Dissertations - M Tech (CS)
Permanent URI for this collectionhttp://164.52.219.250:4000/handle/10263/2147
These Dissertations were submitted in partial fulfilment of the requirements for the award of M TECH (Computer Science) Degree of Indian Statistical Institute
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Item 2.5D Dual-Encoder U-Net for Lesion Segmentation in Chest CT Scans(Indian Statistical Institute, Kolkata, 2025-06) Mukkara, JagannathAccurate segmentation of lesions in chest CT scans plays a vital role in diagnosing and monitoring pulmonary diseases such as COVID-19. In this, we introduce a novel 2.5D[1] dual-encoder U-Net model[2] that utilizes both the central slice and its neighboring slices to improve segmentation accuracy while keeping computational demands manageable. Our model incorporates residual connections[3] and feature fusion[4] to effectively merge multi-slice contextual information, overcoming the limitations found in traditional 2D and 3D methods. To ensure a reliable evaluation and avoid data leakage, we used patient-level data splitting. We validate our approach on a carefully curated chest CT dataset, showing enhanced segmentation performance and better generalization compared to standard U-Net models. Through extensive experiments, including ablation studies and visualizations, we demonstrate the advantages of combining 2.5D learning with a dual-encoder architecture for medical image segmentation tasks.Item Access structures for an image database(Indian Statistical Institute, Kolkata, 1992) Kuila, Sudhansu SekharItem Acyclicity Tests in Classes of Dense Digraphs in Streaming Model(Indian Statistical Institute, Kolkata, 2020-07) Kundu, MadhumitaGraph is a popular model to represent highly structured data which involves entities who have pairwise relations between them. In many applications, computing graph theoretic properties after modelling the entire dataset as graph, provides us interesting informations which gives us insights about the whole dataset. However, in case of application, the datasets in question can be so large that it's di cult to store in the main memory and the dataset can even be dynamic(can change with time). These days in so many applications, the algorithm that requires to solve the problem which takes massive dataset as input, has limitations on time as well as space taken to store the information. These constraints leads us for the development of new techniques. Streaming model of computation takes all these challenges into account and provides us solutions with limited resources in cost of accuracy. Graph stream is a sequence of imcoming edges and we are only allowed to insert(insertion only model) or both insert and delete(dynamic model) into an initially empty graph. Finally our objective is to nd out certain properties of the graph at the end of the stream which minimizes the amount of space the algorithm uses. Sometimes this algorithm needs to provide the trade of between the space usage and the time taken. There is a large volume work on undirected graphs in streaming model but the area of directed graph stream is a pretty unexplored. In this project, we study the problem of testing acyclicity in dense digraphs in semi-streaming model. Here the graph on n vertices is presented as a stream of edges and using O(n polylog(n))-space, we must determine if it is acyclic or notItem Addressing class imbalance problems to improve animal detection through aerial image data(Indian Statistical Institute, Kolkata, 2025-06) Koushal, SuryangMonitoring animal populations in wildlife reserves is essential for conservation, especially for endangered species, but manual censuses are costly, risky, and logistically challenging due to vast, inaccessible terrains. Unmanned Aerial Vehicles (UAVs) with digital cameras provide a safer, scalable solution for collecting aerial imagery to estimate animal populations. However, semi-automated processing of these images faces significant challenges due to class imbalance in datasets, including foreground-background disparities, where background terrain dominates over sparse animal instances, and inter-class imbalances from uneven species representation and varied visual appearances (e.g., species, sizes, fur patterns) against diverse backgrounds like deserts or forests. These imbalances hinder Convolutional Neural Networks (CNNs) used for object detection, leading to inaccurate population estimates. This project addresses these issues using a dataset of 561 aerial images from Tsavo National Parks (March 2014) and Laikipia-Samburu Ecosystem (May 2015), collected by the Kenya Wildlife Service. We propose a clustering-based approach to categorize background terrain into distinct classes (e.g., desert, grassland), aiming to mitigate imbalances and improve animal detection accuracy in UAV imagery, supporting reliable, data-driven conservation strategies.Item Administrative document processing(Indian Statistical Institute, Kolkata, 2016) Chandra, SatishItem Adversarial Attack on Neural Machine Translation System(Indian Statistical Institute, Kolkata, 2019-06) Abijith, K PNowadays Deep Neural Network based solutions are deployed to solve numerous tasks. Thus, it has become absolutely important to study the robustness of these systems. Machine Translation is one of the popular applications of Deep Neural Networks. This thesis studies the robustness of Neural Machine Translation systems by generating adversarial examples with the objective to fool the model. Whenever there is a change in the source, i.e. when a word in the input sentence is replaced by an unrelated word, the translation system is supposed to re ect the changes while doing translation. These unwanted invariance learned by the model is undesirable. With intention to exploit this undesirable property learned by a Neural Machine Translation system we design an attack called: Invariance-based targeted attack. This attack introduces multiple changes(replacement of words) to the original input sentence, keeping the translation unchanged. In-order to facilitate the explanation of the design of the attack we introduce two methods: (i) Min-Grad method: To identify the position where a replacement of the word makes the least change in the translation, and (ii) Soft-Attn method: To search for a new word to replace, given a list of choices. The initial part of the report explain the preliminary explorations we did in-order to get some insights on how to do the problem formulation. These experiments are run on LSTM based models with single replacement policy. Using the learning from the rst part we extend the experiments to Transformer and BLSTM based models, which are considered as the state-of-the-art systems for machine translation.Item Algorithm for mapping boolean network to LUT based FPGAs(Indian Statistical Institute, Kolkata, 2001) Bhattacharyya, JayasriItem Algorithms and bounds in online learning(Indian Statistical Institute, Kolkata, 2016) Sharma, AnkitItem Algorithms for biological cell storing(Indian Statistical Institute, Kolkata, 2010) Chatterjee, SoumyottamItem Algorithms for Boundary Labeling of Horizontal Line Segments(Indian Statistical Institute, Kolkata, 2019-06) Kurmi, AbhilashIn boundary labelling problem the target is to labeling a set P of n points in the plane with labels that are aligned to side of the bounding box of P . In this work, we investigate a variant of this problem. In our problem, we consider a set of sites inside a rectangle R and label are placed in the compliment of R and touches the left boundary of it. Labels are axis- parallel rectangles of same size and no two labels overlaps. We introduce a set V , called visibility , which is a set of subsets of labels correspond to points of sites. Before connecting site (say p) at point (say p1 2 p) with some label (say l), first we need to check weather subset of label correspond to p1 is in set V or not. If it is then we check the label l belongs to that subset of label or not. If it contains that label then we can join site to the label, otherwise not. In our problem we used po-leaders, that is starting from site it is parallel to the side of R where its label resides and then orthogonal to that side of R. We considered various geometric objects as sites, such as point, same length horizontal segment, different length horizontal segments. As a solution, we derive a dynamic algorithm that minimizes the arbitrary cost function and give us planar solution where sites connects to labels by po- leaders and induces a matching such that no two po-leader intersects, also no two leaders shares common site (or label) and every leader satisfies visibility V . For points as sites, our dynamic algorithm runs in O(n3) time and optimizes the cost function. This running time also same for the case of unit length horizontal line segments as sites. Then we taken arbitrary length horizontal segment, algorithms runs in O(n4) time. We assumed that only one end point of any horizontal line segment can be used to connect label (by po-leader).Item Algorithms for finding isomorphic subgraphs(Indian Statistical Institute, Kolkata, 2001) Giri, Pradeep KumarItem Algorithms for thinning of gray level bengali script(Indian Statistical Institute, Kolkata, 1992) Sarkar, ManishItem Algorithms on geometric graphs(Indian Statistical Institute, Kolkata, 2010) De, MinatiItem Analysis and vectorization of line drawings(Indian Statistical Institute, Kolkata, 1998) Mr., RajeevItem Analysis of cell images for identification of cancer(Indian Statistical Institute, Kolkata, 1999) Gupta, ParveenItem Analysis of cluster validity problem(Indian Statistical Institute, Kolkata, 1992) Gupta, Girish KumarItem Analysis of data structures for VLSI tools(Indian Statistical Institute, Kolkata, 2009) Dey, Sandeep KumarItem ANALYSIS OF GREEDY ALGORITHM FOR DOMINATING SET PROBLEM ON ANCHORED RECTANGLES(Indian Statistical Institute, Kolkata, 2019-07) Choudhury, SoubornoWe are given a set R of n Axis-Parallel Rectangles in the plane. We study the Dominating set problem on R. The bottom left vertex of each rectangle in set R is constrained to touch a straight diagonal line of 135 . We study the performance of greedy algorithm for Minimum Dominating set (MDS) problem on the Intersection Graph of R. We give a construction, on R, where Greedy technique yields (log n)-factor approximation. This proves that the approximation ratio for Greedy algorithm for MDS problem is (log n) even for this constrained version of MDS problem. We also do an experimental study of Greedy algorithm of MDS problem for randomly generated arbitrary rectangles. We compare the performance of greedy algorithm with optimal result obtained by solving Integer Linear program- ming (ILP) formulation of MDS problem.Item Analysis of mammogram for detection of micro-calcification(Indian Statistical Institute, Kolkata, 2005) Hota, Rudra NarayanItem Analysis of multiple scan path architecture(Indian Statistical Institute, Kolkata, 2004) Kumar, Ranjan