The success of an e-Learning system depends on several factors: a supportive infrastructure, high-quality content, effective format, and high availability to satisfy ongoing user needs. In this paper, we perform explo...
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The success of an e-Learning system depends on several factors: a supportive infrastructure, high-quality content, effective format, and high availability to satisfy ongoing user needs. In this paper, we perform exploratory data analysis and data mining on an e-Learning web log, which spans one academic year. The study uncovers the e-Learning users’ usage behavior in accessing the content. The study discovers e-Learning media popularity and usage patterns, and helps the institution fine tune future courseware, from strategic changes to the fine-grain of lesson content improvement.
Numerous genome-wide association studies (GWAS) have been performed in order to determine the susceptible loci of diseases. However, most diseases still cannot be explained by a single locus. Pathway-based methods add...
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Monitoring every single email takes a lot of effort especially when the size of email transaction log is very large. This study proposed to find a wise option to monitor only the contents of important emails. Depth Fi...
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Monitoring every single email takes a lot of effort especially when the size of email transaction log is very large. This study proposed to find a wise option to monitor only the contents of important emails. Depth First Search algorithm, multi-digraph, email scoring model, WordNet, and Vector Space Model are used to create a model for filtering important emails and mining email contents. The findings showed that using email filtering module together with term enhancing module can help in reducing the processing time and keeping high precision and recall values of the system.
Query-based diagnostics (Agosta, Gardos, & Druzdzel, 2008) offers passive, incremental construction of diagnostic models that rest on the interaction between a diagnostician and a computer-based diagnostic system....
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Query-based diagnostics (Agosta, Gardos, & Druzdzel, 2008) offers passive, incremental construction of diagnostic models that rest on the interaction between a diagnostician and a computer-based diagnostic system. Effectively, this approach minimizes knowledge engineering, the main bottleneck in practical application of Bayesian networks. While this idea is appealing, it has undergone only limited testing in practice. We describe a series of experiments that subject a prototype implementing passive, incremental model construction to a rigorous practical test. We show that the prototype's diagnostic accuracy reaches reasonable levels after merely tens of cases and continues to increase with the number of cases, comparing favorably to state of the art approaches based on learning.
Numerous genome-wide association studies (GWAS) have been performed in order to determine the susceptible loci of diseases. However, most diseases still cannot be explained by a single locus. Pathway-based methods add...
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Numerous genome-wide association studies (GWAS) have been performed in order to determine the susceptible loci of diseases. However, most diseases still cannot be explained by a single locus. Pathway-based methods address this problem by combining those small effects into groups of related genes or pathways. However, because pathway-based approaches consist of multiple steps, there is much room for improvement. This paper presents an improved method for translating list of SNPs from GWAS into list of genes. We incrementally adjusted and compared different modifications applied to Parkinson's disease (PD) genome-wide SNP data and then used GSEA-P to analyze the resulting list of genes. After careful adjustments, we are able to identify more relevant pathways that were not found previously.
Identifying transcription factor binding sites (TFBSs) is crucial for understanding the mechanism of transcriptional regulation. It is known that transcription factors (TFs) often cooperate to regulate genes. While tr...
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With the tremendous volume of email messages, it is difficult to determine significant messages and users in an email system without knowing the message contents. This paper presents a simple and intuitive model to mi...
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With the tremendous volume of email messages, it is difficult to determine significant messages and users in an email system without knowing the message contents. This paper presents a simple and intuitive model to mine an email transactions log for significant messages and users. No use is made of NLP or semantic analysis. The model is based only on scoring messages and users from a graph-theoretic analysis of the communication pattern represented in the transaction log. Practical experiments indicate the potential of the model.
In this paper, Gait Energy Image (GEI) has constructed to apply Principal Component Analysis (PCA) with and without Radon Transform (RT). The Radon Transform is used to detect features within an image and PCA is used ...
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In this paper, Gait Energy Image (GEI) has constructed to apply Principal Component Analysis (PCA) with and without Radon Transform (RT). The Radon Transform is used to detect features within an image and PCA is used to reduce dimension of the images without much loss of information. The side view of slow walk;fast walk and carrying a ball walk have been selected from the CMU MoBo database for experimental purposes. The two techniques achieved equal error rates (EER) of 94.23%, 82.28%, and 90.38% for PCA only and 96.15%, 82.70% and 92.30% for PCA with RT for slow walk, fast walk and carrying a ball walk respectively.
In this paper, we describe the concept and design of a novel visualization tool to aid in academic writing of English as a Second Language students. The tool makes use of theory in classroom discourse, WordNet API, an...
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In this paper, we describe the concept and design of a novel visualization tool to aid in academic writing of English as a Second Language students. The tool makes use of theory in classroom discourse, WordNet API, and linguistics rules given by a linguistics expert, by analyzing English language essays for their linguistic bond counts and links within and between paragraphs. These linguistic indicators reveal the structure and flow of essays, clusters of main ideas, as well as incoherent sentences, which obstruct essay unity. The lack of essay unity is one of the most common writing errors of English as a Second Language learners. The output of the system is shown as several kinds of visualizations that provide writing feedback to users, as well as an autocorrect functionality to improve essay unity. Novice English learners may benefit greatly from this system.
Most practical uses of Dynamic Bayesian Networks (DBNs) involve temporal inuences of the first order, i.e., inuences between neighboring time steps. This choice is a convenient approximation inuenced by the existence ...
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Most practical uses of Dynamic Bayesian Networks (DBNs) involve temporal inuences of the first order, i.e., inuences between neighboring time steps. This choice is a convenient approximation inuenced by the existence of efficient algorithms for first order models and limitations of available tools. We focus on the question whether constructing higher time-order models is worth the effort when the underlying system's memory goes beyond the current state. We present the results of an experiment with a series of DBN models monitoring woman's monthly cycle. We show that higher order models are significantly more accurate. However, we have also observed overfitting and a resulting decrease in accuracy when the time order chosen is too high.
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