With the continuous improvement in the efficiency of the heavy-haul railway freight transportation, the pressure on on-site maintenance is increasing. In-depth research on fault characteristics carries significant imp...
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With the continuous improvement in the efficiency of the heavy-haul railway freight transportation, the pressure on on-site maintenance is increasing. In-depth research on fault characteristics carries significant importance for fault scientific judgment and fault prevention. This study proposes an efficient association rule mining (ARM) algorithm, HM-Rdhp, for analyzing fault data from heavy-haul railway freight trains. The algorithm introduces distributed parallel computing technology, integrating the MapReduce framework and HBase on the Hadoop platform to process large volumes of complex fault data efficiently. Experimental results show that the HM-Rdhp algorithm can efficiently uncover hidden patterns and associations within the fault data of heavy-haul railway freight trains. The mined association rules provide a valuable reference model to aid in predictive maintenance and fault prevention strategies for freight train maintenance departments.
It has been observed that the growth of communication technologies has led to increased use of computer networks in dissemination of education. An important application area in this perspective where there has been a ...
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ISBN:
(纸本)9783319119328
It has been observed that the growth of communication technologies has led to increased use of computer networks in dissemination of education. An important application area in this perspective where there has been a lot of research is Intelligent Tutoring Systems (ITS). ITS aid the process of learning and evaluation of attainments without human intervention. However ITS are unable to pin point the exact area in a lesson plan where the student is deficient in. In this context, several researchers have used concept maps to perform this identification. However it is time consuming for the educators to construct a concept map of learning manually. Several data mining algorithms have thus been used by the researchers to generate association rules which are used for automated concept map construction. This study proposes automated construction of concept maps using Direct Hashing and Pruning algorithm. The proposed approach was tested with a set of students enrolled in an introductory Java course in some undergraduate colleges in Kolkata and was found to diagnose their learning problems satisfactorily.
With the rapid development of Internet technology, online learning and online education are becoming more and more popular. Intelligent learning diagnosis has become an effective means to guarantee the quality of onli...
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With the rapid development of Internet technology, online learning and online education are becoming more and more popular. Intelligent learning diagnosis has become an effective means to guarantee the quality of online learning, and has become a research hotspot in the direction of education informatization. Concept map is an intuitive visual tool that can discover the concepts poorly mastered by students, and provide useful clues for identifying learning disabilities of students. This paper proposed a learning diagnosis method constructed based on the concept map. First, it groups learners, then uses direct hashing and pruning (dhp) to generate association rules between the different concepts, and finally uses the dhp algorithm to produce and construct the concept map automatically to discover the concepts poorly mastered by students, and realize the automatic diagnosis of learning problems. Case studies that have been done in college mathematics classes in some universities of Lianyungang have fully verified the effectiveness of our method.
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