005.1 Programmierung
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Anomaly Detection is a very acute technical problem among various business enterprises. In this thesis a combination of the Growing Neural Gas and the Generalized Matrix Learning Vector Quantization is presented as a solution based on collected theoretical and practical knowledge. The whole network is described and implemented along with references and experimental results. The proposed model is carefully documented and all the further open researching questions are stated for future investigations.
In this work, we discuss the key role that “conflict minerals” (Gold, Coltan, Cobalt, Tin, Tungsten) play in global supply chains and high-technology industries, and the issues surrounding their extraction and trade in origin
countries, particularly in the African Congo Basin and the Great Lakes Region. We discuss ongoing international efforts to combat violence, child labour and human rights violations at mineral extraction areas, particularly in the Democratic Republic of the Congo (DRC), where very large mineral reserves have been discovered. We present the OECD Due Diligence Guidance for Responsible Supply Chains of Minerals from Conflict-Affected and High-Risk Areas, and the
GOTS MineralTrace mineral proof-of-origin and trade chain certification solution developed by ibes AG in Germany, which automates and simplifies the implementation of the OECD Guidance. We discuss a pilot project in DRC involving the GOTS GoldTrace application, based on the MineralTrace platform. We point out MineralTrace’s benefits and its limitations. We analyse possible solutions to said limitations, including an analysis of blockchain-based transactional information exchange and record keeping systems, and finally we propose a new MineralTrace Application Programming Interface (API) that solves current limitations, introduces configuration flexibility for client applications, introduces workflow flexibility to adapt MineralTrace to any country or region, and simplifies data export functionality.