In higher education, students face challenges when choosing elective courses in their study programmes. Most higher education institutions employ advisors to assist with this task. Recommender systems have their origi...
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In higher education, students face challenges when choosing elective courses in their study programmes. Most higher education institutions employ advisors to assist with this task. Recommender systems have their origins in commerce and are used in other sectors such as education. Recommender systems offer an alternative to the use of human advisors. This paper aims to examine the scope of recommender systems that assist students in choosing elective courses. To achieve this, a systematic literature review (SLR) on recommender systems corpus for choosing elective courses published from 2010-2019 was conducted. Of the 16 981 research articles initially identified, only 24 addressed recommender systems for choosing elective courses and were included in the final analysis. These articles show that several recommender systems approaches and data mining algorithms are used to achieve the task of recommending elective courses. This study identified gaps in current research on the use of recommender systems for choosing elective courses. Further work in several unexplored areas could be examined to enhance the effectiveness of recommender systems for elective courses. This study contributes to the body of literature on recommender systems, in particular those applied for assisting students in choosing elective courses within higher education.
This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Cons...
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This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.
in the paper we introduced the soft margin SVC to solve linearly inseparable problems. Compared with the kernel trick, it is obvious that the two approaches actually solve the problems in different manners. Then we pr...
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in the paper we introduced the soft margin SVC to solve linearly inseparable problems. Compared with the kernel trick, it is obvious that the two approaches actually solve the problems in different manners. Then we provided a novel view to design a kernel function based on a general proximity relation mapping. It shows better classification performance than the common Mercer kernels experimentally in the iatrology area.
datamining has been attracted much attention from practitioners and researchers in recent years. Association rules are one of the most important research areas of datamining. Association Rule mining (ARM) aims to di...
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ISBN:
(纸本)9781605584218
datamining has been attracted much attention from practitioners and researchers in recent years. Association rules are one of the most important research areas of datamining. Association Rule mining (ARM) aims to discovers the relationship between the most frequent itemsets. Many algorithms have been developed for mining static datasets. It is nontrivial to maintain such discovered rules from large datasets, this was the main idea behind Incremental Association Rules mining (IARM), which recently has received much attention from the datamining researcher. In this paper, a survey of Incremental Association Rule mining (IARM) techniques and algorithms that had been developed are categorized and analyzed.
Discovering complex and incomplete periodic patterns in the logs of events is a complicated and time consuming *** work shows that it is possible to discover complex and incomplete periodic patterns through finding si...
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Discovering complex and incomplete periodic patterns in the logs of events is a complicated and time consuming *** work shows that it is possible to discover complex and incomplete periodic patterns through finding simple patterns first and through logical derivations of complex and incomplete patterns later *** paper defines a syntax and semantics of a class of periodic patterns that frequently occur in the logs of events.A system of derivation rules proposed in the paper can be used to transform a set of periodic patterns into a logically equivalent set of *** rules are used in the algorithms that derive complex and incomplete periodic patterns.A prototype implementation of the algorithms that discover complex and incomplete periodic patterns in the logs of events is presented.
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