LEVERAGING MULTI-TASK LEARNING GRAPH NEURAL NETWORKS FOR IMPROVING FRAUD DETECTION

This thesis explores the challenges of detecting fraudulent activities such as money laundering detection in the financial ecosystems and forged review detection in e-commerce websites. One of the major differences between fraud detection and other classification problems is the class imbalance ratio. Class imbalance is a phenomenon that occurs when the number of examples in each class of a dataset is not evenly distributed, for example, the ratio between the number of illicit transactions and that of licit transactions in a fraud detection problem is very small. In this thesis, we explored three graph datasets commonly used for benchmarking fraud detection techniques, the Elliptic dataset, the fraud Amazon dataset, and the fraud Yelp dataset. Our goal is to increase the raw feature set by node embeddings generated by complementary tasks such as link prediction, and link classification before the final classification task. The current limitations of existing tools in accurately estimating fraud, along with the difficulties associated with detecting fraudulent activities in general, are discussed. First, we use interrelated tasks such as link prediction, and link classification to generate node embeddings that are added to the raw features to capture graph topological information, which is then used for training a supervised machine learning algorithm to detect fraudulent nodes

Selective Facet Engineering of Ni₁₂P₅ Nanoparticle for Maximization of Electrocatalytic Oxidative Reaction of Biomass Chemicals

Electrocatalytic hydrogen generation is a prime research topic for the large-scale production of hydrogen fuel. High energy demanding oxygen evolution process impedes the production of H2 at low potentials. Conversion of biomass to value-added chemicals or fuels is appraised as an upcycling process, which is advantageous for resource management. Coupling of hydrogen generation at the cathode with oxidative conversion of biomass to market-demanded chemicals at the anode is a sustainable approach to increase energy efficiency in hybrid electrolysis. For that purpose, Ni-based anode electrocatalysts are in the forefront for ease of formation of hypervalent NiIII species, at a mild anodic potential, which act as an oxidant to propagate the oxidation and dehydrogenation reactions. Herein, we synthesized Ni12P5 nanohexagon via kinetic stabilization of high index {425̅} facets and compared the electrocatalytic activity toward various biomass-derived platform chemicals oxidation with the thermodynamically stable Ni12P5 nanosphere. The Ni12P5 nanohexagon outperforms the current state-of-the-art catalysts regarding mass activity, product conversion, and Faradaic yield. Ease of formation of active species, faster charge transfer, and enhanced adsorption of substrates over {425̅} facets resulted in this superior activity. This shape-directing effects on Ni12P5 ensured potential advantage of 150 mV in hybrid electrolysis over water splitting reaction when ethanol was used as a substrate in a two-electrode electrolyzer cell