Theses
Permanent URI for this collectionhttp://164.52.219.250:4000/handle/10263/2744
Browse
105 results
Search Results
Item Development of Some Scalable Pattern Recognition Algorithms for Real Life Data Analysis(2017-11-20) Garai, ParthaA huge amount of data is being generated continuously as a result of recent advancement and wide use of high-throughput technologies. With the rapid increase in size of data distributed worldwide, understanding the data has become critical. In this regard, dimensionality reduction and clustering have become the necessary preprocessing steps of multiple research areas and applications. One of the important problems of real life large data sets is uncertainty. Some of the sources of this uncertainty include imprecision in computation and vagueness in class denitions. The uncertainty may also be present in the denition of class membership function. In this background, the thesis addresses the problem of dimensionality reduction and clustering of real life data sets, in the presence of noise and uncertainty. The thesis rst presents the problem of feature selection using both type-1 and interval type-2 fuzzyrough sets, which are eective for dimensionality reduction of real life data sets when uncertainty is present in the data set. The properties of fuzzy-rough sets allow greater exibility in handling noisy and real valued data. While the concept of lower approximation and boundary region of rough sets deals with uncertainty, incompleteness, and vagueness in class denition, the use of either type-1 or interval type-2 fuzzy sets enables ecient handling of overlapping classes in uncertain environment. Moreover, a new concept of \simultaneous attribute selection and feature extraction" is introduced for dimensionality reduction, integrating judiciously the merits of both feature selection and extraction. A scalable rough-fuzzy clustering algorithm is introduced for large real life data sets, where the theory of rough hypercuboid approach, interval type-2 fuzzy sets, and c-means algorithm are integrated judiciously to handle the uncertainty present in a data set. While the concept of rough hypercuboid approach deals with uncertainty, incompleteness, and vagueness in cluster denition, the use of fuzzy membership of interval type-2 fuzzy sets in the boundary region of a cluster enables ecient handling of overlapping partitions in uncertain environment. Finally, the application of both clustering and feature selection algorithms is demonstrated by grouping functionally similar microRNAs from microarray data. The proposed approach can automatically select the optimum set of features while clustering the microRNAs, making the complexity of the algorithm lower.Item Some Results on Combinatorial Batch Codes and Permutation Binomials over Finite Fields(Indian Statistical Institute,Kolkata, 2015) Bhattacharya, SrimantaIn this thesis,we study combinatorial batch codes (CBCs) and permutation binomials (PBs) over �nite �elds of even characteristic. Our primary motivation for considering these problems comes from their importance in cryptography. CBCs are replication based variants of batch codes, which were introduced in [IKOS04a] as a tool for reducing the computational overhead of private information retrieval protocols (a cryptographic primitive). On the other hand, permutation polynomials, with favourable cryptographic properties, have applications in symmetric key encryption schemes, especially in block ciphers. Moreover, these two objects are interesting in their own right, and they have connections with other important combinatorial objects. CBCs are much similar to unbalanced expanders, a much studied combinatorial object having numerous applications in theoretical computer science. On the other hand, the speci�c class of PBs that we consider in this work, are intimately related to orthomorphisms. Orthomorphisms are relevant in the construction of mutually orthogonal latin squares, a classical combinatorial objects having applications in design of statistical experiments. These aspects motivate us to explore theoretical properties of CBCs and PBs over �nite �elds. However, these two objects are inherently widely di�erent; CBCs are purely combinatorial objects, and PBs are algebraic entities. So, we explore these two objects independently in two di�erent parts, where our entire focus lies in exploring theoretical aspects of these objects. In Part I, we consider CBCs. There, we provide bounds on the parameters of CBCs and obtain explicit constructions of optimal CBCs. In Part II, we consider PBs over �nite �elds. There, we obtain explicit characterization and enumeration of subclasses of PBs under certain restrictions. Next, we describe these two parts in more detail.Item Orbit Spaces of Unimodular rows over Smooth Real Affine Algebras(Indian Statistical Institute, Kolkata, 2018) Tikader, SoumiItem Infinite Mode Quantum Gaussian States(Indian Statistical Institute, New Delhi, 2018) John, Tiju CherianItem Higher Chow Cycles on the Jacobian of curves(Indian Statistical Institute, Bangalore, 2019-03) Sarkar, SubhamItem A study of operators on the discrete analogue of Hardy spaces on homogeneous trees and on other structures(Indian Statistical Institute, Bangalore, 2019-01) MUTHUKUMAR, PItem On free-type rigid C*-tensor categories and their annular representations(Indian Statistical Institute, Kolkata, 2018-07) MADHAV REDDY, BItem Characterization of Eigenfunctions of the Laplace-Beltrami operator through Radial averages on Rank one symmetric spaces(Indian Statistical Institute,Kolkata, 2019) Naik, MunaItem Studies on Polynomial Rings through locally Nilpotient Derivations(Indian Statistical Institute,Kolkata, 2019-06) Dasgupta, NikhileshItem Some Topics involving Derived Categories over Noetherian Formal Schemes(Indian Statistical Institute, Bangalore, 2019-09) Singh, Saurabh