Anjaly Anand currently serves as Assistant Professor in the Department of Mathematics, School of Arts & Sciences, Amrita Vishwa Vidyapeetham, Kochi.

Qualification - M.Sc.(Tech) in Mathematics and Scientific Computing, UGC-NET.


Publication Type: Journal Article

Year of Publication Title


Athira N and Anjaly Anand, “Numerical Experimentation Research in Optimizing the Signal Rate & other Related Factors on Traffic Flow”, International Journal of Recent Technology and Engineering (IJRTE), vol. 2, no. 2, 2019.[Abstract]

Traffic congestion is one of the current issues faced by the major cities constantly, since past few years. A well organized traffic system and its infrastructure can make a positive impact in reducing the block and thereby affecting the social strata and hence, the economy. Considering the current scenario in the city of Thiruvananthapuram, this study is conducted to forecast the optimal delay time of the signals, such as it reduces the congestion problem effectively. Similar to other cities, Thiruvananthapuram is also prone to the traffic jam in roads on a regular basis. This affects the social system immensely by consuming a large amount of time and fuel. The primal objective of this paper is to optimize the delay time provided for the vehicles that could make the system more systematic, by reducing the wastage of idle time.

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A. Varghese, Keerthana, A., and Anjaly Anand, “Forecasting the Crude Oil Prices and their Effects: A Numerical Approach”, International Journal of Pure and Applied Mathematics , vol. 119, pp. 607-615 , 2018.[Abstract]

Crude oil, one of the world’s incredibly salient fuel is a price determinant for various commodities in the present scenario. It has effects on the economic growth and stability, foreign currency and import and export rates. Forecasting crude oil prices are beneficial for the companies, policy makers of the government and even for the individuals. Considering all these, researchers have been using diverse methods to study fluctuations in crude oil prices. In this paper, we put forward a numerical approach - forecasting using extrapolation by collecting a large set of available market data of crude oil prices in the Indian context

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