Qualification: 
M.Tech, BE
Email: 
r_anisha@cb.amrita.edu

Ms. Anisha Radhakrishnan joined Amrita School of Engineering, Coimbatore in July 2014. She received her BE degree in Computer Science and Engineering from Annamalai University, and M Tech. degree in Computer Science and Engineering from Karunya University. She currently serves as Assistant Professor in the Department of Computer Science and Engineering, Amrita School of Engineering. Her areas of interest include Evolutionary computing, Image processing.

Participation in Faculty Development / STTP / Workshops /Conferences

Sl. No. Title Organization Period Outcome
1.  8th International Conference on Innovations in Computer Science and Engineering (ICICSE 2020) GuruNanak  College Hyderabad August 28-29, 2020 Research Paper
2. 10th International Conference ON Soft Computing for Problem Solving - SocProS 2020 IIT Indore and Soft Computing Research Society December 19-20, 2020 Research Paper

Publications

Publication Type: Conference Paper

Year of Publication Title

2021

B. Subramanian, Selvakumar, A. S., Sachithanantham, M., Saikumar, T., and Anisha Radhakrishnan, “Automatic Railway Gate Control System Using GPS”, in Inventive Communication and Computational Technologies, Singapore, 2021.[Abstract]


Nowadays, the errors caused due to the manual operation at the railway gate level crossing have been increased due to various reasons like the receiving of data about the exact location of the train through various means. In these situations, it would be a boon for the people crossing the railway gate if there are an automatic opening and closing system of the railway gate for an effective means of automation of the railway gate. This paper proposes to introduce an active gate opening system that uses live GPS data collected from GPS sensors on the train. This GPS location data is sent from the GSM transmitter to the receiver at the railway gate. The data is compared with the location of the current railway gate and if both matches with each other, the gate is closed automatically at the receiver end, and by the same means it is opened again once the train crosses the gate.

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2020

K. Keerthanaa and Anisha Radhakrishnan, “Performance Enhancement of Adaptive Image Contrast Approach by using Artificial Bee Colony Algorithm”, in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), 2020.[Abstract]


Image Contrast enhancement emphasizes the information present in low dynamic range of grey scale image. Good quality image facilitates increased extraction of information from them. The objective of this paper is to find the optimal values of parameters using Artificial Bee colony algorithm, which when applied on the transformation function enhances the grey scale image. We propose a new fitness function for evaluating contrast of image, that will guide the Artificial Bee colony algorithm into picking the optimal values. The performance of the proposed method is quantitatively computed using peak signal to noise ratio (PSNR), SSIM(Structural Similarity Index), SNR(Signal to Noise Ratio), MSE(Mean Squared Error) with existing methods.

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2016

N. Chithirala, Natasha, B., Rubini, N., and Anisha Radhakrishnan, “Weighted Mean Filter for removal of high density Salt and Pepper noise”, in ICACCS 2016 - 3rd International Conference on Advanced Computing and Communication Systems: Bringing to the Table, Futuristic Technologies from Arround the Globe, 2016.[Abstract]


The essential constraint on the input images to any computer vision technology is its quality. Acquiring noise free digital images is a challenge as it depends on several factors. Developing algorithms to remove noise is one way to improve the image quality. Salt and pepper noise degrades the image. The challenge here is to restore the lost information without distorting the edges. This paper introduces a new algorithm that reduces high density salt and pepper noise from images. Restoration is done by calculating the weighted mean of the nearby pixels. Weights are assigned unsymmetrically to pre-processed and unprocessed pixels. The quality was judged based on the PSNR value. The algorithm restores information for highly corrupted images. Salt and pepper noise are usually filtered with variants of the median filter. This paper provides an alternate way for noise reduction. © 2016 IEEE.

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Publication Type: Journal Article

Year of Publication Title

2015

Anisha Radhakrishnan and Divya, M., “Survey of data fusion and tumor segmentation techniques using brain images”, International Journal of Applied Engineering Research, vol. 10, pp. 20559-20570, 2015.[Abstract]


Medical image fusion combines several medical images from different modalities to a single image. It gives more quality image and clinical information dropping redundancy. The fusion methods have proved better information accuracy and thereby increase clinical applicability like diagnosis, segmentation, surgery planning etc. MRI has gained wide acceptance due to its ability to provide tissue details. This survey article provides list of methods for the image fusion and MRI brain segmentation. The image fusion review is mainly based on three levels 1) pixel level, feature level and decision level. 2) Widely used techniques for image fusion. 3) Techniques for MRI brain segmentation. This paper concludes that these methods will help physician in medical diagnosis and analysis within short span of time. © Research India Publications.

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Faculty Research Interest: