Industrial Symbiosis Recommender Systems

For a long time, humanity has lived upon the paradigm that the amounts of natural resources are unlimited and that the environment has ample regenerative capacity. However, the notion to shift towards sustainability has resulted in a worldwide adoption of policies addressing resource efficiency and preservation of natural resources.One of the key environmental and economic sustainable operations that is currently promoted and enacted in the European Union policy is Industrial Symbiosis. In industrial symbiosis, firms aim to reduce the total material and energy footprint by circulating traditional secondary production process outputs of firms to become part of an input for the production process of other firms.This thesis directs attention to the design considerations for recommender systems in the highly dynamic domain of industrial symbiosis. Recommender systems are a promising technology that may facilitate in multiple facets of the industrial symbiosis creation as they reduce the complexity of decision making. This typical strength of recommender systems has been responsible for improved sales and a higher return of investments. That provides the prospect for industrial symbiosis recommenders to increase the number of synergistic transactions that reduce the total environmental impact of the process industry in particular

Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts

A Framework for Industrial Symbiosis Systems for Agent-Based Simulation

The industrial symbiosis (IS) practice refers to replacing production inputs with wastes generated by other production processes, thus helping to close the loop of manufacturing processes. Companies adopting IS can reduce the amounts of wastes disposed of in landfills and primary inputs used in production processes, enhancing their production efficiency and reducing their production cost. The Agent-based modeling (ABM) approach has been recently adopted to advance the knowledge in the IS field, in particular to explore the extent to which the establishment of IS relationships can be affected by policy measures, benefit-sharing contracts, information-sharing tools, or social factors. However, the agentbased models proposed in the literature are highly different in terms of agents considered, their attributes, possible actions, and decisional rules. In particular, these models might result far from the real world, in terms of how the agents are modeled, as well as on the actions that they can undertake. The aim of this paper is to provide a general framework on how to model IS systems with ABM in a comprehensive way. The paper provides a literature review on IS aimed at identifying the potential agents in IS, their attributes, and their potential actions, shaped by technical, economic, and regulatory issues affecting the behavior of agents in terms of strategic management. Based on the results of the literature review, a general framework is proposed and discussed. This paper is expected to advance the knowledge on ABM for IS by providing useful suggestions to modelers and academicians