The Experts below are selected from a list of 390750 Experts worldwide ranked by ideXlab platform
Rob Koper - One of the best experts on this subject based on the ideXlab platform.
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Social Support System in Learning Network for lifelong learners: A Conceptual framework
International journal of continuing engineering education and life-long learning, 2009Co-Authors: Danish Nadeem, Slavi Stoyanov, Rob KoperAbstract:Nadeem, D., Stoyanov, S., & Koper, R. (2009). Social support system in Learning Network for lifelong learners: A Conceptual framework [Special issue]. International Journal of Continuing Engineering Education and Life-Long Learning, 19(4/5/6), 337-351.
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Learning Network Services for Professional Development - A Conceptual Model of Learning Networks
Learning Network Services for Professional Development, 2009Co-Authors: Rob KoperAbstract:In the TENCompetence project a set of UML models (Booch et al. 1999) have been developed to specify the core concepts for Learning Networks Services that support professional competence development. The three most important, high-level models are (a) the use case model, (b) the conceptual model, and (c) the domain model. The first model identifies the primary use cases we need in order to support professional competence development. The second model describes the concept of competence and competence development from a theoretical point of view. What is a competence? How does it relate to the cognitive system of an actor? How are competences developed? The third model is a UML Domain Model that defines, among other things, the components of a Learning Network, defines the concepts and relationships between the concepts in a Learning Network and provides a starting point for the design of the overall architecture for Learning Network Services, including the data model. Open image in new window
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Learning Network Services for Professional Development - Learning Network Services for Professional Development
2009Co-Authors: Rob KoperAbstract:A "Learning Network" is a community of people who help each other to better understand and handle certain events and concepts in work or life. As a result and sometimes also as an aim participating in Learning Networks stimulates personal development, a better understanding of concepts and events, career development, and employability. "Learning Network Services" are Web services that are designed to facilitate the creation of distributed Learning Networks and to support the participants with various functions for knowledge exchange, social interaction, assessment and competence development in an effective way. The book presents state-of-the-art insights into the field of Learning Networks and Web-based services which can facilitate all kinds of processes within these Networks. The main emphasis of the contributions is to explain what services a Learning Network requires and what the reader should do to design and run Learning Network Services, including guidelines on how to evaluate the effectiveness of these services. This book is a rich source of information for practitioners and professional developers who want to stimulate Learning by professionals through web-based social interaction. Managers of educational institutions and training companies will find many ideas about possible services to offer. At the same time, it is an excellent introduction for other researchers in the field, and for students interested in the Learning sciences or technology-enhanced Learning.
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Social support system in Learning Network for lifelong learners : a conceptual framework
International Journal of Continuing Engineering Education and Life-Long Learning, 2009Co-Authors: Danish Nadeem, Slavi Stoyanov, Rob KoperAbstract:Learning Networks are favourable model for supporting self-directed Learning for lifelong learners. Learners can decide about their Learning plans to learn at their own pace irrespective of place and time. However, such learners remain hidden from others in the Learning Network, which makes their Learning detrimental and less effective. Bringing learners together would benefit them in sharing each others expertise and learn effectively by collaboration. We tackle the problem of finding people in Learning Networks by developing a social support system (SoSuSy). The paper presents a conceptual framework for designing SoSuSy in a Learning Network. Such a system connects learners dealing with similar problems by using their combined skills and increasing their social interaction. We propose the use of people's profile in a social Network and the public text content they create (blogs and book-marking) as supported by Web 2.0 applications to search for suitable people in a Learning Network.
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Knowledge dating in Learning Networks
2006Co-Authors: Peter Van Rosmalen, Francis Brouns, Peter Sloep, Liesbeth Kester, Malik Koné, Rob KoperAbstract:Van Rosmalen, P., Sloep, P., Brouns, F., Kester, L., Kone, M., & Koper, R. (2006). Knowledge matchmaking in Learning Networks: Alleviating the tutor load by mutually connecting Learning Network users. British Journal of Educational Technology, 37(6), 881-895.
Linlin Shen - One of the best experts on this subject based on the ideXlab platform.
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skin lesion analysis towards melanoma detection using deep Learning Network
Sensors, 2018Co-Authors: Linlin ShenAbstract:Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep Learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep Learning framework consisting of two fully convolutional residual Networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep Learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.
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skin lesion analysis towards melanoma detection using deep Learning Network
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Linlin ShenAbstract:Skin lesion is a severe disease in world-wide extent. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons, e.g. low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. International Skin Imaging Collaboration (ISIC) is a challenge focusing on the automatic analysis of skin lesion. In this paper, we proposed two deep Learning methods to address all the three tasks announced in ISIC 2017, i.e. lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep Learning framework consisting of two fully-convolutional residual Networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. To our best knowledges, we are not aware of any previous work proposed for this task. The proposed deep Learning frameworks were evaluated on the ISIC 2017 testing set. Experimental results show the promising accuracies of our frameworks, i.e. 0.718 for task 1, 0.833 for task 2 and 0.823 for task 3 were achieved.
Lisa Brown - One of the best experts on this subject based on the ideXlab platform.
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impact of participation in nasa s digital Learning Network on science attitudes of rural mid level students
The Electronic Journal of Science Education, 2013Co-Authors: Christine Moseley, Lisa BrownAbstract:The purpose of this research was to determine whether videoconferencing combined with face-to-face instruction, as used in the delivery of the NASA Digital Learning Network Can A Shoebox Fly? Challenge module, was a feasible instructional method to increase positive student attitudes towards science. Overall, the data indicated that this was the case. This study utilized a mixed method approach to data collection. A pre-test, post-test one group design was used to collect quantitative data obtained from a science attitudinal survey. Qualitative data were gathered from face-to-face interviews with the subjects as well as informal observations by the researcher during the design process. Social presence theory, a sub-area of communication theory, was used as the theoretical framework for this study. It was determined that the NASA Digital Learning Network modules do create social presence within the distance Learning environment.
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Impact of participation in NASA’s Digital Learning Network on science attitudes of rural, mid-level students
The Electronic Journal of Science Education, 2013Co-Authors: Christine Moseley, Lisa BrownAbstract:The purpose of this research was to determine whether videoconferencing combined with face-to-face instruction, as used in the delivery of the NASA Digital Learning Network Can A Shoebox Fly? Challenge module, was a feasible instructional method to increase positive student attitudes towards science. Overall, the data indicated that this was the case. This study utilized a mixed method approach to data collection. A pre-test, post-test one group design was used to collect quantitative data obtained from a science attitudinal survey. Qualitative data were gathered from face-to-face interviews with the subjects as well as informal observations by the researcher during the design process. Social presence theory, a sub-area of communication theory, was used as the theoretical framework for this study. It was determined that the NASA Digital Learning Network modules do create social presence within the distance Learning environment.
David A. Wood - One of the best experts on this subject based on the ideXlab platform.
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Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data
Technology and Economics of Smart Grids and Sustainable Energy, 2020Co-Authors: David A. WoodAbstract:The stability of decentralized electricity grids is influenced by real-time electricity prices and the cost sensitivity and reaction times of power producers and consumers. The decentral smart grid control (DSGC) system is designed to provide demand-side control of decentralized electricity grids by linking real-time electricity prices to changes in grid frequency over the time scale of a few seconds. This stimulates electricity demand-side consumption / production on similar time scales. Grid stability of DSGC systems can be simulated by considering a wide range of assumptions for the electricity volumes consumed / produced (P) by each grid participant, their cost-sensitivity (G) and reaction times (Tau) to changing grid conditions. Such a simulation (10,000 cases) published for a simple four-node star decentralized grid configuration with randomized values for P, G and Tau quantifies dynamic grid stability ( Stb _ in ) in terms of grid mechanical and pricing influences. This study applies an optimized data-matching machine-Learning algorithm, the, transparent open box (TOB) Learning Network to predict Stb _ in (ranging from −0.0808 to +0.1094 s^−2) for this published simulation from its independent variables. TOB manages to predict Stb _ in to a high degree of accuracy (RMSE ~0.016 s^−2; R^2 ~0.85) for this grid configuration in which independent variables P, G and Tau are poorly correlated with Stb _ in . By involving average G and Tau values for the three consumers as input variables TOB prediction accuracy is further improved (RMSE ~0.0075 s^−2; R^2 ~0.90). The study highlights the importance of compound feature selection when predicting grid stability of decentralized electricity grids.
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Bakken Stratigraphic and Type Well-Log Learning Network for Transparent Prediction and Rigorous Data Mining
Natural Resources Research, 2019Co-Authors: David A. WoodAbstract:A Bakken formation Learning Network is established based upon type well-log data (seven petrophysical variables) and a discrete stratigraphic index (Str) comprising 1000 records extending into the underlying Three Forks formation. The transparent open box (TOB) Learning Network is applied to this dataset to predict Str, which it achieves with only two erroneous predictions for the 1000 records and high statistical accuracy (root mean squared error (RMSE) = 0.1057, for Str scale of 1–4; coefficient of determination ( R ^2) = 0.9870). Data mining reveals that the few prediction errors are located in the transition zones between the stratigraphic members. Feature selection focused on those transition zones has the potential to further reduce errors. The TOB algorithm demonstrates its potential to be applied for more extensive and complex lithofacies and stratigraphic sequence modeling. The Bakken TOB Network is also configured to predict shear wave velocity, which it does with high accuracy (RMSE = 11 m/s and R ^2 = 0.9994), highlighting the flexibility of the TOB algorithm to assess both continuous and discrete dependent variables.
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transparent open box Learning Network and artificial neural Network predictions of bubble point pressure compared
Petroleum, 2018Co-Authors: David A. Wood, Abouza ChoubinehAbstract:Abstract The transparent open box (TOB) Learning Network algorithm offers an alternative approach to the lack of transparency provided by most machine-Learning algorithms. It provides the exact calculations and relationships among the underlying input variables of the datasets to which it is applied. It also has the capability to achieve credible and auditable levels of prediction accuracy to complex, non-linear datasets, typical of those encountered in the oil and gas sector, highlighting the potential for underfitting and overfitting. The algorithm is applied here to predict bubble-point pressure from a published PVT dataset of 166 data records involving four easy-to-measure variables (reservoir temperature, gas-oil ratio, oil gravity, gas density relative to air) with uneven, and in parts, sparse data coverage. The TOB Network demonstrates high-prediction accuracy for this complex system, although it predictions applied to the full dataset are outperformed by an artificial neural Network (ANN). However, the performance of the TOB algorithm reveals the risk of overfitting in the sparse areas of the dataset and achieves a prediction performance that matches the ANN algorithm where the underlying data population is adequate. The high levels of transparency and its inhibitions to overfitting enable the TOB Learning Network to provide complementary information about the underlying dataset to that provided by traditional machine Learning algorithms. This makes them suitable for application in parallel with neural-Network algorithms, to overcome their black-box tendencies, and for benchmarking the prediction performance of other machine Learning algorithms.
Danish Nadeem - One of the best experts on this subject based on the ideXlab platform.
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Social Support System in Learning Network for lifelong learners: A Conceptual framework
International journal of continuing engineering education and life-long learning, 2009Co-Authors: Danish Nadeem, Slavi Stoyanov, Rob KoperAbstract:Nadeem, D., Stoyanov, S., & Koper, R. (2009). Social support system in Learning Network for lifelong learners: A Conceptual framework [Special issue]. International Journal of Continuing Engineering Education and Life-Long Learning, 19(4/5/6), 337-351.
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Social support system in Learning Network for lifelong learners : a conceptual framework
International Journal of Continuing Engineering Education and Life-Long Learning, 2009Co-Authors: Danish Nadeem, Slavi Stoyanov, Rob KoperAbstract:Learning Networks are favourable model for supporting self-directed Learning for lifelong learners. Learners can decide about their Learning plans to learn at their own pace irrespective of place and time. However, such learners remain hidden from others in the Learning Network, which makes their Learning detrimental and less effective. Bringing learners together would benefit them in sharing each others expertise and learn effectively by collaboration. We tackle the problem of finding people in Learning Networks by developing a social support system (SoSuSy). The paper presents a conceptual framework for designing SoSuSy in a Learning Network. Such a system connects learners dealing with similar problems by using their combined skills and increasing their social interaction. We propose the use of people's profile in a social Network and the public text content they create (blogs and book-marking) as supported by Web 2.0 applications to search for suitable people in a Learning Network.
