@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} } @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{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} } @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} }