Automation Anywhere Tool for Student University Result

Cryptographic algorithms play a vital role in providing security to the data and from malicious attacks. Now a day’s storing the data in the Cloud became essential for organizations to reduce the overhead of securing the data within the organization. “Stream ciphers” are more vulnerable to attacks when compared to “Block Ciphers”. In this article we used java platform to evaluate and compare all the block cipher techniques used for converting plain text to cipher text and we have identified the best block cipher technique which can be used in cloud.

Suitable encrypting algorithms in Parallel Processing for improved efficiency

Tallapally¹,

Manjula

2020

RETRACTED: RNN based prediction of spatiotemporal data mining

Shaik¹,

Verma²,

Praveen³

et al. 2020

The Spatiotemporal pattern is considered by most of the researchers to be a rehashed arrangement or relationship of specific occasions or highlights of spatiotemporal and to distinguish these groupings or affiliations are related to the spatiotemporal patterns of wrongdoing events and proper separation are clearly based on length based estimations that are expected to oblige the size or state of the pattern and ST patterns comprises of various sizes and shapes after some time are non-consistently disseminate over space by performing analytical learning of spatiotemporal successions as it is capable of creating future pictures by knowledge from the authentic edges. Spatial advents and temporal varieties are two pivotal structures which are considered in this paper which proposes the predictive methodology which utilizes recurrent neural network where the approach of persistent neural networks stands apart as a suitable worldview for without model as the data is based on the prediction of nonlinear dynamical frameworks by applying the methodology in Spatiotemporal pattern which predicts the limited mistake.

Enhanced ANN training model to smooth and time series forecast

Shaik¹,

Verma²

2020

In the present era most of the researchers has done extensive research in forecasting the time series data using distinct “linear, nonlinear and hybrid linear models” that combines models like “Auto Regressive Integrated Moving Average (ARIMA)” using “Artificial Neural Networks (ANN)” by combining the “Time Series Forecasting (TSF)”. In this paper we proposed a new algorithm which will smooth the data and evaluate the error rate to obtain time series predictions by considering Telangana state rainfall dataset. Our experimental illustrations propose that the results attained are statistically promising with the datasets that are used for generating the acceptable prediction result set.