Dr. M. V. Judy currently serves as Associate professor , H.O.D. & Research Supervisor in the Department of Computer Science and I.T., School of Arts & Sciences, Kochi. She has 12 years experience, including 10 years in research and published papers in referred national and international journals. She also served as Principal Investigator of research grant from Department of Science and Technology Government of India. 


Publication Type: Journal Article
Year of Publication Publication Type Title
2016 Journal Article A. Asok, Jisha, T. J., S. Ashok, and M.V. Judy, “Integrated framework using frequent pattern for clustering numeric and nominal data sets”, Advances in Intelligent Systems and Computing, vol. 408, pp. 523-530, 2016.[Abstract]

Clustering is an exploratory technique in data mining that aligns objects which have a maximum degree of similarity in the same group. The real-world data are usually mixed in nature, i.e., it can contain both numeric and nominal data. Performance degradation is a major challenge in existing mixed data clustering due to multiple iterations and increased complexities. We propose an integrated framework using frequent pattern analysis, frequent pattern-based framework for mixed data clustering (FPMC) algorithm, to cluster mixed data in a competent way by performing a one-time clustering along with attribute reduction. This algorithm comes under divide-and-conquer paradigm, with three phases, namely crack, transformation, and merging. The results are promising when the algorithm is applied on benchmark datasets. © Springer Science+Business Media Singapore 2016.

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2016 Journal Article N. T. Vijayakumar and M.V. Judy, “Autism spectrum disorders: Integration of the genome, transcriptome and the environment”, Journal of the Neurological Sciences, vol. 364, pp. 167-176, 2016.[Abstract]

Autism spectrum disorders denote a series of lifelong neurodevelopmental conditions characterized by an impaired social communication profile and often repetitive, stereotyped behavior. Recent years have seen the complex genetic architecture of the disease being progressively unraveled with advancements in gene finding technology and next generation sequencing methods. However, a complete elucidation of the molecular mechanisms behind autism is necessary for potential diagnostic and therapeutic applications. A multidisciplinary approach should be adopted where the focus is not only on the 'genetics' of autism but also on the combinational roles of epigenetics, transcriptomics, immune system disruption and environmental factors that could all influence the etiopathogenesis of the disease. ASD is a clinically heterogeneous disorder with great genetic complexity; only through an integrated multidimensional effort can modern autism research progress further. © 2016 Elsevier B.V. All rights reserved. More »»
2016 Journal Article S. Ashok and M.V. Judy, “Process flow for information visualization in biological data”, Advances in Intelligent Systems and Computing, vol. 438, pp. 541-549, 2016.[Abstract]

Every day new discoveries are made in the field of molecular biology and genetics and the sheer volume of data coming out of scientific journals are overwhelming. To collect process and integrate this raw and complex information is probably the most challenging task of current generation of academicians and research scholars. Creating a combined platform by integrating various forms of biological data like DNA sequences, protein structures, or metabolic pathways helps bioinformaticians and computational biologists for efficient data analysis. Current work proposes a structured process flow by integrating different data exploration techniques and visualization techniques that aid in visual extraction of information from biological data. © Springer Science+Business Media Singapore 2016.

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2015 Journal Article S. S. Kumar, Krishnan, T., S. Ashok, and M.V. Judy, “Clutter reduction in parallel coordinates using binning approach for improved visualization”, International Journal of Electrical and Computer Engineering, vol. 5, pp. 1564-1568, 2015.[Abstract]

As the data and number of information sources keeps on mounting, the mining of necessary information and their presentation in a human delicate form becomes a great challenge. Visualization helps us to pictorially represent, evaluate and uncover the knowledge from the data under consideration. Data visualization offers its immense opportunity in the fields of trade, banking, finance, insurance, energy etc. With the data explosion in various fields, there is a large importance for visualization techniques. But when the quantity of data becomes elevated, the visualization methods may take away the competency. Parallel coordinates is an eminent and often used method for data visualization. However the efficiency of this method will be abridged if there are large amount of instances in the dataset, thereby making the visualization clumsier and the data retrieval very inefficient. Here we introduced a data summarization approach as a preprocessing step to the existing parallel coordinate method to make the visualization more proficient. © 2015 Institute of Advanced Engineering and Science. All rights reserved.

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2015 Journal Article S. Sivasankar, Nair, S., and M.V. Judy, “Feature reduction in clinical data classification using augmented genetic algorithm”, International Journal of Electrical and Computer Engineering, vol. 5, pp. 1516-1524, 2015.[Abstract]

In clinical data, we have a large set of diagnostic feature and recorded details of patients for certain diseases. In a clinical environment a doctor reaches a treatment decision based on his theoretical knowledge, information attained from patients, and the clinical reports of the patient. It is very difficult to work with huge data in machine learning; hence to reduce the data, feature reduction is applied. Feature reduction has gained interest in many research areas which deals with machine learning and data mining, because it enhances the classifiers in terms of faster execution, cost-effectiveness, and accuracy. Using feature reduction we intend to find the relevant features of the data set. In this paper, we have analyzed Modified GA (MGA), PCA and the combination of PCA and Modified Genetic algorithm for feature reduction. We have found that correctly classified rate of combination of PCA and Modified Genetic algorithm higher compared to the other feature reduction method. © 2015 Institute of Advanced Engineering and Science. All rights reserved.

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Publication Type: Conference Paper
Year of Publication Publication Type Title
2012 Conference Paper M.V. Judy, Krishnakumar, U., and Narayanan, A. G. H., “Constructing a personalized e-learning system for students with autism based on soft semantic web technologies”, in Proceedings - 2012 IEEE International Conference on Technology Enhanced Education, ICTEE 2012, Kerala, 2012.[Abstract]

Computer use offers a flexible, high status means of providing opportunities for people with autism in education, communication, creativity, leisure and employment. It may offer a range of very useful tools for a person with autism, but this must be embedded in a wider care for educational system to be effective. People with autism have a psychoeducational profile that is different from normally developing individuals. Planning the instructional program for students with autism is complex, because these students have significant differences from most other students in learning style, communication, and social skill development, and often have challenging behaviours. There is considerable individual variability in how these characteristics affect a particular person. Programs must be individualized based on the unique needs and abilities of each student. Context aware e-learning system provides the learning contents based on the individual characteristics of the learner. An adaptive e-learning system based on soft semantic web technologies can be developed to teach the students with autism. © 2012 IEEE.

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