2.Conference Paper (08)

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    EXPERIMENTAL AND DENSITY FUNCTION THEORY CALCULATIONS TO INVESTIGATE THE ADSORPTION OF AN ANTI-INFLAMMATORY DRUG ON ALUMINUM SURFACE IN ACID SOLUTION
    (NACE - International Corrosion Conference Series, 2016-03-06) Monisha, R; Sowmya, Ramkumar
    The literature dealing with aluminium behavior in acid media in the presence of an anti-inflammatory drug is studied in order to understand its action mechanism, protective film formed and the possibility of its application according to the inhibition efficiency achieved. Aceclofenac, an anti-inflammatory drug is studied and its inhibitive performance on aluminium corrosion was studies by both dry and wet lab studies. Dry lab process using DFT is used to explore the relationship between the inhibitor molecular property and its inhibition efficiency. Wet lab studies have been carried out using weight loss, Tafel polarization and impedance measurements to evaluate their inhibitive performance in both 1M HCl and 0.5M H2SO4. PZC were calculated from impedance studies in order to understand the mechanism of inhibition. Polarization studies prove mixed type of inhibition. Good inhibition efficiency from weight loss studies was evidenced in both the acid medium, furnishing an inhibition efficiency of more than 80 %. The inhibition effect results from the adsorption of the inhibitor molecule via the lone pair of electrons on the hetero atoms together with the adjacent aromatic ring, on the metal surface forming a protective complex film. According to the results summarized, aluminium corrosion can be successfully inhibited by the drug used in the study in both the acid solutions. Results obtained from dry lab process are in good agreement with those recorded from wet lab experiments.
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    CORROSION ABATEMENT IN ACID PICKLING INDUSTRIES BY EFFECTIVE N-HETEROCYCLIC COMPOUNDS: DRY AND WET LAB STUDIES
    (NACE - International Corrosion Conference Series, 2016-03-06) Sowmya, Ramkumar; Nalini, D
    During the chemical cleaning process using acids, in many electroplating and other descaling industries, there exists the problem of heavy metal loss. Hence there is always a need for abatement of this metal loss. A critical (steady state) value of the resistivity to corrosion of two organic compounds, 5-[2-(4-methoxyphenyl)-vinyl]-3-phenyl isoxazole (MVI) and 5-[2-(4-methoxyphenyl)-vinyl]-1,3,8-triazacyclopenta[ a]indene (MCI) were determined by a combination of non-electrochemical and electrochemical monitoring techniques. The behavior of organic compound, on a metallic alloy, i.e., mild steel, was investigated over a temperature range of 30 to 70°C. Efficiency of MVI and MCI were determined by correlating the electrochemical response of the compound (by electrochemical impedance and potentiodynamic polarization) in 1M HCl and 0.5M H2SO4 solution with their structural features. EIS measurement predicted the physical adsorption of both MVI and MCI on the metal surface from PZC calculation. Polarization studies proved that the inhibitors retard both the electrode process during inhibition. The integrity of the inhibitors efficiency with respect to time was assessed from mass loss measurements at different immersion period. Mass loss measurements proved that both MVI and MCI inhibit corrosion of mild steel in 1M HCl with a maximum efficiency of 91.07% and 78.23% at 20 ppm in HCl and H2SO4 respectively for MVI, 97.72% and 88.64% at 20 ppm in HCl and H2SO4 respectively for MCI. Hence MVI and MCI were found to be effective inhibitors for acid cleaning process in industries. The mechanism of the inhibition process was discussed in the light of the chemical structure and quantum chemical calculations of the investigated inhibitor. MVI and MCI were modeled in order to assess its absorbability using density functional theory (DFT) and revealed remarkably high interaction energies, which corroborate the experimental findings.
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    GRAPH CUT BASED SEGMENTATION METHOD FOR TAMIL CONTINUOUS SPEECH
    (Springer Link, 2016-11-23) Laxmi Sree, B R; Vijaya, M S
    Automatic segmentation of continuous speech plays an important role in building promising acoustic models for a standard continuous speech recognition system. This needs a lot of segmented data which is rarely available for many languages. As there are no industry standard speech segmentation tools for Indian languages like Tamil, there arises a need to work on Tamil speech segmentation. Here, a segmentation algorithm that is based on Graph cut is proposed for automatic phonetic level segmentation of continuous speech. Using graph cut for speech segmentation allows viewing speech globally rather locally which helps in segmentation of vocabulary, speaker independent speech. The input speech is represented as a graph and the proposed algorithm is applied on it. Experiments on the speech database comprising utterances of various speakers shows the proposed method outperforms the existing methods Blind Segmentation using Non-Linear Filtering and Non-Uniform Segmentation using Discrete Wavelet Transform.
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    COMMUNITY DETECTION BASED ON GIRVAN NEWMAN ALGORITHM AND LINK ANALYSIS OF SOCIAL MEDIA
    (Springer Link, 2016-11-23) Sathiyakumari, K; Vijaya, M S
    Social networks have acquired much attention recently, largely due to the success of online social networking sites and media sharing sites. In such networks, rigorous and complex interactions occur among numerous one-of-a-kind entities, main to massive statistics networks with notable enterprise capacity. Community detection is an unsupervised learning task that determines the community groups based on common interests, occupation, modules and their hierarchical organization, using the information encoded in the graph topology. Finding communities from the social network is a difficult task because of its topology and overlapping of different communities. In this research, the Girvan-Newman algorithm based on Edge-Betweenness Modularity and Link Analysis (EBMLA) is used for detecting communities in networks with node attributes. The twitter data of the well-known cricket player is used right here and community of friends and fans is analyzed based on three exclusive centrality measures together with a degree, betweenness, and closeness centrality. Also, the strength of extracted communities is evaluated based on modularity score using proposed method and the experiment results confirmed that the cricket player’s network is dense.
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    PREDICTING MUSCULAR DYSTROPHY WITH SEQUENCE BASED FEATURES FOR POINT MUTATIONS
    (IEEE, 2016-03-17) Sathyavikasini, K; Vijaya, M S
    Hefty amounts of biological data are accumulated for research with the advancement of sequencing technologies. Genetic diseases are caused by the deformity in the inherited genes. Identifying trait diseases through DNA analysis is a prime task in diagnosing an ailment. Identification of disease based on mutations in the gene sequences is an essential and challenging task in the medical diagnosis of genetic disorders such as Muscular dystrophy. Muscular dystrophy is a rare disease that alters the structure and nature of the muscles that deteriorate the musculoskeletal system and hinder locomotion. There are nine major kinds of muscular dystrophy and it is vital to identify the type of muscular dystrophy for proper diagnosis and treatment. Hence a new model is proposed for predicting the disease accurately with the gene sequences, which are mutated by adopting an approach like positional cloning on the reference cDNA sequence. This paper addresses the problem by considering mutated gene sequences of fifty five genes that causes five types of muscular dystrophy and developing an efficient pattern recognition model using supervised pattern classification technique. The resultant the trained model shows the prediction accuracy of 100% by estimating using 10-fold cross validation.
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    CLASSIFICATION OF HEART RATE DATA USING BFO-KFCM CLUSTERING AND IMPROVED EXTREME LEARNING MACHINE CLASSIFIER
    (IEEE, 2016-05-30) Kavitha, R; Christopher, T
    The Electrocardiogram is a tool used to access the electrical recording and muscular function of the heart and in last few decades it is extensively used in the investigation and diagnosis of heart related diseases. It must be noted that the heart rate fluctuates not only because of cardiac demand, however is also influenced as a result of the occurrence of cardiac disease and diabetes. In addition, it has been shown that Heart Rate Variability (HRV) may well be utilized as an early indicator of cardiac disease susceptibility and the existence of diabetes. As a result, the HRV can be exercised for early clinical test of these diseases. Most existing systems make use of Support Vector Machine (SVM), owing to the generalization performance, it is not sufficient for the accurate classification of heart rate data. In order to overcome this complication, Improved Extreme Learning Machine (IELM) classifier is used, to obtain the best parameter value and best feature subset through the use of Bacterial Foraging Optimization (BFO) that feed the classifier. Here in this work, features of linear and nonlinear are extracted from the HRV signals. Following the preprocessing, feature extraction is done effectively together with feature selection with the assistance of BFO for the purpose of data reduction. Subsequently, proposed a scheme to integrate Kernel Fuzzy C-Means (KFCM) clustering and classifier to adequately enhance the accuracy result for ECG beat classification. The accuracy result for classification of heart rate data is shown in the proposed scheme.
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    CANCELLABLE MULTIMODAL BIOMETRIC USER AUTHENTICATION SYSTEM WITH FUZZY VAULT
    (IEEE, 2016-05-30) Soruba Sree, S R; Radha, N
    Biometrics refers to authentication techniques that rely on humans physical and behavioral characteristics that can be automatically checked. Biometric based authentication system provides robust security and ease of use than conventional methods of verification system. Multimodal biometric system is one of the major areas of study identified with large applications in recognition system. Unimodal biometric systems challenge with a wide variety of problems such as noisy data, Intra-class variations, non-universality, and spoof attacks. Some of these limitations can be solved in multimodal biometric system. In proposed work, face and fingerprint biometric traits are used for multimodal biometric authentication system. Biometric traits are transformed using distortion algorithm. After the transformation processes pre-processing of images are done to improve the clear visibility of images. The extractions of minutiae features from fingerprint are achieved using Crossing Number concept and the face features are extracted using the Local Binary Pattern algorithm. To combine both the face and fingerprint features feature level fusion is used. In order to provide additional security to the proposed work the fuzzy vault is introduced by adding duplicate values and having a secret key to lock and unlock the system. Fuzzy vault and distortion acts as an additional layer of security in multimodal biometric user authentication system.
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    MULTIMODAL BIOMETRIC TEMPLATE AUTHENTICATION OF FINGER VEIN AND SIGNATURE USING VISUAL CRYPTOGRAPHY
    (IEEE, 2016-05-30) Nandhinipreetha, A; Radha, N
    In this paper personal verification method using finger-vein and signature is presented. Among many authentication systems finger-vein is promising as the foolproof method of automatic personal identification. Finger-vein and signature image is pre-processed and features are extracted using cross number concept and principle compound analysis. Fusion technique is used to fuse the finger vein and signature images. Then the visual cryptographic scheme is applied for the biometric template to generate the shares. The shares are stored in a separate database, and then the biometric image is revealed only when both the shares are simultaneously available. At the same time, the individual image does not reveal the identity of the biometric image. The proposed work is evaluated with evaluation metrics FAR, FRR and accuracy.