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

Abstract

Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

Published in: Applied Sciences
DOI: 10.3390/app9061154

Abstract

Purpose

The purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.

Design/methodology/approach

In order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.

Findings

First, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.

Research limitations/implications

This research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.

Practical implications

The proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.

Social implications

The proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.

Originality/value

The key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

Published in: Asia Pacific Journal of Marketing and Logistics
DOI: 10.1108/APJML-03-2018-0088

Abstract

Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events.

Published in: Applied Sciences
DOI: 10.3390/app9010084

Abstract

Current technology such as Bluetooth Low Energy (BLE) provides an efficient way for Real-Time Location System (RTLS). This study proposes a BLE-based Real-Time Location System that utilizes Smartphone and NoSQL database as gateway and data storage respectively. Firstly, we develop a smartphone-based tracking app to gather the location of employees. Secondly, the generated sensor data from gateway is then stored into NoSQL MongoDB. The proposed system was tested for monitoring the movement of employees in the workplace. The results showed that commercial versions of the BLE-based device and the proposed system are sufficiently efficient for RTLS. Furthermore, proposed system is capable of processing a massive input/output of sensor data efficiently when the number of BLE-based devices and users increases.

Published in: BDIOT 2018 Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things, ACM New York, NY, USA ©2018
DOI: 10.1145/3289430.3289470

Abstract

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

Published in: Sensors
DOI: 10.3390/s18092946

Abstract

As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.

Published in: Applied Sciences
DOI: 10.3390/app8081325