Indiana University-Purdue University Indianapolis (IUPUI) / Recommender systems are widely used in many domains. In this work, the importance of a recommender system in an online learning platform is discussed. After explaining the concept of adding an intelligent agent to online education systems, some features of the Course Networking (CN) website are demonstrated. Finally, the relation between CN, the intelligent agent (Rumi), and the recommender system is presented. Along with the argument of three different approaches for building a community recommendation system. The result shows that the Neighboring Collaborative Filtering (NCF) outperforms both the transfer learning method and the Continuous bag-of-words approach. The NCF algorithm has a general format with two various implementations that can be used for other recommendations, such as course, skill, major, and book recommendations.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Intrusion Detection Systems (IDS) that provide high detection rates but are black boxes leadto models that make predictions a security analyst cannot understand. Self-Organizing Maps(SOMs) have been used to predict intrusion to a network, while also explaining predictions throughvisualization and identifying significant features. However, they have not been able to compete withthe detection rates of black box models. Growing Hierarchical Self-Organizing Maps (GHSOMs)have been used to obtain high detection rates on the NSL-KDD and CIC-IDS-2017 network trafficdatasets, but they neglect creating explanations or visualizations, which results in another blackbox model.This paper offers a high accuracy, Explainable Artificial Intelligence (XAI) based on GHSOMs.One obstacle to creating a white box hierarchical model is the model growing too large and complexto understand. Another contribution this paper makes is a pruning method used to cut down onthe size of the GHSOM, which provides a model that can provide insights and explanation whilemaintaining a high detection rate.

Causal inference on networks often encounters interference problems. The potentialoutcomes of a unit depend not only on its treatment but also on the treatments of its neighbors in the network. The classic causal inference assumption of no interference among units is untenable in networks, and many fundamental results in causal inference may no longer hold in the presence of interference. To address interference problems in networks, this thesis proposes a novel Network Embedding Matching (NEM) framework for estimating causal effects under network interference. We recover causal effects based on network structure in an observed network. Furthermore, we extend the network interference from direct neighbors to k-hop neighbors. Unlike most previous studies, which had strong assumptions on interference among units in the network and did not consider network structure, our framework incorporates network structure into the estimation of causal effects. In addition, our NEM framework can be implemented in networks for randomized experiments and observational studies. Our approach is interpretable and can be easily applied to networks. We compare our approach with other existing methods in simulations and real networks, and we show that our approach outperforms other methods under linear and nonlinear network interference. / Statistics