Latent Dirichlet allocation

JOMO KENYATTA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY SCHOOL OF COMPUTING AND INFORMATION TECHNOLOGY Optimized Dynamic Latent Topic Model for Big Text Data Analytics NAME: Geoffrey Mariga Wambugu REGISTRATION NUMBER: CS481-4692/2014 LECTURER: Prof. Waweru Mwangi A thesis proposal submitted in partial fulfilment of the requirement for the Unit SCI 4201 Advanced Research Methodology of the degree of Doctor of Philosophy in Information Technology at the School of Computing and Information Technology

data, i.e., users’ check-in records, are modeled as observed random variables, shown as shaded circles in Figure 1. As a POI has both semantic and geographical attributes, we introduce two latent random variables, topic z and region r, which are responsible for generating them, respectively. Based on the two latent factors, TRM aims to model and infer users’

Dynamic News Classification using Machine Learning Introduction Why this classification is needed ? (Ashutosh) The exponential growth of the data may lead us to a time in future where huge amount of data would not be able to be managed easily. Text Classification is done through Text Mining study which would help sorting the important texts from the content or a document to manage the data or information easily. //Give a scenario, where classification would be mandatory. Advantages of classification

Iowa state University’s focus has always been its students. As part of their ISU Strategic Plan FY 2017-2022 one of its goal is to provide quality education, conduct research which has high impact on the society through which quality of life is improved. This goal can be achieved through tracking the interests and motivation of students to apply to the graduate programs. Due to ever challenging financial situation and increased tuition fee it is has become very important to develop more competitive

My decision to pursue a PhD is derived from my passion for science and engineering paired with my abilities in the field of machine learning and applied statistics. I consider myself fortunate to be part of the Department of Computer Science, University of Florida for my master studies. More importantly, I am glad to have two excellent professors in this field as advisors, Dr. Pader and Dr. Jilson, who are guiding me throughout my graduate studies. They assisted me to decide and pursue the courses

Abstract- Nowadays medical data has been search by people for gathering the more and more information about drugs. The dialog discussions on chronic diseases and drug, as well as online audits and sites are getting to be more important assets for patients. Patients read online reviews, blogs and discussion forum ideas to get information from other patients with similar condition. Reviews of medication from patients are largely available on the internet. Partitioning data from these significant collections

Many digital libraries (such as: Google Scholar, PubMed, CiteSeer etc..) exist which help in retrieving the scholarly research publications. The only unique key to search in these libraries is the author name. The problem of ambiguity in name creates difficulty in identifying the publications of the same author. This author name ambiguity in research publications occur due to multiple authors having identical names (Polysem) or same author having multiple names (Synonym). This type of ambiguity problem

Knowledge-driven DSS Yaw et al (2014) wanted to develop a knowledge elicitation strategy to create and acquire home energy retrofit knowledge which can be integrated in the development of an intelligent decision support system to gain more understanding of home energy retrofits and serve as a single source of comprehensive and reliable information for homeowners. However, the adoption rate is low regardless of well-established benefits due to insufficient or unsuitable information for decision making

Nowadays, information can easily be accessed through multiple channels such as search engines, emails, and social networks. The rapid innovations innovations in technology contributes to the increasing social network usage, wherein users can now access them through their mobile devices (Yakushev & Mityagin, 2014). A report shows that a Filipinos averagely use social networking services for 4.3 hours a day, which ranks Philippines as the second country with the most hours spent on social networks

Building blocks of Machine learning: Mahout and Spark Machine learning is the new boom in software industry which helps in training the computer to think, organize and process data by itself. The main intent of machine learning is that machine learns to observe data, extract important information from it and grasp on its own to predict, recommend or alter any action without any human mediation. This requires various algorithms over varied systems. For the ease of these algorithms, Apache has come