Distance learning is a learning style that can overcome the limitation of time and space. Because of the distance, teachers can not handle the student's learning situation, and they do not know whether the student...
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Distance learning is a learning style that can overcome the limitation of time and space. Because of the distance, teachers can not handle the student's learning situation, and they do not know whether the student is attentive, drowsy or absent. If teachers can know the student's affective state, they can overcome the difficult. The research applies the image recognition technologies to capture the face images of students when they are learning and analyzes their face features to evaluate the student's affective state by Fuzzy Integral. Finally, teachers can monitor the student's behavior by the detection results on the system interface.
Recommendation accuracy is especially important in mobile e-commerce environments due to the limited screen size of mobile devices and relatively expensive connection costs. Mobile content tends to be fashionable and ...
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ISBN:
(纸本)1601320639
Recommendation accuracy is especially important in mobile e-commerce environments due to the limited screen size of mobile devices and relatively expensive connection costs. Mobile content tends to be fashionable and are geared for young users. This paper presents a novel method of building a more accurate recommender system for mobile content in a mobile ecommerce environment. The method is based on collaborative filtering, and models content diffusion and user preference transition and incorporates them in constructing pseudo ratings from implicit feedback data. In a variety of experiments, recommender systems based on the method showed significantly better recommendation accuracy than a pure collaborative filtering-based recommender system.
The success rate of computerscience and engineering students in private universities are not high. It is helpful to find the model to assist students in registration planning. The objective of this research is to pro...
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The success rate of computerscience and engineering students in private universities are not high. It is helpful to find the model to assist students in registration planning. The objective of this research is to propose the classifier algorithm for building course registration planning model (CRPM) from historical dataset. The algorithm is selected by comparing performances of four classifiers include Bayesian network, C4.5, Decision Forest and NBTree. The dataset were obtained from student enrollments including grade point average (GPA) and grades of undergraduate students whose majors were computerscience or computer engineering. These dataset included grades in each subject of first and second year students from a private university in Thailand. Results showed that NBTree seemed to be the best of four classifiers which had highest prediction power. NBTree was used to generate CRP model which can be used to predict student class of GPA and consider student course sequences for registration planning.
Nanoscale porous silicon waveguides, both on silicon substrates and free-standing membranes, are explored for biosensing applications. Measured detection limits in the nanomolar range are reported for DNA sensing. ...
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In emergency response organizations with very limited resources, information technologies are not adequately explored. In such organizations, the simple adoption of new information technologies is not productive, as t...
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ISBN:
(纸本)9781424416509
In emergency response organizations with very limited resources, information technologies are not adequately explored. In such organizations, the simple adoption of new information technologies is not productive, as their efficient use depends on many other interrelated technologies. This work describes a model to help understanding these interrelationships. The model allows the cooperative evaluation of an organization through different perspectives. Using the model, an organization can measure its maturity level and guide the investment in emergency response capabilities. The information technology dimension of the model has been applied to the firefight organization in Brazil.
The objective of this study is to propose a model for planning course registration by using a data mining technique: Bayesian network. The proposed model can be used to predict the sequences of courses to be registere...
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ISBN:
(纸本)9781424414895
The objective of this study is to propose a model for planning course registration by using a data mining technique: Bayesian network. The proposed model can be used to predict the sequences of courses to be registered by undergraduate students whose majors are computerscience or engineering. The data set was obtained from student enrollments and include GPA and grades in each subject for first and second year students from a private university in Thailand. Evaluations show that the predictive power of this model is acceptable. The implications from this studypsilas findings suggest that the model can be applied for advising students in planning courses to be registered in each semester. Further, the model appears to be useful for improving curriculum development in order to fit both studentspsila and university requirements.
Learner attention affects learning efficiency. However, in many classes, teachers cannot assess the degree of attention of every student. When a teacher is capable of addressing inattentive students immediately, he ca...
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Learner attention affects learning efficiency. However, in many classes, teachers cannot assess the degree of attention of every student. When a teacher is capable of addressing inattentive students immediately, he can avoid situations in which students are inattentive. Many studies have analyzed student attentiveness by the applying of image detection technologies. If this mechanism can be applied to in-class learning, it will help teachers keep students attentive, and reduce teacher load during class. This study mainly applies fuzzy logic analysis of student facial images when participating in class. Applying fuzzy logic can prevent erroneous judgments associated with a single term, and help teachers deal with student attentiveness.
Given a percentage-threshold and readings from a pair of consecutive upstream and downstream sensors, flow anomaly discovery identifies dominant time intervals where the fraction of time instants of significantly mis-...
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Given a percentage-threshold and readings from a pair of consecutive upstream and downstream sensors, flow anomaly discovery identifies dominant time intervals where the fraction of time instants of significantly mis-matched sensor readings exceed the given percentage-threshold. Discovering flow anomalies (FA) is an important problem in environmental flow monitoring networks and early warning detection systems for water quality problems. However, mining FAs is computationally expensive because of the large (potentially infinite) number of time instants of measurement and potentially long delays due to stagnant (e.g. lakes) or slow moving (e.g. wetland) water bodies between consecutive sensors. Traditional outlier detection methods (e.g. t-test) are suited for detecting transient FAs (i.e., time instants of significant mis-matches across consecutive sensors) and cannot detect persistent FAs (i.e., long variable time-windows with a high fraction of time instant transient FAs) due to a lack of a pre-defined window size. In contrast, we propose a Smart Window Enumeration and Evaluation of persistence-Thresholds (SWEET) method to efficiently explore the search space of all possible window lengths. Computation overhead is brought down significantly by restricting the start and end points of a window to coincide with transient FAs, using a smart counter and efficient pruning techniques. Experimental evaluation using a real dataset shows our proposed approach outperforms Nainodotve alternatives.
The rapid growth of the biomedical literature and genomic information presents a major challenge for determining the functional relationships among genes. In this study, we develop a Web-based bioinformatics software ...
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The rapid growth of the biomedical literature and genomic information presents a major challenge for determining the functional relationships among genes. In this study, we develop a Web-based bioinformatics software environment called FAUN or feature annotation using nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. We tested FAUN on three manually constructed gene document collections, and then used it to analyze several microarray-derived gene sets obtained from studies of the developing cerebellum in normal and mutant mice. FAUN provides utilities for collaborative knowledge discovery and identification of new gene relationships from text streams and repositories (e.g., MEDLINE). It is particularly useful for the validation and analysis of gene associations suggested by microarray experimentation.
This paper discusses phase noise characteristics of an Fr oscillator focusing on a signal output position. Oscillation circuits can be divided into an amplifier and a feedback circuit. We compared that phase noise cha...
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