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Multiple Learning for Generalized Linear Models in Big DataXiang Liu (11819735) 19 December 2021 (has links)
Big data is an enabling technology in digital transformation. It perfectly complements ordinary linear models and generalized linear models, as training well-performed ordinary linear models and generalized linear models require huge amounts of data. With the help of big data, ordinary and generalized linear models can be well-trained and thus offer better services to human beings. However, there are still many challenges to address for training ordinary linear models and generalized linear models in big data. One of the most prominent challenges is the computational challenges. Computational challenges refer to the memory inflation and training inefficiency issues occurred when processing data and training models. Hundreds of algorithms were proposed by the experts to alleviate/overcome the memory inflation issues. However, the solutions obtained are locally optimal solutions. Additionally, most of the proposed algorithms require loading the dataset to RAM many times when updating the model parameters. If multiple model hyper-parameters needed to be computed and compared, e.g. ridge regression, parallel computing techniques are applied in practice. Thus, multiple learning with sufficient statistics arrays are proposed to tackle the memory inflation and training inefficiency issues.
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A FRAMEWORK TO ASSESS POST-CONFLICT ENVIRONMENT IMPACT ON CONSTRUCTION ORGANIZATION PERFORMANCEQais Amarkhil (6616994) 15 May 2019 (has links)
<p>In the
field of the construction industry, the research work has been widely focused
on identifying key performance indicators and critical success factors without
assessing the impact of conflict environment factors. This study focusses on
the impact of post-conflict environment factors on local construction
organization performance. This research presents a performance prediction model
comprising the effect of post-conflict environment factors on construction
organization performance. The proposed framework of this study has four stages:
identify key performance indicators (KPIs), identify post-conflict environment
impacting factors, determine critical success factors (CSFs), and formulate
success strategy to improve performance. Analytical hierarchy process (AHP) and
multiple linear regression (MLR) techniques are
applied to analyze the data.</p>
<p>The
study finding indicates that there is a significant relationship between the
post-conflict condition impacting factors and local construction organization
performance, which is insufficiently studied
in previous research work. Thus, the
developed framework will benefit academic scholars and industry practitioners
to analyze and evaluate challenges and opportunities caused by different
external environment conditions in the post-conflict construction industry. </p>
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