Browsing Division of Interdisciplinary Research by Subject "Machine Learning"
Now showing items 1-14 of 14
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Applications Of Machine Learning To Anomaly Based Intrusion Detection
(2009-03-02)This thesis concerns anomaly detection as a mechanism for intrusion detection in a machine learning framework, using two kinds of audit data : system call traces and Unix shell command traces. Anomaly detection systems ... -
Data Driven Stabilization Schemes for Singularly Perturbed Differential Equations
This thesis presents a novel way of leveraging Artificial Neural Network (ANN) to aid conventional numerical techniques for solving Singularly Perturbed Differential Equation (SPDE). SPDEs are challenging to solve with ... -
Data-efficient Deep Learning Algorithms for Computer Vision Applications
The performance of any deep learning model depends heavily on the quantity and quality of the available training data. The generalization of the trained deep models improves with the availability of a large number of ... -
Epistasis Detection and Phenotype Prediction in GWAS Using Machine Learning Methods
Genome-wide association studies (GWAS) are used to find the association between genetic variants, Single Nucleotide Polymorphisms (SNPs), and phenotypic traits or diseases in a population. The number of GWAS has increased ... -
Genomics-based Assessments of Stemness and DNA Damage Response in Oral Cancer
Gingivobuccal oral squamous cell carcinoma (OSCC-GB) is a prominent clinical subtype of head and neck squamous cell carcinoma in India, predominantly affecting habitual users of smokeless tobacco. The genetic basis of ... -
Learning Compact Architectures for Deep Neural Networks
(2018-05-22)Deep neural networks with millions of parameters are at the heart of many state of the art computer vision models. However, recent works have shown that models with much smaller number of parameters can often perform just ... -
Malware Analysis using Profile Hidden Markov Models and Intrusion Detection in a Stream Learning Setting
(2018-02-18)In the last decade, a lot of machine learning and data mining based approaches have been used in the areas of intrusion detection, malware detection and classification and also traffic analysis. In the area of malware ... -
Methods for Improving Data-efficiency and Trustworthiness using Natural Language Supervision
Traditional strategies to build machine learning based classification systems employ discrete labels as targets. This limits the usefulness of such systems in two ways. First, the generalizability of these systems is limited ... -
On Leveraging Dynamic Processes in Large Social Networks for Smart Cities
The concept of smart city which began as being synonymous with electronically networked community underwent significant changes with the growth in mobile devices and social networking. This expanded the outlook of smart ... -
On the Optimality of Generative Adversarial Networks — A Variational Perspective
Generative adversarial networks (GANs) are a popular learning framework to model the underlying distribution of images. GANs comprise a min-max game between the generator and the discriminator. While the generator transforms ... -
Predictive Motion Planning for Safe and Efficient Autonomous Driving
The advent of Autonomous Vehicles (AVs) has the potential to revolutionize transportation systems, promising significant improvements in safety, efficiency, and passenger comforts. Safety, the cornerstone of AVs, demands ... -
Quantifying the past and future variability in the Bay of Bengal using statistical and deep learning methods
The Bay of Bengal, the world's largest bay, along with the Andaman Sea, a peripheral sea situated in the southeastern part of the bay, is crucial to the economic and maritime security of India. Understanding the dynamics ... -
Relating Representations in Deep Learning and the Brain
Deep Neural Networks (DNN) inspired by the human brain have redefined the state-of-the-art performance in AI during the past decade. Much of the research is still trying to understand and explain the function of these ... -
Systems Optimizations for DNN Training and Inference on Accelerated Edge Devices
Deep Neural Networks (DNNs) have had a significant impact on a wide variety of domains, such as Autonomous Vehicles, Smart Cities, and Healthcare, through low-latency inferencing on edge computing devices close to the data ...