SwePub
Sök i SwePub databas

  Utökad sökning

Träfflista för sökning "WFRF:(Funk Peter) "

Sökning: WFRF:(Funk Peter)

  • Resultat 1-50 av 158
Sortera/gruppera träfflistan
   
NumreringReferensOmslagsbildHitta
1.
  • Falster, Daniel, et al. (författare)
  • AusTraits, a curated plant trait database for the Australian flora
  • 2021
  • Ingår i: Scientific Data. - : Nature Portfolio. - 2052-4463. ; 8:1
  • Tidskriftsartikel (refereegranskat)abstract
    • We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge.
  •  
2.
  • Olsson, Ella, et al. (författare)
  • Graph-Based Knowledge Representation and Algorithms for Air and Maintenance Operations
  • 2022
  • Ingår i: ICAS Proceedings 33rd Congress of the International Council of the Aeronautical Sciences, Stockholm, Sweden. - : International Council of the Aeronautical Sciences. - 9781713871163
  • Konferensbidrag (refereegranskat)abstract
    • This work presents an approach for information exchange between adjacent air operations domains by means of graph technologies. The approach has the ability to leverage interoperability and collaboration between air- and ground-based systems and stakeholders in respective domains. In its foundation, it provides a means for relevant actors to access and assess relevant data, information and knowledge, and thus provide input in terms of viable action alternatives in a complex and dynamic operational context. As a proof-of-concept, we have utilizeda full-stack application framework to implement a decision support demonstrator for operational aircraft maintenance. Our solution facilitates a lightweight and dynamic representation of relevant domain knowledge,readily available for exploitation by graph algorithms, adapted to our domain. We have based our implementation on the full-stack application framework Grand-Stack, which is an architecture designed to exploit the power of graphs throughout its stack.
  •  
3.
  • 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.
  •  
4.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • A Case-Based Reasoning System for Knowledge and Experience Reuse
  • 2007
  • Ingår i: Proceedings of the 24th annual workshop of the Swedish Artificial Intelligence Society. ; , s. 70-80
  • Konferensbidrag (refereegranskat)abstract
    • Experience is one of the most valuable assets technicians and engineer have and may have been collected during many years and both from successful solutions as well as from very costly mistakes. Unfortunately industry rarely uses a systematic approach for experience reuse. This may be caused by the lack of efficient tools facilitating experience distribution and reuse. We propose a case-based approach and tool to facilitate experience reuse more systematically in industry. It is important that such a tool allows the technicians to give the problem case in a flexible way to increase acceptance and use. The proposed tool enables more structured handling of experience and is flexible and can be adapted to different situations and problems. The user is able to input text in a structured way and possibly in combination with other numeric or symbolic features. The system is able to identify and retrieve relevant similar experiences for reuse.
  •  
5.
  • Ahmed, Mobyen Uddin, 1976-, et al. (författare)
  • A Case-Based Retrieval System for Post-Operative Pain Treatment
  • 2011
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a clinical decision support system based on case-basedretrieval approach to assist physicians in post-operative pain treatment. Here,the cases are formulated by combining regular features and features using anumerical visual analogue scale (NVAS) through a questionnaire. Featureabstraction is done both in problem and outcome description of a case in order toreduce the number of attributes. The system retrieves most similar cases with theiroutcomes. The outcome of each case brings benefits for physicians since it presentsboth severity and fast recovery by the applied treatment in post-operative patients.Therefore, we have introduced a two-layer case structure i.e., solution is the firstlayer and outcome is the second layer that better suits this medical application. Inthe system, the solution presents the treatment and the outcome contains recoveryinformation of a patient, something physicians are interested in, especially the riskof side effects and complications.
  •  
6.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • A Computer Aided System for Post-operative Pain Treatment Combining Knowledge Discovery and Case-Based Reasoning
  • 2012
  • Ingår i: Lecture Notes in Computer Science, vol. 7466. - Berlin, Heidelberg : Springer. - 9783642329852 ; , s. 3-16
  • Bokkapitel (refereegranskat)abstract
    • The quality improvement for individual postoperative-pain treatment is an important issue. This paper presents a computer aided system for physicians in their decision making tasks in post-operative pain treatment. Here, the system combines a Case-Based Reasoning (CBR) approach with knowledge discovery. Knowledge discovery is applied in terms of clustering in order to identify the unusual cases. We applied a two layered case structure for case solutions i.e. the treatment is in the first layer and outcome after treatment (i.e. recovery of the patient) is in the second layer. Moreover, a 2nd order retrieval approach is applied in the CBR retrieval step in order to retrieve the most similar cases. The system enables physicians to make more informed decisions since they are able to explore similar both regular and rare cases of post-operative patients. The two layered case structure is moving the focus from diagnosis to outcome i.e. the recovery of the patient, something a physician is especially interested in, including the risk of complications and side effects.
  •  
7.
  • 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.
  •  
8.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • A Multi-Modal Case-Based System for Clinical Diagnosis and Treatment in Stress Management
  • 2009
  • Konferensbidrag (refereegranskat)abstract
    • A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often base their diagnosis and decision on manual inspection of signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with the manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. A computer system, classifying the sensor signals is one valuable property assisting a clinician. This paper presents a case-based system that assist a clinician in diagnosis and treatment of stress. 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 such as textual information retrieval, rule-based reasoning, and fuzzy logic have been combined together with case-based reasoning to enable more reliable and efficient diagnosis and treatment of stress. The performance has been validated implementing a research prototype and close collaboration with the experts. The experimental results suggest that such a system is valuable both for the less experienced clinicians and for experts where the system may be seen as a second option.
  •  
9.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • A Multi-Module Case Based Biofeedback System for Stress Treatment
  • 2011
  • Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 0933-3657 .- 1873-2860. ; 51:2, s. 107-115
  • Tidskriftsartikel (refereegranskat)abstract
    • Biofeedback is today a recognized treatment method for a number of physical and psychological problems. Experienced clinicians often achieve good results in these areas and their success largely builds on many years of experience and often thousands of treated patients. Unfortunately many of the areas where biofeedback is used are very complex, e.g. diagnosis and treatment of stress. Less experienced clinicians may even have difficulties to initially classify the patient correctly. Often there are only a few experts available to assist less experienced clinicians. To reduce this problem we propose a computer assisted biofeedback system helping in classification, parameter setting and biofeedback training. By adopting a case based approach in a computer-based biofeedback system, decision support can be offered to less experienced clinicians and provide a second opinion to experts. We explore how such a system may be designed and validate the approach in the area of stress where the system assists in the classification, parameter setting and finally in the training. In a case study we show that the case based biofeedback system outperforms novice clinicians based on a case library of cases authorized by an expert.
  •  
10.
  • 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.
  •  
11.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • A Three Phase Computer Assisted Biofeedback Training System Using Case-Based Reasoning
  • 2008
  • Ingår i: Proc. 9th European Conference on Case-based Reasoning. ; , s. 57-68
  • Konferensbidrag (refereegranskat)abstract
    • Biofeedback is a method gaining increased interest and showing good results for a number of physical and psychological problems. Biofeedback training is mostly guided by an experienced clinician and the results largely rely on the clinician's competence. In this paper we propose a three phase computer assisted sensor-based biofeedback decision support system assisting less experienced clinicians, acting as second opinion for experienced clinicians. The three phase CBR framework is deployed to classify a patient, estimate initial parameters and to make recommendations for biofeedback training by retrieving and comparing with previous similar cases in terms of features extracted. The three phases work independently from each other. Moreover, fuzzy techniques are incorporated into our CBR system to better accommodate uncertainty in clinicians reasoning as well as decision analysis. All parts in the proposed framework have been implemented and primarily validated in a prototypical system. The initial result shows how the three phases functioned with CBR technique to assist biofeedback training. Eventually the system enables the clinicians to allow a patient to train himself/herself unsupervised.
  •  
12.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • An Overview of three Medical Applications Using Hybrid Case-Based Reasoning
  • 2012
  • Konferensbidrag (refereegranskat)abstract
    • Today more and more patient journals are stored electronically but they are rarely used for more than statistical purpose. In this paper we present an approach where clinical patient journals are used for improved clinical decision making on an individual level. The underlying assumption is that medical staff benefit from comparing a specific patient with similar patient. By comparing symptoms, diagnosis, medication and outcome in an individual level they are able to make more informed decisions at the point of care. This paper presents some parts of our more than ten years research efforts in the area and some of the projects and their underlying hybrid approaches. As a foundation for all our projects we use case-based reasoning (CBR) research in combination with techniques from artificial intelligence, data mining, statistics and search techniques. Three systems are presented in two medical domains 1) decision support for stress diagnosis 2) decision support for stress treatment and 3) decision support for post-operative pain treatment and discuss results and lessons learned.
  •  
13.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • Analysis of Breakdown Reports Using Natural Language Processing and Machine Learning
  • 2022
  • Ingår i: Lecture Notes in Mechanical Engineering. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030936389 ; , s. 40-52
  • Konferensbidrag (refereegranskat)abstract
    • Proactive maintenance management of world-class standard is close to impossible without the support of a computerized management system. In order to reduce failures, and failure recurrence, the key information to log are failure causes. However, Computerized Maintenance Management System (CMMS) seems to be scarcely used for analysis for improvement initiatives. One part of this is due to the fact that many CMMS utilizes free-text fields which may be difficult to analyze statistically. The aim of this study is to apply Natural Language Processing (NPL), Ontology and Machine Learning (ML) as a means to analyze free-textual information from a CMMS. Through the initial steps of the study, it was concluded though that none of these methods were able to find any suitable hidden patterns with high-performance accuracy that could be related to recurring failures and their root causes. The main reason behind that was that the free-textual information was too unstructured, in terms of for instance: spelling- and grammar mistakes and use of slang. That is the quality of the data are not suitable for the analysis. However, several improvement potentials in reporting and to develop the CMMS further could be provided to the company so that they in the future more easily will be able to analyze its maintenance data.
  •  
14.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity
  • 2008
  • Ingår i: Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity. - 1867-366X. ; 1, s. 3-19
  • Tidskriftsartikel (refereegranskat)abstract
    • Intelligent analysis of heterogeneous data and information sources for efficient decision support presents an interesting yet challenging task in clinical envi-ronments. This is particularly the case in stress medicine where digital patient re-cords are becoming popular which contain not only lengthy time series measurements but also unstructured textual documents expressed in form of natural languages. This paper develops a hybrid case-based reasoning system for stress di-agnosis which is capable of coping with both numerical signals and textual data at the same time. The total case index consists of two sub-parts corresponding to signal and textual data respectively. For matching of cases on the signal aspect we present a fuzzy similarity matching metric to accommodate and tackle the imprecision and uncertainty in sensor measurements. Preliminary evaluations have revealed that this fuzzy matching algorithm leads to more accurate similarity estimates for improved case ranking and retrieval compared with traditional distance-based matching crite-ria. For evaluation of similarity on the textual dimension we propose an enhanced cosine matching function augmented with related domain knowledge. This is im-plemented by incorporating Wordnet and domain specific ontology into the textual case-based reasoning process for refining weights of terms according to available knowledge encoded therein. Such knowledge-based reasoning for matching of tex-tual cases has empirically shown its merit in improving both precision and recall of retrieved cases with our initial medical databases. Experts in the domain are very positive to our system and they deem that it will be a valuable tool to foster wide-spread experience reuse and transfer in the area of stress diagnosis and treatment.
  •  
15.
  • 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.
  •  
16.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • Case studies on the clinical applications using case-based reasoning
  • 2012
  • Ingår i: 2012 Federated Conference on Computer Science and Information Systems, FedCSIS 2012. - 9781467307086 - 9788360810514 ; , s. 3-10
  • Konferensbidrag (refereegranskat)abstract
    • Case-Based Reasoning (CBR) is a promising Artificial Intelligence (AI) method that is applied for problem solving tasks. This approach is widely used in order to develop Clinical Decision Support System (CDSS). A 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. This paper presents the case studies on 3 clinical Decision Support Systems as an overview of CBR research and development. Two medical domains are used here for the case studies: case-study-1) CDSS for stress diagnosis case-study-2) CDSS for stress treatment and case-study-3) CDSS for postoperative pain treatment. The observation shows the current developments, future directions and pros and cons of the CBR approach. Moreover, the paper shares the experiences of developing 3CDSS in medical domain in terms of case study.
  •  
17.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • Efficient Condition Monitoring and Diagnosis Using a Case-Based Experience Sharing System
  • 2007
  • Ingår i: The 20th International Congress and Exhibition on Condition Monitoring and Diagnostics Engineering Management, COMADEM 2007, Faro, Portugal. ; , s. 305-314
  • Konferensbidrag (refereegranskat)abstract
    • Industry has to adjust quickly to changes in their surroundings, for example reducing staff during recession and increasing staff when the market demands it. These factors may cause rapid loss of experience, collected during many years, or require experienced staff to spend considerable resources in training new staff, instead of focusing on production. This is recognised as very costly for companies and organisations today and also reduces competitiveness and productivity. Condition Monitoring, diagnostics and selection of efficient preventive or corrective actions is a task that often requires a high degree of expertise. This expertise is often gained through sometimes very expensive mistakes and can take many years to acquire leading to a few skilled experts. When they are not available due to changes in staff or retirements the company often faces serious problems that may be very expensive, e.g. leading to a reduced productivity.If some deviation occurs in a machine, a fault report is often written; an engineer makes a diagnosis and may order spare parts to repair the machine. Fault report, spare parts, required time and statistics on performance after repair are often stored in different databases but so far not systematically reused. In this paper we present a Case-Based experience sharing system that enables reuse of experience in a more efficient way compared with what is mostly practiced in industry today. The system uses Case-Based-Reasoning (CBR) and limited Natural Language Processing. An important aspect of the experience management tool is that it is user-friendly and web-based to promote efficient experience sharing. The system should be able to handle both experiences that are only in house as well as sharing experience with other industries when there is no conflicting interest. Such a CBR based tool enables efficient experience gathering, management and reuse in production industries. The tool will facilitate the users with an interactive environment to communicate with each other for sharing their experiences. Depend on the user; the security level of the system will be varied to share knowledge among the collaborating companies.The system identifies the most relevant experiences to assess and resolve the current situation. The experience is stored and retrieved as a case in the collaborative space where experience from various companies may have been stored under many years. Reusing experience and avoiding expensive mistakes will increase the participating companies' competitiveness and also transfer valuable experience to their employees. One of the benefits is also the opportunity and facility to identify people with similar tasks and problems at different companies and enable them to share their experience, e.g. if a technician has solved a similar problem recently and is in the near, the most efficient solution may be to call the expert and ask for assistance. In future, one may access this tool through his/her mobile device via wireless or mobile communications using Global Positioning System, GPS, enables the system to suggest experts nearby, willing and able to share the knowledge and quickly assist in resolve the problem.
  •  
18.
  • Ahmed, Mobyen Uddin, 1976-, et al. (författare)
  • FUZZY RULE-BASED CLASSIFICATION TO BUILD INITIAL CASE LIBRARY FOR CASE-BASED STRESS DIAGNOSIS
  • 2009
  • Ingår i: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009. - 9780889867802 ; , s. 225-230
  • Konferensbidrag (refereegranskat)abstract
    • Case-Based Reasoning (CBR) is receiving increasedinterest for applications in medical decision support.Clinicians appreciate the fact that the system reasons withfull medical cases, symptoms, diagnosis, actions takenand outcomes. Also for experts it is often appreciated toget a second opinion. In the initial phase of a CBR systemthere are often a limited number of cases available whichreduces the performance of the system. If past cases aremissing or very sparse in some areas the accuracy isreduced. This paper presents a fuzzy rule-basedclassification scheme which is introduced into the CBRsystem to initiate the case library, providing improvedperformance in the stress diagnosis task. Theexperimental results showed that the CBR system usingthe enhanced case library can correctly classify 83% ofthe cases, whereas previously the correctness of theclassification was 61%. Consequently the proposedsystem has an improved performance with 22% in termsof accuracy. In terms of the discrepancy in classificationcompared to the expert, the goodness-of-fit value of thetest results is on average 87%. Thus by employing thefuzzy rule-based classification, the new hybrid system cangenerate artificial cases to enhance the case library.Furthermore, it can classify new problem cases previouslynot classified by the system.
  •  
19.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • Intelligent Stress Management System
  • 2009
  • Ingår i: Medicinteknikdagarna 2009.
  • Konferensbidrag (refereegranskat)abstract
    • Today, in our daily life we are subjected to a wide range of pressures. When the pressures exceed the extent that we are able to deal with then stress is trigged. High level of stress may cause serious health problems i.e. it reduces awareness of bodily symptoms. So, people may first notice it weeks or months later meanwhile the stress could cause more serious effect in the body and health. A difficult issue in stress management is to use biomedical sensor signals in the diagnosis and treatment of stress. This paper presents a case-based system that assists a clinician in diagnosis and treatment of stress. 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 such as textual information retrieval, rule-based reasoning (RBR), and fuzzy logic have been combined together with case-based reasoning to enable more reliable and efficient diagnosis and treatment of stress. The performance has been validated implementing a research prototype and close collaboration with experts.
  •  
20.
  • 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.
  •  
21.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • Multi-Modal and Multi-Purpose Case-based Reasoning in the Health Sciences
  • 2009
  • Ingår i: PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES. - Cambridge, UK : WSEAS press. - 9789604740512 ; , s. 378-383
  • Konferensbidrag (refereegranskat)abstract
    • Case-based reasoning systems for medical application are increasingly multi-purpose systems and also multi-modal, using a variety of different methods and techniques to meet the challenges from the medical domain. It this paper, some of the recent medical case-based reasoning systems are classified according to their functionality and development properties. It shows how a particular multi-purpose and multi-modal case-based reasoning system solved these challenges. For this a medical case-based reasoning system in the domain of psychophysiology is used. 
  •  
22.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • System Overview on a Clinical Decision Support System for Stress Management
  • 2012
  • Ingår i: Proceedings of the ICCBR 2012 Workshops. ; , s. 111-116
  • Konferensbidrag (refereegranskat)abstract
    • There is an increased need for Clinical Decision Support Systems (CDSS) in the medical community as ICT technology is increasingly used in hospitals as more and more patient data is stored in computers. A CDSS has the potential to play a vital role and bring essential information and knowledge to the clinicians and function as a second opinion in their decision-making tasks. In this paper, a CDSS in stress management is presented where the CDSS can help the clinicians in order to diagnosis and treat stress related disorders. As a foundation for the CDSS, the Case-Based Reasoning (CBR) approach has been used as a core method of the system. The systems also combine other techniques from artificial intelligence in a multimodal manner, such as fuzzy logic, rule-based reasoning and textual information retrieval. In this paper we review our experiences and research efforts while developing the CDSS. The performance of the CDSS shows that the system can be useful both for trainee clinicians as an expert and as well as for senior clinicians as a second option. Moreover, the observation shows the current developments, and pros and cons of the CBR approach.
  •  
23.
  • Ahmed, Mobyen Uddin, et al. (författare)
  • The 3 CDSSs: An Overview and Application in Case-Based Reasoning
  • 2012
  • Ingår i: The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS). - Linköping : Linköping University Electronic Press. ; , s. 25-32
  • Konferensbidrag (refereegranskat)abstract
    • A computer-aided Clinical Decision SupportSystem (CDSS) for diagnosis and treatment often plays a vital role and brings essential benefits for clinicians. Such a CDSScould function as an expert for a less experienced clinician oras a second option/opinion of an experienced clinician to their decision making task. This paper presents 3 clinical DecisionSupport Systems as an overview of case-based reasoning (CBR) research and development. Two medical domains are used here for the case study 1) CDSS for stress diagnosis 2) CDSS for stress treatment and 3) CDSS for post-operative pain treatment.The observation shows the current developments, future direction and pros and cons of the CBR approach. Moreover,the paper shares the experiences of developing 3CDSS in medical domain.
  •  
24.
  • Algaba, Juan-Carlos, et al. (författare)
  • Broadband Multi-wavelength Properties of M87 during the 2017 Event Horizon Telescope Campaign
  • 2021
  • Ingår i: Astrophysical Journal Letters. - : American Astronomical Society. - 2041-8213 .- 2041-8205. ; 911:1
  • Forskningsöversikt (refereegranskat)abstract
    • In 2017, the Event Horizon Telescope (EHT) Collaboration succeeded in capturing the first direct image of the center of the M87 galaxy. The asymmetric ring morphology and size are consistent with theoretical expectations for a weakly accreting supermassive black hole of mass ∼6.5 × 109 M o˙. The EHTC also partnered with several international facilities in space and on the ground, to arrange an extensive, quasi-simultaneous multi-wavelength campaign. This Letter presents the results and analysis of this campaign, as well as the multi-wavelength data as a legacy data repository. We captured M87 in a historically low state, and the core flux dominates over HST-1 at high energies, making it possible to combine core flux constraints with the more spatially precise very long baseline interferometry data. We present the most complete simultaneous multi-wavelength spectrum of the active nucleus to date, and discuss the complexity and caveats of combining data from different spatial scales into one broadband spectrum. We apply two heuristic, isotropic leptonic single-zone models to provide insight into the basic source properties, but conclude that a structured jet is necessary to explain M87's spectrum. We can exclude that the simultaneous γ-ray emission is produced via inverse Compton emission in the same region producing the EHT mm-band emission, and further conclude that the γ-rays can only be produced in the inner jets (inward of HST-1) if there are strongly particle-dominated regions. Direct synchrotron emission from accelerated protons and secondaries cannot yet be excluded.
  •  
25.
  • Allahverdiyeva, Yagut, et al. (författare)
  • NordAqua, a Nordic Center of Excellence to develop an algae-based photosynthetic production platform
  • 2021
  • Ingår i: Physiologia Plantarum. - : John Wiley & Sons. - 0031-9317 .- 1399-3054. ; 173:2, s. 507-513
  • Tidskriftsartikel (refereegranskat)abstract
    • NordAqua is a multidisciplinary Nordic Center of Excellence funded by NordForsk Bioeconomy program (2017–2022). The research center promotes Blue Bioeconomy and endeavours to reform the use of natural resources in a environmentally sustainable way. In this short communication, we summarize particular outcomes of the consortium. The key research progress of NordAqua includes (1) improving of photosynthetisis, (2) developing novel photosynthetic cell factories that function in a “solar-driven direct CO2 capture to target bioproducts” mode, (3) promoting the diversity of Nordic cyanobacteria and algae as an abundant and resilient alternative for less sustainable forest biomass and for innovative production of biochemicals, and (4) improving the bio-based wastewater purification and nutrient recycling technologies to provide new tools for integrative circular economy platforms.
  •  
26.
  • Andersson, Alf, et al. (författare)
  • Inline Process Control – a concept study of efficient in-line process control and process adjustment with respect to product geometry
  • 2016
  • Ingår i: Swedish Production Symposium 2016 SPS 2016. - Lund, Sweden.
  • Konferensbidrag (refereegranskat)abstract
    • All manufacturing processes have variation which may violate the fulfillment of assembly, functional, geometrical or esthetical requirements and difficulties to reach desired form in all areas. The cost for geometry defects rises downstream in the process chain. Therefore, it is vital to discover these defects as soon as they appear. Then adjustments can be done in the process without losing products or time. In order to find a solution for this, a project with the overall scope “development of an intelligent process control system” has been initiated. This project consists of five different work packages: Inline measurement, Process Evaluation, Corrective actions, Flexible tooling and demonstrator cell. These work packages address different areas which are necessary to fulfill the overall scope of the project. The system shall both be able to detect geometrical defects, propose adjustments and adjust simple process parameters. The results are demonstrated in a demo cell located at Chalmers University of Technology. In the demonstrator all the different areas have been verified in an industrial case study – assembly of GOR Volvo S80. Efficient offline programming for robot based measurement, efficient process evaluation based on case base reasoning (CBR) methodology, flexible fixtures and process adjustments based on corrective actions regarding in going part positioning.
  •  
27.
  • Anzt, Hartwig, et al. (författare)
  • An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action
  • 2020
  • Ingår i: F1000 Research. - : F1000 Research Ltd. - 2046-1402. ; 9
  • Tidskriftsartikel (refereegranskat)abstract
    • Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin.
  •  
28.
  •  
29.
  • Barua, Shaibal, et al. (författare)
  • Automated EEG Artifact Handling with Application in Driver Monitoring
  • 2018
  • Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 22:5, s. 1350-1361
  • Tidskriftsartikel (refereegranskat)abstract
    • Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a pre-processing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.
  •  
30.
  • Barua, Shaibal, 1982- (författare)
  • Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals
  • 2015
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving.This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well.For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%. 
  •  
31.
  • Barua, Shaibal, 1982- (författare)
  • Multivariate Data Analytics to Identify Driver’s Sleepiness, Cognitive load, and Stress
  • 2019
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Driving a vehicle in a dynamic traffic environment requires continuous adaptation of a complex manifold of physiological and cognitive activities. Impaired driving due to, for example, sleepiness, inattention, cognitive load or stress, affects one’s ability to adapt, predict and react to upcoming traffic events. In fact, human error has been found to be a contributing factor in more than 90% of traffic crashes. Unfortunately, there is no robust, objective ground truth for determining a driver’s state, and researchers often revert to using subjective self-rating scales when assessing level of sleepiness, cognitive load or stress. Thus, the development of better tools to understand, measure and monitor human behaviour across diverse scenarios and states is crucial. The main objective of this thesis is to develop objective measures of sleepiness, cognitive load and stress, which can later be used as research tools, either to benchmark unobtrusive sensor solutions or when investigating the influence of other factors on sleepiness, cognitive load, and stress.This thesis employs multivariate data analysis using machine learning to detect and classify different driver states based on physiological data. The reason for using rather intrusive sensor data, such as electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), skin conductance, finger temperature, and respiration is that these methods can be used to analyse how the brain and body respond to internal and external changes, including those that do not generate overt behaviour. Moreover, the use of physiological data is expected to grow in importance when investigating human behaviour in partially automated vehicles, where active driving is replaced by passive supervision.Physiological data, especially the EEG is sensitive to motion artifacts and noise, and when recorded in naturalistic environments such as driving, artifacts are unavoidable. An automatic EEG artifact handling method ARTE (Automated aRTifacts handling in EEG) was therefore developed. When used as a pre-processing step in the classification of driver sleepiness, ARTE increased classification performance by 5%. ARTE is data-driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered. In addition, several machine-learning algorithms have been developed for sleepiness, cognitive load, and stress classification. Regarding sleepiness classification, the best achieved accuracy was achieved using a Support Vector Machine (SVM) classifier. For multiclass, the obtained accuracy was 79% and for binary class it was 93%. A subject-dependent classification exhibited a 10% improvement in performance compared to the subject-independent classification, suggesting that much can be gained by using personalized classifiers. Moreover, by embedding contextual information, classification performance improves by approximately 5%. In regard to cognitive load classification, a 72% accuracy rate was achieved using a random forest classifier. Combining features from several data sources may improve performance, and indeed, we observed classification performance improvement by 10%-20% compared to using features from a single data source. To classify drivers’ stress, using the Case-based reasoning (CBR) and data fusion approach, the system achieved an 83.33% classification accuracy rate.This thesis work encourages the use of multivariate data for detecting and classifying driver states, including sleepiness, cognitive load, and stress. A univariate data source often presents challenges, since features from a single source or one just aspect of the feature are not entirely reliable; Therefore, multivariate information requires accurate driver state detection. Often, driver states are a subjective experience, in which other contextual data plays a vital role. Thus, the implication of incorporating contextual information in the classification scheme is presented in this thesis work. Although there are several commonalities, physiological signals are modulated differently in different driver states; Hence, multivariate data could help detect multiple driver states simultaneously – for example, cognitive load detection when a person is under the influence of different levels of stress.
  •  
32.
  • Begum, Shahina, 1977-, et al. (författare)
  • A Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching
  • 2009
  • Ingår i: Computational intelligence. - : Blackwell Publishing. - 0824-7935 .- 1467-8640. ; 25:3, s. 180-195
  • Tidskriftsartikel (refereegranskat)abstract
    • Stress diagnosis based on finger temperature signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret and analyze complex, lengthy sequential measurements in order to make a diagnosis and treatment plan. The paper presents a case-based decision support system to assist clinicians in performing such tasks. Case-based reasoning is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness-of-fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation which shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with case-based reasoning is a valuable approach in domains where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho-physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.
  •  
33.
  • Begum, Shahina, 1977- (författare)
  • A Case-Based Reasoning System for the Diagnosis of Individual Sensitivity to Stress in Psychophysiology
  • 2009
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Increased stress is a continuing problem in our present world. Especiallynegative stress could cause serious health problems if it remainsundiagnosed/misdiagnosed and untreated. In the stress medicine, clinicians’measure blood pressure, ECG, finger temperature and breathing rate during anumber of exercises to diagnose stress-related disorders. One of the physiologicalparameters for quantifying stress levels is the finger temperature that helps theclinicians in diagnosis and treatment of stress. However, in practice, it is difficultand tedious for a clinician to understand, interpret and analyze complex, lengthysequential sensor signals. There are only few experts who are able to diagnose andpredict stress-related problems. A system that can help the clinician in diagnosingstress is important, but the large individual variations make it difficult to build sucha system.This research work has attempted to investigate several artificial Intelligencetechniques to develop an intelligent, integrated sensor system for diagnosis andtreatment plan in the Psychophysiological domain. To diagnose individualsensitivity to stress, case-based reasoning is applied as a core technique to facilitateexperience reuse by retrieving previous similar cases. Further, fuzzy techniques arealso employed and incorporated into the case-based reasoning system to handlevagueness, uncertainty inherently existing in clinicians reasoning process. Thevalidation of the approach is based on close collaboration with experts andmeasurements from twenty four persons used as reference.Thirty nine time series from these 24 persons have been used to evaluate theapproach (in terms of the matching algorithms) and an expert has ranked andestimated similarity which shows a level of performance close to an expert. Theproposed system could be used as an expert for a less experienced clinician or as asecond option for an experienced clinician to their decision making process.
  •  
34.
  • Begum, Shahina, et al. (författare)
  • A computer-based system for the assessment and diagnosis of individual sensitivity to stress in Psychophysiology
  • 2007
  • Konferensbidrag (refereegranskat)abstract
    • Increased exposure to stress may cause serious health problems leading to long term sick leave if undiagnosed and untreated. The practice amongst clinicians' to use a standardized procedure measuring blood pressure, ECG, finger temperature, breathing speed etc. to make a reliable diagnosis of stress and stress sensitivity is increasing. But even with these measurements it is still difficult to diagnose due to large individual variations. A computer-based system as a second option for the assessment and diagnosis of individual stress level is valuable in this domain.A combined approach based on a calibration phase and case-based reasoning is proposed exploiting data from finger temperature sensor readings from 24 individuals. In calibration phase, a standard clinical procedure with six different steps helps to establish a person's stress profile and set up a number of individual parameters. When acquiring a new case, patients are also asked to provide a fuzzy evaluation on how reliable was the procedure to define the case itself. Such a reliability "level" could be used to further discriminate among similar cases. The system extracts key features from the signal and classifies individual sensitivity to stress. These features are stored into a case library and similarity measurements are taken to assess the degrees of matching and create a ranked list containing the most similar cases retrieved by using the nearest-neighbor algorithm.A current case (CC) is compared with two other stored cases (C_92 and C_115) in the case library. The global similarity between the case CC and case C_92 is 67% and case CC and case C_115 is 80% shown by the system. So the case C_115 has ranked higher than the case C_92 and is more similar to current case CC. If necessary, the solution for the best matching case can be revised by the clinician to fit the new patient. The current problem with confirmed solution is then retained as a new case and added to the case library for future use.The system allows us to utilize previous experience and at the same time diagnose stress along with a stress sensitivity profile. This information enables the clinician to make a more informed decision of treatment plan for the patients. Such a system may also be used to actively notify a person's stress levels even in the home environment.
  •  
35.
  • Begum, Shahina, et al. (författare)
  • A Fusion Based System for Physiological Sensor Signal Classification
  • 2014
  • Ingår i: Medicinteknikdagarna 2014 MTD10.
  • Konferensbidrag (refereegranskat)abstract
    • Today, usage of physiological sensor signals is essential in medical applications for diagnoses and classification of diseases. Clinicians often rely on information collected from several physiological sensor signals to diagnose a patient. However, sensor signals are mostly non-stationary and noisy, and single sensor signal could easily be contaminated by uncertain noises and interferences that could cause miscalculation of measurements and reduce clinical usefulness. Therefore, an apparent choice is to use multiple sensor signals that could provide more robust and reliable decision. Therefore, a physiological signal classification approach is presented based on sensor signal fusion and case-based reasoning. To classify Stressed and Relaxed individuals from physiological signals, data level and decision level fusion are performed and case-based reasoning is applied as classification algorithm. Five physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, data level fusion is performed using Multivariate Multiscale Entropy (MMSE) and extracted features are then used to build a case- library. Decision level fusion is performed on the features extracted using traditional time and frequency domain analysis. Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.
  •  
36.
  • Begum, Shahina, 1977- (författare)
  • A Personalised Case-Based Stress Diagnosis System Using Physiological Sensor Signals
  • 2011
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Stress is an increasing problem in our present world. It is recognised that increased exposure to stress may cause serious health problems if undiagnosed and untreated. In stress medicine, clinicians’ measure blood pressure, Electrocardiogram (ECG), finger temperature and respiration rate etc. during a number of exercises to diagnose stress-related disorders. However, in practice, it is difficult and tedious for a clinician to understand, interpret and analyze complex, lengthy sequential sensor signals. There are few experts who are able to diagnose and predict stress-related problems. Therefore, a system that can help clinicians in diagnosing stress is important. This research work has investigated Artificial Intelligence techniques for developing an intelligent, integrated sensor system to establish diagnosis and treatment plans in the psychophysiological domain. This research uses physiological parameters i.e., finger temperature (FT) and heart rate variability (HRV) for quantifying stress levels.  Large individual variations in physiological parameters are one reason why case-based reasoning is applied as a core technique to facilitate experience reuse by retrieving previous similar cases. Feature extraction methods to represent important features of original signals for case indexing are investigated. Furthermore, fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness and uncertainty inherently existing in clinicians’ reasoning. The evaluation of the approach is based on close collaboration with experts and measurements of FT and HRV from ECG data. The approach has been evaluated with clinicians and trial measurements on subjects (24+46 persons). An expert has ranked and estimated the similarity for all the subjects during classification. The result shows that the system reaches a level of performance close to an expert in both the cases. The proposed system could be used as an expert for a less experienced clinician or as a second opinion for an experienced clinician to supplement their decision making tasks in stress diagnosis.
  •  
37.
  • Begum, Shahina, et al. (författare)
  • An Overview on Recent Case-Based Reasoning Systems in the Medicine
  • 2009
  • Ingår i: 25th annual workshop of the Swedish Artificial Intelligence Society.
  • Konferensbidrag (refereegranskat)abstract
    • Case-based reasoning systems for medical application are increasingly applied to meet the challenges from the medical domain. This paper looks at the state of the art in case-based reasoning and some systems are classified in this respect. A survey is performed based on the recent publications and research projects in CBR in medicine. Also, the survey is based on e-mail questionnaire to the authors’ to complete the missing property information. Some clear trends in recent projects/systems have been identified such as most of the systems are multi-modal, using a variety of different methods and techniques to serve multipurpose i.e. address more than one task.
  •  
38.
  • Begum, Shahina, et al. (författare)
  • Case-Based Reasoning Systems in the Health Sciences : A Survey of Recent Trends and Developments
  • 2011
  • Ingår i: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews. - 1094-6977 .- 1558-2442. ; 41:4, s. 421-434
  • Tidskriftsartikel (refereegranskat)abstract
    • The Health Sciences are, nowadays, one of the major application areas for case-based reasoning (CBR). The paper presents a survey of recent medical CBR systems based on a literature review and an e-mail questionnaire sent to the corresponding authors of the papers where these systems are presented. Some clear trends have been identified, such as multipurpose systems: more than half of the current medical CBR systems address more than one task. Research on CBR in the area is growing, but most of the systems are still prototypes and not available on the market as commercial products. However, many of the projects/systems are intended to be commercialized.
  •  
39.
  • Begum, Shahina, et al. (författare)
  • Case-Based Systems in the Health Sciences - A Case Study in the Field of Stress Management
  • 2009
  • Ingår i: WSEAS Transactions on Systems. - 1109-2777. ; 8:3, s. 344-354
  • Tidskriftsartikel (refereegranskat)abstract
    • Now-a-days medical domain is a popular area for the artificial intelligence (AI) research. Many of the early AI systems were attempted to apply rule-based reasoning in developing computer-based diagnosis system in medical domain. However, for a broad and complex medical domain the effort of applying rule-based system has encountered several problems. Today many systems are serving multi-purpose i.e. tend to support not only in diagnosis but also in number of other complex tasks and combining more than one AI techniques in the health care domain. In this paper, we will investigate the state-of-the art of casebased reasoning (CBR), a recent AI method in the medical domain. A case study in the stress medicine domain is presented here. Today stress has become a major concern in our society. The demand of the decision support system (DSS) in stress domain is increasing rapidly. However, the application of DSS in this domain is limited so far due to the weak domain theory. In our on going research, we have proposed a solution analyzing the relation between stress and finger temperature using case-based reasoning and other AI techniques namely case-based reasoning, textual CBR, rule-based reasoning, and fuzzy logic to support classification and diagnosis in stress management.
  •  
40.
  • Begum, Shahina, et al. (författare)
  • Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning
  • 2007
  • Ingår i: Case-Based Reasoning Research and Development. - Berlin, Heidelberg : Springer. - 9783540741381 ; , s. 478-491
  • Bokkapitel (refereegranskat)abstract
    • Increased exposure to stress may cause health problems. An experi-enced clinician is able to diagnose a person's stress level based on sensor read-ings. Large individual variations and absence of general rules make it difficult to diagnose stress and the risk of stress-related health problems. A decision sup-port system providing clinicians with a second opinion would be valuable. We propose a novel solution combining case-based reasoning and fuzzy logic along with a calibration phase to diagnose individual stress. During calibration a num-ber of individual parameters are established. The system also considers the feedback from the patient on how well the test was performed. The system uses fuzzy logic to incorporating the imprecise characteristics of the domain. The cases are also used for the individual treatment process and transfer experience between clinicians. The validation of the approach is based on close collabora-tion with experts and measurements from 24 persons used as reference.
  •  
41.
  • Begum, Shahina, et al. (författare)
  • Development of a Stress Questionnaire : A Tool for Diagnosing Mental Stress
  • 2010
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • Stress and its relation with health, behavioral and environmental factors are known today. The stress questionnaire is a scientific screening instrument to understand individual’s causes of stress in different parts of life e.g. in the work place and at home. The 38-item stress questionnaire (SQ) is developed to assess the appraisal of stress personally experienced in a patient’s life. This questionnaire cannot diagnose any illness or psychological disorder. However it can be a helpful tool for developing the individual stress management plan by assessing data about the current demands of individual’s life and work.
  •  
42.
  • Begum, Shahina, et al. (författare)
  • Diagnosis and Biofeedback System for Stress
  • 2009
  • Ingår i: Proceedings of the 6th International Workshop on Wearable, Micro, and Nano Technologies for Personalized Health: "Facing Future Healthcare Needs", pHealth 2009. - 9781424452538 ; , s. 17-20
  • Konferensbidrag (refereegranskat)abstract
    • Today, everyday life for many people contain many situations that may trigger stress or result in an individual living on an increased stress level under long time. High level of stress may cause serious health problems. It is known that respiratory rate is an important factor and can be used in diagnosis and biofeedback training, but available measurement of respiratory rate are not especially suitable for home and office use. The aim of this project is to develop a portable sensor system that can measure the stress level, during everyday situations e.g. at home and in work environment and can help the person to change the behaviour and decrease the stress level. The sensor explored is a finger temperature sensor. Clinical studies show that finger temperature, in general, decreases with stress; however this change pattern shows large individual variations. Diagnosing stress level from the finger temperature is difficult even for clinical experts. Therefore a computer-based stress diagnosis system is important. In this system, case-based reasoning and fuzzy logic have been applied to assists in stress diagnosis and biofeedback treatment utilizing the finger temperature sensor signal. An evaluation of the system with an expert in stress diagnosis shows promising result.
  •  
43.
  • Begum, Shahina, et al. (författare)
  • Driver's Mental State Monitoring System Using CBR Based on Heart Rate Variability Analysis
  • 2012
  • Ingår i:
  • Konferensbidrag (refereegranskat)abstract
    • The consequences of tiredness, drowsiness, stress and lack of concentration caused by a variety of different factors such as illness, sleep depletion, drugs and alcohol is a serious problem in traffic and when operating industrial equipment. This is especially important for professional drivers since both expensive equipment and lives may be at stake, e.g. in mining, construction and personal transportation, reduced concentration, stress or tiredness are known to be the cause of many accidents. A system which recognizes the state of the driver and e.g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. Today different sensors enable clinician to determine a driver’s status with high accuracy. The aim of the paper is to develop an intelligent system that can monitor drivers’ stress depending on psychological and behavioral conditions/status using heart rate variability. An experienced clinician is able to diagnose a person’s stress level based on sensor readings. Here, we propose a solution using case-based reasoning to diagnose individual driver’s stress. During calibration a number of individual parameters are established. The system also considers the feedback from the driver’s on how well the test was performed The validation of the approach is based on close collaboration with experts and measurements from 18 driver’s from Volvo Construction Equipment are used as reference.
  •  
44.
  • Begum, Shahina, et al. (författare)
  • ECG Sensor Signal Analysis to Represent Cases in a Case-based Stress Diagnosis System
  • 2010
  • Ingår i: Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB. - Corfu, Greece. - 9781424465606
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) sensor signal. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress using ECG sensor signal.
  •  
45.
  • Begum, Shahina, et al. (författare)
  • Individualized Stress Diagnosis Using Calibration and Case-Based Reasoning
  • 2007
  • Ingår i: Proceedings of the 24th annual workshop of the Swedish Artificial Intelligence Society, Borås, Sweden. ; , s. 59-69
  • Konferensbidrag (refereegranskat)abstract
    • Diagnosing stress is difficult even for experts due to large individual variations. Clinician's use today manual test procedures where they measure blood pressure, ECG, finger temperature and breathing speed during a number of exercises. An experienced clinician makes diagnosis on different readings shown in a computer screen. There are only very few experts who are able to diagnose and predict stress-related problems. In this paper we have proposed a combined approach based on a calibration phase and case-based reasoning to provide assistance in diagnosing stress, using data from the finger temperature sensor readings. The calibration phase helps to establish a number of individual parameters. The system uses a case-based reasoning approach and also feedback on how well the patient succeeded with the different test, used for giving similar cases reliability estimates.
  •  
46.
  • Begum, Shahina, et al. (författare)
  • Induction of an Adaptive Neuro-Fuzzy Inference System for Investigating Fluctuation in Parkinson´s Disease : The 23rd Annual Workshop of the Swedish Artificial Intelligence Society Umeå, May 10-12, 2006
  • 2006
  • Ingår i: Proceedings of SAIS 2006. ; , s. 67-72
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a methodology to formulate natural language rules for an adaptive neuro-fuzzy system based on discovered knowledge, supported by prior knowledge and statistical modeling. These rules could be improved using statistical methods and neural nets. This gives clinicians a valuable tool to explore the importance of different variables and their relations in a disease and could aid treatment selection. A prototype using the proposed methodology has been used to induce an Adaptive Neuro Fuzzy Inference Model that has been used to "discover" relationships between fluctuation, treatment and disease severity in Parkinson. Preliminary results from this project are promising and show that Neuro-fuzzy techniques in combination with statistical methods may offer medical research and medical applications a useful combination of methods.
  •  
47.
  • Begum, Shahina, et al. (författare)
  • Induction of an Adaptive Neuro-Fuzzy Inference System for investing fluctuation in Parkinson’s disease
  • 2006
  • Ingår i: 23rd annual workshop of the Swedish Artificial Intelligence Society. - Umeå.
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a methodology to formulate natural language rules for an adaptive neuro-fuzzy system based on discovered knowledge, supported by prior knowledge and statistical modeling. Relationships between disease related variables and fluctuations in Parkinson’s disease is often complex. Experts have simplified but mostly reliable “fuzzy” rules based on experience. These rules could be improved using statistical methods and neural nets. This gives clinicians a valuable tool to explore the importance of different variables and their relations in a disease and could aid treatment selection. A prototype using the proposed methodology has been used to induce an Adaptive Neuro Fuzzy Inference Model that has been used to “discover” relationships between fluctuation, treatment and disease severity. More data is needed to confirm these findings. The project shows that artificial intelligence techniques and methods in combination with statistical methods offer medical research and applications valuable opportunities.
  •  
48.
  • Begum, Shahina, et al. (författare)
  • Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management
  • 2010
  • Ingår i: Computational Intelligence in Healthcare 4. - : Springer Berlin/Heidelberg. - 9783642144639 ; , s. 159-189
  • Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
    • The complexity of modern lifestyle and society brings many advantages but also causes increased levels of stress for many people. It is known that increased exposure to stress may cause serious health problems if undiagnosed and untreated and a report from the Swedish government estimates that 1/3 of all long term sick leave is stress related. One of the physiological parameters for quantifying stress levels is the finger temperature that helps the clinician in diagnosis and treatment of stress. However, in practice, the complex and varying nature of signals makes it difficult and tedious to interpret and analyze the lengthy sequential measurements. A computer based system diagnosing stress would be valuable both for clinicians and for treatment. Since stress diagnosis has a week domain theory and there are large individual variations, Case-Based Reasoning is proposed as the main methodology. Feature extraction methods abstracting the original signals without losing key features are investigated. A fuzzy technique is also incorporated into the system to perform matching between the features derived from signals to better accommodate vagueness, uncertainty often present in clinical reasoning Validation of the approach is based on close collaboration with experts and measurements from twenty four persons. The system formulates a new problem case with 17 extracted features from the fifteen minutes (1800 samples) of biomedical sensor data. Thirty nine time series from twenty four persons have been used to evaluate the approach (matching algorithms) in which the system shows a level of performance close to an experienced expert. The system can be used as an expert for a less experienced clinician, as a second option for an experienced clinician or for treatment in home environment.
  •  
49.
  • Begum, Shahina, et al. (författare)
  • K-NN Based Interpolation to Handle Artifacts for Heart Rate Variability Analysis
  • 2011
  • Ingår i: IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2011. - : IEEE. ; , s. 387-392
  • Konferensbidrag (refereegranskat)abstract
    • Heart rate variability (HRV) is a popular parameter for depicting activities of autonomous nervous system and helps to explain various physiological activities of the body. A small amount of artifacts can produce significant changes especially, for time domain HRV features. Manual correction of artifacts performed by visual inspection of the signal by experts is tedious and time consuming and often leads to incorrect result especially for long term recordings. Therefore, an automatic artifact removing approach that helps to provide clinically useful HRV analysis is valuable. This paper proposes an algorithm that detects and replaces artifacts from inter-beat interval (IBI) signal for HRV analysis. The detection is mainly based on windowing technique and interpolation is performed using the k-nearest neighbour (K-NN) algorithm. The experimental work shows a promising performance in handling artifacts for HRV analysis using electrocardiogram (ECG) sensor signal.
  •  
50.
  • Begum, Shahina, et al. (författare)
  • Mental State Monitoring System for the Professional Drivers Based on Heart Rate Variability Analysis and Case-based Reasoning
  • 2012
  • Ingår i: 2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS). - NEW YORK : IEEE. - 9788360810484 ; , s. 35-42
  • Konferensbidrag (refereegranskat)abstract
    • The consequences of tiredness, drowsiness, stress and lack of concentration caused by a variety of different factors such as illness, sleep depletion, drugs and alcohol is a serious problem in traffic and when operating industrial equipment. A system that recognizes the state of the driver and e. g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. So, the aim of the project is to develop an intelligent system that can monitor drivers' stress depending on psychological and behavioral conditions/status using Heart Rate Variability (HRV). Here, we have proposed a solution using Case-Based Reasoning (CBR) to diagnose individual driver's level of stress. The system also considers feedback from the driver's on how well the test was performed. The validation of the approach is based on close collaboration with experts and measurements from 18 drivers from Volvo Construction Equipment (Volvo CE) are used as reference.
  •  
Skapa referenser, mejla, bekava och länka
  • Resultat 1-50 av 158
Typ av publikation
konferensbidrag (77)
tidskriftsartikel (43)
doktorsavhandling (11)
bokkapitel (10)
licentiatavhandling (6)
rapport (4)
visa fler...
forskningsöversikt (4)
annan publikation (3)
visa färre...
Typ av innehåll
refereegranskat (125)
övrigt vetenskapligt/konstnärligt (33)
Författare/redaktör
Funk, Peter (103)
Xiong, Ning (45)
Ahmed, Mobyen Uddin (40)
Begum, Shahina (32)
Funk, Peter, 1957- (24)
Olsson, Erik (14)
visa fler...
von Schéele, Bo (13)
Olsson, Tomas (11)
Begum, Shahina, 1977 ... (10)
Ahmed, Mobyen Uddin, ... (8)
Brodelius, Peter (8)
Ahmed, Mobyen Uddin, ... (7)
Funk, Peter, Profess ... (7)
Jackson, Mats (6)
Bengtsson, Marcus (6)
Rahman, Hamidur (5)
Folke, Mia (4)
Westin, Jerker (4)
Dougherty, Mark (4)
Bengtsson, Marcus, 1 ... (4)
Barua, Shaibal (4)
Funk, Christiane (4)
Nilsson, Markus (3)
Tomasic, Ivan (3)
Olsson, E (3)
Andersson, Alf (3)
Erdem, Ilker (3)
Sakao, Tomohiko, 196 ... (3)
Lindén, Maria (2)
Svensson, Magnus (2)
Memedi, Mevludin, 19 ... (2)
Klemencic, Marina (2)
Leon, Miguel (2)
Tuominen, Hannele (2)
Ahlström, Christer (2)
Schéele, Bo von (2)
Salonen, Antti (2)
Larsson, Jonas (2)
Rawal, N (2)
Carlsson, Johan (2)
Bozhkov, Peter (2)
Jansson, Peter, 1971 ... (2)
Engel, C. (2)
Aregbe, Yetunde (2)
Bonino, François (2)
Funk, Pierre (2)
Hildingsson, Lars (2)
Janssens, Willem (2)
Martikka, Elina (2)
Medici, Fausto (2)
visa färre...
Lärosäte
Mälardalens universitet (124)
Linnéuniversitetet (10)
RISE (10)
Linköpings universitet (7)
Uppsala universitet (5)
Chalmers tekniska högskola (5)
visa fler...
Högskolan Dalarna (5)
Umeå universitet (4)
Jönköping University (4)
Luleå tekniska universitet (3)
Örebro universitet (3)
Stockholms universitet (2)
Sveriges Lantbruksuniversitet (2)
Kungliga Tekniska Högskolan (1)
Lunds universitet (1)
Gymnastik- och idrottshögskolan (1)
Karolinska Institutet (1)
Blekinge Tekniska Högskola (1)
VTI - Statens väg- och transportforskningsinstitut (1)
visa färre...
Språk
Engelska (156)
Svenska (2)
Forskningsämne (UKÄ/SCB)
Naturvetenskap (58)
Teknik (58)
Medicin och hälsovetenskap (5)
Samhällsvetenskap (3)
Lantbruksvetenskap (1)
Humaniora (1)

År

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy