@article{BandJanizadehChandraPaletal.,
  author    = {Band, Shahab S. and Janizadeh, Saeid and Chandra Pal, Subodh and Chowdhuri, Indrajit and Siabi, Zhaleh and Norouzi, Akbar and Melesse, Assefa M. and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration},
  series = {Sensors},
  volume    = {2020},
  journal   = {Sensors},
  number    = {Volume 20, issue 20, article 5763},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/s20205763},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43364},
  pages     = {1 -- 23},
  abstract  = {Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70\%) and testing (30\%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.},
  subject      = {Grundwasser},
  language  = {en}
}
@article{GhazvineiDarvishiMosavietal.,
  author    = {Ghazvinei, Pezhman Taherei and Darvishi, Hossein Hassanpour and Mosavi, Amir and Yusof, Khamaruzaman bin Wan and Alizamir, Meysam and Shamshirband, Shahaboddin and Chau, Kwok-Wing},
  title     = {Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network},
  series = {Engineering Applications of Computational Fluid Mechanics},
  volume    = {2018},
  journal   = {Engineering Applications of Computational Fluid Mechanics},
  number    = {12,1},
  publisher = {Taylor \& Francis},
  doi       = {10.1080/19942060.2018.1526119},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181017-38129},
  pages     = {738 -- 749},
  abstract  = {Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.},
  subject      = {K{\"u}nstliche Intelligenz},
  language  = {en}
}
@unpublished{KhosraviSheikhKhozaniCooper,
  author    = {Khosravi, Khabat and Sheikh Khozani, Zohreh and Cooper, James R.},
  title     = {Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms},
  volume    = {2021},
  doi       = {10.25643/bauhaus-universitaet.4499},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20211004-44998},
  abstract  = {Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of hydraulic geometry is lacking. This study provides the first quantification of this potential. Using at-a-station field data, predictions of flow depth, water-surface width and longitudinal water surface slope are made using three standalone data mining techniques -, Instance-based Learning (IBK), KStar, Locally Weighted Learning (LWL) - along with four types of novel hybrid algorithms in which the standalone models are trained with Vote, Attribute Selected Classifier (ASC), Regression by Discretization (RBD), and Cross-validation Parameter Selection (CVPS) algorithms (Vote-IBK, Vote-Kstar, Vote-LWL, ASC-IBK, ASC-Kstar, ASC-LWL, RBD-IBK, RBD-Kstar, RBD-LWL, CVPSIBK, CVPS-Kstar, CVPS-LWL). Through a comparison of their predictive performance and a sensitivity analysis of the driving variables, the results reveal: (1) Shield stress was the most effective parameter in the prediction of all geometry dimensions; (2) hybrid models had a higher prediction power than standalone data mining models, empirical equations and traditional machine learning algorithms; (3) Vote-Kstar model had the highest performance in predicting depth and width, and ASC-Kstar in estimating slope, each providing very good prediction performance. Through these algorithms, the hydraulic geometry of any river can potentially be predicted accurately and with ease using just a few, readily available flow and channel parameters. Thus, the results reveal that these models have great potential for use in stable channel design in data poor catchments, especially in developing nations where technical modelling skills and understanding of the hydraulic and sediment processes occurring in the river system may be lacking.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@phdthesis{Moosbrugger,
  author    = {Moosbrugger, Jennifer},
  title     = {Design Intelligence - Human-Centered-Design for the development of industrial AI/ML agents},
  doi       = {10.25643/bauhaus-universitaet.6409},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20230719-64098},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {201},
  abstract  = {This study deals with design for AI/ML systems, more precisely in the industrial AI context based on case studies from the factory automation field. It therefore touches on core concepts from Human-Centered-Design (HCD), User Experience (UX) and Human Computer Interaction (HCI) on one hand, as well as concepts from Artificial Intelligence (AI), Machine Learning (ML) and the impact of technology on the other. The case studies the research is based on are within the industrial AI domain. However, the final outcomes, the findings, solutions, artifacts and so forth, should be transferable to a wider spectrum of domains. The study's aim is to examine the role of designers in the age of AI and the factors which are relevant, based on the hypothesis that current AI/ML development lacks the human perspective, which means that there are pitfalls and challenges that design can help resolve. The initial literature review revealed that AI/ML are perceived as a new design material that calls for a new design paradigm. Additional research based on qualitative case study research was conducted to gain an overview of the relevant issues and challenges. From this, 17 themes emerged, which together with explorative expert interviews and a structured literature review, were further analyzed to produce the relevant HCD, UX and HCI themes. It became clear that designers need new processes, methods, and tools in the age of AI/ML in combination with not only design, but also data science and business expertise, which is why the proposed solution in this PhD features process modules for design, data science and business collaboration. There are seven process modules and their related activities and dependencies that serve as guidelines for practitioners who want to design intelligence. A unified framework for collecting use case exemplars was created, based on a workshop with different practitioners and researchers from the area of AI/ML to support and enrich the process modules with concrete projects examples.},
  subject      = {K{\"u}nstliche Intelligenz},
  language  = {en}
}
@inproceedings{ParmeeAbraham2004,
  author    = {Parmee, Ian and Abraham, Johnson},
  title     = {User-centric Evolutionary Design Systems - the Visualisation of Emerging Multi-Objective Design Information},
  doi       = {10.25643/bauhaus-universitaet.109},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-1094},
  year      = {2004},
  abstract  = {The paper describes further developments of the interactive evolutionary design concept relating to the emergence of mutually inclusive regions of high performance design solutions. These solutions are generated from cluster-oriented genetic algorithm (COGAs) output and relate to a number of objectives introduced during the preliminary design of military airframes. The data-mining of multi-objective COGA (moCOGA) output further defines these regions through the application of clustering algorithms, data reduction and variable attribute relevance analyses. A number of visual representations of the COGA output projected onto both variable and objective space are presented. The multi-objective output of the COGA is compared to output from a Strength Pareto Evolutionary Algorithm (SPEA-II) to illustrate the manner in which moCOGAs can generate good approximations to Pareto frontiers.},
  subject      = {Konzipieren <Technik>},
  language  = {en}
}
@article{SchwenkeSoebkeKraft,
  author    = {Schwenke, Nicolas and S{\"o}bke, Heinrich and Kraft, Eckhard},
  title     = {Potentials and Challenges of Chatbot-Supported Thesis Writing: An Autoethnography},
  series = {Trends in Higher Education},
  volume    = {2023},
  journal   = {Trends in Higher Education},
  number    = {Volume 2, issue 4},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/higheredu2040037},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20231207-65016},
  pages     = {611 -- 635},
  abstract  = {The release of the large language model-based chatbot ChatGPT 3.5 in November 2022 has brought considerable attention to the subject of artificial intelligence, not only to the public. From the perspective of higher education, ChatGPT challenges various learning and assessment formats as it significantly reduces the effectiveness of their learning and assessment functionalities. In particular, ChatGPT might be applied to formats that require learners to generate text, such as bachelor theses or student research papers. Accordingly, the research question arises to what extent writing of bachelor theses is still a valid learning and assessment format. Correspondingly, in this exploratory study, the first author was asked to write his bachelor's thesis exploiting ChatGPT. For tracing the impact of ChatGPT methodically, an autoethnographic approach was used. First, all considerations on the potential use of ChatGPT were documented in logs, and second, all ChatGPT chats were logged. Both logs and chat histories were analyzed and are presented along with the recommendations for students regarding the use of ChatGPT suggested by a common framework. In conclusion, ChatGPT is beneficial for thesis writing during various activities, such as brainstorming, structuring, and text revision. However, there are limitations that arise, e.g., in referencing. Thus, ChatGPT requires continuous validation of the outcomes generated and thus fosters learning. Currently, ChatGPT is valued as a beneficial tool in thesis writing. However, writing a conclusive thesis still requires the learner's meaningful engagement. Accordingly, writing a thesis is still a valid learning and assessment format. With further releases of ChatGPT, an increase in capabilities is to be expected, and the research question needs to be reevaluated from time to time.},
  subject      = {Chatbot},
  language  = {en}
}
@article{Schweppenhaeuser1997,
  author    = {Schweppenh{\"a}user, Gerhard},
  title     = {Der Sklavenaufstand der instrumentellen Vernunft : philosophische {\"U}berlegungen zur k{\"u}nstlichen Intelligenz},
  doi       = {10.25643/bauhaus-universitaet.1155},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-11559},
  year      = {1997},
  abstract  = {Wissenschaftliches Kolloquium vom 27. bis 30. Juni 1996 in Weimar an der Bauhaus-Universit{\"a}t zum Thema: ‚Techno-Fiction. Zur Kritik der technologischen Utopien'},
  subject      = {K{\"u}nstliche Intelligenz},
  language  = {de}
}
@inproceedings{VossCoulonGebhardt1997,
  author    = {Voß, A. and Coulon, Carl-Helmut and Gebhardt, F.},
  title     = {KI-Methoden beim Entwurf komplexer Geb{\"a}ude},
  doi       = {10.25643/bauhaus-universitaet.420},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-4207},
  year      = {1997},
  abstract  = {Anhand von Ergebnissen aus dem FABEL-Projekt wird gezeigt, welche Beitr{\"a}ge Methoden der K{\"u}nstlichen Intelligenz, insbesondere der Wissensverarbeitung beim Entwurf komplexer Geb{\"a}ude leisten k{\"o}nnen. Exemplarisch werden spezialisierte wissensintensive Methoden, und allgemeine fallbasierte Methoden zum Retrieval und zur Wiederverwendung fr{\"u}herer Entw{\"u}rfe vorgestellt. Es werden Fragen der Integration von Wissen, F{\"a}llen und Daten diskutiert. Der Prototyp des FABEL-Projekts verwendet die Metapher der virtuellen Baustelle, um die verschiedenen Methoden als Planungswerkzeuge in einem CAD-System integriert anzubieten. Ein Planungsmodell dient der zus{\"a}tzlichen Orientierung des Planers. Die Ergebnisse sind interessant f{\"u}r den Entwurf komplexer Unikate, d{\"u}rften aber auch als Zusatz zu elektronisch angebotenen Katalogen relevant sein.},
  subject      = {Bauentwurf},
  language  = {de}
}