M Tech Dissertations

Now showing 1 - 4 of 4

Open Access
Explanations by Counterfactual Argument in Recommendation Systems
(Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Pathak, Yash; Rana, Arpit
In recent advances in the domains of Artificial Intelligence (AI) and MachineLearning (ML), complex models are used. Due to their complexity and approaches,they have black box type of nature and raise the question of a trustworthy for decisionprocess especially in the high cost decisions scenario. To overcome thisproblem, users of these systems can ask for an explanation about the decisionwhich can be provided by system in various ways. One way of generating theseexplanations is by the help of Counterfactual (CF) arguments. Although there is adebate on how AI can generate these explanations, either by Correlation or CausalInference, in Recommendation Systems (RecSys) the aim is to generate these explanationswith minimum Oracle calls and have near optimal length (eg., in termsof interactions) of provided explanations. In this study we analyze the nature ofCFs and different methods (eg., Model Agnostic approach, Genetic Algorithms(GA)) to generate them along with the quality measures. Extensive experimentsshow that the generation of CFs can be done through multiple approaches andselecting optimal CFs will improve the explanations.
Open Access
Deep Learning for Severity Level-based Classification of Dysarthria
(2021) Gupta, Siddhant; Patil, Hemant A.
Dysarthria is a motor speech disorder in which muscles required to speak somehow gets damaged or paralyzed resulting in an adverse effect to the articulatory elements in the speech and rendering the output voice unintelligible. Dysarthria is considered to be one of the most common form of speech disorders. Dysarthria occurs as a result of several neurological and neuro-degenerative diseases, such as Parkinson’s Disease, Cerebral palsy, etc. People suffering from dysarthria face difficulties in conveying vocal messages and emotions, which in many cases transform into depression and social isolation amongst the individuals. Dysarthria has become a major speech technology issue as the systems that work efficiently for normal speech, such as Automatic Speech Recognition systems, do not provide satisfactory results for corresponding dysarthric speech. In addition, people suffering from dysarthria are generally limited by their motor functions. Therefore, development of voice assisted systems for them become all the more crucial. Furthermore, analysis and classification of dysarthric speech can be useful in tracking the progression of disease and its treatment in a patient. In this thesis, dysarthria has been studied as a speech technology problem to classify dysarthric speech into four severity-levels. Since, people with dysarthria face problem during long speech utterances, short duration speech segments (maximum 1s) have been used for the task, to explore the practical applicability of the thesis work. In addition, analysis of dysarthric speech has been done using different methods such as time-domain waveforms, Linear prediction profile, Teager Energy Operator profile, Short-Time Fourier Transform etc., to distinguish the best representative feature for the classification task. With the rise in Artificial Intelligence, deep learning techniques have been gaining significant popularity in the machine classification and pattern recognition tasks. Therefore, to keep the thesis work relevant, several machine learning and deep learning techniques, such as Gaussian Mixture Models (GMM), Convolutional Neural Network (CCN), Light Convolutional Neural Network (LCNN), and Residual Neural Network (ResNet) have been adopted. The severity levelbased classification task has been evaluated on various popular measures such as, classification accuracy and F1-scores. In addition, for comparison with the short duration speech, classification has also been done on long duration speech (more than 1 sec) data. Furthermore, to enhance the relevance of the work, experiments have been performed on statically meaningful and widely used Universal Access-Speech Corpus.
Open Access
The Study of Cycles in 2-connected Graphs Specifically Odd Graphs
(Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Thakker, Avani; Muthu, Rahul
Counting the number of cycles in an undirected graph is a classical problemwhich is known to be intractable and so research on this problem typically focuses on approximation algorithms, special cases, heuristics and some variants of the problem. This problem has been extensively studied for its applications in areas of communication systems, artificial intelligence and signal processing. In Complexity theory, this problem lies in the class of #P-complete problem. There may be exponentially many simple cycles in a graph. We observed growth in the number of cycles by adding ears to a 2-connected graph. As analyzed, the growth was exponential. Counting or finding cycles and paths of graphs like complete graphs, presents no interest, in particular since everything is already known analytically. Hence, we studied the cycle structure in Odd Graphs. We analytically obtained cycle lengths that are certainly present in an odd graph without traversing the graph structure. Further, we added minimal number of edges to an odd graph to make the graph pancyclic.
Open Access
Tikitaka based attack strategy for soccer simulation 2D
(Dhirubhai Ambani Institute of Information and Communication Technology, 2014) Jain, Priyanka; Dasgupta, Sourish
Soccer Simulation 2D has grown as a testbed for MultiAgent research over the years. As it provides a complete distributed platform for simulating the algorithm proposed. I have also used the same platform for my research. Chapter-1, describes about the History of robocup. It also describes the robocup as a research challenge. It also describes the problem statement in detail. Additionally, the salient features of the problem, the assumptions and gives the proposed methodology overview. Chapter-2, gives the working details of the simulator used for the experiments. Chapter-3, describes the work done in Soccer Simulation 2D league. It describes the last years top 8 teams of the competition. It also describes the techniques used by some other research groups for solving the problem. Chapter-4, is the detailed explanation of the proposed work. The proposed tikitaka based attack strategy, is a reactive strategy. Every player is supposed to react on the basis of the information available in that cycle. Chapter-5, gives the implementation details and the results. The proposed algorithm is implemented on Agent 2D base, which is available under free license. Chapter-6,This is the concluding chapter. It decribes what all things are learnt during research and how the proposed method is useful. Chapter-7,Followed is the Appendices which describes the defense strategy that could be implemented and additional is the reformation technique which after observing the game was considered important.

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