A web-aware interoperable data mining system

Li, Sheng-Tun

doi:10.1016/s0957-4174(01)00050-1

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…of The Seventh International Conference on Distributed Multimedia Systems (DMS2001), Tamkang University, Taipei, Taiwan, September, 26-28. Li, S.-T. (2002). A Web-aware interoperable data mining system.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…The prototype of ADL sample LMS 1.2 environment is oversimplified and cannot fit the requirement of interoperability and reusability. On the other hand, component-based computing has been recognized as a successful paradigm that allows two or more software components to cooperate in a seamless manner, despite heterogeneities in implementation of languages, service interfaces, and deployment platforms (Li, 2002). Therefore, we choose the emerging Enterprise Java Bean (EJB), a distributed component-based computing environment, to develop the proposed LMS for realizing SCORM-compliant SMIL-enabled multimedia streaming contents.…”

Section: Introductionmentioning

confidence: 99%

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On a Design of SCORM-Compliant SMIL-Enabled Multimedia Streaming E-Learning System

Lin

2005

International Journal of Distance Education Technologies

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The emergence of the SCORM specification has shed a light toward standardization of e-learning which makes the reusability and interoperability of learning resources feasible. However, the existing SCORM-compliant asset model is over-simplified; only few asset types are defined. On the other hand, W3C's declarative-style SMIL is becoming prevalent in designing Webbased instructions with the consideration of temporality and spatiality of the presentations. In addition, advances in real-time multimedia technologies can further vitalize these instructions. In this study, we propose a SMIL-enabled asset model with the enhancement of multimedia streaming. To render SCORMcompliant SMIL-enabled streaming contents, a Java applet-based SMIL RTP/RTSP LMS system is designed in the Java EJB environment to tackle the issue of platform interoperability. The resulting system demonstrates an encouraging direction towards more vivid and interactive SCORM-compliant e-learning.

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Section: Acknowledgmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On a Design of SCORM-Compliant SMIL-Enabled Multimedia Streaming E-Learning System

Lin

2005

International Journal of Distance Education Technologies

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“…Other alternatives can be found in Flexer (2001), Kiang (2001) and Vesanto and Alhoniemi, 2000. A comprised approach is the twolevel SOM neural network which augments the conventional SOM network by an additional one-dimensional Kohonen layer in which each neuron is connected to the ones in the previous Kohonen layer (Li, 2002;Martín--del-Brío & Medrano, 1995). Fig.…”

Section: Self-organizing Map Neural Networkmentioning

confidence: 99%

“…An interoperable web-aware data mining system based on the proposed two-level SOM network is constructed by applying RMI and high-level code wrapper mechanisms of Java distributed object computing to address the issues of interoperability in heterogeneous environments. The details of design and implementation were presented in (Li, 2002). In this study, we adopt the two-level SOM network approach to mine the air pollution data and identify the number of clusters.…”

Section: Self-organizing Map Neural Networkmentioning

confidence: 99%

Data mining to aid policy making in air pollution management

Shue

2004

Expert Systems with Applications

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“…Various algorithms (including k-means and fuzzy c-mean clustering techniques, SOM, and fuzzy adaptive resonance theory (ART) have been applied to the dive profiles of penguins and seals (Schreer et al 1998). Unsupervised classification methods such as SOM (Li 2002) have been attempted to identify and classify crop weeds (Moshou et al 2001;Hemming and Rath 2001). The long-term uses of SOMassisted discrimination of weedy rice from cultivated rice can be potentially employed in monitoring edible rice cultivations, combating unwanted eco-types of rice, supporting weedy rice combustion with artificial intelligence tools used by drones, etc.…”

Section: Introductionmentioning

confidence: 99%

Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM)

Weerakoon

Somaratne

2021

Asian J Agric

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Abstract. Weerakoon SR, Somaratne S. 2021. Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM). Asian J Agric 5: 35-43. Weedy rice (WR) (Oryza sativa f. spontanea) has become a major threat in rice cultivation. Discrimination of WR from cultivated rice is difficult since agro-morphology of WR and cultivated rice are overlapping. Molecular markers are useful and can be an informative tool for estimating genetic diversity and relationships in closely related WR eco-types. Self-Organizing Maps (SOM) is an interesting and promising classification tool employing an innovative and data-driven classification method based on unsupervised artificial neural networks. The present study focused on exploring the potential use of SOM to classify WR populations of different eco-climatic zones in Sri Lanka using agro-morphological and molecular data. Separate SOMs for each set of variables, agro-morphological and molecular data were developed. The best SOM was chosen based on the error performance. Findings of SOM analyses showed that certain morphological characters (seedling height, leaf blade width, leaf blade length, culm strength, panicle shattering, seed coat color and leaf angle) and certain molecular characters detected from SSR primers (RM 11, RM 21, RM 14, and RM 280) are important in separation of different WR eco-types satisfactorily. SOM clustering of cultivated, wild, and WR eco-types indicated specific patterns of grouping with respect to climatic conditions of the country. WR eco-types in dry zone and wet zone of the country are closely related to Oryza nivara and O. rufipogon respectively.

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A web-aware interoperable data mining system

Cited by 13 publications

References 20 publications

On a Design of SCORM-Compliant SMIL-Enabled Multimedia Streaming E-Learning System

On a Design of SCORM-Compliant SMIL-Enabled Multimedia Streaming E-Learning System

Data mining to aid policy making in air pollution management

Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM)

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