GLCM and LSTM Recurrent Neural Networks Integrated with Machine Learning Techniques to Identify Plant Disease

-449210151823c775641-73a0 20220805022626922 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 08 30 2022 11 9 GLCM and LSTM Recurrent Neural Networks Integrated with Machine Learning Techniques to Identify Plant Disease Department of Computer Science, R N Shetty PU College, Kundapura (Karnataka), India. Nithyananda B Devadiga Akshatha K N Department of Botany, RN Shetty PU College, Kundapura (Karnataka), India. Plant diseases are very impactful towards the overall effectiveness and quality management of the agricultural sector. In recent years, deep learning methods have been used as a way to identify these diseases, based on neural networks. The study presents GLCM and LSTM Recurrent Neural Networks Integrated with Machine Learning towards the identification of plant diseases. It has been found that the process is very accurate and can assess diverse plants disease characteristics dataset as well. 08 30 2022 44 46 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.G9243.0811922 https://www.ijitee.org/portfolio-item/g92430811922/ Volatility Clustering of Select Sectoral Indices in the Bse Stock Market Department of Commerce, PSGR Krishnammal College for Women, Coimbatore (Tamil Nadu), India. Dr. D. Vijayalakshmi Thanya. G.C Department of Commerce, PSGR Krishnammal College for Women, Coimbatore (Tamil Nadu), India. Volatility is a standard measure of financial vulnerability and it plays a vital role in analyzing the risk of the securities market. It is traditionally measured using the standard deviation, which indicates how the price of a stock is clustered around the mean or moving average. The intent of the study is to analyse the volatility clustering of six select sectoral indices such as S&P BSE AUTO (Automobile), S&P BSE BANKEX (Bank) , S&P BSE FMCG (Fast Moving Consumer Goods), S&P BSE IT (Information Technology), S&P BSE METAL ( Metals), and S&P BSE OIL & GAS (Oil & Gas Industries). A sample of 2726 days of observations for 11 years period from 03.01.2011 to 31.12.2021 has been taken for the study. The econometric model namely ARCH and GARCH have been applied to analyse the data. The result of the study reveals the presence of volatility clustering in the select six sectoral indices. Metal Sector has shown the higher phase of volatility. 08 30 2022 47 54 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.G9247.0811922 https://www.ijitee.org/portfolio-item/g92470811922/