This page has only limited features, please log in for full access.
Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.
Ganjar Alfian; Muhammad Syafrudin; Muhammad Fazal Ijaz; M. Alex Syaekhoni; Norma Latif Fitriyani; Jongtae Rhee. A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing. Sensors 2018, 18, 2183 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Muhammad Fazal Ijaz, M. Alex Syaekhoni, Norma Latif Fitriyani, Jongtae Rhee. A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing. Sensors. 2018; 18 (7):2183.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Muhammad Fazal Ijaz; M. Alex Syaekhoni; Norma Latif Fitriyani; Jongtae Rhee. 2018. "A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing." Sensors 18, no. 7: 2183.
Due to increasing concerns about environmental protection, the environmental sustainability of businesses has been widely considered in the manufacturing and supply chain context. Further, its adoption has been implemented in the retail industry for marketing field, including green product promotion. This study aimed to propose a customer purchasing behavior analysis as an alternative for supporting decision-making in order to promote green products in retail stores. Hence, right-on-target marketing strategies can be implemented appropriately. The study was carried out using shopping path data collected by radio frequency identification (RFID) from a large retail store in Seoul, South Korea. In addition, the store layout and its traffic were also analyzed. This method is expected to help experts providing appropriate decision alternatives. In addition, it can help retailers in order to increase product sales and achieve high levels of customer satisfaction.
M. Alex Syaekhoni; Ganjar Alfian; Young S. Kwon. Customer Purchasing Behavior Analysis as Alternatives for Supporting In-Store Green Marketing Decision-Making. Sustainability 2017, 9, 2008 .
AMA StyleM. Alex Syaekhoni, Ganjar Alfian, Young S. Kwon. Customer Purchasing Behavior Analysis as Alternatives for Supporting In-Store Green Marketing Decision-Making. Sustainability. 2017; 9 (11):2008.
Chicago/Turabian StyleM. Alex Syaekhoni; Ganjar Alfian; Young S. Kwon. 2017. "Customer Purchasing Behavior Analysis as Alternatives for Supporting In-Store Green Marketing Decision-Making." Sustainability 9, no. 11: 2008.
Ganjar Alfian; Jongtae Rhee; Hyejung Ahn; Jaeho Lee; Umar Farooq; Muhammad Fazal Ijaz; M. Alex Syaekhoni. Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering 2017, 212, 65 -75.
AMA StyleGanjar Alfian, Jongtae Rhee, Hyejung Ahn, Jaeho Lee, Umar Farooq, Muhammad Fazal Ijaz, M. Alex Syaekhoni. Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering. 2017; 212 ():65-75.
Chicago/Turabian StyleGanjar Alfian; Jongtae Rhee; Hyejung Ahn; Jaeho Lee; Umar Farooq; Muhammad Fazal Ijaz; M. Alex Syaekhoni. 2017. "Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system." Journal of Food Engineering 212, no. : 65-75.
Radio frequency identification (RFID) technology has been successfully applied to gather customers’ shopping habits from their motion paths and other behavioral data. The customers’ behavioral data can be used for marketing purposes, such as improving the store layout or optimizing targeted promotions to specific customers. Some data mining techniques, such as clustering algorithms can be used to discover customers’ hidden behaviors from their shopping paths. However, shopping path data has peculiar challenges, including variable length, sequential data, and the need for a special distance measure. Due to these challenges, traditional clustering algorithms cannot be applied to shopping path data. In this paper, we analyze customer behavior from their shopping path data by using a clustering algorithm. We propose a new distance measure for shopping path data, called the Operation edit distance, to solve the aforementioned problems. The proposed distance method enables the RFID customer shopping path data to be processed effectively using clustering algorithms. We have collected a real-world shopping path data from a retail store and applied our method to the dataset. The proposed method effectively determined customers’ shopping patterns from the data.
M. Alex Syaekhoni; ChanSeung Lee; Young S. Kwon. Analyzing customer behavior from shopping path data using operation edit distance. Applied Intelligence 2016, 48, 1912 -1932.
AMA StyleM. Alex Syaekhoni, ChanSeung Lee, Young S. Kwon. Analyzing customer behavior from shopping path data using operation edit distance. Applied Intelligence. 2016; 48 (8):1912-1932.
Chicago/Turabian StyleM. Alex Syaekhoni; ChanSeung Lee; Young S. Kwon. 2016. "Analyzing customer behavior from shopping path data using operation edit distance." Applied Intelligence 48, no. 8: 1912-1932.
This paper proposes a new clustering approach for customer shopping paths. The approach is based on the Apriori algorithm and LCS (Longest Common Subsequence) algorithms. We devised new similarity and performance measurements for the clustering. In this approach, we do not require data normalization for preprocessing, which leads to an easy and practical application and implementation of the proposed approach. The experiment results show that the proposed approach performs well compared with k-medoids clustering.
In-Chul Jung; M. Alex Syaekhoni; Young Sig Kwon. A Practical Approach to the Shopping Path Clustering. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 9101, 675 -682.
AMA StyleIn-Chul Jung, M. Alex Syaekhoni, Young Sig Kwon. A Practical Approach to the Shopping Path Clustering. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; 9101 ():675-682.
Chicago/Turabian StyleIn-Chul Jung; M. Alex Syaekhoni; Young Sig Kwon. 2015. "A Practical Approach to the Shopping Path Clustering." Transactions on Petri Nets and Other Models of Concurrency XV 9101, no. : 675-682.