Qualification: 
MSc, BSc
krishnas@am.amrita.edu

Krishna S. currently serves as Faculty Associate at the Department of Computer Science Applications at Amrita School of Engineering, Amritapuri. He completed his B. Sc. Computer Science and M. Sc. Computer Science from Amrita School of Arts and Sciences, Amritapuri campus. 

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2018

Conference Paper

Ani R., Krishna S., H, A., and U, A., “An Approach Towards Building an IoT Based Smart Classroom”, in International Conference on Advances in Computing, Communications and Informatics (ICACCI) Cite this publication, 2018.[Abstract]


IoT (Internet of Things) is a dynamic innovation with a powerful impact on today's world which can make human life simple and effortless. The scope of this field is limitless and has emerged as a winner in various areas ranging from Medicine, Engineering, Computer Science, Space and Technology, Automobiles and so on. The center of purpose is utilizing IoT based technology in accomplishing automation for classrooms. In this paper, we propose an approach to control and manage electrical equipments such as fans and lights based on human presence. Our focus is towards building a solution which could help in reducing overutilization of energy resources. A camera is used for recognizing the presence of people in the classroom and for analyzing their seating position. Here a classroom is divided into two segments. Whenever a human presence is detected in a particular segment then the light and fan will be switched ON. The reasonable objective of this paper is how to build up a smart classroom where we can automate the electrical equipments with a focus towards energy conservation.

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2017

Conference Paper

Ani R., Krishna S., Anju, N., Sona, A. M., and Deepa, O. S., “IoT based patient monitoring and diagnostic prediction tool using ensemble classifier”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017, vol. 2017-January, pp. 1588-1593.[Abstract]


The ubiquitous growth of Internet of Things (IoT) and its medical applications has improved the effectiveness in remote health monitoring systems of elderly people or patients who need long-term personal care. Nowadays, chronic illnesses, such as, stroke, heart disease, diabetes, cancer, chronic respiratory diseases are major causes of death, in many parts of the world. In this paper, we propose a patient monitoring system for strokeaffected people to minimize future recurrence of the same by alarming the doctor and caretaker on variation in risk factors of stroke disease. Data analytics and decision-making, based on the real-time health parameters of the patient, helps the doctor in systematic diagnosis followed by tailored restorative treatment of the disease. The proposed model uses classification algorithms for the diagnosis and prediction. The ensemble method of treebased classification-Random Forest give an accuracy of 93%. © 2017 IEEE.

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2014

Conference Paper

Manjusha Nair, Krishna S., Dr. Bipin G. Nair, and Diwakar, S., “Parameter optimization and nonlinear fitting for computational models in neuroscience on GPGPUs”, in 2014 International Conference on High Performance Computing and Applications (ICHPCA), 2014.[Abstract]


One of the main challenges in computational modeling of neurons is to reproduce the realistic behaviour of the neurons of the brain under different behavioural conditions. Fitting electrophysiological data to computational models is required to validate model function and test predictions. Various tools and algorithms exist to fit the spike train recorded from neurons to computational models. All these require huge computational power and time to produce biologically feasible results. Large network models rely on the single neuron models to reproduce population activity. A stochastic optimization technique called Particle Swam Optimisation (PSO) was used here to fit spiking neuron model called Adaptive Exponential Leaky Integrate and Fire (AdEx) model to the firing patterns of different types of neurons in the granular layer of the cerebellum. Tuning a network of different types of spiking neurons is computationally intensive, and hence we used Graphic Processing Units (GPU) to run the parameter optimisation of AdEx using PSO. Using the basic principles of swam intelligence, we could optimize the n-dimensional space search of the parameters of the spiking neuron model. The results were significant and we observed a 15X performance in GPU when compared to CPU. We analysed the accuracy of the optimization process with the increase in width of the search space and tuned the PSO algorithm to suit the particular problem domain. This work has promising roles towards applied modeling and can be extended to many other disciplines of model-based predictions.

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Faculty Research Interest: