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Articles by S. Ramakrishnan
Total Records ( 3 ) for S. Ramakrishnan
  A.S. Muthanantha Murugavel and S. Ramakrishnan
  Epilepsy is the most widely recognized chronic neurological diseases and the most well-known neurological chronic disease of childhood. The Electroencephalogram (EEG) signal furnishes significant information neurologists contemplate in the investigation and analysis of epileptic seizures. EEG being the non-stationary signals, legitimate analysis is greatly needed to classify the normal, inter-ictal and ictal EEGs. This srudy presents a propose method is to classify the EEG signals regarding the existence or absence of seizure. Modified EELM is more accurate for automatic epileptic seizure detection. Multi wavelet transform contains both scaling and wavelet functions, simultaneously offers orthogonality and symmetry which is not possible for scalar wavelet transform. With these major properties, the multi wavelet transform is promising in signal processing applications. Multi wavelet based characteristic is utilized to differentiate the normal EEGs of healthy subjects, inter-ictal and ictal EEGs of epileptic patients. To acquire the detail and approximation wavelet coefficients, the EEG signals are decomposed into sub-groups. The proposed strategy decomposes with wavelet transform, reduce dimensionality by a set of features and have a classification with Modified Effective Extreme Learning Machine (Modified-EELM). The modified-EELM has a better accuracy than ELM. The Modified-EELM Classification algorithm with the sigmoid function has 98.01% testing accuracy, good performance, easy to implementing and consumes only 0.0008 sec of time. This is very less amount of time compared with other learning machines.
  C. Arunkumar and S. Ramakrishnan
  This study proposes a novel method that employs correlation based filter for dimensionality reduction followed by fuzzy rough quick reduct for feature selection on a particle swarm optimization search space. The first phase removed the redundant genes using correlation coefficient filter on a particle swarm optimization search space. The second phase produced a fuzzy rough quick reduct that would be used for classification. The genes obtained after feature selection are subjected to classification using traditional classifiers. It has been determined that the proposed method contributes to reduction in the total number of genes and improvement in the classifier accuracy compared to gene selection and classification using correlation coefficient and traditional fuzzy rough quick reduct algorithm. This approach also reduces the number of misclassifications that might occur in other approaches.
  M. Amutha , R. Arunachalam , M. Umamaheswari , A. Usharamalakshmi , S. Ramakrishnan and G. Annadurai
  The present investigation was aimed at to test different Camellia sinensis (tea) extracts such as Magholai (I), 3 roses (II) and chakra gold (III) for their lactose reduction capability in milk with more lactose. Cow, goat, buffalo and two commercial milk samples such as KC and Star were tested for its lactose content. Among these buffalo milk as used as a control due to its high content of lactose (8 mL dL-1). Buffalo milk was treated by tea extracts I, II and III and 0.018, 0.018 and 0.042 mg dL-1 of lactose content were found. Its reveals that the lactose content of milk was well reduced by adding tea extracts and also its suggested peoples to have milk with herbal extracts (tea) who have gastrointestinal problems.
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