| 1. |
- Dougherty, Mark, et al.
(författare)
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The April Fool Turing Test
- 2006
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Ingår i: tripleC. - 1726-670X. ; 4:2
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Tidskriftsartikel (refereegranskat)abstract
- This paper explores certain issues concerning the Turing test; non-termination, asymmetry and the need for a control experiment. A standard diagonalisation argument to show the non-computability of AI is extended to yields a socalled “April fool Turing test”, which bears some relationship to Wizard of Oz experiments and involves placing several experimental participants in a symmetrical paradox – the “April Fool Turing Test”. The fundamental question which is asked is whether escaping from this paradox is a sign of intelligence. An important ethical consideration with such an experiment is that in order to place humans in such a paradox it is necessary to fool them. Results from an actual April Fool Turing Test experiment are reported. It is concluded that the results clearly illustrate some of the difficulties and paradoxes which surround the classical Turing Test.
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| 2. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease
- 2020
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Ingår i: Journal of Sensors. - London : Hindawi Publishing Corporation. - 1687-725X .- 1687-7268. ; , s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson's disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD. © 2020 Somayeh Aghanavesi et al.
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| 3. |
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| 4. |
- Aghanavesi, Somayeh, 1981-
(författare)
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Sensor-based knowledge- and data-driven methods : A case of Parkinson’s disease motor symptoms quantification
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The overall aim of this thesis was to develop and evaluate new knowledge- and data-driven methods for supporting treatment and providing information for better assessment of Parkinson’s disease (PD).PD is complex and progressive. There is a large amount of inter- and intravariability in motor symptoms of patients with PD (PwPD). The current evaluation of motor symptoms that are done at clinics by using clinical rating scales is limited and provides only part of the health status of PwPD. An accurate and clinically approved assessment of PD is required using frequent evaluation of symptoms.To investigate the problem areas, the thesis adopted the microdata analysis approach including the stages of data collection, data processing, data analysis, and data interpretation. Sensor systems including smartphone and tri-axial motion sensors were used to collect data from advanced PwPD experimenting with repeated tests during a day. The experiments were rated by clinical experts. The data from sensors and the clinical evaluations were processed and used in subsequent analysis.The first three papers in this thesis report the results from the investigation, verification, and development of knowledge- and data-driven methods for quantifying the dexterity in PD. The smartphone-based data collected from spiral drawing and alternate tapping tests were used for the analysis. The results from the development of a smartphone-based data-driven method can be used for measuring treatment-related changes in PwPD. Results from investigation and verification of an approximate entropy-based method showed good responsiveness and test-retest reliability indicating that this method is useful in measuring upper limb temporal irregularity.The next two papers, report the results from the investigation and development of motion sensor-based knowledge- and data-driven methods for quantification of the motor states in PD. The motion data were collected from experiments such as leg agility, walking, and rapid alternating movements of hands. High convergence validity resulted from using motion sensors during leg agility tests. The results of the fusion of sensor data gathered during multiple motor tests were promising and led to valid, reliable and responsive objective measures of PD motor symptoms.Results in the last paper investigating the feasibility of using the Dynamic Time-Warping method for assessment of PD motor states showed it is feasible to use this method for extracting features to be used in automatic scoring of PD motor states.The findings from the knowledge- and data-driven methodology in this thesis can be used in the development of systems for follow up of the effects of treatment and individualized treatments in PD.
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| 5. |
- Aghanavesi, Somayeh, 1981-
(författare)
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Smartphone-based Parkinson’s disease symptom assessment
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of four research papers presenting a microdata analysis approach to assess and evaluate the Parkinson’s disease (PD) motor symptoms using smartphone-based systems. PD is a progressive neurological disorder that is characterized by motor symptoms. It is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Both patients’ perception regarding common symptom and their motor function need to be related to the repeated and time-stamped assessment; with this, the full extent of patient’s condition could be revealed. The smartphone enables and facilitates the remote, long-term and repeated assessment of PD symptoms. Two types of collected data from smartphone were used, one during a three year, and another during one-day clinical study. The data were collected from series of tests consisting of tapping and spiral motor tests. During the second time scale data collection, along smartphone-based measurements patients were video recorded while performing standardized motor tasks according to Unified Parkinson’s disease rating scales (UPDRS).At first, the objective of this thesis was to elaborate the state of the art, sensor systems, and measures that were used to detect, assess and quantify the four cardinal and dyskinetic motor symptoms. This was done through a review study. The review showed that smartphones as the new generation of sensing devices are preferred since they are considered as part of patients’ daily accessories, they are available and they include high-resolution activity data. Smartphones can capture important measures such as forces, acceleration and radial displacements that are useful for assessing PD motor symptoms.Through the obtained insights from the review study, the second objective of this thesis was to investigate whether a combination of tapping and spiral drawing tests could be useful to quantify dexterity in PD. More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. The results from this study showed that tapping and spiral drawing tests that were collected by smartphone can detect movements reasonably well related to under- and over-medication.The thesis continued by developing an Approximate Entropy (ApEn)-based method, which aimed to measure the amount of temporal irregularity during spiral drawing tests. One of the disabilities associated with PD is the impaired ability to accurately time movements. The increase in timing variability among patients when compared to healthy subjects, suggests that the Basal Ganglia (BG) has a role in interval timing. ApEn method was used to measure temporal irregularity score (TIS) which could significantly differentiate the healthy subjects and patients at different stages of the disease. This method was compared to two other methods which were used to measure the overall drawing impairment and shakiness. TIS had better reliability and responsiveness compared to the other methods. However, in contrast to other methods, the mean scores of the ApEn-based method improved significantly during a 3-year clinical study, indicating a possible impact of pathological BG oscillations in temporal control during spiral drawing tasks. In addition, due to the data collection scheme, the study was limited to have no gold standard for validating the TIS. However, the study continued to further investigate the findings using another screen resolution, new dataset, new patient groups, and for shorter term measurements. The new dataset included the clinical assessments of patients while they performed tests according to UPDRS. The results of this study confirmed the findings in the previous study. Further investigation when assessing the correlation of TIS to clinical ratings showed the amount of temporal irregularity present in the spiral drawing cannot be detected during clinical assessment since TIS is an upper limb high frequency-based measure.
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| 6. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Verification of a Method for Measuring Parkinson's Disease Related Temporal Irregularity in Spiral Drawings
- 2017
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Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220. ; 17:10
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Tidskriftsartikel (refereegranskat)abstract
- -value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.
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| 7. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A fuzzy rule-based decision support system for Duodopa treatment in Parkinson
- 2006
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Ingår i: 23rd annual workshop of the Swedish Artificial Intelligence Society. - Umeå.
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Konferensbidrag (refereegranskat)abstract
- A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.
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| 8. |
- Arridge, Christopher S., et al.
(författare)
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Uranus Pathfinder : exploring the origins and evolution of Ice Giant planets
- 2012
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Ingår i: Experimental astronomy. - : Springer Science and Business Media LLC. - 0922-6435 .- 1572-9508. ; 33:2-3, s. 753-791
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Tidskriftsartikel (refereegranskat)abstract
- The "Ice Giants" Uranus and Neptune are a different class of planet compared to Jupiter and Saturn. Studying these objects is important for furthering our understanding of the formation and evolution of the planets, and unravelling the fundamental physical and chemical processes in the Solar System. The importance of filling these gaps in our knowledge of the Solar System is particularly acute when trying to apply our understanding to the numerous planetary systems that have been discovered around other stars. The Uranus Pathfinder (UP) mission thus represents the quintessential aspects of the objectives of the European planetary community as expressed in ESA's Cosmic Vision 2015-2025. UP was proposed to the European Space Agency's M3 call for medium-class missions in 2010 and proposed to be the first orbiter of an Ice Giant planet. As the most accessible Ice Giant within the M-class mission envelope Uranus was identified as the mission target. Although not selected for this call the UP mission concept provides a baseline framework for the exploration of Uranus with existing low-cost platforms and underlines the need to develop power sources suitable for the outer Solar System. The UP science case is based around exploring the origins, evolution, and processes at work in Ice Giant planetary systems. Three broad themes were identified: (1) Uranus as an Ice Giant, (2) An Ice Giant planetary system, and (3) An asymmetric magnetosphere. Due to the long interplanetary transfer from Earth to Uranus a significant cruise-phase science theme was also developed. The UP mission concept calls for the use of a Mars Express/Rosetta-type platform to launch on a Soyuz-Fregat in 2021 and entering into an eccentric polar orbit around Uranus in the 2036-2037 timeframe. The science payload has a strong heritage in Europe and beyond and requires no significant technology developments.
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| 9. |
- Ashfaq, Awais, 1990-
(författare)
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Deep Evidential Doctor
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Recent years have witnessed an unparalleled surge in deep neural networks (DNNs) research, surpassing traditional machine learning (ML) and statistical methods on benchmark datasets in computer vision, audio processing and natural language processing (NLP). Much of this success can be attributed to the availability of numerous open-source datasets, advanced computational resources and algorithms. These algorithms learn multiple levels of simple to complex abstractions (or representations) of data resulting in superior performances on downstream applications. This has led to an increasing interest in reaping the potential of DNNs in real-life safety-critical domains such as autonomous driving, security systems and healthcare. Each of them comes with their own set of complexities and requirements, thereby necessitating the development of new approaches to address domain-specific problems, even if building on common foundations.In this thesis, we address data science related challenges involved in learning effective prediction models from structured electronic health records (EHRs). In particular, questions related to numerical representation of complex and heterogeneous clinical concepts, modelling the sequential structure of EHRs and quantifying prediction uncertainties are studied. From a clinical perspective, the question of predicting onset of adverse outcomes for individual patients is considered to enable early interventions, improve patient outcomes, curb unnecessary expenditures and expand clinical knowledge.This is a compilation thesis including five articles. It begins by describing a healthcare information platform that encapsulates clinical, operational and financial data of patients across all public care delivery units in Halland, Sweden. Thus, the platform overcomes the technical and legislative data-related challenges inherent to the modern era's complex and fragmented healthcare sector. The thesis presents evidence that expert clinical features are powerful predictors of adverse patient outcomes. However, they are well complemented by clinical concept embeddings; gleaned via NLP inspired language models. In particular, a novel representation learning framework (KAFE: Knowledge And Frequency adapted Embeddings) has been proposed that leverages medical knowledge schema and adversarial principles to learn high quality embeddings of both frequent and rare clinical concepts. In the context of sequential EHR modelling, benchmark experiments on cost-sensitive recurrent nets have shown significant improvements compared to non-sequential networks. In particular, an attention based hierarchical recurrent net is proposed that represents individual patients as weighted sums of ordered visits, where visits are, in turn, represented as weighted sums of unordered clinical concepts. In the context of uncertainty quantification and building trust in models, the field of deep evidential learning has been extended. In particular for multi-label tasks, simple extensions to current neural network architecture are proposed, coupled with a novel loss criterion to infer prediction uncertainties without compromising on accuracy. Moreover, a qualitative assessment of the model behaviour has also been an important part of the research articles, to analyse the correlations learned by the model in relation to established clinical science.Put together, we develop DEep Evidential Doctor (DEED). DEED is a generic predictive model that learns efficient representations of patients and clinical concepts from EHRs and quantifies its confidence in individual predictions. It is also equipped to infer unseen labels.Overall, this thesis presents a few small steps towards solving the bigger goal of artificial intelligence (AI) in healthcare. The research has consistently shown impressive prediction performance for multiple adverse outcomes. However, we believe that there are numerous emerging challenges to be addressed in order to reap the full benefits of data and AI in healthcare. For future works, we aim to extend the DEED framework to incorporate wider data modalities such as clinical notes, signals and daily lifestyle information. We will also work to equip DEED with explainability features.
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| 10. |
- Asiimwe, Edgar Napoleon, 1984-
(författare)
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On Emerging Mobile Learning Environments
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis investigates issues pertaining to the implementation of mobile learning and particularly the use of learning content management systems and mobile devices in university education. The thesis is positioned at the intersection of the research areas of information systems and education. The general view of technology is grounded in the field of information systems, but it is connected to the field of learning using the framework for the rational analysis of mobile education (FRAME). The point of enquiry was chosen based on the fact that the number of people who use mobile devices today is higher than the number of people who use desktop computers. This presents an opportunity for higher education institutions to increase the reach of education services by means of delivering them on mobile devices. This, of course, also presents challenges.The thesis is situated in the interpretive paradigm and the methodological approach was action research. Data was collected through review of literature, interviews, focus group discussions, observations and online surveys. Findings suggest that mobile learning cannot replace existing forms of learning in least developed countries but blended learning is a feasible alternative. Three units of analysis were used and found to be significant for studying and evaluating mobile learning, the technical artefact, the social artefact and the information artefact, together making up the information systems artefact. The thesis contributes to theory by discussing how mobile learning can be seen to be constructed as an information systems artefact through these three constructs. Advancements in virtual learning technology may bring a new wave of learning management systems. The nature of the next generation of learning content management systems is a topic for further research.
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| 11. |
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| 12. |
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| 13. |
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| 14. |
- Begum, Shahina, et al.
(författare)
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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
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Ingår i: Proceedings of SAIS 2006. ; , s. 67-72
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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.
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| 15. |
- Begum, Shahina, et al.
(författare)
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Induction of an Adaptive Neuro-Fuzzy Inference System for investing fluctuation in Parkinson’s disease
- 2006
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Ingår i: 23rd annual workshop of the Swedish Artificial Intelligence Society. - Umeå.
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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.
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| 16. |
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| 17. |
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| 18. |
- Dougherty, Mark
(författare)
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Exploring traffic systems by elasticity analysis of neural networks
- 2019
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Ingår i: Neural Networks in Transport Applications. - : Taylor and Francis. - 9780429817649 - 9781138334465 ; , s. 211-228
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter shows how elasticity testing of neural networks can greatly aid our understanding of transport systems. It examines several different pieces of work which have the common theme of using neural networks, coupled with a technique of elasticity analysis, in order to reach a better understanding of transport related problems. One of the main reasons for using neural networks is that they can easily represent complex functions, often with nonlinear interactions between different parameters. The chapter focuses on the elasticity of a single parameter with respect to a single network output. However, the elasticity technique can easily be extended to explore mutual interactions between parameters. A three-dimensional elasticity plot is shown of elasticity response against occupancy and speed. When neural networks are coupled with advanced computer visualization tools they provide an immensely powerful tool for general analysis. © V. Himanen, P. Nijkamp, A. Reggiani and J. Raitio 1998. All rights reserved.
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| 19. |
- Dougherty, Mark
(författare)
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International Master Programme: two intakes per year for a better peer group support
- 2005
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Ingår i: LIEE: LMD Informatique en Europe et Emploi. - Montpellier.
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Konferensbidrag (refereegranskat)abstract
- In 2001 Ho"gskolan Dalarna launched a masters programme in computer science. This programme hasattracted a large number of applications from international students. This has yielded many exciting opportunities, but also given rise to some problems, both practical and academic. A key element of thesuccess in solving some of these problems has been to make the programme highly modular in structure, allowing two intakes per year. This has been the key to developing a peer group support system that ismuch appreciated by the students. Another key element in the modular structure is that studies can be partly done in partner universities member of the INHEE network. INHEE is a International Network forHigher Education in Engineering and most partners are small european universities.
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| 20. |
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| 21. |
- Dougherty, Mark
(författare)
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Something old, something new, something borrowed, something blue : Part 3 - An elephant never forgets
- 2017
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Ingår i: Journal of Intelligent Systems. - : Walter de Gruyter GmbH. - 0334-1860 .- 2191-026X. ; 26:3, s. 433-437
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Tidskriftsartikel (refereegranskat)abstract
- Forgetting is an oft-forgotten art. Many artificial intelligence (AI) systems deliver good performance when first implemented; however, as the contextual environment changes, they become out of date and their performance degrades. Learning new knowledge is part of the solution, but forgetting outdated facts and information is a vital part of the process of renewal. However, forgetting proves to be a surprisingly difficult concept to either understand or implement. Much of AI is based on analogies with natural systems, and although all of us have plenty of experiences with having forgotten something, as yet we have only an incomplete picture of how this process occurs in the brain. A recent judgment by the European Court concerns the "right to be forgotten" by web index services such as Google. This has made debate and research into the concept of forgetting very urgent. Given the rapid growth in requests for pages to be forgotten, it is clear that the process will have to be automated and that intelligent systems of forgetting are required in order to meet this challenge.
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| 22. |
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| 23. |
- Dougherty, Mark
(författare)
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The affect of binocular vision disorders on cave survey accuracy.
- 2006
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Ingår i: Cave and Karst Science. - : British Cave Research Association. - 1356-191X. ; 33:2, s. 53-56
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Tidskriftsartikel (refereegranskat)abstract
- Binocular vision disorders such as heterophoria and hyperphoria are relatively common. This paper shows, that if a cave survey team does not take careful account of the possibility of one or more of its members suffering from such a disorder, very serious errors can occur. Both theoretical as well as empirical evidence is presented. The likely magnitude of these errors implies that BCRA grade 5 cannot be achieved unless surveyors check for, or if necessary correct for, binocular vision disorders. Various techniques to eliminate such errors are explained and compared. Finally a recommendation is made concerning an additional clause to the notes which accompany the BCRA cave surveying grade definitions. To achieve BCRA grade 5, the possibility of binocular vision disorders must be taken into account.
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| 24. |
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| 25. |
- Dougherty, Mark
(författare)
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What has literature to offer computer science?
- 2004
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Ingår i: Human IT. - 1402-1501 .- 1402-151X. ; 7:1, s. 74-91
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Forskningsöversikt (refereegranskat)abstract
- In this paper I ask the question: what has literature to offer computer science? Can a bilateral programme of research be started with the aim of discovering the same kind of deep intertwining of ideas between computer science and literature, as already exists between computer science and linguistics? What practical use could such results yield? I begin by studying a classic forum for some of the most unintelligible pieces of prose ever written, the computer manual. Why are these books so hard to understand? Could a richer diet of metaphor and onomatopoeia help me get my laser printer working? I then dig down a little deeper and explore computer programs themselves as literature. Do they exhibit aesthetics, emotion and all the other multifarious aspects of true literature? If so, does this support their purpose and understandability? Finally I explore the link between computer code and the human writer. Rather than write large amounts of code directly, we encourage students to write algorithms as pseudo-code as a first step. Pseudo-code tells a story within a semi-formalised framework of conventions. Is this the intertwining we should be looking for?
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| 26. |
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| 27. |
- Fleyeh, Hasan, et al.
(författare)
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Invariant road sign recognition with fuzzy ARTMAP and zernike moments
- 2007
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Ingår i: 2007 IEEE Intelligent Vehicles Symposium, vols 1-3. ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- In this paper, a novel approach to recognize road signs is developed. Images of road signs are collected by a digital camera mounted in a vehicle. They are segmented using colour information and all objects which represent signs are extracted, normalized to 36x36 pixels, and used to train a Fuzzy ARTMAP neural network by calculating Zernike moments for these objects as features. Sign borders and pictograms are investigated in this study. Zernike moments of sign borders and speed-limit signs of 210 and 150 images are calculated as features. A fuzzy ARTMAP is trained directly with features, or by using PCA for dimension reduction, or by using LDA algorithm as dimension reduction and data separation algorithm. Two Fuzzy ARTMAP Neural Networks are trained. The first NN determines the class of the sign from the shape of its border and the second one determines the sign itself from its pictogram. Training and testing of both NNs is done offline by using still images. In the online mode, the system loads the Fuzzy ARTMAP Neural Networks, and performs recognition process. An accuracy of about 99% is achieved in sign border recognition and 96% for Speed-Limit recognition.
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| 28. |
- Fleyeh, Hasan, et al.
(författare)
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Road and traffic sign detection and recognition
- 2005
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Ingår i: 10th EWGT Meeting and 16th Mini-EURO Conference. - Poznan, Poland.
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Konferensbidrag (refereegranskat)abstract
- This paper presents an overview of the road and traffic sign detection and recognition. It describes the characteristics of the road signs, the requirements and difficulties behind road signs detection and recognition, how to deal with outdoor images, and the different techniques used in the image segmentation based on the colour analysis, shape analysis. It shows also the techniques used for the recognition and classification of the road signs. Although image processing plays a central role in the road signs recognition, especially in colour analysis, but the paper points to many problems regarding the stability of the received information of colours, variations of these colours with respect to the daylight conditions, and absence of a colour model that can led to a good solution. This means that there is a lot of work to be done in the field, and a lot of improvement can be achieved. Neural networks were widely used in the detection and the recognition of the road signs. The majority of the authors used neural networks as a recognizer, and as classifier. Some other techniques such as template matching or classical classifiers were also used. New techniques should be involved to increase the robustness, and to get faster systems for real-time applications.
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| 29. |
- Fleyeh, Hasan, et al.
(författare)
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Road sign detection and recognition using fuzzy artmap : a case study Swedish speed-limit signs
- 2006
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Ingår i: The 10th IASTED International Conference on Artificial Intelligence and Soft Computing. - Palma de Mallorca, Spain.
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Konferensbidrag (refereegranskat)abstract
- In this paper, a novel approach is developed using Fuzzy ARTMAP Neural Networks to recognize and classify Swedish road and traffic signs. The Swedish Speed-Limit signs are selected as a case study, but the system can be applied to other signs. A new color detection and segmentation algorithm is presented in which the effects of shadows and highlights are eliminated. Images are taken by a digital camera mounted in a car. Segmented images are created by converting RGB images into HSV color space and applying the shadow-highlight invariant method. The method is tested on hundreds of outdoor images under shadow and highlight conditions, and it shows high robustness; in 95% of cases of correct segmentation is achieved. Classification is carried out by two stages of Fuzzy ARTMAP which are trained by 210 and 150 images, respectively. The first stage determines the border of the sign and the second stage determines the pictogram. Training and testing of both stages are made offline, using still images. In online mode, the system loads the Fuzzy ARTMAP and performs recognition process. An accuracy of 96.7% is achieved in Speed-Limit recognition and more than 90% as whole accuracy.
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| 30. |
- Fleyeh, Hasan, et al.
(författare)
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Segmentation of fingerprint images based on bi-level combination of global and local processing
- 2012
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Ingår i: Journal of Intelligent Systems. - Berlin : Walter de Gruyter. - 2191-026X .- 0334-1860. ; 21:2, s. 97-120
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a new approach to segment low quality fingerprint imageswhich are collected by low quality fingerprint readers. Images collected using such readersare easy to collect but difficult to segment. The proposed approach is based on combiningglobal and local processing to achieve segmentation of fingerprint images. On the globallevel, the fingerprint is located and extracted from the rest of the image by using a globalthresholding followed by dilation and edge detection of the largest object in the image.On the local level, fingerprint’s foreground and its border image are treated using differentfuzzy rules. These rules are based on the mean and variance of the block under consideration.The approach is implemented in three stages: pre-processing, segmentation, andpost-processing.Segmentation of 100 images was performed and compared with manual examinationsby human experts. The experiments showed that 96% of images under test are correctlysegmented. The results from the quality of segmentation test revealed that the averageerror in block segmentation was 2.84% and the false positive and false negatives wereapproximately 1.4%. This indicates the high robustness of the proposed approach.
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| 31. |
- Fleyeh, Hasan, et al.
(författare)
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Segmentation of low quality fingerprint images
- 2010
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Konferensbidrag (refereegranskat)abstract
- This paper presents a new algorithm to segment fingerprint images. The algorithm uses four features, the global mean, the local mean, variance and coherence of the image to achieve the fingerprint segmentation. Based on these features, a rule based system is built to segment the image. The proposed algorithm is implemented in three stages; pre-processing, segmentation, and post-processing. Gaussian filter and histogram equalization are applied in the pre-processing stage. Segmentation is applied using the local features. Finally, fill the gaps algorithm and a modified version of Otsu thresholding are invoked in the post-processing stage. In order to evaluate the performance of this method, experiments are performed on FVC2000 DB1. Segmentation of 100 images is performed and compared with manual examinations of human experts. It shows that the proposed algorithm achieves a correct segmentation of 82% of images under test.
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| 32. |
- Fleyeh, Hasan, et al.
(författare)
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SVM based traffic sign classification using legender moments
- 2007
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Ingår i: Proceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007. ; , s. 957-968
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel approach to recognize traffic signs using Support Vector Machines (SVMs) and Legendre Moments. Images of traffic signs are collected by a digital camera mounted in a vehicle. They are color segmented and all objects which represent signs are extracted and normalized to 36×36 pixels images. Legendre moments of sign borders and speed-limit signs of 350 and 250 images are computed and the SVM classifier is trained with theses features. Two stages of SVM are trained; the first stage determines the class of the sign from the shape of its border and the second one determines the pictogram of the sign. Training and testing of both SVM classifiers are done offline by using still images. In the online mode, the system loads the SVM training model and performs recognition. Copyright © 2007 IICAI.
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| 33. |
- Fleyeh, Hasan, et al.
(författare)
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Traffic sign classification using invariant features and support vector machines
- 2008
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Ingår i: Intelligent Vehicles Symposium, 2008 IEEE. ; , s. 530-535
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel approach to recognize traffic signs using invariant features and support vector machines (SVM). Images of traffic signs are collected by a digital camera mounted in a vehicle. They are color segmented and all objects which represent signs are extracted and normalized to 36 x 36 pixels images. Invariant features of sign rims and speed-limit sign interiors of 350 and 250 images are computed and the SVM classifier is trained with these features. Two stages of SVM are trained; the first stage determines the shape of sign rim and the second determines the pictogram of the sign. Training and testing of both SVM classifiers are done using still images. The best performance achieved is 98% for sign rims and 93% for speed limit signs.
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| 34. |
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| 35. |
- Grek, Åsa, 1989-, et al.
(författare)
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An Inductive Approach to Quantitative Methodology—Application of Novel Penalising Models in a Case Study of Target Debt Level in Swedish Listed Companies
- 2024
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Ingår i: Journal of Risk and Financial Management. - Basel : MDPI. - 1911-8074. ; 17:5
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a method for conducting quantitative inductive research on survey data when the variable of interest follows an ordinal distribution. A methodology based on novel and traditional penalising models is described. The main aim of this study is to pedagogically present the method utilising the new penalising methods in a new application. A case was employed to outline the methodology. The case aims to select explanatory variables correlated with the target debt level in Swedish listed companies. The survey respondents were matched with accounting information from the companies’ annual reports. However, missing data were present: to fully utilise penalising models, we employed classification and regression tree (CART)-based imputations by multiple imputations chained equations (MICEs) to address this problem. The imputed data were subjected to six penalising models: grouped multinomial lasso, ungrouped multinomial lasso, parallel element linked multinomial-ordinal (ELMO), semi-parallel ELMO, nonparallel ELMO, and cumulative generalised monotone incremental forward stagewise (GMIFS). While the older models yielded several explanatory variables for the hypothesis formation process, the new models (ELMO and GMIFS) identified only one quick asset ratio. Subsequent testing revealed that this variable was the only statistically significant variable that affected the target debt level. © 2024 by the authors.
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| 36. |
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| 37. |
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| 38. |
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| 39. |
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| 40. |
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| 41. |
- Grek, Åsa, 1989-
(författare)
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Nonresponse issues when analysing business survey data
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Data issues due to nonresponse or missing data arises often in company surveys or in firm data. Missing data and nonresponse causes bias. Another problem that causes bias is omitted variables. Accordingly, it will lead to wrong conclusions. The idea behind this licentiate thesis is to address these problems. The aim is to develop an insight into how common problems can be solved by transforming the data and changing the statistical method. There is no claim that the method suggested in the papers is always optimal. Rather, the goal of the papers is to give an awareness of problems that occurs in quantitative business research.
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| 42. |
- Hansson, Karl, et al.
(författare)
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Machine Learning Algorithms in Heavy Process Manufacturing
- 2016
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Ingår i: American Journal of Intelligent Systems. - 2165-8978 .- 2165-8994. ; 6:1, s. 1-13
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Tidskriftsartikel (refereegranskat)abstract
- In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.
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| 43. |
- Jomaa, Diala, 1978-, et al.
(författare)
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A comparative study between vehicle activated signs and speed indicator devices
- 2017
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Ingår i: Transportation Research Procedia. - : Elsevier. - 2352-1465. ; 22, s. 115-123
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Tidskriftsartikel (refereegranskat)abstract
- Vehicle activated signs and Speed indicator devices are safety signs used to warn and remind drivers that they are exceeding the speed limit on a particular road segment. This article has analysed and compared such signs with the aim of reporting the most suitable sign for relevant situations. Vehicle speeds were recorded at different test sites and the effects of the signs were studied by analyzing the mean and standard deviation. Preliminary results from the work indicate that both types of signs have variable effects on the mean and standard deviation of speed on a given road segment. Speed indicator devices were relatively more effective than vehicle activated signs on local roads; in contrast their effectivity was only comparable when tested on highways.
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| 44. |
- Jomaa, Diala, 1978-
(författare)
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A data driven approach for automating vehicle activated signs
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated.Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.
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| 45. |
- Jomaa, Diala, 1978-, et al.
(författare)
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Data based Calibration System for Radar used by Vehicle Activated Signs
- 2014
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Ingår i: Journal of Data Analysis and Information Processing. - : Scientific Research Publishing. - 2327-7203 .- 2327-7211. ; :2, s. 106-116
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Tidskriftsartikel (refereegranskat)abstract
- The accurate measurement of a vehicle’s velocity is an essential feature in adaptive vehicle activated sign systems. Since the velocities of the vehicles are acquired from a continuous wave Doppler radar, the data collection becomes challenging. Data accuracy is sensitive to the calibration of the radar on the road. However, clear methodologies for in-field calibration have not been carefully established. The signs are often installed by subjective judgment which results in measurement errors. This paper develops a calibration method based on mining the data collected and matching individual vehicles travelling between two radars. The data was cleaned and prepared in two ways: cleaning and reconstructing. The results showed that the proposed correction factor derived from the cleaned data corresponded well with the experimental factor done on site. In addition, this proposed factor showed superior performance to the one derived from the reconstructed data.
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| 46. |
- Jomaa, Diala, 1978-, et al.
(författare)
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Effectiveness of trigger speed of vehicle-activated signs on mean and standard deviation of speed
- 2016
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Ingår i: Journal of Transportation Safety and Security. - : Informa UK Limited. - 1943-9962 .- 1943-9970. ; 8:4, s. 293-309
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Tidskriftsartikel (refereegranskat)abstract
- Excessive or inappropriate speeds are a key factor in traffic fatalities and crashes. Vehicle-activated signs (VASs) are therefore being extensively used to reduce speeding to increase traffic safety. A VAS is triggered by an individual vehicle when the driver exceeds a speed threshold, otherwise known as trigger speed (TS). The TS is usually set to a constant, normally proportional to the speed limit on the particular segment of road. Decisions concerning the TS largely depend on the local traffic authorities. The primary objective of this article is to help authorities determine the TS that gives an optimal effect on the Mean and Standard Deviation of speed. The data were systematically collected using radar technology whilst varying the TS. The results show that when the applied TS was set near the speed limit, the standard deviation was high. However, the Standard Deviation decreased substantially when the threshold was set to the 85th percentile. This decrease occurred without a significant increase in the mean speed. It is concluded that the optimal threshold speed should approximate the 85th percentile, though VASs should ideally be individually calibrated to the traffic conditions at each site.
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| 47. |
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| 48. |
- Jomaa, Diala, et al.
(författare)
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Review of the effectiveness of vehicle activated signs
- 2013
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Ingår i: Journal of Transportation Technologies. - Irvine, CA : Scientific Research Publishing. - 2160-0481 .- 2160-0473. ; 3:2, s. 123-130
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Tidskriftsartikel (refereegranskat)abstract
- This paper reviews the effectiveness of vehicle activated signs. Vehicle activated signs are being reportedly used in recent years to display dynamic information to road users on an individual basis in order to give a warning or inform about a specific event. Vehicle activated signs are triggered individually by vehicles when a certain criteria is met. An example of such criteria is to trigger a speed limit sign when the driver exceeds a pre-set threshold speed. The preset threshold is usually set to a constant value which is often equal, or relative, to the speed limit on a particular road segment.This review examines in detail the basis for the configuration of the existing sign types in previous studies and explores the relation between the configuration of the sign and their impact on driver behavior and sign efficiency. Most of previous studies showed that these signs have significant impact on driver behavior, traffic safety and traffic efficiency. In most cases the signs deployed have yielded reductions in mean speeds, in speed variation and in longer headways. However most experiments reported within the area were performed with the signs set to a certain static configuration within applicable conditions. Since some of the aforementioned factors are dynamic in nature, it is felt that the configurations of these signs were thus not carefully considered by previous researchers and there is no clear statement in the previous studies describing the relationship between the trigger value and its consequences under different conditions. Bearing in mind that different designs of vehicle activated signs can give a different impact under certain conditions of road, traffic and weather conditions the current work suggests that variable speed thresholds should be considered instead.
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| 49. |
- Jomaa, Diala, 1978-, et al.
(författare)
-
Speed prediction for triggering vehicle activated signs
- 2016
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.
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| 50. |
- Jomaa, Diala, et al.
(författare)
-
Triggering Radar Speed Warning signs using Association Rules and Clustering Techniques
- 2012
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Konferensbidrag (refereegranskat)abstract
- Radar speed warning signs (RSWS) have been used in recent years across Sweden and elsewhere in the world. Such signs measure vehicle speed using radar and are designed to display a message when the driver exceeds a pre-set threshold speed, which is often relative to the speed limit on a particular road segment. RSWS are typically placed on locations which are perceived to be problematic by relevant authorities. Excessive speeding or road accidents are examples of such perceived problems. Deploying RSWS in many relevant locations is often impractical due to the lack of necessary power supply needed for operation. Battery driven RSWS are an alternative but are less attractive because of limited running time and frequent maintenance (changing batteries etc). Therefore, solar powered RSWS are more desirable. However, these signs are also dependent on batteries that need to be charged. The duration of operation of solar powered RSWS largely depend on how often the sign is triggered. Constant activation of the sign drains the battery. It is desirable to trigger the sign only when necessary. Hence, the main goal of this research is to design a model that optimises the performance of RSWS depending on prevailing conditions i.e traffic flows during different times of the day and so on. Vehicle speed data had been collected at a test site in Sweden all hours of the day. This paper attempts to use a hybrid system based on Apriori and K-means clustering algorithm. Apriori algorithm is simple and efficient to determine associations’ rules among attributes in particular to discover the most common combination that can occur within the data set. K-means clustering is basically used to quantize the input variables into smaller clusters that can easily derive the trigger threshold value. The proposed hybrid system indicated that the system was able to trigger solar RWWS efficiently.
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