Dany Varghese

Dany Varghese


Postgraduate Research Student
M. Tech & B. Tech in Computer Science & Engineering

Academic and research departments

Department of Computer Science.

My research project

Research

Research interests

My teaching

My publications

Publications

Dany Varghese, Viju Shankar (2014). Cognitive computing simulator-COMPASS
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—Cognitive computing is an emerging method which helps to analyse the human brain behaviour and simulate it mathematically. Cognitive computing systems learn and interact naturally with people to extend what either humans or machine could do on their own. Cognitive science consists of multiple research disciplines, including psychology, artificial intelligence, philosophy, neuroscience, linguistics, and anthropology. Cognitive computing helps autonomous systems to work as human Brain. COMPASS is a simulator which simulates the working of cognitive computing. It is fully based on the architecture TrueNorth developed by IBM. COMPASS enables to simulate brain-like functions in a hardware platform.
Dany Varghese, Viju Shankar (2015). A Novel Approach for Single Image Super Resolution using Statistical Mathematical Model
View abstract
Images with high resolution are always a necessity in almost all image processing applications. .Super Resolution is a method in image processing to create High Resolution image from several or single low resolution image so that high spatial frequency information can be recovered. SR methods are applied on LR images in order to increase spatial resolution for a new image. The super resolution processing includes two main tasks: up-sampling of the image, removing degradations that arise during the image capture. In effect, the super-resolution process tries to generate the missing high frequency components. Applications may include HDTV, biological imaging etc. In this work we deal the problem of producing a HR image from a single low-resolution image using some statistical mathematical model. Performance of these algorithms was checked by using objective image quality criteria PSNR, MSSIM and compared with other existing methods.
KS Neethu, Dany Varghese (2017). An incremental semi-supervised approach for visual domain adaptation
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The general focus of domain adaptation methodology is transferring learned knowledge from labeled train domain to unlabeled test domain. Domain adaptation tries to minimize the domain shift problem by modeling a classifier using labeled training domain data which taken under definite conditions and this classifier is utilize to test the data which taken under distinct conditions. Common adaptation approaches will learn a freshly acquired feature vector space using labeled data domain (source) and unlabeled train (target) data domain having alike characteristics and a supervised, unsupervised or semi-supervised classifier will carry out the further task. Here is a design of an incremental KM-ELM classifier which can utilize for better classification of various domain adaptation task. This classifier is a fusion of high performing K-Means algorithm and fast neural network Extreme learning machine (ELM). Here utilizes the cross-domain learning capability of ELM with PCA, GFK (Geodesic flow Kernel) methods for addressing domain adaptation task. First PCA and PLS are used to create the subspaces of testing data and training data and these subspaces will considered as a points in Grassmann manifold. After that the geodesic based domain shift representation will carry out and integration of these data points creates the intermediate cross domain. This will form a new space having feature vectors from training domain and testing domain where the likelihood of these vectors in this space is maximum.
Krishna Thulasi NP, Dany Varghese (2015). A Novel Approach for Diagnosing Alzheimer's Disease Using SVM
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Alzheimer's disease (AD) is one of the most intensifying brain disorder that gradually damage memory and thinking skills and later the ability to carry out the normal tasks. It is the most common cause of dementia in older adults. While dementia is more common as people grow older, it is not a normal part of aging. One of the first signs of Alzheimer's disease is memory loss. AD accounts for up to 80% of cases of dementia. The 3 stages of AD is mild, moderate and severe AD. In mild cognitive impairment (MCI), the loss of cognitive skills only slightly affects a person's daily life, moderate stage is the middle stage of AD. While in severe AD, a person is no longer able to function independently and becomes totally reliant on others for care. In this paper, Support Vector Machine (SVM) is used for diagnosing Alzheimer's disease of brain MRI and for classifying it into specific stages. The algorithm was trained and tested using the MRI data from Alzheimer's Disease Neuroimaging Initiative (ADNI). The data used include the MRI scanning of about 70 AD patients and 30 normal controls.