The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

Birim, Şule; Kazançoğlu, İpek; Kumar, Sachin; Kahraman, Aysun; Kazançoğlu, Yiğit

doi:10.1007/s10479-021-04429-x

Cited by 23 publications

(10 citation statements)

References 163 publications

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“…This trend is supported by the previous literature, which has consistently demonstrated the value of data in improving firms' operations, including supply chain configuration (Wang et al 2016), safety stock allocation (Hamister et al 2018), risk management (Choi et al 2016), and promotion planning (Cohen et al 2017). With the advances in artificial intelligence in recent years, techniques such as deep learning have been increasingly adopted to improve several aspects related to retail operations, such as procurement (Cui et al 2022), demand forecasting (Birim et al 2022), inventory replenishment (Qi et al 2020), and risk management (Wu and Chien 2021). This paper extends the literature in this research stream in the wake of the COVID-19 pandemic, which imposes significant burdens on supply chains and product distribution (e.g., Armani et al 2020;Ivanov and Dolgui 2020).…”

Section: Data-driven Retail Operationssupporting

confidence: 63%

Using AI to detect panic buying and improve products distribution amid pandemic

Adulyasak

Benomar²,

Chaouachi³

et al. 2023

AI & Soc

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The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%.

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Section: Data-driven Retail Operationssupporting

confidence: 63%

Using AI to detect panic buying and improve products distribution amid pandemic

Adulyasak

Benomar²,

Chaouachi³

et al. 2023

AI & Soc

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“…Compared with other neural networks, LSTM is better at processing data with sequence changes, such as speech signals [ 64 ]. In our study, spectral data were regarded as data of sequence changes, and the LSTM model as shown in Figure S14 was constructed (the basic unit of LSTM can be seen in Figure S15 [ 65 ]). The function of the dropout layer is to add a probabilistic process to the neurons of each layer on the basis of the normal neural network to randomly discard some neurons to prevent overfitting.…”

Section: Resultsmentioning

confidence: 99%

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

Tian

Zang

et al. 2023

Molecules

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Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA–A and LMWHA–E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA–A and LMWHA–E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA–A and LMWHA–E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares–discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)–SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA–A and LMWHA–E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules.

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“…Due to the fluctuation and complexity of the tourism industry, the prediction methods must capture even the finest nuances of non-stationary property and accurately describe its evolutionary trend. Some authors have used machine learning methods [71][72][73][74] or neural networks to predict tourism demand more accurately [40,67,[75][76][77][78][79].…”

Section: Specialized Literature That Addresses the Subject Of Tourism...mentioning

confidence: 99%

A Projection Approach of Tourist Circulation under Conditions of Uncertainty

et al. 2022

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This paper explores an important problem in tourism demand analysis, namely, the inherent uncertainty involved in projecting tourism demand. Tourism demand continues to be severely affected by unforeseen events associated with the current global health crisis, which has led to an examination of ways to predict the devastating effects of the COVID-19 pandemic on tourism. Tourism flow forecasting relating to arrivals is of particular importance for tourism and the entire hospitality industry, because it is an indicator of future demand. Thus, it provides fundamental information that can be applied in the planning and development of future strategies. Accurate forecasts of seasonal tourist flows can help decision-makers increase the efficiency of their strategic planning and reduce the risk of decision-making failure. Due to the growing interest in more advanced forecasting methods, we applied the ARMA model method to analyze the evolution of monthly arrival series for Romania in the period from January 2010 to September 2021, in order to ascertain the best statistical forecasting model for arrivals. We conducted this research to find the best method of forecasting tourist demand, and we compared two forecasting models: AR(1)MA(1) and AR(1)MA(2). Our study results show that the superior model for the prediction of tourist demand is AR(1)MA(1).

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The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

Cited by 23 publications

References 163 publications

Using AI to detect panic buying and improve products distribution amid pandemic

Using AI to detect panic buying and improve products distribution amid pandemic

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

A Projection Approach of Tourist Circulation under Conditions of Uncertainty

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