Forecasting Tourism Demand in Marrakech with SQD-PCA-SVR

Laaroussi, Houria; Guerouate, Fatima; Sbihi, Mohamed

doi:10.1007/978-3-030-73882-2_62

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…This model is tested for accuracy in the validation data set, which is unseen for the model. Principal Component Analysis (PCA) from sklearn.decomposition is applied to extract principal components (PCs) 30 from the original variables, and their explained variance is checked for adequacy. High‐dimensional data are common in engineering research and arise when multiple variables are involved.…”

Section: Materials and Methodologymentioning

confidence: 99%

Predicting dimensional accuracy in 3D printed polydimethylsiloxane‐carbon nanotubes composites via machine learning

Raj,

Mahato,

Moharana

et al. 2023

Polymer Composites

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Direct ink writing (DIW) is a rapidly expanding additive manufacturing (AM) technique valued for its cost‐efficiency and material adaptability. While DIW offers substantial process flexibility, achieving precise manufacturing demands a thorough understanding of its printability parameters. Key process variables such as printing speed, nozzle‐bed gap, pulses for extrusion, and extrusion multiplier wield significant influence over the shape and dimensions of printed components. In this study, a machine learning model based on k‐nearest neighbors is employed to predict dimensional accuracy. The model is trained using measured width and thickness data from printed rings, with flow and printing‐speed tests defining parameter boundaries for printing. The final model demonstrates an accuracy of 81% and 74% for two PDMS‐CNT composite ink compositions. The principal components analysis underscores the pivotal roles of the extrusion factor and printing speed in achieving superior geometric fidelity. Additionally, retraction‐residual tests were conducted, revealing that the initial 10 seconds of extrusion hold critical importance as they can lead to distortions when traversing printed regions or perimeters, necessitating consideration in part design. To evaluate layer adhesion strength, a crucial aspect of AM, T‐peel tests were performed. Furthermore, rheological tests provided insights into extrudability, printable reinforcing percentages, shape retention, and yield stress.Highlights Rheological tests yield insights into minimum reinforcing% and shape retention. Developed k‐NN model predicts accuracy using measured printed rings' data. PC analysis shows key roles of extrusion factor and print speed. Retraction‐residual tests reveal initial 10 seconds are critical. Model validation results show 83.45% and 77.95% accuracy, close to prediction.

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Section: Materials and Methodologymentioning

confidence: 99%

Predicting dimensional accuracy in 3D printed polydimethylsiloxane‐carbon nanotubes composites via machine learning

Raj,

Mahato,

Moharana

et al. 2023

Polymer Composites

View full text Add to dashboard Cite

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“…In this step, we identified six categories related to the destination. Categories and keywords are in the same discussed in [22]. For each category we determine the seed search keywords and subsequently convert these keywords into Arabic and French, as they are the main languages in Marrakech, Morocco using Google Translate.…”

Section: Search Volume Datamentioning

confidence: 99%

A novel hybrid deep learning approachfor tourism demand forecasting

Laaroussi

Guerouate

Sbihi

2023

IJECE

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This paper proposes a new hybrid deep learning framework that combines search query data, autoencoders (AE) and stacked long-short term memory (staked LSTM) to enhance the accuracy of tourism demand prediction. We use data from Google Trends as an additional variable with the monthly tourist arrivals to Marrakech, Morocco. The AE is applied as a feature extraction procedure to dimension reduction, to extract valuable information and to mine the nonlinear information incorporated in data. The extracted features are fed into stacked LSTM to predict tourist arrivals. Experiments carried out to analyze performance in forecast results of proposed method compared to individual models, and different principal component analysis (PCA) based and AE based hybrid models. The experimental results show that the proposed framework outperforms other models.

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