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Sökning: db:Swepub > Övrigt vetenskapligt/konstnärligt > Mälardalens universitet

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51.
  • Ahlström, Karin, 1964-, et al. (författare)
  • Municipalities working for Agenda 2030: Review and Agenda for future Research
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Considering the population density of urban areas, their levels of consumption and emissions, their influence on social equality and inclusion, and the resources and power allocated to urban actors, it is important to examine how municipalities organize work as they proceed to implement Agenda 2030 and to what effect. A systematic literature review is used to analyse 77 articles, published between 2017 and 2022, that examine the sustainability activities of municipal actors in Europe. Two themes are identified and explained: a) a paradigm shift in local government; b) the mobilization of socio-technical approaches (e.g., smart cities and regenerative land use). Also, the limitations of the literature and some areas for future research are discussed: research comparing practices across regions and countries; research analysing the interaction of different initiatives within a municipality; and research examining the role of municipal companies.
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52.
  • Ahlström, Karin, 1964-, et al. (författare)
  • Negotiating shared Responsibility for sustainable urban Development: Pronouns and In-here-ness as rhetorical Resources
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • This article explores how issues of shared responsibility are negotiated in the realm of managing collaborative efforts between organizational actors for sustainable urban development abiding to the Agenda 2023 – with a particular focus on the rhetorical resources used in such negotiations. Through empirical material from recorded meetings in a one-year project at a municipal company, the article contributes to previous literature on collaborative ways of organizing and managing complex public challenges. With a focus on the discursive construction of shared responsibility, the concept of in-here-ness is introduced to denote accepted and assumed responsibility, which may shift through the use of pronouns: from a narrow ‘I’ or ‘we’ of stakeholders to a wider ‘we’ of collaborating parties. The article further contributes to the empirical field of sustainable development at the municipal level.
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53.
  • Ahmadpanah, Seyed Mohammad Mehdi, 1996, et al. (författare)
  • Poster : Data Minimization by Construction for Trigger-Action Applications
  • 2023
  • Ingår i: CCS 2023 - Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. - : Association for Computing Machinery, Inc. - 9798400700507 ; , s. 3522-3524
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • Trigger-Action Platforms (TAPs) enable applications to integrate various devices and services otherwise unconnected. Recent features of TAPs introduce additional sources of data such as queries in IFTTT. The current TAPs, like IFTTT, demand that trigger and query services transmit excessive amounts of user data to the TAP. To limit the data to what is actually necessary for the execution to comply with the principle of data minimization, input services should send no more than the necessary data. LazyTAP proposes a new paradigm of data minimization by construction in TAPs, introducing a novel perspective for data collection from input services. While the existing push-all approach of TAPs entails coarse-grained data over-approximation, LazyTAP pulls input data on-demand at the level of attributes, once accessed by the app execution. Thanks to the fine granularity provided by LazyTAP, multiple trigger and query services can be naturally minimized while the behavior of app executions is preserved. In addition, a great benefit of LazyTAP is being seamless for third-party app developers. By leveraging laziness, LazyTAP defers computation and proxies objects to load necessary remote data behind the scenes. Our evaluation study on app benchmarks shows that on average LazyTAP improves minimization by 95% over IFTTT and by 38% over minTAP, with a tolerable performance overhead. This poster goes into further details about LazyTAP and elaborates on its prototype implementation. 
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54.
  • Ahmed, Mobyen Uddin (författare)
  • A case-based multi-modal clinical system for stress management
  • 2010
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements.  A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions. A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option.
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55.
  • Ahmed, Mobyen Uddin, 1976-, et al. (författare)
  • A Hybrid Case-Based System in Stress Diagnosis and Treatment
  • 2012
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Computer-aided decision support systems play anincreasingly important role in clinical diagnosis and treatment.However, they are difficult to build for domains where thedomain theory is weak and where different experts differ indiagnosis. Stress diagnosis and treatment is an example of such adomain. This paper explores several artificial intelligencemethods and techniques and in particular case-based reasoning,textual information retrieval, rule-based reasoning, and fuzzylogic to enable a more reliable diagnosis and treatment of stress.The proposed hybrid case-based approach has been validated byimplementing a prototype in close collaboration with leadingexperts in stress diagnosis. The obtained sensitivity, specificityand overall accuracy compared to an expert are 92%, 86% and88% respectively.
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56.
  • Ahmed, Mobyen Uddin, 1976- (författare)
  • A Multimodal Approach for Clinical Diagnosis and Treatment
  • 2011
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • A computer-aided Clinical Decision Support System (CDSS) for diagnosis and treatment often plays a vital role and brings essential benefits for clinicians. Such a CDSS could function as an expert for a less experienced clinician or as a second option/opinion of an experienced clinician to their decision making task. Nevertheless, it has been a real challenge to design and develop such a functional system where accuracy of the system performance is an important issue. This research work focuses on development of intelligent CDSS based on a multimodal approach for diagnosis, classification and treatment in medical domains i.e. stress and post-operative pain management domains. Several Artificial Intelligence (AI) techniques such as Case-Based Reasoning (CBR), textual Information Retrieval (IR), Rule-Based Reasoning (RBR), Fuzzy Logic and clustering approaches have been investigated in this thesis work. Patient’s data i.e. their stress and pain related information are collected from complex data sources for instance, finger temperature measurements through sensor signals, pain measurements using a Numerical Visual Analogue Scale (NVAS), patient’s information from text and multiple choice questionnaires. The proposed approach considers multimedia data management to be able to use them in CDSSs for both the domains. The functionalities and performance of the systems have been evaluated based on close collaboration with experts and clinicians of the domains. In stress management, 68 measurements from 46 subjects and 1572 patients’ cases out of ≈4000 in post-operative pain have been used to design, develop and validate the systems. In the stress management domain, besides the 68 measurement cases, three trainees and one senior clinician also have been involved in order to conduct the experimental work. The result from the evaluation shows that the system reaches a level of performance close to the expert and better than the senior and trainee clinicians. Thus, the proposed CDSS could be used as an expert for a less experienced clinician (i.e. trainee) or as a second option/opinion for an experienced clinician (i.e. senior) to their decision making process in stress management. In post-operative pain treatment, the CDSS retrieves and presents most similar cases (e.g. both rare and regular) with their outcomes to assist physicians. Moreover, an automatic approach is presented in order to identify rare cases and 18% of cases from the whole cases library i.e. 276 out of 1572 are identified as rare cases by the approach. Again, among the rare cases (i.e. 276), around 57.25% of the cases are classified as ‘unusually bad’ i.e. the average pain outcome value is greater or equal to 5 on the NVAS scale 0 to 10. Identification of rear cases is an important part of the PAIN OUT project and can be used to improve the quality of individual pain treatment.
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57.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • Bibliometric Profiling of a Group: A Discussion on Different Indicators
  • 2011
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • Now-a-days in some advanced countries bibliometric profiling plays a vital role when making decision on promotion, fund allocation and award prizes. Accurate identification of this is important since it is becoming important to assess scientific output for a researcher or a group of researcher. This paper presents and discusses several most common indicators of bibliometric profiling together with h- and g-indexes. A case study has been conducted on 101 scientific articles with three most well known search engines. The study results using several indicators are presented in this report.
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58.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • Case-Based Reasoning for Medical and Industrial Decision Support Systems
  • 2010
  • Ingår i: Successful Case-based Reasoning Applications. - Berlin, Heidelberg : Springer. - 9783642140778 ; , s. 7-52
  • Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
    • The amount of medical and industrial experience and knowledge is rapidly growing and it is almost impossible to be up to date with everything. The demand of decision support system (DSS) is especially important in domains where experience and knowledge grow rapidly. However, traditional approaches to DSS are not always easy to adapt to a flow of new experience and knowledge and may also show a limitation in areas with a weak domain theory. This chapter explores the functionalities of Case-Based Reasoning (CBR) to facilitate experience reuse both in clinical and in industrial decision making tasks. Examples from the field of stress medicine and condition monitoring in industrial robots are presented here to demonstrate that the same approach assists both for clinical applications as well as for decision support for engineers. In the both domains, DSS deals with sensor signal data and integrate other artificial intelligence techniques into the CBR system to enhance the performance in a number of different aspects. Textual information retrieval, Rule-based Reasoning (RBR), and fuzzy logic are combined together with CBR to offer decision support to clinicians for a more reliable and efficient management of stress. Agent technology and wavelet transformations are applied with CBR to diagnose audible faults on industrial robots and to package such a system. The performance of the CBR systems have been validated and have shown to be useful in solving such problems in both of these domains.
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59.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • Heart Rate and Inter-beat Interval Computation to Diagnose Stress
  • 2010
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • Problem in diagnosing of stress is an important issue. The variations in beat-to-beat alteration in the heart rate (HR) can provide an identification of stress. HR can be determined from the Electrocardiogram (ECG) signal. However, accurate detection of HR and inter-beat interval (IBI) values from the ECG waveform is important. This report presents a way of measuring the ECG signal together with the ECG component analysis such as QRS peak detection and HR calculation to use it in a computer-based stress diagnosis system.
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60.
  • Ahmed, Mobyen Uddin, 1976-, et al. (författare)
  • Mining Rare Cases in Post-Operative Pain by Means of Outlier Detection
  • 2011
  • Ingår i: IEEE Symposium on Signal Processing and Information Technology (ISSPIT) 2011. - : IEEE. - 9781467307536 ; , s. 35-41
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Rare cases are often interesting for healthprofessionals, physicians, researchers and clinicians in order toreuse and disseminate experiences in healthcare. However,mining, i.e. identification of rare cases in electronic patientrecords, is non-trivial for information technology. This paperinvestigates a number of well-known clustering algorithms andfinally applies a 2nd order clustering approach by combining theFuzzy C-means algorithm with the Hierarchical one. Theapproach is used in order to identify rare cases from 1572patient cases in the domain of post-operative pain management.The results show that the approach enables identification of rarecases in the domain of post-operative pain management and 18%of cases are identified as rare case.
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