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Research Project
Intelligent system for occupational safety in retail sector
Funder
Authors
Publications
Integrated feature selection and classification algorithm in the prediction of work-related accidents in the retail sector: a comparative study
Publication . Sena, Inês; Lima, Laíres; Silva, Felipe G.; Braga, Ana Cristina; Novais, Paulo; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara; Lima, José; Pereira, Ana I.
Assessing the different factors that contribute to accidents in the workplace is essential to ensure the safety and well-being of employees. Given the importance of risk identification in hazard prediction, this work proposes a comparative study between different feature selection techniques (χ2 test and Forward Feature Selection) combined with learning algorithms (Support Vector Machine, Random Forest, and Naive Bayes), both applied to a database of a leading company in the retail sector, in Portugal. The goal is to conclude which factors of each database have the most significant impact on the occurrence of accidents. Initial databases include accident records, ergonomic workplace analysis, hazard intervention and risk assessment, climate databases, and holiday records. Each method was evaluated based on its accuracy in the forecast of the occurrence of the accident. The results showed that the Forward Feature Selection-Random Forest pair performed better among the assessed combinations, considering the case study database. In addition, data from accident records and ergonomic workplace analysis have the largest number of features with the most significant predictive impact on accident prediction. Future studies will be carried out to evaluate factors from other databases that may have meaningful information for predicting accidents.
Exploring Features to Classify Occupational Accidents in the Retail Sector
Publication . Sena, Inês; Braga, Ana Cristina; Novais, Paulo; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara B.; Lima, José; Pereira, Ana I.
The Machine Learning approach is used in several application
domains, and its exploitation in predicting accidents in occupational
safety is relatively recent. The present study aims to apply different
Machine Learning algorithms for classifying the occurrence or nonoccurrence
of accidents at work in the retail sector. The approach consists
of obtaining an impact score for each store and work unit, considering
two databases of a retail company, the preventive safety actions, and
the action plans. Subsequently, each score is associated with the occurrence
or non-occurrence of accidents during January and May 2023. Of
the five classification algorithms applied, the Support Vector Machine
was the one that obtained the best accuracy and precision values for
the preventive safety actions. As for the set of actions plan, the Logistic
Regression reached the best results in all calculated metrics. With this
study, estimating the impact score of the study variables makes it possible
to identify the occurrence of accidents at work in the retail sector
with high precision and accuracy.
Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine Learning Models
Publication . Vaz, Clara B.; Sena, Inês; Braga, Ana Cristina; Novais, Paulo; Lima, José; Pereira, Ana I.
Retail transactions represent sales of consumer goods, or
final goods, by consumer companies. This sector faces security challenges
due to the hustle and bustle of sales, affecting employees’ workload. In
this context, it is essential to estimate the number of customers who
will appear in the store daily so that companies can dynamically adjust
employee schedules, aligning workforce capacity with expected demand.
This can be achieved by forecasting transactions using past observations
and forecasting algorithms. This study aims to compare the ARIMA time
series algorithm with several Machine Learning algorithms to predict the
number of daily transactions in different store patterns, considering data
variability. The study identifies four typical store patterns based on these
criteria using daily transaction data between 2019 and 2023 from all retail
stores of the leading company in Portugal. Due to data variability and
the results obtained, the algorithm that presents the most minor errors
in predicting daily transactions is selected for each store. This study’s
ultimate goal is to fill the gap in forecasting daily customer transactions
and present a suitable forecasting model to mitigate risks associated with
transactions in retail stores.
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Funders
Funding agency
Fundação para a Ciência e a Tecnologia
Funding programme
OE
Funding Award Number
UI/BD/153348/2022