M Tech Dissertations
Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3
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Item Open Access Modelling and short term forecasting of flash floods in an urban environment(Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Ogale, Suraj; Srivastava, SanjayRapid urbanization, climate change and extreme rainfall has resulted in a growingnumber of cases of urban flash floods. It is important to predict the occurrenceof flood so that the aftermath of the flood can be minimized. Flood forecasting isa major exercise performed to determine the chances of a flood when suitableconditions are present. Short term forecasting or nowcasting is a dominant techniqueused in urban cities for prediction of the very near future incident up to sixhours. In orthodox methods of flood forecasting, current weather conditions areexamined using conventional methods such as use of radar, satellite imaging andcomplex calculation involving complicated mathematical equations.Recent developments in Information and Communication Technology(ICT) andMachine Learning(ML) has helped us to study this hydrological problem alongwith many real world situation in different perspective. The main aim of thisthesis is to design a theoretical model that accounts parameters causing an urbanflash flood and develop a prediction tool for the forecasting of near future event.To test the soundness of a model, data syntheses is performed and the results areseen using the artificial neural network.Item Open Access Text retrieval from the degraded document images(Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Vasani, Hiral; Mitra, Suman K.Image binarization is used to obtain a black and white text document from a colored one. Basically, it can be taken as an image segmentation task that segments the text part from the background. Such a black and white document can be used in many applications, namely Optical Character Recognition (OCR). Text documents suffer from various types of degradations that make image binarization a challenging task. This thesis presents the work done to design a technique that segments text from the background. In this method, the document image is first darkened in order to enhance the text (foreground) in it. The text image is again processed separately so as to suppress the background. The two images so obtained are combined in such a way that the suppressed background is retained from the last image and enhanced text is used from the first image. Then this pre-processed image is binarized using an existing thresholding technique. The first binarized image is subjected to some post-processing in order to remove unwanted smaller components and other noise. The output image so obtained is compared to the ground truth results using some evaluation parameters. The results of the algorithm are compared to the existing Binarization techniques.Item Open Access Retrieval of legal documents using query expansion(Dhirubhai Ambani Institute of Information and Communication Technology, 2014) Agrawal, Madhulika; Majumder, PrasenjitStructure of query by a lawyer and a layman is different. Legal content in the layman’s query is very less. Thus pre-processing of these queries is required for better retrieval performance. In this thesis, we used various query expansion techniques and found that increasing query size increases system performance. MAP of 0.5034 was obtained by using BM25 retrieval model with query expansion up to 2550 terms using Bo1 query expansion model. By explicitly adding terms to the query, using topics obtained from topic modelling, a MAP value of 0.4281 was obtained. Further by relevance feedback of documents using topic modelling and only 2 cycles of feedback, we got MAP of 0.3832. Baseline result that we had was MAP value of 0.3799 using In_expC2 retrieval model. We also compared the relevance judgment of a lawyer and a non-lawyer and found out that for relative evaluation of two systems, non-lawyer’s relevance judgment is at par with the lawyer’s judgment.Item Open Access Feature based approach for singer identification(Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Radadia, Purushotam G.; Patil, Hemant A.One of the challenging and difficult problems under the category of Music Information Retrieval (MIR) is to identify a singer of a given song under strong instrumental accompaniments. Besides instrumental sounds, other parameters are also severely affecting Singer IDentification (SID) accuracy, such as quality of song recording devices, transmission channels and other singing voices present within a song. In our work, we propose singer identification with large database of 500 songs (largest database ever used in any of the SID problem) prepared from Hindi (Indian Language) Bollywood songs. In addition, vocal portions are segmented manually from each of the songs. Different features have been employed in addition to state-of-the-art feature set, Mel Frequency Cepstral Coefficients (MFCC) in this thesis work. To identify a singer, three classifiers are employed, viz., 2nd order polynomial classifier, 3rd order polynomial classifier and state-of-the-art GMM classifier. Furthermore, to alleviate the effect of recording devices and transmission channels, Cepstral Mean Subtraction (CMS) technique on MFCC is utilized for singer identification and it is providing better results than the baseline MFCC alone. Moreover, the 3rd order classifier outperforms amongst all three classifiers. Score-level fusion technique of MFCC and CMSMFCC is also used in this thesis and it improves the results significantly.Item Open Access SMS query processing for information retrieval(Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Shinghal, Khushboo; Majumder, PrasenjitSMS text messaging is one of the fast and popular communication mode on mobile phones these days. This study presents a query processing system for information retrieval system when queries are Short-message-Service (SMS). SMS contains various user improvisation and typographical errors. Proposed approach uses approximate string matching techniques and context extraction to normalize SMS queries with minimum linguistic resources. We have tested the system on FIRE 2011 SMS based FAQ retrieval corpus. Results seems encouragingItem Open Access Learning to rank: using Bayesian networks(Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Gupta, Parth; Mjumder, Prasenjit; Mitra, Suman K.Ranking is one of the key components of an Information Retrieval system. Recently supervised learning is involved for learning the ranking function and is called 'Learning to Rank' collectively. In this study we present one approach to solve this problem. We intend to test this problem in di erent stochastic environment and hence we choose to use Bayesian Networks for machine learning. This work also involves experimentation results on standard learning to rank dataset `Letor4.0'[6]. We call our approach as BayesNetRank. We compare the performance of BayesNetRank with another Support Vector Machine(SVM) based approach called RankSVM [5]. Performance analysis is also involved in the study to identify for which kind of queries, proposed system gives results on either extremes. Evaluation results are shown using two rank based evaluation metrics, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG).