Computer Science and Informatics

Artikel (refereegranskat)

• Li, Z., Tan, H., Jarfors, A., Jansson, P., Lattanzi, L. (2024). Smart-Cast: An AI-Based System for Semisolid Casting Process Control. Procedia Computer Science (pp. 2440-2447).
• Löfström, H., Löfström, T., Johansson, U., Sönströd, C. (2024). Calibrated explanations: With uncertainty information and counterfactuals. Expert systems with applications
• Peretz-Andersson, E., Tabares, S., Mikalef, P., Parida, V. (2024). Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach. International Journal of Information Management
• Flyckt, J., Andersson, F., Westphal, F., Mansson, A., Lavesson, N. (2023). Explaining rifle shooting factors through multi-sensor body tracking. Intelligent Data Analysis 27(2), 535-554
• Gleicher, M., Riveiro, M., Von Landesberger, T., Deussen, O., Chang, R., Gillman, C. (2023). A Problem Space for Designing Visualizations. IEEE Computer Graphics and Applications 43(4), 111-120
• Johansson, U., Löfström, T., Boström, H. (2023). Conformal Predictive Distribution Trees. Annals of Mathematics and Artificial Intelligence
• Lidberg, W., Paul, S., Westphal, F., Richter, K., Lavesson, N., Melniks, R. ... agren, A. (2023). Mapping Drainage Ditches in Forested Landscapes Using Deep Learning and Aerial Laser Scanning. Journal of irrigation and drainage engineering 149(3),
• Löfström, H., Löfström, T., Johansson, U., Sönströd, C. (2023). Investigating the impact of calibration on the quality of explanations. Annals of Mathematics and Artificial Intelligence
• Pomarlan, M., Hedblom, M., Porzel, R. (2023). Curiously exploring affordance spaces of a pouring task. Expert systems (Print) 40(6),
• Sadri, H., Yitmen, I., Tagliabue, L., Westphal, F., Tezel, A., Taheri, A., Sibenik, G. (2023). Integration of Blockchain and Digital Twins in the Smart Built Environment Adopting Disruptive Technologies—A Systematic Review. MDPI
• Shimizu, C., Hammar, K., Hitzler, P. (2023). Modular ontology modeling. Semantic Web 14(3), 459-489
• Höffner, S., Porzel, R., Hedblom, M., Pomarlan, M., Cangalovic, V., Pfau, J. ... Malaka, R. (2022). Deep understanding of everyday activity commands for household robots. Semantic Web 13(5), 895-909
• Johansson, U., Sönströd, C., Löfström, T., Boström, H. (2022). Rule extraction with guarantees from regression models. Pattern Recognition
• Kebede, R., Moscati, A., Tan, H., Johansson, P. (2022). Integration of manufacturers' product data in BIM platforms using semantic web technologies. Automation in Construction
• Peretz-Andersson, E., Torkar, R. (2022). Empirical AI Transformation Research: A Systematic Mapping Study and Future Agenda. e-Informatica Software Engineering Journal 16(1),
• Ståhl, N., Weimann, L. (2022). Identifying wetland areas in historical maps using deep convolutional neural networks. Ecological Informatics
• Arvidsson, S., Gullstrand, M., Sirmacek, B., Riveiro, M. (2021). Sensor fusion and convolutional neural networks for indoor occupancy prediction using multiple low-cost low-resolution heat sensor data. Sensors 21(4), 1-21
• Beauxis-Aussalet, E., Behrisch, M., Borgo, R., Chau, D., Collins, C., Ebert, D. ... Rhyne, T. (2021). The Role of Interactive Visualization in Fostering Trust in AI. IEEE Computer Graphics and Applications 41(6), 7-12
• García-Martín, E., Bifet, A., Lavesson, N. (2021). Energy modeling of Hoeffding tree ensembles. Intelligent Data Analysis 25(1), 81-104
• García-Martín, E., Lavesson, N., Grahn, H., Casalicchio, E., Boeva, V. (2021). Energy-aware very fast decision tree. International Journal of Data Science and Analytics 11(2), 105-126
• Kusetogullari, H., Yavariabdi, A., Hall, J., Lavesson, N. (2021). DIGITNET: A Deep Handwritten Digit Detection and Recognition Method Using a New Historical Handwritten Digit Dataset. Big Data Research
• Oucheikh, R., Löfström, T., Ahlberg, E., Carlsson, L. (2021). Rolling cargo management using a deep reinforcement learning approach. Logistics 5(1),
• Oucheikh, R., Pettersson, T., Löfström, T. (2021). Product verification using OCR classification and Mondrian conformal prediction. Expert systems with applications
• Ouchiekh, R., Fri, M., Touil, A., Echchatbi, A. (2021). Total weighted tardiness in the permutation flow shop under uncertainty. IFAC-PapersOnLine series 54(1), 1174-1180
• Riveiro, M., Thill, S. (2021). “That's (not) the output I expected!” On the role of end user expectations in creating explanations of AI systems. Artificial Intelligence
• Tan, H., Tarasov, V., Jarfors, A., Seifeddine, S. (2021). A design of fuzzy inference systems to predict tensile properties of as-cast alloy. The International Journal of Advanced Manufacturing Technology 113(3-4), 1111-1123
• Ulfenborg, B., Karlsson, A., Riveiro, M., Andersson, C., Sartipy, P., Synnergren, J. (2021). Multi-assignment clustering: Machine learning from a biological perspective. Journal of Biotechnology (pp. 1-10).
• Ventocilla, E., Martins, R., Paulovich, F., Riveiro, M. (2021). Scaling the Growing Neural Gas for Visual Cluster Analysis. Big Data Research
• Bae, J., Helldin, T., Riveiro, M., Nowaczyk, S., Bouguelia, M., Falkman, G. (2020). Interactive Clustering: A Comprehensive Review. ACM Digital Library
• Linusson, H., Johansson, U., Boström, H. (2020). Efficient conformal predictor ensembles. Neurocomputing (pp. 266-278).
• Riveiro, M. (2020). Explainable AI for maritime anomaly detection and autonomous driving. Dagstuhl Reports 9(11), 29-30
• Sirmacek, B., Riveiro, M. (2020). Occupancy prediction using low-cost and low-resolution heat sensors for smart offices. Sensors 20(19),
• Tarasov, V., Tan, H., Jarfors, A., Seifeddine, S. (2020). Fuzzy logic-based modelling of yield strength of as-cast A356 alloy. Neural Computing & Applications 32(10), 5833-5844
• Ventocilla, E., Riveiro, M. (2020). A comparative user study of visualization techniques for cluster analysis of multidimensional data sets. Information Visualization 19(4), 318-338
• Buendia, R., Kogej, T., Engkvist, O., Carlsson, L., Linusson, H., Johansson, U. ... Ahlberg, E. (2019). Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors. Journal of Chemical Information and Modeling 59(3), 1230-1237
• Dórea, F., Vial, F., Hammar, K., Lindberg, A., Lambrix, P., Blomqvist, E., Revie, C. (2019). Drivers for the development of an Animal Health Surveillance Ontology (AHSO). Preventive Veterinary Medicine (pp. 39-48).
• Johansson, U., Löfström, T., Linusson, H., Boström, H. (2019). Efficient Venn Predictors using Random Forests. Machine Learning 108(3), 535-550
• Tan, H., Tarasov, V., Jarfors, A., Seifeddine, S. (2019). Fuzzy Logic Based Modelling of Cast Component Properties. IFAC-PapersOnLine 52(13), 1132-1137
• Alirezaie, M., Hammar, K., Blomqvist, E. (2018). SmartEnv as a Network of Ontology Patterns. Semantic Web 9(6), 903-918
• Johansson, U., Linusson, H., Löfström, T., Boström, H. (2018). Interpretable regression trees using conformal prediction. Expert systems with applications (pp. 394-404).
• Löfström, H., Löfström, T., Johansson, U. (2018). Interpretable instance-based text classification for social science research projects. Archives of Data Science, Series A 5(1),
• Westphal, F., Grahn, H., Lavesson, N. (2018). Efficient document image binarization using heterogeneous computing and parameter tuning. International Journal on Document Analysis and Recognition 21(1-2), 41-58
• Boström, H., Linusson, H., Löfström, T., Johansson, U. (2017). Accelerating difficulty estimation for conformal regression forests. Annals of Mathematics and Artificial Intelligence 81(1-2), 125-144