“…A conversion rate 0.05 was reported for the data collected within a specific interval. The transformation rate is a metric that indicates the speed at which the scores fluctuate over time [ 33 ]. Fig.…”
Section: Deep Learning Approachmentioning
confidence: 99%
“…This reward function depends on the rate of change of the proposed approach data, the sleep duration, and the TT level. The reward function, Equation (14) , achieves this goal [ 32 , 33 ]: …”
“…A conversion rate 0.05 was reported for the data collected within a specific interval. The transformation rate is a metric that indicates the speed at which the scores fluctuate over time [ 33 ]. Fig.…”
Section: Deep Learning Approachmentioning
confidence: 99%
“…This reward function depends on the rate of change of the proposed approach data, the sleep duration, and the TT level. The reward function, Equation (14) , achieves this goal [ 32 , 33 ]: …”
“…This situation causes customer dissatisfaction and loss of money and time for businesses. The large data size in machine learning increases the success of the trained model [1], [2], [3], [4], [5]. For this reason, machine-learning algorithms used in e-commerce web pages can be successful in understanding customer behaviors, making appropriate product recommendations or making sales forecasts.…”
The e-retail sector is growing day by day and the competitive environment is getting harder. Businesses have to compete with their competitors in order to survive. In parallel with the increasing internet penetration, the trade volume in E-Retail sites is also increasing therefore the data generated on these sites is enormous. Understanding these data with traditional analysis methods is difficult due to the size problem mentioned. Difficult to understand data causes loss of time, money and customers. In recent years, machine-learning algorithms have been frequently used to analyse these large-sized data and to use them in decision-making. This study aimed to perform predictive analysis for the product recommendation system established by using logistic regression, which is a supervised machine-learning algorithm. In addition, the binary classification algorithm preferred to predict whether customers make a purchase or not. As a result, the accuracy degree of the model was 79.73%. This study has the potential to affect the understanding of customers, ensuring customer satisfaction, increasing profit and market share, and contributes to a sustainable business purpose.
“…Default NP also has achieved the best forecasting performance for the COVID-19 problem 15 compared to Random Forest and Poisson distribution models. Borges et al 16 applied the Prophet-LSTM hybrid model to forecast daily COVID-19 ICU entrances for a Brazilian city and found smaller values for MAEs compared to standalone models.…”
In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP’s efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases.
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