M. S. Ramaiah Institute of Technology

About: M. S. Ramaiah Institute of Technology is a based out in . It is known for research contribution in the topics: Feature extraction & Photoluminescence. The organization has 2853 authors who have published 2434 publications receiving 23507 citations.

Automated recognition of ROIs for breast thermograms of lateral view-a pilot study

Abstract: This research study proposes an automated region of interest (ROI) for breast thermograms by considering lateral view as well as the frontal view of the breasts. The lateral view helps in contralat...

Ion conductivity and dielectric relaxation behavior in FIC silver based phospho-molybdate glasses

Abstract: Electrical conductivity and dielectric relaxation studies of silver ion-conducting glasses have been prepared using xAg(2)SO(4)-15Ag(2)O-(90-X)(90P(2)O(5)-10MoO(3)) glass system over a temperature range of 298-353 K and frequencies of 10 Hz to 10 MHz. DC conductivities exhibit Arrhenius behavior over the entire temperature range with a single activation barrier. The ac conductivity behavior of these glasses has been analyzed using single power law; conductivity increases linearly in logarithmic scale with Ag2SO4 concentration. The power law exponent (s) decreases, while stretched exponent (beta) is insensitive to increase of temperature. Scaling behavior has also been carried out using the reduced plots of conductivity and frequency, which suggest that ion transport mechanism remains unaffected at all temperatures and compositions.

Design and Implementation of a Consensus Algorithm to build Zero Trust Model

Abstract: Zero Trust Model ensures each node is responsible for the approval of the transaction before it gets committed The data owners can track their data while it’s shared amongst the various data custodians ensuring data security The consensus algorithm enables the users to trust the network as malicious nodes fail to get approval from all nodes, thereby causing the transaction to be aborted The use case chosen to demonstrate the proposed consensus algorithm is the college placement system The algorithm has been extended to implement a diversified, decentralized, automated placement system, wherein the data owner ie the student, maintains an immutable certificate vault and the student’s data has been validated by a verifier network ie the academic department and placement department The data transfer from student to companies is recorded as transactions in the distributed ledger or blockchain allowing the data to be tracked by the student

Characterization of EEG signals for identification of alcoholics using ANOVA ranked approximate entropy and classifiers

Abstract: This work focuses on non-linear characterization of 61-channel electroencephalogram (EEG) signal for detecting alcoholics using ranked Approximate Entropy (ApEn) parameters. Significant channels that contribute to the detection of alcoholism are selected by ranking the ApEn features based on ANOVA test. In order to classify alcoholics from control, the ranked feature set is applied to two non-linear classifiers, namely Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) Classifiers respectively. The performance of the classifiers is evaluated in terms of classification accuracy as well as computational processing time. Experimental results reveal that the BPNN classifier with 40 hidden neurons and SVM classifier with a polynomial kernel of order 3 perform with an accuracy of 90% with only 32 ranked ApEn coefficients.