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Analysis of Pima Dataset in R

11 minute read

Published:

In this blog post I would like to provide an anylsis of the Pima dataset that is available in the faraway R-package. The exercises below are part of the course MAST90139: Statistical Modelling for Data Science at the University of Melbourne.

Analysis of Swiss Dataset in R

23 minute read

Published:

In this blog post I would like to provide an anylsis of the Swiss dataset which can be accessed in R. The exercises below are part of the course MAST90139: Statistical Modelling for Data Science at the University of Melbourne.

AI for Social Impact - A great leap forward

16 minute read

Published:

Over the past years, there have been major advancements in Artificial Intelligence and given the intense interest and investment in AI by industry and Academia, we believe that now is the time to focus our energies in applying AI to solve complex social problems in health, sustainability, community violence, and in assisting low resource communities.

Leveraging car diagnostics capabilities via OBD

less than 1 minute read

Published:

In a recent project that I did for my current employer (BCG Platinion), we were tasked with developing a digital Proof of Concept app that retrieves data from cars and analyzes it for interesting insights. The specifics of our work are of course under NDA, but nonetheless we were allowed to publish two short blog posts on LinkedIn that describe the great results that we have accomplished as a team.

Various articles on Business Managed IT

less than 1 minute read

Published:

Over the last couple of weeks, I published various articles on the concept of Business Managed IT on LinkedIn. Make sure to check them out if you would like to get the latest insights from practitioners in the industry and understand how Business Managed IT could be beneficial for your organization.

Deep Learning for toxic comment classification

1 minute read

Published:

Last month, I had the pleasure to take part in the GermEval 2018 workshop on the identification of offensive comments in German language microposts. During my stay at KAUST, me and my supervisors took part in Task 1 (binary classification) of the shared task for German language classification.

Expanding the knowledge graph of research

less than 1 minute read

Published:

From April to July 2018, I had the chance to work as a Visiting Student for the Laboratory Machine Intelligence and kNowledge Engineering (MINE) at King Abdullah University for Science and Technology (KAUST).

portfolio

publications

Success Factors in Business-Managed IT - A Case Study Analysis at a Large German Industrial Company

Published in SKILL 2018-Studierendenkonferenz Informatik, 2018

The terminology of Business-Managed IT refers to Shadow-IT systems which are operated overtly in the business units (BUs) and with the awareness from the IT department. They are a common phenomenon in corporations and usually emerge if the formal IT organization is unable to provide the BUs with solutions that meet their requirements. Because of this, Business-Managed IT is a highly significant area for an exploratory study, and both academia and practitioners can benefit from knowledge on how such systems can be successfully managed. In this present research, a case study analysis of five Business-Managed IT systems at a large German industrial company was conducted. Interviews with stakeholders involved in these systems were transcribed and analyzed to derive Critical Success Factors (CSFs) for Business-Managed IT. After a careful analysis of the data, a total of nine CSFs for Business-Managed IT systems were obtained. They fall into three dimensions: Project Dimension, Organizational Dimension and System Dimension.

Recommended citation: Bachfischer, Matthias. "Success Factors in Business-Managed IT-A Case Study Analysis at a Large German Industrial Company." SKILL 2018-Studierendenkonferenz Informatik (2018). https://dl.gi.de/bitstream/handle/20.500.12116/28985/SKILL2018-07.pdf?sequence=1&isAllowed=y

Offensive Comment Classification on German Language Microposts

Published in 14th Conference on Natural Language Processing KONVENS 2018, 2018

In this paper, we present two deep-learning based classifier systems for the identification of offensive comments in German Language microposts: A bidirectional LSTM model and a CNN model. Our objective is to compare the performance of these two systems with a traditional, machine-learning based SVM classifier and to evaluate our approach on Task 1 (binary classification) of the GermEval 2018 shared task.

Recommended citation: Bachfischer, M., Akujuobi, U., & Zhang, X. (2018, September). KAUSTmine-Offensive Comment Classification on German Language Microposts. In 14th Conference on Natural Language Processing KONVENS 2018. https://ids-pub.bsz-bw.de/files/8490/Ruppenhofer_Siegel_Wiegand_GermEval2018_Proceedings.pdf#page=39

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teaching