Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Steganography & Support vector machine. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Need Accumulation Over DTN

Abstract: The demand forecast model and the utility function, derived in Chap. 2, are used to assess the exact need for emergency resources at different shelters. As mentioned earlier, these assessed needs are required to be transmitted and accumulated at the control station for maintaining a fully functional relief chain.

Design and Performance Analysis of a Modified MRAC for Second-Order Integrating Processes

Abstract: For the processes having time varying nature performance of conventional PID controller is not satisfactory. In such cases adaptive controllers are suitable as they are capable to modify the control action according to the changes of process dynamics and undesired load variations. In model reference adaptive control (MRAC), desired process response is provided with the choice of a reference model. Adaptation algorithm changes the control action according to the difference between the outputs of the actual process and reference model with a fixed adaptation gain. This paper deals with a modified MRAC technique designed for the second-order integrating processes with dead-time. In the proposed modified MRAC scheme, proportional-derivative (PD) feedback is incorporated in the conventional MRAC structure and the final modified controller is termed as MRAC-PD. It ensures improved response compared to conventional MRAC. Enhanced set-point tracking with fewer oscillations and lesser oscillations during undesired load changes substantiate the superiority of the proposed MRAC-PD controller compared to conventional MRAC and this fact is also reflected in the calculated values of performance indices – integral absolute error (IAE) and integral time absolute error (ITAE) under closed loop operation.

Forecasting COVID-19 Outbreak in India Using Time Series Dataset: An Ensemble of ARIMA, Abbasov-Mamedova, and Multilayer Perceptron Models

Abstract: Recently the COVID-19 pandemic outbreak is inflicting devastation on human civilization. This infectious virus spreads like wildfire, already affected millions worldwide, and the numbers are still increasing. This situation warrants a comprehensive strategy backed by futuristic estimations to counter COVID-19 adversities. Like any other country globally, India is also encountering an uphill task to fight against this unfortunate pandemic, with six million-plus COVID-19 cumulative infected cases by the first week of October 2020. This publication elucidates the use of four state-of-art models, namely the Abbasov - Mamedova (AM) Fuzzy, proposed Multilayer Perceptron (MLP), Auto-ARIMA, and Auto-MLP, to forecast the number of cumulative infected COVID-19 cases in India. These models exhibited high forecast accuracy for 30 days ahead scenario with MAPE ranges from 0.44 to 1.83% in the test condition, whereas a MAPE range of 1.09 to 2.39% in real-time. We estimated the COVID-19 cases fortnightly and observed that the proposed MLP exhibited the flattening of the COVID-19 curve, whereas other models exhibited a rising trend. Though our proposed MLP outperformed other models, we employed all four methods and estimated a range between 8.53 to 13.77 million COVID-19 positives by 4th January 2021 in India.