Multi Faceted Text Classification using Supervised Machine Learning Models

In recent year’s document management tasks (known as information retrieval) increased a lot due to availability of digital documents everywhere. The need of automatic methods for extracting document information became a prominent method for organizing information and knowledge discovery. Text Classification is one such solution, where in the natural language text is assigned to one or more predefined categories based on the content. In my research classification of text is mainly focused on sentiment label classification. The idea proposed for sentiment analysis is multi-class classification of online movie reviews. Many research papers discussed the classification of sentiment either positive or negative, but in this approach the user reviews are classified based on their sentiment to multi classes like positive, negative, neutral, very positive and very negative. This classification task would help the business to classify the user reviews same as star ratings, which are manually given by users. This paper also proposes a better classification approach with multi-tier prediction model. The goal of this research is to provide a better understanding classification for sentiment analysis by applying different preprocessing techniques and selecting suitable features like bag of words, stemming and removing stop words, POS Tagging etc. These features are adjusted to fit with some of the machine learning text classification algorithms such as Naïve Bayes, SVM, sand SGD on frameworks like WEKA, SVMLight & Scikit Learn

Energy efficient processor operation and vibration based energy harvesting schemes for wireless sensor nodes

A wireless Sensor Network (WSN) is a network of spatially distributed autonomous sensors deployed in the environment in order to cooperatively monitor physical or environmental conditions such as temperature, sound, pressure, motion or pollutants at different locations. Each node in a sensor network is equipped with a radio transceiver, a microprocessor and an energy source such as a battery which should be replaced periodically. To increase the lifetime of the network keeping the small size in mind, methods should be put in place to reduce the power consumption of the sensor node or increase the node life and/or to supply power to the battery from external sources. In this thesis, the first paper presents an energy-efficient frequency adaptation based approach to minimize the power consumption of the microprocessor in an attempt to increase the lifetime of the sensor node...The second paper, on the other hand, presents an energy harvesting circuitry to charge the battery of the sensor node so that the time to replacement can be extended --Abstract, page iv

Fault-tolerance embedding of rings and arrays in star and pancake graphs

The star and pancake graphs are useful interconnection networks for connecting processors in a parallel and distributed computing environment. The star network has been widely studied and is shown to possess attactive features like sublogarithmic diameter, node and edge symmetry and high resilience. The star/pancake interconnection graphs, {dollar}S\sb{n}/P\sb{n}{dollar} of dimension n have n! nodes connected by {dollar}{(n-1).n!\over2}{dollar} edges. Due to their large number of nodes and interconnections, they are prone to failure of one or more nodes/edges; In this thesis, we present methods to embed Hamiltonian paths (H-path) and Hamiltonian cycles (H-cycle) in a star graph {dollar}S\sb{n}{dollar} and pancake graph {dollar}P\sb{n}{dollar} in a faulty environment. Such embeddings are important for solving computational problems, formulated for array and ring topologies, on star and pancake graphs. The models considered include single-processor failure, double-processor failure, and multiple-processor failures. All the models are applied to an H-cycle which is formed by visiting all the ({dollar}{n!\over4!})\ S\sb4/P\sb4{dollar}s in an {dollar}S\sb{n}/P\sb{n}{dollar} in a particular order. Each {dollar}S\sb4/P\sb4{dollar} has an entry node where the cycle/path enters that particular {dollar}S\sb4/P\sb4{dollar} and an exit node where the path leaves it. Distributed algorithms for embedding hamiltonian cycle in the presence of multiple faults, are also presented for both {dollar}S\sb{n}{dollar} and {dollar}P\sb{n}{dollar}

Interaction between palladium and silicon carbide: A study for Triso nuclear fuel

The unique properties of SiC (wide band gap, high thermal conductivity, high electron mobility, and resistance to radiation effects) permits it to operate reliably at very high temperatures even in harsh environments and as coating layers in TRISO nuclear fuels. To optimize the SiC for use as a coating material in the nuclear reactor fuel design, it is important to elucidate the chemical bonding and interface formation of metal fission products (Pd, Ag, Cs, etc.) with SiC coating layers and to study the diffusion behavior of fission products into TRISO coating materials. It is known in the TRISO community that Pd is able to corrode the SiC layer. However, the detailed nature of this corrosion is still unknown; The objective of this thesis is to study the influence of fission products (Pd) on the chemical and electronic properties of the SiC coating layer in TRISO nuclear fuel particles. For this purpose, three series of interfaces (Pd/SiC) were prepared and studied using X-ray Photoelectron Spectroscopy (XPS) and Ultraviolet Photoelectron Spectroscopy (UPS); The experimental approach comprises the preparation of Pd/SiC interfaces in-situ in our ultra-high vacuum system by electron-beam deposition of Pd onto suitable prepared SiC single crystal surfaces. In order to understand the impact of the SiC surface properties on the interface formation, a variety of surface preparation and modification schemes were employed. The results obtained give detailed information about the Pd/SiC interface formation. Thereby the study shows a diffusion at this interface, which is an important first step in understanding the corrosion of the SiC-layer in the TRISO particles

SYNTHESIS, SINTERING, AND ELECTRONIC CONDUCTIVITY STUDIES OF MEDIUM- AND HIGH-ENTROPY PEROVSKITE OXIDES

The application of the entropy concept to stabilize oxide systems opens the possibility of discovering new materials with unique structural and functional properties. High-entropy alloys and oxides, which are based on the entropy stabilization concept and composed of multi-principal elements, have the potential to tailor structural and functional properties to meet specific needs. The study of lanthanum-based perovskite materials that benefit from the entropy stabilization approach is a promising area of research.However, the inherent randomness of multi-principal elements presents new challenges, making it difficult to predict their behavior. To understand these difficulties, we have initiated a methodical investigation of La-based medium- and high-entropy perovskite oxides. This study focuses on the synthesis, characterization, sintering mechanism, and electrical conductivity properties of nine La1-xCax(A1/3, B1/3, C1/3)O3 medium-entropy perovskite oxide systems (A, B, and C = three combination of Cr or Co or Fe or Ni or Mn) and one La1-xCax(Cr0.2Co0.2Fe0.2Ni0.2Mn0.2)O3 high-entropy perovskite oxide system (for x = 0.1 to 0.3). This research aims to provide better understanding of: (1) synthesis process, (2) temperature of single-phase formation, (3) the impact of various combinations of multiple B-site transitional elements and Ca doping on crystal structure, and microstructure (4) sintering mechanism and (5) electrical conductivity properties

Perfect matchings and Quantum physics: Bounding the dimension of GHZ states

Greenberger-Horne-Zeilinger (GHZ) states are quantum states involving at least three entangled particles. They are of fundamental interest in quantum information theory and have several applications in quantum communication and cryptography. Motivated by this, physicists have been designing various experiments to create high-dimensional GHZ states using multiple entangled particles. In 2017, Krenn, Gu and Zeilinger discovered a bridge between experimental quantum optics and graph theory. A large class of experiments to create a new GHZ state are associated with an edge-coloured edge-weighted graph having certain properties. Using this framework, Cervera-Lierta, Krenn, and Aspuru-Guzik proved using SAT solvers that through these experiments, the maximum dimension achieved is less than $3,4$ using $6,8$ particles, respectively. They further conjectured that using $n$ particles, the maximum dimension achievable is less than $\dfrac{n}{{2}}$ [Quantum 2022]. We make progress towards proving their conjecture by showing that the maximum dimension achieved is less than $\dfrac{n}{\sqrt{2}}$.Comment: 11 pages, 3 figure

Impact of COVID-19 on chronic disease management

Routine care for chronic disease is an ongoing major challenge. We aimed to evaluate the impact of COVID-19 on routine care for chronic diseases. A deeper understanding helps to increase the health system’s resilience and adequately prepare for the next waves of the pandemic. Diabetes, heart failure, chronic kidney disease, and hypertension were the most impacted conditions due to the reduction in access to care. It is important routine care continues in spite of the pandemic, to avoid a rise in non-COVID-19-related morbidity and mortality. This is a review article discussing the potential impact of COVID-19 on chronic disease management

Edge-coloured graphs with only monochromatic perfect matchings and their connection to quantum physics

Krenn, Gu and Zeilinger initiated the study of PMValid edge-colourings because of its connection to a problem from quantum physics. A graph is defined to have a PMValid $k$-edge-colouring if it admits a $k$-edge-colouring (i.e. an edge colouring with $k$-colours) with the property that all perfect matchings are monochromatic and each of the $k$ colour classes contain at least one perfect matching. The matching index of a graph $G$, $\mu(G)$ is defined as the maximum value of $k$ for which $G$ admits a PMValid $k$-edge-colouring. It is easy to see that $\mu(G)\geq 1$ if and only if $G$ has a perfect matching (due to the trivial $1$-edge-colouring which is PMValid). Bogdanov observed that for all graphs non-isomorphic to $K_4$, $\mu(G)\leq 2$ and $\mu(K_4)=3$. However, the characterisation of graphs for which $\mu(G)=1$ and $\mu(G)=2$ is not known. In this work, we answer this question. Using this characterisation, we also give a fast algorithm to compute $\mu(G)$ of a graph $G$. In view of our work, the structure of PMValid $k$-edge-colourable graphs is now fully understood for all $k$. Our characterisation, also has an implication to the aforementioned quantum physics problem. In particular, it settles a conjecture of Krenn and Gu for a sub-class of graphs.Comment: 18 pages and 7 figure

Longitudinal Analysis of Readmission Risk Using Machine Learning

Unnecessary hospital readmissions are a major problem impacting millions of patients and costing billions of dollars per year. Unfortunately, accurate assessment of readmission risk remains an open problem. In this study, several methods and tools for readmission prediction were developed using UNC hospital data available from April 1, 2014 to November 1, 2014. This study investigated the change in readmission risk for patients over time to explore at which times high-risk patients can be most effectively identified. Toward this goal, multiple Machine Learning models of hospital readmission using patient history prior to admission and comparing them with baseline model which uses data during hospitalization were developed. The results of this study find that patients history did not produce better predictive performance than the baseline model that considered just hospitalization data. However, the dataset considered is small and results may not generalize to large data sets over longer period of time.Master of Science in Information Scienc

Generalizations of Length Limited Huffman Coding for Hierarchical Memory Settings

In this paper, we study the problem of designing prefix-free encoding schemes having minimum average code length that can be decoded efficiently under a decode cost model that captures memory hierarchy induced cost functions. We also study a special case of this problem that is closely related to the length limited Huffman coding (LLHC) problem; we call this the soft-length limited Huffman coding problem. In this version, there is a penalty associated with each of the n characters of the alphabet whose encodings exceed a specified bound D(? n) where the penalty increases linearly with the length of the encoding beyond D. The goal of the problem is to find a prefix-free encoding having minimum average code length and total penalty within a pre-specified bound P. This generalizes the LLHC problem. We present an algorithm to solve this problem that runs in time O(nD). We study a further generalization in which the penalty function and the objective function can both be arbitrary monotonically non-decreasing functions of the codeword length. We provide dynamic programming based exact and PTAS algorithms for this setting