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Orthogonal schemes for bidirectional associative memories

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1997年第3期27卷 543-551页

作者： Haryono Sadananda, R Phien, HN Department of Computer Science and Information Management Program of the Asian Institute of Technology Bangkok Thailand

Two issues are addressed in this paper. Firstly, it investigates some important properties of bidirectional associative memories (BAM) and proposes an improved capacity estimate. Those properties are the encoding form of the input pattern pairs as well as their decoding, the orthogonality of the pattern pairs, the similarity of associated patterns, and the density of the pattern pairs. Secondly, it proposes an implementation approach to improve the storage capacity. The approach embraces three proposed methods, i.e., the bipolar-orthogonal augmentation, the set partition, and the combined method. Along with those methods is the construction of the set of bipolar orthogonal patterns.

关键词： associative memory bidirectional memory heteroassociative memory neural networks

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A decision support system for evaluating international student applications

A decision support system for evaluating international stude...

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37th Annual Frontiers in EducationConference

作者： Hien, Nguyen Thi Ngoc Haddawy, Peter Computer Science and Information Management Program Asian Institute of Technology

ISBN: (纸本)9781424410835

In today's transnational admission environment, evaluating applicant qualifications is becoming increasingly challenging. While standardized tests can be helpful, studies have shown that they are rather noisy predictors of performance. Predicting educational outcome is a viable alternative in such heterogeneous environments. Performance prediction models can be built by applying data mining techniques to enrollment data. In this paper we present an approach to using Bayesian networks to predict graduating cumulative Grade Point Average based on applicant background at the time of admission. While such prediction models can be helpful, their recommendations may not be followed by departmental faculty members making admission decisions if they are presented as black boxes. We thus present a novel approach to deriving a case-based retrieval mechanism from the Bayesian network prediction model in such a way that the similarity measure used by the case-based system is consistent with the predictive model. The case-based component retrieves the past student most similar to the applicant being evaluated. The Bayesian network model is evaluated using stratified ten-fold cross validation.

关键词： Bayesian networks case-based retrieval student evaluation

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Deriving financial aid optimization models from admissions data

Deriving financial aid optimization models from admissions d...

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37th ASEE/IEEE Frontiers in Education Conference, FIE

作者： Van Thanh, Le Haddawy, Peter Asian Institute of Technology Computer Science and Information Management Program

ISBN: (纸本)1424410843

This paper presents a novel approach to deriving probabilistic models that predict enrollment given applicant background and the amount of financial aid offered. Our Bayesian network models can be used to optimize various enrollment objectives. We present a novel efficient optimization algorithm that uses the models to maximize expected tuition revenue under capacity constraints including student-faculty ratio and accommodation. We demonstrate and evaluate our approach using four years of graduate admissions data from the asian institute of technology, consisting of 7,788 applicants from 84 different countries. This data set is particularly challenging since reliable family income data is not available for students from most of these countries. Evaluating the Bayesian network model with 10-fold cross validation yields an ROC Az value of 0.8451, with a predictive accuracy of 82.70% at a threshold of 0.5. Comparing the results of the tuition revenue optimization model to the institute's current financial aid allocation practice shows that if single-term tuition revenue is the sole optimization criterion, the institute can achieve its current enrollment numbers while realizing significant savings in its financial aid budget. The prediction and optimization software is currently being incorporated into the institute's online admissions processing system. ©2007 IEEE.

关键词： Bayesian networks

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Expanding the plausible solution space for robustness in an intelligent tutoring system

Expanding the plausible solution space for robustness in an ...

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9th International Conference on Intelligent Tutoring Systems

作者： Kazi, Hameedullah Haddawy, Peter Suebnukarn, Siriwan Computer Science and Information Management Program Asian Institute of Technology Thailand School of Dentistry Thammasat University Thailand Department of Computer Science Isra University Pakistan

ISBN: (纸本)9783540691303

The knowledge acquisition bottleneck is a problem pertinent to the authoring of any intelligent tutoring system. Allowing students a broad scope of reasoning and solution representation whereby a wide range of plausible student solutions are accepted by the system, places additional burden on knowledge acquisition. In this paper we present a strategy to alleviate the burden of knowledge acquisition for building a tutoring system for medical problem-based learning (PBL). The Unified Medical Language System (UMLS) is deployed as domain ontology and information structure in the ontology is exploited to make intelligent inferences and expand the domain model. Using these inferences and expanded domain model, the tutoring system is able to accept a broader range of plausible student solutions that lie beyond the scope of explicitly encoded solutions. We describe the development of a tutoring system prototype and report the evaluation of system correctness in accepting such plausible solutions. The system evaluation indicates an average accuracy of 94.59% when compared against human domain experts, who agreed among themselves with a statistical agreement based on Pearson Correlation Coefficient of 0.48 and p < 0.05.

关键词： robustness intelligent tutoring systems medical problem-based learning UMLS knowledge acquisition bottleneck.

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Developing a conceptual framework to evaluate public B2B E-marketplaces 19

Developing a conceptual framework to evaluate public B2B E-m...

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19th Pacific Asia Conference on information Systems, PACIS 2015

作者： Thitimajshima, Wiyada Esichaikul, Vatcharaporn Krairit, Donyaprueth Computer Science and Information Management Asian Institute of Technology Thailand School of Management Asian Institute of Technology Thailand

Business-to-business (B2B) e-marketplaces are Internet-based inter-organizational trading platforms that facilitate and foster the exchange of information, products and services, and other business transactions among many buyers and sellers. Despite the proliferation of B2B e-marketplaces, many B2B e-marketplaces have failed. A well-developed framework or standard for evaluating B2B emarketplaces is scarce in the literature. Previous studies indicate the need for a comprehensive evaluation framework for evaluating performance of B2B e-marketplaces. This research presented a framework to evaluate performance of public B2B e-marketplaces that support small exporters. Factors contributing to the performance and effectiveness of B2B e-marketplaces were explored. The proposed conceptual framework integrated factors from both B2B e-marketplace performance and Web site evaluation perspectives. Expert interviews using a semi-structured approach were conducted in order to verify the proposed conceptual framework. On the basis of thorough review of literature and expert interviews conducted, this study proposed eleven factors to evaluate public B2B e-marketplaces. This research contributes to the literature by building an integrated framework to evaluate the performance public B2B e-marketplaces. It also contributes to the B2B e-marketplace industry by offering a practical mean for public B2B e-marketplace market makers or managers to evaluate and improve their emarketplaces.

关键词： information systems

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Modeling and forecasting of hourly transactions on a WWW and proxy cache server

Modeling and forecasting of hourly transactions on a WWW and...

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1999 Internet Workshop, IWS 1999

作者： Tantatsanawong, Panjai Phien, Huynh Ngoc Kanchanasut, Kanchana Computer Science and Information Management Program School of Advanced Technologies Asian Institute of Technology Thailand

ISBN: (纸本)0780359259

A modeling approach is introduced in this paper to capture the real characteristics of the WWW traffic, using a combination of Box-Jenkins models with seasonality removal. Fourier analysis and standardization are used to render seasonal data into non-seasonal data. However, seasonality cannot be completely removed by this approach, so both seasonal and non-seasonal models are employed and the best models are selected using posterior possibility criterion (PPC). In order to improve the forecasting accuracy, forecast values obtained by the developed models are adjusted by the difference percentage of the day of the week and by using this percentage adjusted values in place of the actual data, which are not available at the time of forecasting. With this adjustment, the developed models can provide very accurate forecast values, except for the case where the higher level of hierarchical proxy is shared by child proxies located in a different time zone. © 1999 IEEE.

关键词： Forecasting

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Clustering human behaviors with dynamic time warping and hidden Markov models for a video surveillance system

Clustering human behaviors with dynamic time warping and hid...

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7th Annual International Conference on Electrical Engineering/Electronics, computer, Telecommunications and information technology, ECTI-CON 2010

作者： Ouivirach, Kan Dailey, Matthew N. Computer Science and Information Management Asian Institute of Technology Thailand

ISBN: (纸本)9789746724913

We propose and experimentally evaluate a new method for clustering human behaviors that is suitable for bootstrapping an anomaly detection module for intelligent video surveillance systems. The method uses dynamic time warping, agglomerative hierarchical clustering, and hidden Markov models to provide an initial partitioning of a set of observation sequences then automatically identifies where to cut off the hierarchical clustering dendrogram. We show that the method is extremely effective, providing 100% accuracy in separating anomalous from typical behaviors on real-world testbed video surveillance data.

关键词： Hidden Markov models

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A quality assurance support system for learning-object development

A quality assurance support system for learning-object devel...

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2012 International Workshops on Web-Based Learning, ICWL 2012 - KMEL, SciLearn, and CCSTED

作者： Kalayanapan, Nopachat Wuwongse, Vilas Dittawit, Kornschnok Computer Science and Information Management Asian Institute of Technology Thailand

ISBN: (纸本)9783662434536

Use of e-Learning has been expanding rapidly in recent years with the development of ICT and e-Learning tools. The trend shows that it will continue to grow as technology is becoming cheaper and more accessible. Despite the trend, e-Learning suffers from low quality learning objects (LOs) due to the lack of supporting tools for proper electronic LO development. This paper proposes a quality assurance support system for LO development that aims to raise quality awareness throughout the development process. The system's underlying data model makes use of RDF to model and implement a workflow for LO development following the ISO/IEC 19796-1 standard. The system also integrates Quality Function Deployment (QFD) and achievement standards from Achievement Standard Network (ASN) to support the proposed workflow. Preliminary evaluation of the system shows that the system can satisfactorily assure the quality of resulting LOs. © 2014 Springer-Verlag.

关键词： Quality function deployment

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Factors influencing the adoption of e-government in Pakistan

Factors influencing the adoption of e-government in Pakistan

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International Conference on E-Business and E-Government

作者： Rehman, Mariam Esichaikul, Vatcharaporn Computer Science and Information Management Asian Institute of Technology Thailand

ISBN: (纸本)9781424486946

Governments around the world are working continuously in order to improve services for their citizens. However, the success of these initiatives is dependent not only on the government support but also on the citizens' willingness to use these services. To implement e-government successfully, it is important to determine the factors related to its adoption. This paper examines the factors influencing adoption of e-government services by the Pakistani citizens. Factors that have been explored in the extant literature present inadequate understanding of the relationship that exists between 'adopter characteristics' and 'behavioral intention' to use e-government services. These inadequacies have been identified through a systematic and thorough review of empirical studies and also by conducting expert reviews from the government officials, software engineers and academic researchers to get their perception according to the context of Pakistan. On the basis of thorough review of literature and expert reviews conducted, an integrated conceptual model is developed and proposed by integrating the influential factors which have been highlighted by the various researchers into a single comprehensive model. In this context, this paper describes and tracks in a fruitful way the factors related to the adoption of e-government services and also proposes conceptual model according to the context of Pakistan. © 2011 IEEE.

关键词： Government data processing

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A comparative analysis of techniques for predicting academic performance

A comparative analysis of techniques for predicting academic...

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37th Annual Frontiers in EducationConference

作者： Nghe, Nguyen Thai Janecek, Paul Haddawy, Peter Computer Science and Information Management Program Asian Institute of Technology (AIT) 12120 Thailand AIT Thailand Can Tho University Viet Nam

ISBN: (纸本)9781424410835

This paper compares the accuracy of Decision Tree and Bayesian Network algorithms for predicting the academic performance of undergraduate and postgraduate students at two very different academic institutes: Can Tho University (CTU), a large national university in Viet Nam;and the asian institute of technology (AIT), a small international postgraduate institute in Thailand that draws students from 86 different countries. Although the diversity of these two student populations is very different, the data-mining tools were able to achieve similar levels of accuracy for predicting student performance: 73/71% for {fail, fair, good, very good} and 94/93% for {fail, pass} at the CTU/AIT respectively. These predictions are most useful for identifying and assisting failing students at CTU (64% accurate), and for selecting Very Good students for scholarships at the AIT (82% accurate). In this analysis, the Decision Tree was consistently 3-12% more accurate than the Bayesian Network. The results of these case studies give insight into techniques for accurately predicting student performance, compare the accuracy of data mining algorithms, and demonstrate the maturity of open source tools.

关键词： Bayesian Networks data mining Decision Trees prediction

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