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.

Protein class prediction based on Count Vectorizer and long short term memory

Abstract: Proteins class and function prediction is one of the most significant task in computational bioinformatics. The information about the protein functions and class plays a vital role in understanding biological cells and has a great impact on human life in factors such as personalized medicine. The technical advancement in the areas of biological aspects and understanding of biological processes results in features and characteristics of important Proteins. Prediction of amino acid sequence involves prediction of amino sequence folding and its structures from the primary sequence obtained. In this work, Machine learning prediction algorithms have applied for protein class prediction. This method takes consideration of macromolecules of biological significances. Later the solution focuses on the understanding of different protein family, subsequently classify the protein family type sequence. This is achieved through machine learning algorithms Naive Bayes (NB) and Random forest (RF) algorithms with count vectorized feature and LSTM. These algorithms are used to classify the protein family on its protein sequence. Finally, result shows that LSTM predicts the protein class more accurately than the RF, and NB algorithm. LSTM achieves an accuracy of 96% whereas RF & NB with an accuracy of 91% and 86%.

Effect of ZTA concentration on structural, thermal, mechanical and dielectric behavior of novel ZTA–PVA nanocomposite films

Abstract: Zirconia toughened alumina (ZTA) nanopowder was synthesized by solution combustion technique. Polymer nanocomposites were prepared using ZTA nanopowder as the reinforcement and polyvinyl alcohol (PVA) as the matrix. ZTA nanopowder composition was varied from 0 to 2.5% by weight. Structural characterization was done using scanning electron microscope (SEM) and X-ray diffraction (XRD). XRD results showed prominent, well defined peaks of zirconia and α-alumina, hence confirmed that ZTA is a crystalline material. SEM images showed that, level of agglomeration kept increasing due to increase in filler content, which might have contributed to film stiffness. Thermal analysis was carried out using differential scanning calorimetry. Addition of ZTA into PVA matrix resulted in increase in melting point as well as glass transition temperature. Influence of the nanofiller concentration on the electrical conductivity was found using Agilent 4249A impedance analyser. Conductivity measurements were carried out for all the nanocomposite films doped with ZTA and were found to exhibit insulating properties. Change in mechanical properties such as Young’s modulus, tensile strength and film toughness of PVA films as a function of nano filler content are reported.