Article Per Year (5 Year)
p-Index From 2020 - 2025
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All Journal International Journal of Electrical and Computer Engineering Journal of Robotics and Control (JRC)
Mishra, Sashikala
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Performance assessment of Deep Learning procedures on Malaria dataset Sinha, Shruti; Srivastava, Udit; Dhiman, Vikas; P.S., Akhilan; Mishra, Sashikala
Journal of Robotics and Control (JRC) Vol 2, No 1 (2021): January
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.2145

Abstract

Malaria detection is a time-consuming procedure. Only blood sample investigation is the practice which provides the confirmation. Now numerous computational methods have been used to make it faster. The proposed model uses the conception of Convolutional Neural Network (CNN) to lessen the time complexity in identification of Malaria. The prototypical model uses different deep learning algorithms which   uses the same dataset to validate the stability. Model uses the two various components of CNN like Sequential and   ResNet.  ResNet uses more of number of hidden layers rather than sequential.  The ResNet model achieved 96.50% accuracy on the training data, 96.78% accuracy on the validation data and 97% accuracy on the testing data. Sequential model on the other hand achieved 98% accuracy on the training data, 96% accuracy on the validation data and 96% accuracy on the testing data. From the initial hypothesis, we get to know that there is no significant difference in the accuracy when we have too many layers.
Vibration Analysis for Engine fault Detection Gude, Angad; Pawar, Shubham; Alhat, Siddharth; Mishra, Sashikala
Journal of Robotics and Control (JRC) Vol 2, No 3 (2021): May (Forthcoming Issue)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the Vibration analysis for engine fault detection, we use different visualization graph. Today‘s world growing fast and machinery part getting complex so it’s difficult to find out fault in the machine so here means in this paper we explain how we find out the fault of the machine with help of visualization it’s easy to find out a fault here we use angular.js, D3.js for visualization and use MQTT protocol for publishing and subscribe sensor data. In the automobile industries machines are the main part of how we find out fault yes we find out fault with help of sensors using sensors here we analyze the machine.
Bitcoin volatility forecasting: a comparative analysis of conventional econometric models with deep learning models Tripathy, Nrusingha; Mishra, Debahuti; Hota, Sarbeswara; Mishra, Sashikala; Das, Gobinda Chandra; Dalai, Sasanka Sekhar; Nayak, Subrat Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp614-623

Abstract

The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold ARCH (TARCH) models with long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and multivariate Bi-LSTM models. Model effectiveness is evaluated by means of root mean squared error (RMSE) and root mean squared percentage error (RMSPE) scores. The multivariate Bi-LSTM model emerges as mostly effective, achieving an RMSE score of 0.0425 and an RMSPE score of 0.1106. This comparative scrutiny contributes to understanding the dynamics of Bitcoin volatility prediction, offering insights that can inform investment strategies and risk management practices in this quickly changing environment of finance.
Co-Authors Alhat, Siddharth Dalai, Sasanka Sekhar Das, Gobinda Chandra Dhiman, Vikas Gude, Angad Hota, Sarbeswara Mishra, Debahuti Nayak, Subrat Kumar P.S., Akhilan Pawar, Shubham Sinha, Shruti Srivastava, Udit Tripathy, Nrusingha