Indian Institute of Technology Hyderabad (IITH) is a premier institute of science and technology established in 2008. IITH has been consistently ranked in the&nbsp;<a href="https://iith.ac.in/reports/" rel="noopener noreferrer">top 10 institutes</a>&nbsp;in India for Engineering&nbsp;<a href="https://www.nirfindia.org/2021/EngineeringRanking.html" rel="noopener noreferrer" target="_blank">according to NIRF</a>&nbsp;making it one of the most coveted schools for science and technology in the country.

IITH offers&nbsp;<a href="https://iith.ac.in/academics/under-graduate/">undergraduate programs</a>&nbsp;in all the classical engineering disciplines, applied sciences, design, as well as several modern interdisciplinary areas. Students are given a flexibility to explore a broad set of areas, and potentially pursue a minor or double major in a discipline that is not their own. Students who wish to seek a deeper understanding of their own discipline are strongly encouraged to get involved in cutting-edge research with the help of a faculty to mentor them, and earn an Honors in their own field.

The very foundation of IIT Hyderabad is based on&nbsp;<a href="https://iith.ac.in/research/">research and innovation</a>. The vibrant research culture is evident from the number of patents and publications that IITH produces consistently every year. IITH offers&nbsp;<a href="https://iith.ac.in/academics/programmes-offered/">graduate programs</a>&nbsp;at both a masters, and a doctoral level, in several diverse areas. There are separate programs for technology, design, science, and liberal arts. The&nbsp;<a href="https://iith.ac.in/academics/post-graduate/">MTech program</a>&nbsp;is offered in various modes and durations to cater to the ever-growing need of postgraduate level professionals in the country.

IITH encourages and supports&nbsp;<a href="https://iic-iith.netlify.app/index.html" rel="noopener noreferrer" target="_blank">innovation and entrepreneurship</a>&nbsp;at every stage. The minor program in entrepreneurship is open to all students of IITH, and many of the courses are offered by industrialists who are entrepreneurs themselves. There are various&nbsp;<a href="https://iith.ac.in/research/centres-incubators/">innovation centers and incubators</a>&nbsp;that promote entrepreneurship and help mentor young innovators. IITH has been very successful in building tie-ups with leading academic institutions around the globe.

IITH enjoys a very special relationship with Japanese universities and industries that goes beyond academic and research collaborations. In fact, some of the iconic buildings in IITH campus will carry the signature of Japanese architecture.

IITH creates a unique holistic ecosystem for education that offers interactive learning, a very flexible academic structure, cutting-edge research, strong industry collaboration, and entrepreneurship. This is an environment which enables students and faculty to translate their dreams into realities.

IIT Hyderabad

Nowadays online social networks are used extensively for personal and commercial purposes. This widespread popularity makes them an ideal platform for advertisements. Social media can be used for both direct and word-of-mouth (WoM) marketing. Although WoM marketing is considered more effective and it requires less advertisement cost, it is currently being under-utilized. To do WoM marketing, we need to identify a set of people who can use their authoritative position in social network to promote a given product. In this paper, we show how to do WoM marketing in Facebook group, which is a question answer type of social network. We also present concept of reinforced WoM marketing, where multiple authorities can together promote a product to increase the effectiveness of marketing. We perform our experiments on Facebook group dataset consisting of 0.3 million messages and 10 million user reactions. © 2017, Springer International Publishing AG.

World University root node

Department of Computer Science and Engineering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Using social media for word-of-mouth marketing

As microblogging services like Twitter are becoming more and more influential in today's globalized world, its facets like sentiment analysis are being extensively studied. We are no longer constrained by our own opinion. Others' opinions and sentiments play a huge role in shaping our perspective. In this paper, we build on previous works on Twitter sentiment analysis using Distant Supervision. The existing approach requires huge computation resource for analyzing large number of tweets. In this paper, we propose techniques to speed up the computation process for sentiment analysis. We use tweet subjectivity to select the right training samples. We also introduce the concept of EFWS (Effective Word Score) of a tweet that is derived from polarity scores of frequently used words, which is an additional heuristic that can be used to speed up the sentiment classification with standard machine learning algorithms. We performed our experiments using 1.6 million tweets. Experimental evaluations show that our proposed technique is more efficient and has higher accuracy compared to previously proposed methods. We achieve overall accuracies of around 80% (EFWS heuristic gives an accuracy around 85%) on a training dataset of 100K tweets, which is half the size of the dataset used for the baseline model. The accuracy of our proposed model is 2-3% higher than the baseline model, and the model effectively trains at twice the speed of the baseline model. © 2017 IEEE.

2017 9th International Conference on Communication Systems and Networks, COMSNETS 2017

Efficient Twitter sentiment classification using subjective distant supervision

At present, news channels are using social media to disseminate news to a large audience. These news channels try to convey the news in such a way that attracts more user interaction in the form of views, likes, comments, and shares. In online news, one of the important factors in getting higher user interaction is to recommend the top news articles that would attract more number of users to give opinion, especially in the form of comments. When a news article starts getting many comments, it automatically attracts other readers to participate in the discussion. We say that a news article has “high-arousal” content if it can attract more number of comments from users. When a new news article is written it has no user interaction information, such as number of views, likes, shares or comments. In this paper, our aim is to predict news articles which have higher potential to generate high-arousal. In other words, they would attract a large number of users to give opinion in the form of comments. Unlike previous studies, we predict the arousal of news articles prior to their release, which brings the possibility of appropriate decision making to modify the article content or its ranking in audience newsfeed. We generate multiple features from the content of news articles and show that our best set of features can predict the arousal with an accuracy of 81%. We perform our experiments on social media page of CNN news channel, containing four years of data with 33,324 news articles and 226.83 million reactions. © 2017, Springer International Publishing AG.

Arousal prediction of news articles in social media

With ever increasing number of publication venues and research topics, it is becoming difficult for users to find out topics of interest for conferences or research areas. Although we have many popular topic modeling techniques, we still find that conferences are listing their topics of interest using a manual approach. Topics that are generated by existing topic modeling algorithms are good for text categorization, but they are not ideal for displaying to users because they generate topics that are not so readable and are often redundant. In this paper, we propose a novel technique to generate topics of interest using association mining and natural language processing. We show that the topics of interest that are generated by our technique is much more similar to manually written topics of interest compared to existing topic modeling algorithms. Our results show that the proposed method generates meaningful, interpretable topics, and leads to 13.9% higher precision than existing techniques. © Springer International Publishing AG 2017.

Generating topics of interests for research communities

We examine the applicability of Composite Items (CIs) for generating customized travel packages consisting of Points of Interest (POIs) in a given city. CIs have been shown to serve complex information needs such as selecting books for a reading club, identifying a set of products for a promotion, or planning a city tour. In the travel domain, a synthesized view of travel options in a city can be provided with a set of cohesive CIs, each of which is covering a different region in the city. In this paper, we attempt to understand the benefit of letting users customize travel packages, and examine the relationship between customization and personalization. For personalization, we gather user preferences on POI features when available or on latent topics extracted from POI tags. For customization, we develop a framework within which a user interacts with proposed travel packages and the system suggests new CIs according to refined user preferences. Our experiments reveal a tension between personalization and the cohesiveness of items forming each CI. As a result, customization is necessary to find a balance between POI personalization and CI cohesiveness. We also show that the refined user preferences obtained from customization in one city help build better travel packages in another city. © 2017 IEEE.

Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017

Customizing travel packages with interactive composite items

In the present chapter, the widely common problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform and artificial neural networks. The technique has been tested and found to be more accurate and economic in respect of the recognition process time of the system. Features of the handwritten character images are extracted by discrete wavelet transform used with appropriate level of multiresolution technique, then the artificial neural networks is trained by extracted features. The unknown input handwritten character images are recognized by trained artificial neural networks system. The proposed method provides good recognition accuracy for handwritten characters with less training time, less no. of samples and less no. of iterations. © 2017, IGI Global. All rights reserved.

Biometrics: Concepts, Methodologies, Tools, and Applications

Wavelet-based recognition of handwritten characters using artificial neural network

A traditional relational database can evaluate complex queries but requires users to precisely express their information need. But users often do not know what information is available in a database, and hence cannot correctly express their information need. Traditional databases do not provide convenient means for users to gain familiarity with the data. In this paper, we study the problem of exploratory search, which a user may wish to perform to get an understanding of the data set. We note that users often have some decisions already made, so what they need is not an overall database summary, but rather a summary "in context" of the relevant portion of the database. Towards this end, we devise a novel data summarization technique called the Conditional Attribute Dependency (CAD) View, which shows the conditional dependencies between attribute values conditioned on applied selections. The CAD View can help users to gain familiarity with structured datasets in an attribute-wise manner. To evaluate the CAD View, we perform a user study comprising three complex exploratory tasks on a real dataset. Our studies show that users are able to do all the tasks about 4-5 times faster and with better accuracy using the CAD View compared to the data summary shown in faceted navigation, which is currently the most popular search interface for e-commerce and has support for exploratory search. © 2016, Copyright is with the authors.

Advances in Database Technology - EDBT

DBExplorer: Exploratory search in databases

Many e-commerce sites struggle to present their data to users in an easily accessible manner, especially when the users have limited knowledge of what is contained in their database or lack technical expertise to form proper queries. Faceted navigation is a central tool that these e-commerce sites use to address this challenge. A typical faceted interface has two main component panels: a query panel and a result panel. Faceted browsing is primarily designed to help users quickly get to a specific item if they know the characteristics they are looking for. However, limitations in the query and the result panel deter effective faceted browsing, especially for users unfamiliar with the data. In this paper, we study why users are not able to explore e-commerce databases. We identify five limitations in the query and result panel that deter exploratory search using faceted browsing. We propose nine addon extensions-four in the query panel and five in the result panel-to address these limitations. © Springer International Publishing Switzerland 2015.

Information exploration in e-commerce databases

This paper describes our experimental work on computational analysis of socio-political blog data through a novel combine of sophisticated language processing and visualization techniques. We have designed an integrated framework by utilizing Topic Modeling, Entity Extraction and Sentiment Analysis; to draw sociologically relevant inferences from unstructured free form blogosphere data. The dataset comprised of more than 9290 blog posts on social-political events related to the Arab spring. We have tried to extract important inferences from the dataset; such as key themes, persons, places, organizations and overall sentiment orientation of the content around different entities in the texts. We have tried to validate the inferences obtained through manual and Google search trends statistics. The results obtained are quite relevant and demonstrate the usefulness of our approach for computational analysis of social media data. © 2013 IEEE.

Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013

Blog text analysis using topic modeling, named entity recognition and sentiment classifier combine

Numerous applications such as wireless communication and telematics need to keep track of evolution of spatio-temporal data for a limited past. Limited retention may even be required by regulations. In general, each data entry can have its own user specified lifetime. It is desired that expired entries are automatically removed by the system through some garbage collection mechanism. This kind of limited retention can be achieved by using a sliding window semantics similar to that from stream data processing. However, due to the large volume and relatively long lifetime of data in the aforementioned applications (in contrast to the real-time transient streaming data), the sliding window here needs to be maintained for data on disk rather than in memory. It is a new challenge to provide fast access to the information from the recent past and, at the same time, facilitate efficient deletion of the expired entries. In this paper, we propose a disk based, two-layered, sliding window indexing scheme for discretely moving spatio-temporal data. Our index can support efficient processing of standard time slice and interval queries and delete expired entries with almost no overhead. In existing historical spatio-temporal indexing techniques, deletion is either infeasible or very inefficient. Our sliding window based processing model can support both current and past entries, while many existing historical spatio-temporal indexing techniques cannot keep these two types of data together in the same index. Our experimental comparison with the best known historical index (i.e., the MV3R tree) for discretely moving spatio-temporal data shows that our index is about five times faster in terms of insertion time and comparable in terms of search performance. MV3R follows a partial persistency model, whereas our index can support very efficient deletion and update. © 2012 IEEE.

Proceedings - International Conference on Data Engineering

SWST: A disk based index for sliding window spatio-temporal data

A relational database often yields a large set of tuples as the result of a query. Users browse this result set to find the information they require. If the result set is large, there may be many pages of data to browse. Since results comprise tuples of alphanumeric values that have few visual markers, it is hard to browse the data quickly, even if it is sorted. In this paper, we describe the design of a system for browsing relational data by scrolling through it at a high speed. Rather than showing the user a fast changing blur, the system presents the user with a small number of representative tuples. Representative tuples are selected to provide a "good impression" of the query result. We show that the information loss to the user is limited, even at high scrolling speeds, and that our algorithms can pick good representatives fast enough to provide for real-time, high-speed scrolling over large datasets. © 2012 ACM.

Proceedings of the ACM SIGMOD International Conference on Management of Data

Skimmer: Rapid scrolling of relational query results

This paper presents our experimental work on evaluation of Machine Learning based classification approaches (Naïve Bayes and SVM) with the Unsupervised Semantic Orientation based SO-PMI-IR algorithm for sentiment analysis of movie review texts. We have used both pre-existing data sets and our own dataset collection comprising of a large number of user reviews for Hindi movies. The Naïve Bayes and SVM approaches were implemented in multiple folds. The results, in addition to presenting a detailed comparative view of these techniques, demonstrate that with suitable selection of features the Naive Bayes algorithm performs reasonably well and at times matches the popularly believed superior performance level of SVM, at least for sentiment analysis task. The SO-PMI-IR algorithm produces substantially accurate sentiment classification without the requirement of any prior training. The accuracy of SO-PMI-IR however depends on POS tags used as features and thresholding/ aggregation schemes used. © 2012 IEEE.

2012 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2012

Evaluating Machine Learning and Unsupervised Semantic Orientation approaches for sentiment analysis of textual reviews

Image processing is a rapidly growing field. There are several transforms that do image transformation i.e image enhancement and segmentation. Among all the transformations spectral transformation is the simplest one because it is similar to the commonly used geometric coordinate transformation except that the geometric coordinates are replaced with the spectral bands. I can perform spectral transformation by different algorithms, in this paper we use spatial domain methods, which operate directly on pixels. In this paper I have implement method for image transformation, segmentation and analysis using spectral transformation technique and develop results using matlab coding for spatial domain methods. In future the matlab code can be converted to VHDL code and implement on FPGA kit inorder to develop ASIC (application specific IC) for image transformation, segmentation and analysis. This will give a generalization to image processing. ©2010 IEEE.