TY - CHAP A1 - Voß, A. A1 - Coulon, Carl-Helmut A1 - Gebhardt, F. T1 - KI-Methoden beim Entwurf komplexer Gebäude N2 - Anhand von Ergebnissen aus dem FABEL-Projekt wird gezeigt, welche Beiträge Methoden der Künstlichen Intelligenz, insbesondere der Wissensverarbeitung beim Entwurf komplexer Gebäude leisten können. Exemplarisch werden spezialisierte wissensintensive Methoden, und allgemeine fallbasierte Methoden zum Retrieval und zur Wiederverwendung früherer Entwürfe vorgestellt. Es werden Fragen der Integration von Wissen, Fällen 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 zusätzlichen Orientierung des Planers. Die Ergebnisse sind interessant für den Entwurf komplexer Unikate, dürften aber auch als Zusatz zu elektronisch angebotenen Katalogen relevant sein. KW - Bauentwurf KW - CAD KW - Künstliche Intelligenz Y1 - 1997 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-4207 ER - TY - JOUR A1 - Schweppenhäuser, Gerhard T1 - Der Sklavenaufstand der instrumentellen Vernunft : philosophische Überlegungen zur künstlichen Intelligenz N2 - Wissenschaftliches Kolloquium vom 27. bis 30. Juni 1996 in Weimar an der Bauhaus-Universität zum Thema: ‚Techno-Fiction. Zur Kritik der technologischen Utopien' T3 - Thesis // Bauhaus-Universität - 43.1997,1-2/153-157 KW - Künstliche Intelligenz KW - Bauhaus-Kolloquium KW - Weimar KW - 1996 Y1 - 1997 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-11559 ER - TY - JOUR A1 - Schwenke, Nicolas A1 - Söbke, Heinrich A1 - Kraft, Eckhard T1 - Potentials and Challenges of Chatbot-Supported Thesis Writing: An Autoethnography JF - Trends in Higher Education N2 - 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. KW - Chatbot KW - Künstliche Intelligenz KW - Hochschulbildung KW - AIEd KW - artificial intelligence KW - academic writing KW - ChatGPT KW - education Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20231207-65016 UR - https://www.mdpi.com/2813-4346/2/4/37 VL - 2023 IS - Volume 2, issue 4 SP - 611 EP - 635 PB - MDPI CY - Basel ER - TY - CHAP A1 - Parmee, Ian A1 - Abraham, Johnson T1 - User-centric Evolutionary Design Systems - the Visualisation of Emerging Multi-Objective Design Information N2 - 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. KW - Konzipieren KW - Bauwerk KW - Künstliche Intelligenz Y1 - 2004 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-1094 ER - TY - THES A1 - Moosbrugger, Jennifer T1 - Design Intelligence - Human-Centered-Design for the development of industrial AI/ML agents N2 - 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. KW - Künstliche Intelligenz KW - Benutzererlebnis KW - Human-centered Design KW - Datenkompetenz KW - Prozessmodell KW - AI, computational thinking KW - Design, UX, Human-Centered-Design KW - process, tools, methods KW - collaboration KW - Artificial Intelligence Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20230719-64098 ER - TY - INPR A1 - Khosravi, Khabat A1 - Sheikh Khozani, Zohreh A1 - Cooper, James R. T1 - Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms N2 - 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. KW - Maschinelles Lernen KW - Künstliche Intelligenz KW - Data Mining KW - Hydraulic geometry KW - Gravel-bed rivers Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20211004-44998 N1 - This is the pre-peer reviewed version of the following article: https://www.sciencedirect.com/science/article/abs/pii/S1364815221002085 ; https://doi.org/10.1016/j.envsoft.2021.105165 VL - 2021 ER - TY - JOUR A1 - Ghazvinei, Pezhman Taherei A1 - Darvishi, Hossein Hassanpour A1 - Mosavi, Amir A1 - Yusof, Khamaruzaman bin Wan A1 - Alizamir, Meysam A1 - Shamshirband, Shahaboddin A1 - Chau, Kwok-Wing T1 - Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network JF - Engineering Applications of Computational Fluid Mechanics N2 - 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. KW - Künstliche Intelligenz KW - Sustainable production KW - ELM KW - prediction KW - machine learning KW - sugarcane KW - estimation KW - growth mode KW - extreme learning machine KW - OA-Publikationsfonds2018 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20181017-38129 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2018.1526119 VL - 2018 IS - 12,1 SP - 738 EP - 749 PB - Taylor & Francis ER - TY - JOUR A1 - Band, Shahab S. A1 - Janizadeh, Saeid A1 - Chandra Pal, Subodh A1 - Chowdhuri, Indrajit A1 - Siabi, Zhaleh A1 - Norouzi, Akbar A1 - Melesse, Assefa M. A1 - Shokri, Manouchehr A1 - Mosavi, Amir Hosein T1 - Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration JF - Sensors N2 - 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. KW - Grundwasser KW - Nitratbelastung KW - Künstliche Intelligenz KW - ground water contamination KW - machine learning KW - big data KW - hydrological model KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43364 UR - https://www.mdpi.com/1424-8220/20/20/5763 VL - 2020 IS - Volume 20, issue 20, article 5763 SP - 1 EP - 23 PB - MDPI CY - Basel ER -