Soumajit Pramanik

Dynamic Image Segmentation using Fuzzy C-Means based Genetic Algorithm

This paper describes an evolutionary approach for unsupervised gray-scale image segmentation that segments an image into its constituent parts automatically. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighborhood relationships. In this paper, fuzzy c-means clustering helps in generating the population of Genetic algorithm which there by automatically segments the image. This technique is a powerful method for image segmentation and works for both single and multiplefeature data with spatial information. Validity index has been utilized for introducing a robust technique for finding the number of components in an image. Experimental results shown that the algorithm generates good quality segmented image.

Edge detection: A statistical approach

This paper describes a novel edge detection algorithm for gray scale images. The proposed method is based on the neighborhood similarity of a pixel using a pre-defined intensity range and simple statistical approach. Then using three or four neighboring boundary pixel to detect a noise and reduced this noise. Many experiments were carried out to evaluate and compare the performance of the proposed algorithm. This new detector outperforms the previously available classical edge detectors.

Can I foresee the success of my meetup group

Success of Meetup groups is of utmost importance for the members who organize them. Given a wide variety of such groups, a single metric may not be indicative of success for different groups; rather, success measure should be specific to the interest of a group. In this paper, accounting for the group diversity, we systematically define Meetup group success metrics and use them to generate labels for our machine learnt models. We crawl the Meetup dataset for three US cities namely New York, Chicago and San Francisco over a period of 8 months. The data study reveals the key players (such as core members, new members etc.) behind the success of the Meetup groups. This study leverages semantic, syntactic, temporal and location based features to discriminate between successful and unsuccessful groups. Finally, we present a model to predict success of the Meetup groups with high accuracy (0.81 with AUC = 0.86). Our approach generalizes well across groups, categories and cities. Additionally, the model performs reasonably well for new groups with little history (cold start problem), exhibiting high accuracy for the cross city validation.

Modeling cascade formation in Twitter amidst mentions and retweets

This paper presents an analytical framework for cascade formation considering both retweet and mentioning activities into account. We introduce two mention strategies (a) random mention and (b) smart mention to model the mention preferences of the users. The proposed framework

An Unsupervised Dynamic Image Segmentation using Fuzzy Hopfield Neural Network based Genetic Algorithm

{\mathcal {C}}^M_F

An empirical approach towards an efficient “whom to mention?” Twitter app

CFM analytically computes the cascade size, depicting tweet popularity and discovers the presence of a critical retweet rate, under which mentioning in a tweet significantly helps in cascade formation. We validate the proposed framework with the help of Monte Carlo simulation; we demonstrate the generality of the framework taking both empirical and synthetic follower networks into consideration. This framework proves the elegance of smart mention strategy in boosting tweet popularity, specially in the low retweeting environment.