National Institute of Technology, Karnataka

About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Computer science & Corrosion. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.

Fabrication and characterization of new PSF/PPSU UF blend membrane for heavy metal rejection

Abstract: Polysulfone and polyphenylsulfone-blend ultrafiltration membranes of different compositions were prepared by the phase inversion method, with and without hydrophilic additive poly (ethylene glycol) 1,000 (PEG). The membrane morphology was studied using scanning electron microscope, which displayed the asymmetric structure of the membrane. The hydrophilicity of the membranes was measured by contact angle, porosity, water uptake, and permeability studies. The blend membrane showed enhanced permeability, hydrophilicity, and antifouling property as compared to the pristine polymer membrane. The pure water flux of the membrane, which was blended with PEG additive was relatively higher than the blend membranes without the additive. The flux recovery ratio (FRR) was measured to study the antifouling property. The membranes with PEG additive exhibited better antifouling property with maximum FRR of 72.84%. The heavy metal rejection by the membrane was carried out by complexing the metal ions with polyethy...

Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique

Abstract: Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 33 different combinations of technical indicators to predict the stock prices. The paper has two objectives, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second objective is an accurate prediction model for stocks. To predict stock prices we have proposed ANN (Artificial Neural Network) Regression prediction model and model performance is evaluated using metrics is Mean absolute error (MAE) and Root mean square error (RMSE). The experimental results are better than the existing method by decreasing the error rate in the prediction to 12%. We have used the National Stock Exchange, India (NSE) data for the experiment.