Mining High Utility Itemsets Using Bio-Inspired Algorithms: A Diverse Optimal Value Framework

Song, Wei; Huang, Chao‐Min

doi:10.1109/access.2018.2819162

Cited by 67 publications

(56 citation statements)

References 30 publications

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“…12. For convenience, methods proposed in [9], [19] and this paper are respectively named as method A, method B and method C. The computer simulations in Fig. 11 show that the performance of CFO estimation in the context of the AWGN channel is the best while that in the context of channel ITU-B is the worst, no matter which method is used.…”

Section: Performance Of the Synchronization Methodsmentioning

confidence: 99%

Joint synchronization estimation based on genetic algorithm for OFDM/OQAM systems

Liu

Chen

Zhao

2020

J. of Syst. Eng. Electron.

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This paper investigates the problem of synchronization for offset quadrature amplitude modulation based orthogonal frequency division multiplexing (OFDM/OQAM) systems based on the genetic algorithm. In order to increase the spectrum efficiency, an improved preamble structure without guard symbols is derived at first. On this basis, instead of deriving the log likelihood function of power spectral density, joint estimation of the symbol timing offset and carrier frequency offset based on the preamble proposed is formulated into a bivariate optimization problem. After that, an improved genetic algorithm is used to find its global optimum solution. Conclusions can be drawn from simulation results that the proposed method has advantages in the joint estimation of synchronization.

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Section: Performance Of the Synchronization Methodsmentioning

confidence: 99%

Joint synchronization estimation based on genetic algorithm for OFDM/OQAM systems

Liu

Chen

Zhao

2020

J. of Syst. Eng. Electron.

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“…These algorithms achieve high performance but are not guaranteed to explore all the HUIs. In 2018, Song and Huang [37] proposed a new model based on bio-inspired algorithms to find a set of HUIs. This approach selects the proportion of HUIs detected as the target values of the next population.…”

Section: A High-utility Itemset Miningmentioning

confidence: 99%

Mining Correlated High Utility Itemsets in One Phase

et al. 2020

IEEE Access

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High-utility itemset mining (HUIM) in transaction databases has been extensively studied to discover interesting itemsets from users' purchase behaviors. With this, business managers can adjust their sale strategies appropriately to increase profit. HUIM approaches usually focus on the utility values of itemsets, but rarely evaluate the correlation of items in itemsets. Many high-utility itemsets are weakly correlated and have no real meaning. To address this issue, we suggest an algorithm, called CoHUI-Miner, to efficiently find correlated high-utility itemsets. In the proposed algorithm, we use the database projection mechanism to reduce the database size and present a new concept, called the prefix utility of projected transactions, to eliminate itemsets which do not satisfy the minimum threshold in the mining process. Experimental evaluation on two types of datasets from sparse to dense ones shows that CoHUI-Miner can efficiently mine correlated high-utility itemsets with regard to both execution time and memory usage. INDEX TERMS Correlated high-utility itemset, data mining, high-utility pattern, projected database.

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“…Finally, the remaining 48 data were used as the final verification set, and the accuracy was found to be 97.91%. The confusion matrix is used to visualize the performance of an algorithm [34]. In predictive analysis, the confusion matrix is a table made up of False Positives (FP), False Negatives (FN), True Positives (TP), and True Negatives (TN).…”

Section: K-nn Classification For Ev Driver Behaviormentioning

confidence: 99%

EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

Shen

Fang

et al. 2020

Electronics

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With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select an EV suitable for scheduling. In order to improve the efficiency of scheduling, we first need to determine define categories of target EV users. We found that grouping on the basis of EV charging behavior is one effective method to identify target EVs. Therefore, we propose a hybrid artificial intelligence classification method based on the charging behavior profile of EVs. Through this classification method, target EVs can be accurately identified. The results of cross-validation experiments and performance evaluations suggest that this method is effective.

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Mining High Utility Itemsets Using Bio-Inspired Algorithms: A Diverse Optimal Value Framework

Cited by 67 publications

References 30 publications

Joint synchronization estimation based on genetic algorithm for OFDM/OQAM systems

Joint synchronization estimation based on genetic algorithm for OFDM/OQAM systems

Mining Correlated High Utility Itemsets in One Phase

EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

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