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Dr. George T. S. HO
The Hang Seng University of Hong Kong

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0 Big Data Analytics
0 Logistics and supply chain management
0 Information Analysis and Decision-making
0 Internet-of-Things Applications
0 Digital Workforces

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Earlycite article
Published: 30 April 2021 in Industrial Management & Data Systems
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Purpose Under the impact of Coronavirus disease 2019 (COVID-19), this paper contributes in the deployment of the Artificial Intelligence of Things (AIoT)-based system, namely AIoT-based Domestic Care Service Matching System (AIDCS), to the existing electronic health (eHealth) system so as to enhance the delivery of elderly-oriented domestic care services. Design/methodology/approach The proposed AIDCS integrates IoT and Artificial Intelligence (AI) technologies to (1) capture real-time health data of the elderly at home and (2) provide the knowledge support for decision making in the domestic care appointment service in the community. Findings A case study was conducted in a local domestic care centre which provided elderly oriented healthcare services to the elderly. By integrating IoT and AI into the service matching process of the mobile apps platform provided by the local domestic care centre, the results proved that customer satisfaction and the quality of the service delivery were improved by observing the key performance indicators of the transactions after the implementation of the AIDCS. Originality/value Following the outbreak of COVID-19, this is a new attempt to overcome the limited research done on the integration of IoT and AI techniques in the domestic care service. This study not only inherits the ability of the existing eHealth system to automatically capture and monitor the health status of the elderly in real-time but also improves the overall quality of domestic care services in term of responsiveness, effectiveness and efficiency.

ACS Style

H.Y. Lam; G.T.S. Ho; Daniel Y. Mo; Valerie Tang. Enhancing data-driven elderly appointment services in domestic care communities under COVID-19. Industrial Management & Data Systems 2021, 121, 1552 -1576.

AMA Style

H.Y. Lam, G.T.S. Ho, Daniel Y. Mo, Valerie Tang. Enhancing data-driven elderly appointment services in domestic care communities under COVID-19. Industrial Management & Data Systems. 2021; 121 (7):1552-1576.

Chicago/Turabian Style

H.Y. Lam; G.T.S. Ho; Daniel Y. Mo; Valerie Tang. 2021. "Enhancing data-driven elderly appointment services in domestic care communities under COVID-19." Industrial Management & Data Systems 121, no. 7: 1552-1576.

Journal article
Published: 28 April 2021 in Expert Systems with Applications
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Aircraft spare parts inventory management (ASPM) has played a critical role in tracing and tracking spare parts as any related maintenance or movement shall be recorded. Traceability and trackability of data ensure the compliance of airworthiness requirements. The International Air Transport Association (IATA) has strongly emphasised the significance of quality traceability data throughout the aircraft part’s life cycle, leading to enhanced inventory control accuracy, reduced maintenance error, and effective decision-making processes. However, with the rapid increase of spare parts types, the complexity of aircraft parts multi-stage supply chains leads to inefficient tracing and tracking operations with unsatisfactory traceability data quality and information security. This paper proposed a blockchain-based system that provided a managerial platform for accurate recording of spare parts traceability data with organisational consensus and validation using Hyperledger Fabric and Hyperledger Composer. A data model has been determined based on the existing ASPM, enabling information integrity during transaction operations. The channel mechanism has yielded a trustful data sharing platform between each contracting organisation for logistics and operational arrangements, which has enhanced information visibility and security. The blockchain-based system, executed under a decentralised ledger mechanism, shall improve the quality of traceability data and reliable information sharing within the spare parts supply chain. The enhanced blockchain-based inventory management system can establish the digital twin of aviation as part of Industry 4.0 in the future.

ACS Style

G.T.S. Ho; Yuk Ming Tang; Kun Yat Tsang; Valerie Tang; Ka Yin Chau. A blockchain-based system to enhance aircraft parts traceability and trackability for inventory management. Expert Systems with Applications 2021, 179, 115101 .

AMA Style

G.T.S. Ho, Yuk Ming Tang, Kun Yat Tsang, Valerie Tang, Ka Yin Chau. A blockchain-based system to enhance aircraft parts traceability and trackability for inventory management. Expert Systems with Applications. 2021; 179 ():115101.

Chicago/Turabian Style

G.T.S. Ho; Yuk Ming Tang; Kun Yat Tsang; Valerie Tang; Ka Yin Chau. 2021. "A blockchain-based system to enhance aircraft parts traceability and trackability for inventory management." Expert Systems with Applications 179, no. : 115101.

Journal article
Published: 22 February 2021 in Journal of Manufacturing Systems
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New product development to enhance companies’ competitiveness and reputation is one of the leading activities in manufacturing. At present, achieving successful product design has become more difficult, even for companies with extensive capabilities in the market, because of disorganisation in the fuzzy front end (FFE) of the innovation process. Tremendous amounts of information, such as data on customers, manufacturing capability, and market trend, are considered in the FFE phase to avoid common flaws in product design. Because of the high degree of uncertainties in the FFE, multidimensional and high-volume data are added from time to time at the beginning of the formal product development process. To address the above concerns, deploying big data analytics to establish industrial intelligence is an active but still under-researched area. In this paper, an intelligent product design framework is proposed to incorporate fuzzy association rule mining (FARM) and a genetic algorithm (GA) into a recursive association-rule-based fuzzy inference system to bridge the gap between customer attributes and design parameters. Considering the current incidence of epidemics, such as the COVID-19 pandemic, communication of information in the FFE stage may be hindered. Through this study, a recursive learning scheme is established, therefore, to strengthen market performance, design performance, and sustainability on product design. It is found that the industrial big data analytics in the FFE process achieve greater flexibility and self-improvement mechanism on the evolution of product design.

ACS Style

Y.P. Tsang; C.H. Wu; Kuo-Yi Lin; Y.K. Tse; G.T.S. Ho; C.K.M. Lee. Unlocking the power of big data analytics in new product development: An intelligent product design framework in the furniture industry. Journal of Manufacturing Systems 2021, 1 .

AMA Style

Y.P. Tsang, C.H. Wu, Kuo-Yi Lin, Y.K. Tse, G.T.S. Ho, C.K.M. Lee. Unlocking the power of big data analytics in new product development: An intelligent product design framework in the furniture industry. Journal of Manufacturing Systems. 2021; ():1.

Chicago/Turabian Style

Y.P. Tsang; C.H. Wu; Kuo-Yi Lin; Y.K. Tse; G.T.S. Ho; C.K.M. Lee. 2021. "Unlocking the power of big data analytics in new product development: An intelligent product design framework in the furniture industry." Journal of Manufacturing Systems , no. : 1.

Journal article
Published: 18 January 2021 in International Journal of Environmental Research and Public Health
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With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.

ACS Style

Rongxiang Rui; Maozai Tian; Man-Lai Tang; George Ho; Chun-Ho Wu. Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model. International Journal of Environmental Research and Public Health 2021, 18, 774 .

AMA Style

Rongxiang Rui, Maozai Tian, Man-Lai Tang, George Ho, Chun-Ho Wu. Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model. International Journal of Environmental Research and Public Health. 2021; 18 (2):774.

Chicago/Turabian Style

Rongxiang Rui; Maozai Tian; Man-Lai Tang; George Ho; Chun-Ho Wu. 2021. "Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model." International Journal of Environmental Research and Public Health 18, no. 2: 774.

Journal article
Published: 10 October 2020 in Technological Forecasting and Social Change
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The digitalisation in healthcare opens opportunities for more effective chronic disease management. Digitalised medical records are valuable data sources for identifying high-risk patients and facilitating early clinical intervention. However, the liberation of data has plagued adoption amongst physicians as massive data mean more difficult to identify important knowledge from the data. In the cervical cancer context, many patients are adherence to prescription medications only when symptoms appear, beyond the earlier point-in-time of the disease progression. Regular screening is the only way to detect abnormal cells that may develop into cancer if left untreated. Yet, without a comprehensive understanding of the relationship between risk factors and healthcare outcomes, inappropriate screening procedures may be conducted, lengthening the treatment process. Delay in the treatment process may have an irreversible influence on patients’ conditions as chronic diseases progress. This study demonstrates a data-mining framework which extracts knowledge that can advance cervical cancer screening processes in the form of association rules and improves the generalisation potential of the rules for deployment. The knowledge discovered serves as an additional supplement for physicians’ experience and uncovers appropriate screening strategies based on patients’ risk factors, increasing the chances of high-risk patients getting treated for cervical pre-cancers.

ACS Style

Carmen Kar Hang Lee; Ying Kei Tse; G.T.S. Ho; S.H. Chung. Uncovering insights from healthcare archives to improve operations: An association analysis for cervical cancer screening. Technological Forecasting and Social Change 2020, 162, 120375 .

AMA Style

Carmen Kar Hang Lee, Ying Kei Tse, G.T.S. Ho, S.H. Chung. Uncovering insights from healthcare archives to improve operations: An association analysis for cervical cancer screening. Technological Forecasting and Social Change. 2020; 162 ():120375.

Chicago/Turabian Style

Carmen Kar Hang Lee; Ying Kei Tse; G.T.S. Ho; S.H. Chung. 2020. "Uncovering insights from healthcare archives to improve operations: An association analysis for cervical cancer screening." Technological Forecasting and Social Change 162, no. : 120375.

Journal article
Published: 11 July 2020 in Sustainability
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The aging population has led to an increase in the variety and volume of transportation demands by people facing travel difficulties. Hence, transportation organisations need to provide flexible and sustainable paratransit services to meet these increasing demands. In this study, we investigate the design of flexible vehicle scheduling systems in order for a community organisation to serve more people and achieve higher operational efficiency. We analyse and propose a system design based on user requirements for different types of paratransit types. Further, we identify an integrated service option and process flow for dial-a-ride passengers to ride on a vehicle with schedule route passengers. Because this option involves a complex decision, we formulate the problem as a two-stage decision model. To verify the effectiveness of our proposed design, we perform numerical simulations and conduct a case study by collaborating with a transportation organisation. We found that the proposed system would enable the organisation to serve more people with fewer vehicles but without an increase in the travelling time. These results demonstrate the importance of a flexible vehicle scheduling system for accessible transportation organisations to sustain their service operations.

ACS Style

Daniel Mo; H. Lam; Weikun Xu; G. Ho. Design of Flexible Vehicle Scheduling Systems for Sustainable Paratransit Services. Sustainability 2020, 12, 5594 .

AMA Style

Daniel Mo, H. Lam, Weikun Xu, G. Ho. Design of Flexible Vehicle Scheduling Systems for Sustainable Paratransit Services. Sustainability. 2020; 12 (14):5594.

Chicago/Turabian Style

Daniel Mo; H. Lam; Weikun Xu; G. Ho. 2020. "Design of Flexible Vehicle Scheduling Systems for Sustainable Paratransit Services." Sustainability 12, no. 14: 5594.

Journal article
Published: 28 February 2020 in Virtual Reality
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Virtual reality (VR) is rapidly developed and bringing advancement in various related technologies through the virtual world. It has high potential and plays an important role in education and training fields. Mixed reality (MR) is a type of hybrid system that involves both physical and virtual elements. While VR/MR has proved to be an effective way to improve the learning attitude and effectiveness for secondary students, however, not much work has been conducted on university students to compare the MR experience and traditional teaching approaches in learning design subjects. In this project, we investigated the effectiveness of students in learning design subjects with the support of MR. The effectiveness was measured based on their creativity and systematic approaches in design. Pretests and posttests were conducted to measure the learning effects. We also compared the learning effectiveness of a student’s study with the MR and traditional teaching materials. Nonparametric analyses were conducted to investigate whether the improvements were significant. Experimental results showed that after studying with the support of the MR technology, the students’ abilities in geometric analysis (mean difference = 4.36, p < 0.01) and creativity (mean difference = 1.59, p < 0.05) were significantly improved. The students’ ability in model visualization was also significantly better than the control group (mean difference = 3.08, p < 0.05). It indicated that the results were positive by using the MR to support their study. The MR was also better than using traditional teaching notes in various measured effects.

ACS Style

Y. M. Tang; K. M. Au; H. C. W. Lau; G. T. S. Ho; C. H. Wu. Evaluating the effectiveness of learning design with mixed reality (MR) in higher education. Virtual Reality 2020, 24, 797 -807.

AMA Style

Y. M. Tang, K. M. Au, H. C. W. Lau, G. T. S. Ho, C. H. Wu. Evaluating the effectiveness of learning design with mixed reality (MR) in higher education. Virtual Reality. 2020; 24 (4):797-807.

Chicago/Turabian Style

Y. M. Tang; K. M. Au; H. C. W. Lau; G. T. S. Ho; C. H. Wu. 2020. "Evaluating the effectiveness of learning design with mixed reality (MR) in higher education." Virtual Reality 24, no. 4: 797-807.

Journal article
Published: 15 April 2019 in Sensors
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In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. In this paper, a fully integrated solution is developed by equipping high-definition smart cameras with wireless communication for traffic surveillance. Henceforth, this system is referred to as a computer vision-based roadside occupation surveillance system (CVROSS). Through a vision-based network, real-time roadside traffic images, such as images of loading or unloading activities, are captured automatically. By making use of the collected data, decision support on roadside occupancy and vacancy can be evaluated by means of fuzzy logic and visualized for users, thus enhancing the transparency of roadside activities. The CVROSS was designed and tested in Hong Kong to validate the accuracy of parking-gap estimation and system performance, aiming at facilitating traffic and fleet management for smart mobility.

ACS Style

George To Sum Ho; Yung Po Tsang; Chun Ho Wu; Wai Hung Wong; King Lun Choy. A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities. Sensors 2019, 19, 1796 .

AMA Style

George To Sum Ho, Yung Po Tsang, Chun Ho Wu, Wai Hung Wong, King Lun Choy. A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities. Sensors. 2019; 19 (8):1796.

Chicago/Turabian Style

George To Sum Ho; Yung Po Tsang; Chun Ho Wu; Wai Hung Wong; King Lun Choy. 2019. "A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities." Sensors 19, no. 8: 1796.

Journal article
Published: 01 August 2018 in Food Control
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In the globalized cold chain network, the effective distribution of perishable food is of utmost importance when transporting multiple types of food with different handling requirements, such as temperature and humidity, for minimizing the food spoilage rate during transportation. Currently, mismanagement of premium fruit and vegetables leads to a huge amount of capital loss such that logistics service providers (LSPs) apply refrigerated trucks to deliver them for the sake of minimizing the food spoilage rate during transportation. Since different types of food have their own different handling requirements, traditional refrigerated distribution management at a fixed environmental condition is insufficient. Without considering such requirements, traditional route planning by merely minimizing the travelling distance is ineffective in maintaining food quality, resulting in an increased likelihood of food deterioration and food chilling injury. In addition, there is a lack of real-time product monitoring to control violations of the required handling requirements in order to prevent delivery of spoilt food to customers. In this paper, an internet of things (IoT)-based route planning system (IRPS) is proposed (i) to design a multi-temperature packaging model, (ii) to develop real-time product monitoring during transportation, and (iii) to optimize routing solutions. Under the IoT framework, the ambient environmental information can be collected automatically by building a wireless sensor network so as to develop total product monitoring during the distribution process. Experiments using the Taguchi method are conducted to examine the most effective packaging model for various products in terms of maximizing duration of optimal environment conditions in tertiary packaging. By integrating the above results and travelling constraints, the optimal delivery routes can be formulated by using genetic algorithms (GAs). With the aid of IRPS, the food spoilage rate during transportation and the time needed in route planning and in the delivery of deteriorated food can be reduced, while customer satisfaction is enhanced.

ACS Style

Y.P. Tsang; K.L. Choy; C.H. Wu; G.T.S. Ho; H.Y. Lam; Valerie Tang. An intelligent model for assuring food quality in managing a multi-temperature food distribution centre. Food Control 2018, 90, 81 -97.

AMA Style

Y.P. Tsang, K.L. Choy, C.H. Wu, G.T.S. Ho, H.Y. Lam, Valerie Tang. An intelligent model for assuring food quality in managing a multi-temperature food distribution centre. Food Control. 2018; 90 ():81-97.

Chicago/Turabian Style

Y.P. Tsang; K.L. Choy; C.H. Wu; G.T.S. Ho; H.Y. Lam; Valerie Tang. 2018. "An intelligent model for assuring food quality in managing a multi-temperature food distribution centre." Food Control 90, no. : 81-97.

Journal article
Published: 16 July 2018 in IEEE Journal of Radio Frequency Identification
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Railways provide convenience and efficiency to the travelling public, with passengers’ safety being the top priority in railway transit systems. Nowadays, with the population increase in many cities, railway safety and punctuality are of concern, and an effective surveillance system is needed to minimize the severity of an accident. The Fallen Object Detection (FOD) system is undoubtedly important in a surveillance system, as it is concerned with accidents that are due to objects falling through platform gaps. With the aid of advanced technology, Radio Frequency Identification (RFID) can be used for further enhancement in providing accurate and prompt information. An RFID-based FOD System (RFFODS) is proposed to ensure railway passengers’ safety. A study of the RFFODS in a mass transit system is reported to illustrate the performance of RF technology for fallen object detection in outdoor environments. The location of an antenna is determined by using the Analytic Hierarchy Process. The feasibility and performance of the proposed system are verified with the results of an extensive on-site experiment conducted in a light rail vehicle station. It is expected that the proposed system would play a key role in establishing an intelligent monitoring system for passengers’ safety in future railway developments.

ACS Style

C. H. Wu; G. T. S. Ho; K. L. Yung; W. W. Y. Tam; W. H. Ip. An RFID-Based Fallen Object Detection System: A Case Study of Hong Kong’s Light Rail System. IEEE Journal of Radio Frequency Identification 2018, 2, 55 -67.

AMA Style

C. H. Wu, G. T. S. Ho, K. L. Yung, W. W. Y. Tam, W. H. Ip. An RFID-Based Fallen Object Detection System: A Case Study of Hong Kong’s Light Rail System. IEEE Journal of Radio Frequency Identification. 2018; 2 (2):55-67.

Chicago/Turabian Style

C. H. Wu; G. T. S. Ho; K. L. Yung; W. W. Y. Tam; W. H. Ip. 2018. "An RFID-Based Fallen Object Detection System: A Case Study of Hong Kong’s Light Rail System." IEEE Journal of Radio Frequency Identification 2, no. 2: 55-67.

Journal article
Published: 14 May 2018 in VINE Journal of Information and Knowledge Management Systems
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Purpose This paper aims to improve operational efficiency and minimize accident frequency in cold storage facilities through adopting an effective occupational safety and health program. The hidden knowledge can be extracted from the warehousing operations to create the comfortable and safe workplace environment. Design/methodology/approach A fuzzy association rule-based knowledge management system is developed by integrating fuzzy association rule mining (FARM) and rule-based expert system (RES). FARM is used to extract hidden knowledge from real operations to establish the relationship between safety measurement, personal constitution and key performance index measurement. The extracted knowledge is then stored and adopted in the RES to establish an effective occupational and safety program. Afterwards, a case study is conducted to validate the performance of the proposed system. Findings The results indicate that the aforementioned relationship can be built in the form of IF-THEN rules. An appropriate safety and health program can be developed and applied to all workers, so that they can follow instructions to prevent cold induced injuries and also improve the productivity. Practical implications Because of the increasing public consciousness of occupational safety and health, it is important for the workers in cold storage facilities where the ambient temperature is at/below 10°C. The proposed system can address the social problem and promote the importance of occupational safety and health in the society. Originality/value This study contributes to the knowledge management system for improving the occupational safety and operational efficiency in the cold storage facilities.

ACS Style

Y.P. Tsang; King Lun Tommy Choy; P.S. Koo; G.T.S. Ho; C.H. Wu; H.Y. Lam; Valerie Tang. A fuzzy association rule-based knowledge management system for occupational safety and health programs in cold storage facilities. VINE Journal of Information and Knowledge Management Systems 2018, 48, 199 -216.

AMA Style

Y.P. Tsang, King Lun Tommy Choy, P.S. Koo, G.T.S. Ho, C.H. Wu, H.Y. Lam, Valerie Tang. A fuzzy association rule-based knowledge management system for occupational safety and health programs in cold storage facilities. VINE Journal of Information and Knowledge Management Systems. 2018; 48 (2):199-216.

Chicago/Turabian Style

Y.P. Tsang; King Lun Tommy Choy; P.S. Koo; G.T.S. Ho; C.H. Wu; H.Y. Lam; Valerie Tang. 2018. "A fuzzy association rule-based knowledge management system for occupational safety and health programs in cold storage facilities." VINE Journal of Information and Knowledge Management Systems 48, no. 2: 199-216.

Journal article
Published: 01 January 2018 in Expert Systems with Applications
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ACS Style

K.H. Leung; K.L. Choy; Paul K.Y. Siu; G.T.S. Ho; H.Y. Lam; Carman Lee. A B2C e-commerce intelligent system for re-engineering the e-order fulfilment process. Expert Systems with Applications 2018, 91, 386 -401.

AMA Style

K.H. Leung, K.L. Choy, Paul K.Y. Siu, G.T.S. Ho, H.Y. Lam, Carman Lee. A B2C e-commerce intelligent system for re-engineering the e-order fulfilment process. Expert Systems with Applications. 2018; 91 ():386-401.

Chicago/Turabian Style

K.H. Leung; K.L. Choy; Paul K.Y. Siu; G.T.S. Ho; H.Y. Lam; Carman Lee. 2018. "A B2C e-commerce intelligent system for re-engineering the e-order fulfilment process." Expert Systems with Applications 91, no. : 386-401.

Journal article
Published: 14 August 2017 in Industrial Management & Data Systems
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Purpose In view of the elderly caregiving service being in high demand nowadays, the purpose of this paper is to develop an intelligent e-healthcare system for the domestic care industry by using the Internet of Things (IoTs) and Fuzzy Association Rule Mining (FARM) approach. Design/methodology/approach The IoTs connected with the e-healthcare system collect real-time vital sign monitoring data for the e-healthcare system. The FARM approach helps to identify the hidden relationships between the data records in the e-healthcare system to support the elderly care management tasks. Findings To evaluate the proposed system and approach, a case study was carried out to identify the association between the specific collected demographic data, behavior data and the health measurements data in the e-healthcare system. It is found that the discovered rules are useful for the care management tasks in the elderly healthcare service. Originality/value Knowledge discovery in databases uses various data mining techniques and rule-based artificial intelligence algorithms. This paper demonstrates complete processes on how an e-healthcare system connected with IoTs can support the elderly care services via a data collection phase, data analysis phase and data reporting phase by using the FARM to evaluate the fuzzy sets of the data attributes. The caregivers can use the discovered rules for proactive decision support of healthcare services and to improve the overall service quality by enhancing the elderly healthcare service responsiveness.

ACS Style

Bennie Wong; G.T.S. Ho; Eric Tsui. Development of an intelligent e-healthcare system for the domestic care industry. Industrial Management & Data Systems 2017, 117, 1426 -1445.

AMA Style

Bennie Wong, G.T.S. Ho, Eric Tsui. Development of an intelligent e-healthcare system for the domestic care industry. Industrial Management & Data Systems. 2017; 117 (7):1426-1445.

Chicago/Turabian Style

Bennie Wong; G.T.S. Ho; Eric Tsui. 2017. "Development of an intelligent e-healthcare system for the domestic care industry." Industrial Management & Data Systems 117, no. 7: 1426-1445.

Articles
Published: 12 January 2017 in Enterprise Information Systems
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As the development of Radio Data System (RDS) technology and its applications are getting more and more attention and promotion, people concern their personal privacy and communication efficiency, and therefore compression and encryption technologies are being more important for transferring RDS data. Unlike most of the current approaches which contain two stages, compression and encryption, we proposed a new algorithm called Swapped Huffman Table (SHT) based on Huffman algorithm to realise compression and encryption in a single process. In this paper, a good performance for both compression and encryption is obtained and a possible application of RDS with the proposed algorithm in smart transportation is illustrated.

ACS Style

C. H. Wu; Kuo-Kun Tseng; C. K. Ng; G. T. S. Ho; Fu-Fu Zeng; Y. K. Tse. An improved Huffman coding with encryption for Radio Data System (RDS) for smart transportation. Enterprise Information Systems 2017, 12, 137 -154.

AMA Style

C. H. Wu, Kuo-Kun Tseng, C. K. Ng, G. T. S. Ho, Fu-Fu Zeng, Y. K. Tse. An improved Huffman coding with encryption for Radio Data System (RDS) for smart transportation. Enterprise Information Systems. 2017; 12 (2):137-154.

Chicago/Turabian Style

C. H. Wu; Kuo-Kun Tseng; C. K. Ng; G. T. S. Ho; Fu-Fu Zeng; Y. K. Tse. 2017. "An improved Huffman coding with encryption for Radio Data System (RDS) for smart transportation." Enterprise Information Systems 12, no. 2: 137-154.

Research article
Published: 01 January 2017 in International Journal of Engineering Business Management
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Differing from managing a general supply chain, handling environmentally sensitive products (ESPs) requires the use of specific refrigeration systems to control the designated range of storage conditions, such as temperature, humidity, and lighting level in a cold chain environment. In general, third-party logistics (3PL) companies are authorized to handle ESPs, who therefore need to have a good cargo monitoring system in the cold chain environment, without which the functional quality is difficult to control and manage. This may result in product deterioration and even inventory obsolescence of the ESPs due to the lack of such systems, so there is a need to develop an effective cargo monitoring system to prevent such situations. This article proposes an Internet of Things-based cargo monitoring system (IoT-CMS) to monitor any environmental changes of ESPs in order to ensure their functional quality throughout the entire cold chain operational environment. Operational efficiency, maintenance strategy, environmental change, and electricity consumption are considered in real-life cold chain operations. Through applying (i) a wireless sensor network to collect real-time product information, together with (ii) fuzzy logic and case-based reasoning techniques to suggest appropriate storage conditions for various ESPs, effective storage guidance can be established. Through conducting the case study in a 3PL company in Hong Kong, the performance in customer satisfaction, obsolescence rate, and inventory visibility after adoption of IoT-CMS is evaluated. It is found that the functional quality of ESPs can be effectively assured, and the overall customer satisfaction is increased.

ACS Style

Yp Tsang; K.L. Choy; Ch Wu; Gts Ho; H.Y. Lam; Ps Koo. An IoT-based cargo monitoring system for enhancing operational effectiveness under a cold chain environment. International Journal of Engineering Business Management 2017, 9, 1 .

AMA Style

Yp Tsang, K.L. Choy, Ch Wu, Gts Ho, H.Y. Lam, Ps Koo. An IoT-based cargo monitoring system for enhancing operational effectiveness under a cold chain environment. International Journal of Engineering Business Management. 2017; 9 ():1.

Chicago/Turabian Style

Yp Tsang; K.L. Choy; Ch Wu; Gts Ho; H.Y. Lam; Ps Koo. 2017. "An IoT-based cargo monitoring system for enhancing operational effectiveness under a cold chain environment." International Journal of Engineering Business Management 9, no. : 1.

Conference paper
Published: 10 November 2016 in 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Due to rapidly ageing population, the need for care and attention homes for the elderly and patient with chronic illnesses has increased significantly in recent years. However, the continuous increase in operation and medical costs and the problem of drugs shortages bring increasing pressure to care and attention homes in regard to medical resource allocation. In such situations, patients may not receive appropriate treatment and hence dissatisfaction with the quality of service may result. Therefore, it is essential to have a decision support system to ensure that an optimal amount of medical resources are stored so as to maintain a sustainable healthcare service. In this paper, an intelligent medical replenishment system (IMRS) is proposed to assist healthcare workers in arranging the appropriate type and quantity of drugs, based on the needs of patients. In IMRS, artificial intelligent techniques, i.e. fuzzy association rules mining and fuzzy logic, are applied to evaluate the historical diagnosis records of patients and determine the amount and frequency of medical resources for replenishment. To validate the feasibility of the proposed system, a pilot study is conducted in a care and attention home located in Hong Kong. The result shows that the IMRS is effective in improving the healthcare service quality for the elderly in terms of the elderly satisfaction and medical resources fulfillment.

ACS Style

Valerie Tang; Stephen W.Y. Cheng; King Lun Tommy Choy; Paul K.Y. Siu; G.T.S. Ho; H.Y. Lam. An intelligent medical Replenishment System for managing the medical resources in the healthcare industry. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2016, 154 -161.

AMA Style

Valerie Tang, Stephen W.Y. Cheng, King Lun Tommy Choy, Paul K.Y. Siu, G.T.S. Ho, H.Y. Lam. An intelligent medical Replenishment System for managing the medical resources in the healthcare industry. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2016; ():154-161.

Chicago/Turabian Style

Valerie Tang; Stephen W.Y. Cheng; King Lun Tommy Choy; Paul K.Y. Siu; G.T.S. Ho; H.Y. Lam. 2016. "An intelligent medical Replenishment System for managing the medical resources in the healthcare industry." 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , no. : 154-161.

Conference paper
Published: 01 November 2016 in 2016 4th International Conference on Enterprise Systems (ES)
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In the competitive and low entrance barrier beauty industry, customer loyalty is a critical factor for business success. Research literature of customer relationship management recommends various factors contributing to customer loyalty in the general setting, however, there are insufficient studies empirically weigh the importance of each critical factor for the beauty industry. This study investigates and ranks empirically the critical factors, which contributes to customer loyalty of Online-to-Offline (O2O) marketing in the beauty industry. Our result shows that customer satisfaction, customer switching costs, customer trust, corporate image and customer value positively influence customer loyalty of O2O marketing and in the order of decreasing importance. Attributes contributing to the five critical factors have also been studied and ranked. Findings of this study can help the beauty industry to develop an effective O2O marketing plan and hence customer loyalty can be enhanced through the process of implementing targeted marketing activities.

ACS Style

P.P.L. Leung; C.H. Wu; W.H. Ip; G.T.S. Ho; V.W.S. Cho; K.K.Y. Kwong. Customer Loyalty Enhancement of Online-to-Offline Marketing in Beauty Industry. 2016 4th International Conference on Enterprise Systems (ES) 2016, 51 -59.

AMA Style

P.P.L. Leung, C.H. Wu, W.H. Ip, G.T.S. Ho, V.W.S. Cho, K.K.Y. Kwong. Customer Loyalty Enhancement of Online-to-Offline Marketing in Beauty Industry. 2016 4th International Conference on Enterprise Systems (ES). 2016; ():51-59.

Chicago/Turabian Style

P.P.L. Leung; C.H. Wu; W.H. Ip; G.T.S. Ho; V.W.S. Cho; K.K.Y. Kwong. 2016. "Customer Loyalty Enhancement of Online-to-Offline Marketing in Beauty Industry." 2016 4th International Conference on Enterprise Systems (ES) , no. : 51-59.

Journal article
Published: 01 November 2016 in International Journal of Production Economics
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Sustainable consumption and production is a critical issue in the chemical industry due to increasing public concerns on environmental and safety issues. Organizations are urged to improve the quality of chemical products while minimizing the environmental impacts during production. In current practice, chemists and formulators have to determine both the ingredients to be used and the machine parameter settings during product development and production. Without appropriate operations strategies for managing sustainable consumption and production, a significant portion of the ingredients, toxic materials and pollutants are wasted or emitted during the trial-and-error processes when developing chemical products. In addition, inappropriate machine parameter settings, such as blending speed and blending temperature, result in inefficient energy use. Motivated by these issues, this paper describes a recursive operations strategy (ROS) model for achieving sustainable consumption and production in the chemical industry. The ROS model first identifies the business strategy, and then defines operations strategies by assessing the competitive priorities and policies with the use of artificial intelligence, including case-based reasoning and fuzzy logic, so as to manage the operations functions. The effectiveness of the model is verified by means of a case study. The results indicate that the model can provide direct guidelines for the users to develop products based on previously developed products. By so doing, the number of trials for testing various ingredient formulae can be reduced, minimizing the ingredient waste. The proposed model is also capable of achieving continuous improvement and determining the optimal production process conditions for avoiding unnecessary energy consumption.

ACS Style

K.L. Choy; G.T.S. Ho; C.K.H. Lee; H.Y. Lam; Stephen W.Y. Cheng; Paul K.Y. Siu; G.K.H. Pang; Valerie Tang; Jason C.H. Lee; Y.P. Tsang. A recursive operations strategy model for managing sustainable chemical product development and production. International Journal of Production Economics 2016, 181, 262 -272.

AMA Style

K.L. Choy, G.T.S. Ho, C.K.H. Lee, H.Y. Lam, Stephen W.Y. Cheng, Paul K.Y. Siu, G.K.H. Pang, Valerie Tang, Jason C.H. Lee, Y.P. Tsang. A recursive operations strategy model for managing sustainable chemical product development and production. International Journal of Production Economics. 2016; 181 ():262-272.

Chicago/Turabian Style

K.L. Choy; G.T.S. Ho; C.K.H. Lee; H.Y. Lam; Stephen W.Y. Cheng; Paul K.Y. Siu; G.K.H. Pang; Valerie Tang; Jason C.H. Lee; Y.P. Tsang. 2016. "A recursive operations strategy model for managing sustainable chemical product development and production." International Journal of Production Economics 181, no. : 262-272.

Conference paper
Published: 01 September 2016 in 2016 Portland International Conference on Management of Engineering and Technology (PICMET)
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Due to the aging population in Hong Kong, the need for home care service is growing rapidly and requires nursing staff to frequently visit the homes of the elderly for service. For years, a shortage of qualified nursing staff and the tight service schedule has brought increasing pressure to the existing home care service, sometimes leading to high complaint rates by the elderly and their family members. In order to maintain the home care service quality, it is critical to have an evaluation approach by assessing the workload and characteristics of the home care nursing staff. In this paper, an intelligent performance assessment system (IPAS) is designed to evaluate the performance of home care nursing staff in the healthcare industry. IPAS integrates Online Analytical Processing (OLAP) for the collecting and storing of data on the elderly patient, nursing staff and healthcare agency when providing home care services, and fuzzy logic for evaluating the service quality of the nursing staff. The healthcare agency can then formulate a follow up plan based on the assessment results. By conducting a pilot study in a local healthcare agency, the nursing staff loyalty can be increased while the quality of home care service can be enhanced.

ACS Style

Valerie Tang; K.L. Choy; Paul K.Y. Siu; H.Y. Lam; G.T.S. Ho; Stephen W.Y. Cheng. An intelligent performance assessment system for enhancing the service quality of home care nursing staff in the healthcare industry. 2016 Portland International Conference on Management of Engineering and Technology (PICMET) 2016, 576 -584.

AMA Style

Valerie Tang, K.L. Choy, Paul K.Y. Siu, H.Y. Lam, G.T.S. Ho, Stephen W.Y. Cheng. An intelligent performance assessment system for enhancing the service quality of home care nursing staff in the healthcare industry. 2016 Portland International Conference on Management of Engineering and Technology (PICMET). 2016; ():576-584.

Chicago/Turabian Style

Valerie Tang; K.L. Choy; Paul K.Y. Siu; H.Y. Lam; G.T.S. Ho; Stephen W.Y. Cheng. 2016. "An intelligent performance assessment system for enhancing the service quality of home care nursing staff in the healthcare industry." 2016 Portland International Conference on Management of Engineering and Technology (PICMET) , no. : 576-584.

Journal article
Published: 25 July 2016 in Enterprise Information Systems
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Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making.

ACS Style

Ckm Lee; Catalin Mitrea; W.H. Ip; King Lun Tommy Choy; Gts Ho. Design and development of inventory knowledge discovery system. Enterprise Information Systems 2016, 11, 1262 -1282.

AMA Style

Ckm Lee, Catalin Mitrea, W.H. Ip, King Lun Tommy Choy, Gts Ho. Design and development of inventory knowledge discovery system. Enterprise Information Systems. 2016; 11 (8):1262-1282.

Chicago/Turabian Style

Ckm Lee; Catalin Mitrea; W.H. Ip; King Lun Tommy Choy; Gts Ho. 2016. "Design and development of inventory knowledge discovery system." Enterprise Information Systems 11, no. 8: 1262-1282.