Sreeja Ashok currently serves as Assistant Professor in the Department of Computer Science and I.T., School of Arts & Sciences, Kochi. She has 15 years of experience, including 14 years IT experience ( Avenir & Wipro Technologies ) , 1 year teaching and research experience. 


Publication Type: Journal Article

Year of Publication Title


S. Ashok and Dr. 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.

More »»


A. Asok, Jisha, T. J., S. Ashok, and Dr. 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.

More »»


S. S. Kumar, Krishnan, T., S. Ashok, and Dr. 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.

More »»


Dr. T. K. Ramesh, S. Ashok, Bithil, K. B., Nayanar, D., and Vaya, P. R., “Distributed traffic grooming multipath routing algorithm for all optical WDM networks”, European Journal of Scientific Research, vol. 57, pp. 305-313, 2011.[Abstract]

In this paper, we have presented an efficient traffic grooming algorithm (DTMR) for selecting the optimal path based on the varying traffic load conditions. In this algorithm, the initial incoming packets are sent through all the possible paths. With the monitored values on each path, the source estimates the blocking probabilities of each path and the source node selects the best three paths. Then the traffic is classified into high priority and low priority by the source. Subsequently, they are classified into high speed and low speed traffic. High priority high speed data is sent through primary path. Low priority high speed data is sent through secondary path. The high priority low speed packets are inserted in a frame of fixed length containing the given packets along with replicas of the same packet. Enough replicas are added so that the size of the frame adds upto a minimum of 3.5 Gigabits and a maximum of 5 Gigabits. These high priority low speed packets are sent through the primary path. The same procedure is done for low priority low speed packets as well. These low priority low speed packets are then sent through the secondary path. Backup path is kept for survivability of the network. However, the initial duplication of packets is only done if there are no other requests waiting in queue. When these high priority low speed or low priority low speed packets arrive at another node, and if there are other low speed requests which arrive at this node to be processed; then the replica packets are discarded and the low speed packets at the other node is added in the frame. Using the number of acknowledgement packets that are received, the blocking probabilities of primary path and secondary path are updated. A threshold value "thresh" is set. Only if the maximum value of the blocking probabilities of primary path or secondary path goes above "thresh", the algorithm restarts and identifies and assigns optimal paths, thus optimizing the overall setup delay of the system. In addition the algorithm has a proactive approach, such that it minimizes the chance of blocking and also the approach is self-acting as it automatically changes with the traffic load variation across the network. By simulation result, we show that our algorithm has low blocking probability and high throughput which improves the QoS of the network.

More »»