Debi Prosad Dogra

Bio: Debi Prosad Dogra is an academic researcher from Indian Institute of Technology Bhubaneswar. The author has contributed to research in topics: Video tracking & Hidden Markov model. The author has an hindex of 22, co-authored 124 publications receiving 1689 citations. Previous affiliations of Debi Prosad Dogra include Electronics and Telecommunications Research Institute & Indian Institute of Technology Kharagpur.

Analysis of EEG signals and its application to neuromarketing

Abstract: Marketing and promotions of various consumer products through advertisement campaign is a well known practice to increase the sales and awareness amongst the consumers. This essentially leads to increase in profit to a manufacturing unit. Re-production of products usually depends on the various facts including consumption in the market, reviewer's comments, ratings, etc. However, knowing consumer preference for decision making and behavior prediction for effective utilization of a product using unconscious processes is called "Neuromarketing". This field is emerging fast due to its inherent potential. Therefore, research work in this direction is highly demanded, yet not reached a satisfactory level. In this paper, we propose a predictive modeling framework to understand consumer choice towards E-commerce products in terms of "likes" and "dislikes" by analyzing EEG signals. The EEG signals of volunteers with varying age and gender were recorded while they browsed through various consumer products. The experiments were performed on the dataset comprised of various consumer products. The accuracy of choice prediction was recorded using a user-independent testing approach with the help of Hidden Markov Model (HMM) classifier. We have observed that the prediction results are promising and the framework can be used for better business model.

Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey

Abstract: Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. Timely detection of traffic violations and abnormal behavior of pedestrians at public places through computer vision and visual surveillance can be highly effective for maintaining traffic order in cities. However, despite a handful of computer vision–based techniques proposed in recent times to understand the traffic violations or other types of on-road anomalies, no methodological survey is available that provides a detailed insight into the classification techniques, learning methods, datasets, and application contexts. Thus, this study aims to investigate the recent visual surveillance–related research on anomaly detection in public places, particularly on road. The study analyzes various vision-guided anomaly detection techniques using a generic framework such that the key technical components can be easily understood. Our survey includes definitions of related terminologies and concepts, judicious classifications of the vision-guided anomaly detection approaches, detailed analysis of anomaly detection methods including deep learning–based methods, descriptions of the relevant datasets with environmental conditions, and types of anomalies. The study also reveals vital gaps in the available datasets and anomaly detection capability in various contexts, and thus gives future directions to the computer vision–guided anomaly detection research. As anomaly detection is an important step in automatic road traffic surveillance, this survey can be a useful resource for interested researchers working on solving various issues of Intelligent Transportation Systems (ITS).

Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

Abstract: We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized da-ta points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approxi-mation for the tangent space at each point, and those tangent spaces are then aligned to give the global coordinates of the data pointswith respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can bequite small in some cases. We illustrate our algorithm using curves and surfaces both in 2D/3D Euclidean spaces and higher dimension-al Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.

Cyber bullying: An old problem in a new guise?

Abstract: Although technology provides numerous benefits to young people, it also has a ’ dark side ’, as it can be used for harm, not only by some adults but also by the young people themselves. Email, texting, chat rooms, mobile phones, mobile phone cameras and web sites can and are being used by young people to bully peers. It is now a global problem with many incidents reported in the United States, Canada, Japan, Scandinavia and the United Kingdom, as well as in Australia and New Zealand. This growing problem has as yet not received the attention it deserves and remains virtually absent from the research literature. This article explores definitional issues, the incidence and potential consequences of cyber bullying, as well as discussing possible prevention and intervention strategies.

Density-based clustering based on hierarchical density estimates

Abstract: We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.