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25 posts tagged with "paper"

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· 2 min read

Abstract

Understanding customer shopping behavior in retail store is important to improve the customers' relationship with the retailer, which can help to lift the revenue of the business. However, compared to online store, the customer browsing activities in the retail store is difficult to be analysed. Therefore, in this study the customer shopping behavior analysis (i.e., browsing activity) in retail store by utilizing radio frequency identification (RFID)-enabled shelf and machine learning model is proposed. First, the RFID technology is installed in the store shelf to monitor the movement tagged products. The dataset was gathered from receive signal strength (RSS) of the tags for different customer behavior scenario. The statistical features were extracted from RSS of tags. Finally, machine learning models were utilized to classify different customer shopping activities. The experiment result showed that the proposed model based on Multilayer Perceptron (MLP) outperformed other models by as much as 97.00%, 96.67%, 97.50%, and 96.57% for accuracy, precision, recall, and f-score, respectively. The proposed model can help the managers better understand what products customer interested in, so that can be utilized for product placement, promotion as well as relevant product recommendations to the customers.

Published in: IOP Conference Series: Materials Science and Engineering
DOI: 10.1088/1757-899X/803/1/012022

· 2 min read

Abstract

Radio Frequency Identification (RFID) technology has significantly improved in the past few years and is presently sought for implementation in the identification and traceability of perishable food in the food sector to safeguard food safety and quality. It is currently considered a worthy successor to the barcode system and has significant advantages for monitoring products in the perishable food supply chain (PFSC). The present study proposes a traceability system that utilizes RFID and Internet of Things (IoT) sensors. RFID technology can be used to track and trace perishable food while IoT sensors can be used to measure temperature and humidity during storage and transportation. Furthermore, it is important that RFID gates can identify the direction of tags and whether products are being received or shipped through the gate. In this study, machine-learning models are utilized to detect the direction of passive RFID tags. The input features are derived from receive signal strength (RSS) and the timestamp of tags. The proposed system has been tested in the perishable food supply chain and has revealed significant benefits to managers and customers by providing real-time product information and complete temperature and humidity history. In addition, by integrating a machine-learning model into the RFID gate, tagged products that move in or out through a gate can be correctly identified and thus improve the efficiency of the traceability system.

Published in: Food Control
DOI: 10.1016/j.foodcont.2019.107016

· 2 min read

Abstract

Early diseases prediction plays an important role for improving healthcare quality and can help individuals avoid dangerous health situations before it is too late. This paper proposes a disease prediction model (DPM) to provide an early prediction for type 2 diabetes and hypertension based on individual’s risk factors data. The proposed DPM consists of isolation forest (iForest) based outlier detection method to remove outlier data, synthetic minority oversampling technique tomek link (SMOTETomek) to balance data distribution, and ensemble approach to predict the diseases. Four datasets were utilized to build the model and extract the most significant risks factors. The results showed that the proposed DPM achieved highest accuracy when compared to other models and previous studies. We also developed a mobile application to provide the practical application of the proposed DPM. The developed mobile application gathers risk factor data and send it to a remote server, so that an individual’s current condition can be diagnosed with the proposed DPM. The prediction result is then sent back to the mobile application; thus, immediate and appropriate action can be taken to reduce and prevent individual’s risks once unexpected health situations occur (i.e., type 2 diabetes and/or hypertension) at early stages.

Published in: IEEE Access
DOI: 10.1109/ACCESS.2019.2945129