Toward Automating and Systematizing the Use of Domain Knowledge in Feature Selection

University of Minnesota Ph.D. dissertation. August 2015. Major: Computer Science. Advisor: Maria Gini. 1 computer file (PDF); xi, 185 pages.Constructing prediction models for real-world domains often involves practical complexities that must be addressed to achieve good prediction results. Often, there are too many sources of data (features). Limiting the set of features in the prediction model is essential for good performance, but prediction accuracy may be degraded by the inadvertent removal of relevant features. The problem is even more acute in situations where the number of training instances is limited, as limited sample size and domain complexity are often attributes of real-world problems. This thesis explores the practical challenges of building regression models in large multivariate time-series domains with known relationships between variables. Further, we explore the conventional wisdom related to preparing datasets for model calibration in machine learning, and discuss best practices for learning time-varying concepts from data. The core contribution of this work is a novel wrapper-based feature selection framework called Developer-Guided Feature Selection (DGFS). It systematically incorporates domain knowledge for domains characterized by a large number of observable features. The observable features may be related to each other by logical, temporal, or spatial relationships, some of which are known to the model developer a priori. The approach relies on limited domain-specific knowledge but can replace or improve upon more elaborate domain specific models and on fully automated feature selection for many applications. As a wrapper-based approach, DGFS can augment existing multivariate techniques used in high-dimensional domains to produce improved modeling results particularly in situations where the volume of training data is limited. We demonstrate the viability of our method in several complex domains (natural and synthetic) that have significant temporal aspects and many observable features

An Empirical Analysis to Control Product Counterfeiting in the Automotive Industry\u27s Supply Chains in Pakistan

The counterfeits pose significant health and safety threat to consumers. The quality image of firms is vulnerable to the damage caused by the expanding flow of counterfeit products in today’s global supply chains. The counterfeiting markets are swelling due to globalization and customers’ willingness to buy counterfeits, fueling illicit activities to explode further. Buyers look for the original parts are deceived by the false (deceptive) signals’ communication. The counterfeiting market has become a multi-billion industry but lacks detailed insights into the supply side of counterfeiting (deceptive side). The study aims to investigate and assess the relationship between the anti-counterfeiting strategies and improvement in the firm’s supply performance within the internal and external supply chain quality management context in the auto-parts industry’s supply chains in Pakistan

URI Undergraduate and Graduate Course Catalog 2021-2022

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URI Undergraduate and Graduate Course Catalog 2016-2017

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URI Undergraduate and Graduate Course Catalog 2015-2016

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URI Undergraduate and Graduate Course Catalog 2020-2021

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WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

URI Undergraduate and Graduate Course Catalog 2019-2020

This is a downloadable PDF version of the University of Rhode Island course catalog.https://digitalcommons.uri.edu/course-catalogs/1071/thumbnail.jp