at 800 °C in the muffle furnace. The nano metal oxides are characterized by IR and XRD studies. The surface morphology and quantative analysis of metal oxides were determined by using SEM analysis. The shape of nano zinc oxide is rock like hexagonal structure while that of nano Mn oxide is rod like structure which was confirmed by transmission electron microscopy analysis.
ARCRECTZONE: A LIGHTWEIGHT CURVED RECTANGLE VECTOR BASED SECURE ROUTING FOR MOBILE AD-HOC SENSOR NETWORK
(International Journal of Intelligent Engineering and Systems, 2017-07-09) Viji Gripsy, Jebaseelan; Anithalakshmi, Srinivasan
Location aided routing in Mobile-Ad hoc networks are the most significant process because it limits the route search and allows routing with least messages. Due to the dynamic and uncertain infrastructure of Ad-hoc and sensor networks also creates several security issues like route misbehaviors. This paper aims to develop a new lightweight route selection scheme with the security concern. In this scheme, the routing attack targets are identified and avoided by deploying intellectual watchdog and lightweight key verification mechanisms, the scheme also utilizes the curved rectangle zone selection for shrinking route search space. The scheme is extended to adopt the curved rectangle zone routing for both Ad-hoc and Sensor Networks. The core functionality of the proposed work in terms of cost effective and secure route selection is validated by NS-2 tool. The experimental results demonstrated that the proposed approach is cost effective and secure and provides significant performance improvements with least energy consumption.
MULTI-LABEL CLASSIFICATION: PROBLEM TRANSFORMATION METHODS IN TAMIL PHONEME CLASSIFICATION
(Elsevier, 2017-08-24) Pushpa, M; Karpagavalli, S
Most of the supervised learning task has been carried out using single label classification and solved as binary or multiclass classification problems. The hierarchical relationship among the classes leads to Multi- Label (ML) classification which is learning from a set of instances that are associated with a set of labels. In Tamil language, phonemes fall into different categories according to place and manner of articulation. This motivates the application of multi-label classification methods to classify Tamil phonemes. Experiments are carried out using Binary Relevance (BR) and Label Powerset (LP) and BR’s improvement Classifier Chains (CC) methods with different base classifiers and the results are analysed.
CARICA PAPAYA PEEL MEDIATED SYNTHESIS OF SILVER NANOPARTICLES AND ITS ANTIBACTERIAL ACTIVITY AGAINST HUMAN PATHOGENS
(Journal of Applied Research and Technology, 2017-08-28) Balavijayalakshmi, J; Ramalakshmi, V
Metallic nanoparticles are traditionally synthesized by wet chemical techniques, in which the chemicals used are quite often toxic and flammable. Ripe carica papaya peel is found to be a suitable source for green synthesis of silver nanoparticles. In the present work, a cost effective and environmental friendly technique for the green synthesis of silver nanoparticles from 1 mM silver nitrate (AgNO3 ) solution through the extract of ripe Carica papaya peel of various concentrations (5 ml, 10 ml, 15 ml, 20 ml, 25 ml) is described. The synthesized silver nanoparticles are characterized by using the UV–vis absorption spectroscopy, FT-IR, XRD, SEM and TEM. The formation of silver nanoparticles is confirmed by surface plasmon resonance, determined by UV–vis spectra at 400–435 nm. The shift in the absorption bands and variation in the calculated optical band gaps for the various concentrations of papaya peels extracts are also observed. The FT-IR spectra reveal that an increase in the concentration of the papaya peel extract shifts the bands to higher wavelengths. The average crystallite size for various concentrations of papaya peel extract is observed from XRD spectral analysis and is found to be around 16–20 nm, which is in good agreement with the TEM analysis. The SEM analysis shows the spherical structure of the silver nanoparticles with some agglomeration for higher concentrations of papaya peel extract. The synthesized silver nanoparticles show good antibacterial activity against human pathogens such as Escherichia coli and Staphylococcus aureus and it has many medical applications.
PROGNOSIS OF MUSCULAR DYSTROPHY WITH EXTRINSIC AND INTRINSIC DESCRIPTORS THROUGH ENSEMBLE LEARNING
(TUBITAK Academic Journals, 2017-10-05) Sathyavikasini, Kalimuthu; Vijaya, Vijayakumar
Muscular dystrophy is a neuromuscular disorder that impairs the functioning of the locomotive muscles. Large deletion and duplication mutations in the gene sequences pave the way for these muscular dystrophies. Any heritable change can be used as input in computational studies such as pattern and classification models. Mutated gene sequences are generated by adopting the positional cloning approach on the reference cDNA sequence with mutational information from the Human Gene Mutational Database (HGMD). The extrinsic and intrinsic descriptors of the mutated gene sequence are indispensable to identifying the disease. This work describes a computational approach of building a disease classification model by extracting the exonic and intronic descriptors from the mutated gene sequences through a combined learning technique. An ensemble hybrid model is developed through LibD3C classifier. The hybrid learned model gained an accuracy of 98.3% in diagnosing the neuromuscular disorder, based on deletion and insertion/duplication mutations. Furthermore, this paper analyzes the implementation of ensemble-learning classifiers based on features related to synonymous and nonsynonymous mutations, in order to detect muscular dystrophy performed with the same data set. Experiments showed high accuracy for the models built using LibD3C classifier, which proves that ensemble learning is effective for predicting disease. To the best of our knowledge, for the first time the models established here explore a scheme of disease prediction through pattern recognition from the sequence of nucleic acid molecule and associated mutations.