Data Analytics plays an important role in the decision making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage. However, the hidden patterns of the frequent itemsets become more time consuming to be mined when the amount of data increases over the time. Moreover, significant memory consumption is needed in mining the hidden patterns of the frequent itemsets due to a heavy computation by the algorithm. Therefore, an efficient algorithm is required to mine the hidden patterns of the frequent itemsets within a shorter run time and with less memory consumption while the volume of data increases over the time period. This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining (FPM) so that a more efficient FPM algorithm can be developed.
Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method's applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.
Purpose
The study aims to analyze the influence of hedonic, utilitarian, and self-esteem motivations on online shopping behavior. Likewise, the mediating role of impulsive shopping and shopping intentions is also analyzed.
Design/methodology/approach
The study was carried out with the results of a survey in which 450 respondents participated, and the data was analyzed by using structural equation modeling (SmartPLS 3.0 software).
Findings
All the hypothesized links were significant and positive except for the relationship of self-esteem motive with impulsive shopping tendency, which was negative as hypothesized. Moreover, hedonic motive had a strong positive impact on impulsive shopping tendency, whereas, in contrast, utilitarian motive had a strong positive impact on shopping intentions.
Practical implications
Managers should focus on functional value rather than emotional value to attract customers who tend to be utilitarian. In contrast, for customers who tend to be hedonic, the product offerings should be visually appealing, stimulating and inspiring, as well as have emotional value.
Originality/value
This study investigates the roles of self-esteem and hedonic motives in impulsive shopping behavior. Moreover, by using the theory of planned behavior, this study highlights the roles of hedonic and utilitarian motives in attitude toward engaging in online shopping.
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