| 1. |
- Sutinen, Martti, et al.
(författare)
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Web-Based Analytical Decision Support System
- 2010
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Ingår i: Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. - : IEEE conference proceedings. - 9781424481347 - 9781424481354 ; , s. 575-579
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Konferensbidrag (refereegranskat)abstract
- This paper presents a web-application supporting structured decision modelling and analysis. The application allows for decision modelling with respect to different preferences and views, allowing for numerically imprecise and vague background probabilities, values, and criteria weights, which further can be adjusted in an interactive fashion when considering calculated decision outcomes. The web-application is based on a decision tool that has been used in a large number of different domains over the last 15 years, ranging from investment decision analysis for companies to public decision support for local governments.
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| 2. |
- Danielson, Mats, et al.
(författare)
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A second-order-based decision tool for evaluating decisions under conditions of severe uncertainty
- 2020
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Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 191
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Tidskriftsartikel (refereegranskat)abstract
- The requirement to assign precise numerical values to model entities such as criteria weights, probabilities, and utilities is too strong in most real-life decision situations, and hence alternative representations and evaluation mechanisms are important to consider. In this paper, we discuss the DecideIT 3.0 state-of-the-art software decision tool and demonstrate its functionality using a real-life case. The tool is based on a belief mass interpretation of the decision information, where the components are imprecise by means of intervals and qualitative estimates, and we discuss how multiplicative and additive aggregations influence the resulting distribution over the expected values.
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| 3. |
- Humble, Niklas, et al.
(författare)
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Design science for Small Scale Studies : Recommendations for Undergraduates and Junior Researchers
- 2023
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Ingår i: European Conference on Research Methodology for Business and Management Studies. - Reading (UK) : Academic Conferences and Publishing International Limited. - 9781914587719 - 9781914587726 ; , s. 87-92
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Konferensbidrag (refereegranskat)abstract
- Design science is a research methodology that can be applied for both small scale studies at undergraduate level and for large scale application in the industry. Design science is a research methodology with several branches, with slightly different processes built around a common foundation. This paper has a focus on the branch developed by Johannesson and Perjons, and the five-phase model that is included in this branch: 1) explicate problem, 2) define requirements, 3) design and develop artefact, 4) demonstrate artefact, and 5) evaluate artefact. All these five phases must of course be carried out in a complete large-scale project in many real-world developments. However, the problem with applying a design science research project for undergraduates is that a thorough implementation of all the five phases is often too demanding for a Bachelor’s or a Master's thesis. A reason for this is that several of the phases are better carried out in an iterative manner to obtain a quality result, which is time-consuming. The aim of this paper is to discuss the challenges and opportunities in applying design science for small scale studies, such as those conducted by undergraduates in their theses or by researchers new to the field. Based on this discussion, the paper concludes with a set of recommendations for how the design science methodology can be modified and applied to accommodate these smaller studies. The main recommendation is, as the principle for quality research, to delimit and to choose a specific focus that is carried out in depth. Some examples of focuses, that also are recommended by Johannesson and Perjons, are requirements and development focused design science research or evaluation focused design science research. An interesting follow-up to this position paper would be to study the application of design science in Bachelor’s theses and where the emphasis is placed? Moreover, it would be interesting to investigate how design science is applied by researchers and compare if their emphasis in the design science methodology differs from that of undergraduates.
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| 4. |
- Ekenberg, Love, et al.
(författare)
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Second order effects in interval valued decision graph models
- 2005
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Ingår i: Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence. - : AAAI Press. - 1577352343 ; , s. 728-733
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Konferensbidrag (refereegranskat)abstract
- Second-order calculations may significantly increase a decision maker's understanding of a decision situation when handling aggregations of imprecise representations, as is the case in decision trees or influence diagrams, while the use of only first-order results gives an incomplete picture. The results apply also to approaches which do not explicitly deal with second-order distributions, instead using only first-order concepts such as upper and lower bounds.
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| 5. |
- Mozelius, Peter, Docent, 1959-, et al.
(författare)
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On the Use of Generative AI for Literature Reviews : an Exploration of Tools and Techniques
- 2024
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Ingår i: 23<sup>rd</sup> European Conference on Research Methodology for Business and Management Studies. - Porto : Academic Conferences and Publishing International Limited. ; , s. 161-168, s. 161-168, s. 161-168
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Konferensbidrag (refereegranskat)abstract
- To carry out a literature review often involves hard and tedious work. There is a tradition of using facilitating tools, that extended to the AI field in 2018 when iris.ai appeared. Today, in the emerging field of Generative AI tools based on Large Language Models, there has been rapid development of new literature search tools and approaches. This study has the aim of exploring this vast array of Generative AI tools, in a literature study where some of the found tools were used to facilitate the selection of relevant publication. Three research questions guided this study: RQ1) "What Generative AI tools can be found in literature?", RQ2) "Which of these tools could be of use in the literature review conducted in this study, and how?", and RQ3) "What are the ethical aspects of using Generative AI tools in literature studies?” The approach has been a scoping review, built around a search that combined the keywords: "AI supported", "AI generated", "AI based" and "Literature review". An initial result set was filtered with inclusion exclusion criteria in a strive for an interesting quality answer to the research questions. However, most publications that passed the filtering lacked any potential to contribute to answer the research questions. The most interesting finding in the first search was a hint about the new feature 'Scopus AI'. A new search with the Scopus AI tool resulted in a small but very relevant set of publications. These publications were analysed in a deductive inductive thematic analysis, and primary sorted into the categories of: 'Generative AI Tools', 'Supportive AI Techniques', and 'Ethical Issues'. Findings indicate that there is a wide variety of tools that can facilitate the skimming process of a literature, and to provide adequate summaries of retrieved publication. However, authors recommendation is to keep the tools on the facilitating support level, and that the main analysis and conclusion should be human conducted. With this, rather traditional approach, researchers will have clearly less ethical issues to consider. Finally, the ethical aspects of Generative AI tools in research ought to be investigated more in detail, in a separate future study.
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| 6. |
- Svanberg, Jan, et al.
(författare)
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Prediction of Controversies and Estimation of ESG Performance : An Experimental Investigation Using Machine Learning
- 2023
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Ingår i: Handbook of Big Data and Analytics in Accounting and Auditing. - Singapore : Springer Publishing Company. - 9789811944598 - 9789811944604 ; , s. 65-87
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Bokkapitel (refereegranskat)abstract
- We develop a new methodology for computing environmental, social, and governance (ESG) ratings using a mode of artificial intelligence (AI) called machine learning (ML) to make ESG more transparent. The ML algorithms anchor our rating methodology in controversies related to non-compliance with corporate social responsibility (CSR). This methodology is consistent with the information needs of institutional investors and is the first ESG methodology with predictive validity. Our best model predicts what companies are likely to experience controversies. It has a precision of 70–84 per cent and high predictive performance on several measures. It also provides evidence of what indicators contribute the most to the predicted likelihood of experiencing an ESG controversy. Furthermore, while the common approach of rating companies is to aggregate indicators using the arithmetic average, which is a simple explanatory model designed to describe an average company, the proposed rating methodology uses state-of-the-art AI technology to aggregate ESG indicators into holistic ratings for the predictive modelling of individual company performance.Predictive modelling using ML enables our models to aggregate the information contained in ESG indicators with far less information loss than with the predominant aggregation method.
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| 7. |
- Tran, Thanh
(författare)
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Drill Failure Detection based on Sound using Artificial Intelligence
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In industry, it is crucial to be able to detect damage or abnormal behavior in machines. A machine's downtime can be minimized by detecting and repairing faulty components of the machine as early as possible. It is, however, economically inefficient and labor-intensive to detect machine fault sounds manual. In comparison with manual machine failure detection, automatic failure detection systems can reduce operating and personnel costs. Although prior research has identified many methods to detect failures in drill machines using vibration or sound signals, this field still remains many challenges. Most previous research using machine learning techniques has been based on features that are extracted manually from the raw sound signals and classified using conventional classifiers (SVM, Gaussian mixture model, etc.). However, manual extraction and selection of features may be tedious for researchers, and their choices may be biased because it is difficult to identify which features are good and contain an essential description of sounds for classification. Recent studies have used LSTM, end-to-end 1D CNN, and 2D CNN as classifiers for classification, but these have limited accuracy for machine failure detection. Besides, machine failure occurs very rarely in the data. Moreover, the sounds in the real-world dataset have complex waveforms and usually are a combination of noise and sound presented at the same time.Given that drill failure detection is essential to apply in the industry to detect failures in machines, I felt compelled to propose a system that can detect anomalies in the drill machine effectively, especially for a small dataset. This thesis proposed modern artificial intelligence methods for the detection of drill failures using drill sounds provided by Valmet AB. Instead of using raw sound signals, the image representations of sound signals (Mel spectrograms and log-Mel spectrograms) were used as the input of my proposed models. For feature extraction, I proposed using deep learning 2-D convolutional neural networks (2D-CNN) to extract features from image representations of sound signals. To classify three classes in the dataset from Valmet AB (anomalous sounds, normal sounds, and irrelevant sounds), I proposed either using conventional machine learning classifiers (KNN, SVM, and linear discriminant) or a recurrent neural network (long short-term memory). For using conventional machine learning methods as classifiers, pre-trained VGG19 was used to extract features and neighborhood component analysis (NCA) as the feature selection. For using long short-term memory (LSTM), a small 2D-CNN was proposed to extract features and used an attention layer after LSTM to focus on the anomaly of the sound when the drill changes from normal to the broken state. Thus, my findings will allow readers to detect anomalies in drill machines better and develop a more cost-effective system that can be conducted well on a small dataset.There is always background noise and acoustic noise in sounds, which affect the accuracy of the classification system. My hypothesis was that noise suppression methods would improve the sound classification application's accuracy. The result of my research is a sound separation method using short-time Fourier transform (STFT) frames with overlapped content. Unlike traditional STFT conversion, in which every sound is converted into one image, a different approach is taken. In contrast, splitting the signal into many STFT frames can improve the accuracy of model prediction by increasing the variability of the data. Images of these frames separated into clean and noisy ones are saved as images, and subsequently fed into a pre-trained CNN for classification. This enables the classifier to become robust to noise. The FSDNoisy18k dataset is chosen in order to demonstrate the efficiency of the proposed method. In experiments using the proposed approach, 94.14 percent of 21 classes were classified successfully, including 20 classes of sound events and a noisy class.
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| 8. |
- Ekenberg, Love, et al.
(författare)
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Hazards and quality control in humanitarian demining
- 2018
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Ingår i: International Journal of Quality & Reliability Management. - 0265-671X .- 1758-6682. ; 35:4, s. 897-913
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The purpose of this paper is to discuss the adequacy of International Mine Action Standards 09.20 (IMAS 09.20) and the used standards ISO 2859 in the context of demining. Design/methodology/approach: The authors show how the actual quality level acceptable quality limit (AQL) significantly affects the average total quality cost for one lot with a single sampling plan and, consequently, the average total quality cost, and as AQL increases, the cost of rejecting a lot and the cost of sampling increase. Findings: The sampling plans for demining are not always optimal given economical and other concerns and that other mechanisms should be considered. Practical implications: Addressing opportunity costs for adopting wide samplings plans instead of clearing uncleared land per default, as well as balancing producer and consumer consequences seems, therefore, to be highly relevant from a socio-economical perspective. Originality/value The general understanding of quality management and the systems involved are limited within the mine action sector. IMAS and most national mine action standards provide only a fairly narrow description of the issue. This implies that the field is missing opportunities to achieve efficiency and effectiveness, as well as to learn from and improve upon past experiences. The authors demonstrate herein that sampling provides little additional confidence as to whether a particular area is free from explosive hazards and substantial savings can be made compared to the current practice.
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| 9. |
- Fasth, Tobias, et al.
(författare)
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Disagreement Constrained Action Selection in Participatory Portfolio Decision Analysis
- 2016
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Ingår i: International Journal of Innovation, Management and Technology. - : EJournal Publishing. - 2010-0248. ; 7:1, s. 1-7
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Tidskriftsartikel (refereegranskat)abstract
- In some portfolio decision problems it is not possible or interesting to constrain portfolios with a monetary budget. Instead it might be of interest to investigate how disagreement among a group of decision makers or stakeholders can be used as a constraint, and how this affects the portfolio composition. In this paper we present complementary decision evaluation methods for group portfolio decision analysis in situations where the stakeholders have conflicting preferences. The approach supports the analysis of a portfolio of planned actions in urban planning when a large group of stakeholders have inconsistent opinions with respect to the performance of each action. The group of stakeholders is, for each criterion, partitioned into two disagreeing groups based upon their views on the actions' performance. The distance between these two groups is then measured. An action's aggregated disagreement taking into account all criteria is then used as the action's associated resource constraint, and portfolios can be generated by solving a sequence of Knapsack problems. The robustness of the portfolios can be further evaluated with an a priori sensitivity analysis. The suggested approach supports decision makers by elucidating how the portfolio composition changes when the actions' aggregated disagreement increases.
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| 10. |
- van Laere, Joeri, et al.
(författare)
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Challenges for critical infrastructure resilience : Cascading effects of payment system disruptions
- 2017
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Ingår i: Proceedings of the 14th ISCRAM Conference. - Linköping : Linköping university. ; , s. 281-292, s. 281-292, s. 281-292
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Konferensbidrag (refereegranskat)abstract
- Critical infrastructures become more and more entangled and rely extensively on information technology. A deeper insight into the relationships between critical infrastructures enables the actors involved to more quickly understand the severity of information technology disruptions and to identify robust cross-functional mitigating actions. This study illustrates how and why disruptions in the payment system in Sweden could create cascading effects in other critical infrastructures with potentially severe consequences for many citizens, government institutions and companies. Data from document studies, interviews and workshops with field experts reveal seven challenges for collective cross-functional critical infrastructure resilience that need to be dealt with: 1) Shortage of food, fuel, cash, medicine; 2) Limited capacity of alternative payment solutions; 3) Cities are more vulnerable than the countryside; 4) Economically vulnerable groups in society are more severely affected; 5) Trust maintenance needs; 6) Crisis communication needs; 7) Fragmentation of responsibility for critical infrastructures across many actors.
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