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Vu, Thomas

doi:10.55277/researchhub.611vklh0

Cited by 3 publications

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“…For this study, the ANN model was created by using algorithms developed in Python Software v. 3.10 on the Jupyter Notebook online platform [26]. For this study, the ANN model was created by using algorithms developed in Python Software v. 3.10 on the Jupyter Notebook online platform [26]. The simulation process steps followed were similar to those of other researchers who have investigated ANN usage in prediction [27], [28].…”

Section: Algorithm Stepsmentioning

confidence: 99%

The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market

Khrais

Ahmed

2023

IJECE

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The main goal of this paper was to explore the use of an artificial neural network (ANN) model in predicting real estate prices in the Middle East market. Although conventional modeling approaches such as regression can be used in prediction, they have a weakness of a predetermined relationship between input and output. In this regard, using the ANN model was expected to reduce the bias and ensure non-linear relationships are also covered in the prediction process for more accurate results. The ANN model was created using Python v.3.10 program. The model exhibited a high correlation between predicted and actual house price data (R=0.658). In this respect, it was realized that the model could be effectively used in appraising real estate by investors. However, a major limitation of the model was realized to be a limited dataset for large and luxurious houses, which were not accurately predicted as data distribution between actual and predicted values became sparse for high house prices. A key recommendation made is that future research should include more variables related to luxurious houses and macroeconomic factors to increase the ANN model accuracy.

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Section: Algorithm Stepsmentioning

confidence: 99%

The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market

Khrais

Ahmed

2023

IJECE

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Mental Health Frequently Asked Questions Chatbot Powered by Machine Learning

Schlegell

Abuomar

2022

2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights (Preprint)

Sahu,

Dubin,

Majowicz

et al. 2023

Preprint

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BACKGROUND The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google’s GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE This study investigates in-home mobility data from ecobee’s smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google’s residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS Motion sensor data were acquired from the ecobee “Donate Your Data” initiative via Google’s BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces—Ontario, Quebec, Alberta, and British Columbia—during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google’s data set. Examination of Google’s daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS This study’s findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google’s out-of-house residential mobility data and ecobee’s in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

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Jupyter Notebook

Cited by 3 publications

References 0 publications

The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market

The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market

Mental Health Frequently Asked Questions Chatbot Powered by Machine Learning

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights (Preprint)

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