Gautham Shekar

VIT University, Computer Science and Engineering, Graduate Student

Sathyabama University, Information technology, Undergraduate

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A highly self-motivated data science enthusiast with passion towards research, broadly in the areas of deep learning algorithms, neural networks, image processing and applications of artificial intelligence.

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David Pierre Leibovitz

Carleton University

Maurizio Forte

Duke University

David Seamon

Kansas State University

Armando Marques-Guedes

UNL - New University of Lisbon

Robin L Hill

University of Edinburgh

Fabio Cuzzolin

Oxford Brookes University

Sharma Balakrishnan

Universiti Sains Islam Malaysia (USIM)

Bálint Molnár

Eötvös Loránd University

Ferhat Bozkurt

Ataturk University

Alex Dommann

University of Bern

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Papers by Gautham Shekar

Malaria Detection using Deep Learning

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 2020

Malaria is the deadliest disease in the earth and big hectic work for the health department. The ... more Malaria is the deadliest disease in the earth and big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. This process is inefficient and the diagnosis depends on the experience and well knowledgeable person needed for the examination. Deep Learning algorithms have been applied to malaria blood smears for diagnosis before. However, practical performance has not been sufficient so far. This paper proposes a new and highly robust machine learning model based on a convolutional neural network (CNN) which automatically classifies and predicts infected cells in thin blood smears on standard microscope slides. A ten-fold cross-validation layer of the convolutional neural network on 27,558 single-cell images is used to understand the parameter of the cell. Three types of CNN models are compared based on their accuracy and select the precise accurate - Basic CNN, VGG-19 Frozen CNN, and VGG-19 Fine Tuned CNN. Then by comparing the accuracy of the three models, the model with a higher rate of accuracy is acquired.

Hardware Acceleration For Image Processing Application

NSETC, 2021

In India nearly thousands of people die every year due to pesticide poisoning. Toxic pesticides l... more In India nearly thousands of people die every year due to pesticide poisoning. Toxic pesticides like chlorpyrifos, ethion, acetamiprid are used without knowing the side effects. Detection of the pesticides using image processing can bring a solution. In order to detect the pesticide residue, three Convolutional Neural Network (CNN) is developed by training the crop leaf image dataset. The Convolutional Neural Network are Basic Convolutional Neural Network, Frozen Convolutional Neural Network and Fine Tuned Convolutional Neural Network. To make this analysis possible, Graphical processing Unit (GPU) is used to run the image processing algorithm. Three Graphical Processing Units are used to understand the accuracy, time taken and to compare their benchmarks.

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Malaria Detection using Deep Learning

by Gautham Shekar, S. Revathy, and Nani Karthik

4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 2020

Malaria is the deadliest disease in the earth and a big hectic work for the health department. Th... more Malaria is the deadliest disease in the earth and a big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. This process is inefficient and the diagnosis depends on the experience and well knowledgeable person needed for the examination. Deep Learning algorithms have been applied to malaria blood smears for diagnosis before. However, practical performance has not been sufficient so far. This paper proposes a new and highly robust machine learning model based on a Convolutional Neural Network (CNN) which automatically classifies and predicts infected cells in thin blood smears on standard microscope slides. We used the tenfold cross-validation layer of convolutional neural network on 27,558 single-cell images to understand the parameter of the cell. We made three types of CNN models to compare the accuracy and select the precise accurate-Basic CNN, VGG-19 Frozen CNN and VGG-19 Fine Tuned CNN. Then by comparing the accuracy of the three models, we found the model with higher rate of accuracy.

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Drafts by Gautham Shekar

Social Distancing Monitoring System Paper (1)

COVID-19 belongs to the family of coronavirus which requires highest level of precautions that sh... more COVID-19 belongs to the family of coronavirus which requires highest level of precautions that should be taken by the people. Social distancing is considered as the best spread stopper in the present scenario. All affected countries are implementing social distancing to solve the problem. This paper is aimed to support and take care of the rules regarding social distancing. It aims at detecting the violation of the rules and helps in keep track of it using certain CCTV footages or any recorded video using YOLOV3. This method uses object detection method to recognize the distance between the individuals and provides the alert where the rules is getting violated so that certain measures can be taken by the user. It also updates the current active and cured cases of certain areas of the particular region.

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Malaria Detection using Deep Learning

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 2020

Hardware Acceleration For Image Processing Application

NSETC, 2021

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Malaria Detection using Deep Learning

by Gautham Shekar, S. Revathy, and Nani Karthik

4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 2020

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Social Distancing Monitoring System Paper (1)

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