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As important immune cells in the human body, white blood cells play a very significant role in the auxiliary diagnosis of many major diseases. Clinically, changes in the number and morphology of white blood cells and their subtypes are the prediction index for important, serious diseases, such as anaemia, malaria, infections, and tumours. The application of image recognition technology and cloud computing to assist in medical diagnosis is a hot topic in current research, which we believe have great potential to further improve real-time detection and improve medical diagnosis. This paper proposes a novel automatic classification framework for the recognition of five subtypes of white blood cells, in the hope of contributing to disease prediction. First, we present an adaptive threshold segmentation method to deal with blood smear images with nonuniform colour and uneven illumination. The method is designed based on colour space information and threshold segmentation. After successfully separating the white blood cell from the blood smear image, a large number of features, including geometrical, colour, and texture features are extracted. However, redundant features can affect the classification speed and efficiency, and in view of that, a feature selection algorithm based on classification and regression trees (CART) is designed to successfully remove irrelevant and redundant features from the initial features. The selected prominent features are fed into a particle swarm optimisation support vector machine (PSO-SVM) classifier to recognise the types of white blood cells. Finally, to evaluate the performance of the proposed white blood cell classification methodology, we build a white blood cell data set containing 500 blood smear images for experiments. The proposed methodology achieves 99.76% classification accuracy, which well demonstrates its effectiveness.
Na Dong; Meng-Die Zhai; Jian-Fang Chang; Chun-Ho Wu. A self-adaptive approach for white blood cell classification towards point-of-care testing. Applied Soft Computing 2021, 111, 107709 .
AMA StyleNa Dong, Meng-Die Zhai, Jian-Fang Chang, Chun-Ho Wu. A self-adaptive approach for white blood cell classification towards point-of-care testing. Applied Soft Computing. 2021; 111 ():107709.
Chicago/Turabian StyleNa Dong; Meng-Die Zhai; Jian-Fang Chang; Chun-Ho Wu. 2021. "A self-adaptive approach for white blood cell classification towards point-of-care testing." Applied Soft Computing 111, no. : 107709.
The estimation of the difference between the new competitive advantages of China's export and the world’s trading powers have been the key measurement problems in China-related studies. In this work, a comprehensive evaluation index system for new export competitive advantages is developed, a soft-sensing model for China’s new export competitive advantages based on the fuzzy entropy weight analytic hierarchy process is established, and the soft-sensing values of key indexes are derived. The obtained evaluation values of the main measurement index are used as the input variable of the fuzzy least squares support vector machine, and a soft-sensing model of the key index parameters of the new export competitive advantages of China based on the combined soft-sensing model of the fuzzy least squares support vector machine is established. The soft-sensing results of the new export competitive advantage index of China show that the soft measurement model developed herein is of high precision compared with other models, and the technical and brand competitiveness indicators of export products have more significant contributions to the new competitive advantages of China's export, while the service competitiveness indicator of export products has the least contribution to new competitive advantages of China's export.
Taosheng Wang; Hongyan Zuo; C. H. Wu; B. Hu. Combined soft measurement on key indicator parameters of new competitive advantages for China's export. Financial Innovation 2021, 7, 1 -24.
AMA StyleTaosheng Wang, Hongyan Zuo, C. H. Wu, B. Hu. Combined soft measurement on key indicator parameters of new competitive advantages for China's export. Financial Innovation. 2021; 7 (1):1-24.
Chicago/Turabian StyleTaosheng Wang; Hongyan Zuo; C. H. Wu; B. Hu. 2021. "Combined soft measurement on key indicator parameters of new competitive advantages for China's export." Financial Innovation 7, no. 1: 1-24.
Recently, global e-commerce businesses have been blooming due to the convenience they offer, their product range, and the individualized products and services they offer. To maintain an entire ecosystem, effective platform-vendor relationships should be considered, through which e-commerce platforms can provide collaborative packages to vendors. E-vendor relationship management (eVRM) should then be developed to identify, attract, retain, and develop existing and new vendors so that groups of loyal vendors can be managed. However, eVRM in e-commerce is an area that has received less attention. This paper proposes an adaptive e-vendor relationship-management system (AVRMS) to provide decision-making support for the formulation of vendor management strategies. The contribution of this study is that it addresses the missing link of platform-vendor relationship management in global e-commerce environments, while integrating data-driven approaches and artificial intelligence techniques to generate a new synergy for the facilitation of eVRM.
H. Y. Lam; Y. P. Tsang; C. H. Wu; C. Y. Chan. Intelligent E-Vendor Relationship Management for Enhancing Global B2C E-Commerce Ecosystems. Journal of Global Information Management 2021, 29, 1 -25.
AMA StyleH. Y. Lam, Y. P. Tsang, C. H. Wu, C. Y. Chan. Intelligent E-Vendor Relationship Management for Enhancing Global B2C E-Commerce Ecosystems. Journal of Global Information Management. 2021; 29 (3):1-25.
Chicago/Turabian StyleH. Y. Lam; Y. P. Tsang; C. H. Wu; C. Y. Chan. 2021. "Intelligent E-Vendor Relationship Management for Enhancing Global B2C E-Commerce Ecosystems." Journal of Global Information Management 29, no. 3: 1-25.
Amid the coronavirus outbreak, many countries are facing a dramatic situation in terms of the global economy and human social activities, including education. The shutdown of schools is affecting many students around the world, with face-to-face classes suspended. Many countries facing the disastrous situation imposed class suspension at an early stage of the coronavirus outbreak, and Asia was one of the earliest regions to implement live online learning. Despite previous research on online teaching and learning, students' readiness to participate in the real-time online learning implemented during the coronavirus outbreak is not yet well understood. This study explored several key factors in the research framework related to learning motivation, learning readiness and student's self-efficacy in participating in live online learning during the coronavirus outbreak, taking into account gender differences and differences among sub-degree (SD), undergraduate (UG) and postgraduate (PG) students. Technology readiness was used instead of conventional online/internet self-efficacy to determine students' live online learning readiness. The hypothetical model was validated using confirmatory factor analysis (CFA). The results revealed no statistically significant differences between males and females. On the other hand, the mean scores for PG students were higher than for UG and SD students based on the post hoc test. We argue that during the coronavirus outbreak, gender differences were reduced because students are forced to learn more initiatively. We also suggest that students studying at a higher education degree level may have higher expectations of their academic achievement and were significantly different in their online learning readiness. This study has important implications for educators in implementing live online learning, particularly for the design of teaching contexts for students from different educational levels. More virtual activities should be considered to enhance the motivation for students undertaking lower-level degrees, and encouragement of student-to-student interactions can be considered.
Yuk Ming Tang; Pen Chung Chen; Kris M.Y. Law; C.H. Wu; Yui-Yip Lau; Jieqi Guan; Dan He; G.T.S. Ho. Comparative analysis of Student's live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Computers & Education 2021, 168, 104211 -104211.
AMA StyleYuk Ming Tang, Pen Chung Chen, Kris M.Y. Law, C.H. Wu, Yui-Yip Lau, Jieqi Guan, Dan He, G.T.S. Ho. Comparative analysis of Student's live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Computers & Education. 2021; 168 ():104211-104211.
Chicago/Turabian StyleYuk Ming Tang; Pen Chung Chen; Kris M.Y. Law; C.H. Wu; Yui-Yip Lau; Jieqi Guan; Dan He; G.T.S. Ho. 2021. "Comparative analysis of Student's live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector." Computers & Education 168, no. : 104211-104211.
Purpose Due to the rapid growth of blockchain technology in recent years, the fusion of blockchain and the Internet of Things (BIoT) has drawn considerable attention from researchers and industrial practitioners and is regarded as a future trend in technological development. Although several authors have conducted literature reviews on the topic, none have examined the development of the knowledge structure of BIoT, resulting in scattered research and development (R&D) efforts. Design/methodology/approach This study investigates the intellectual core of BIoT through a co-citation proximity analysis–based systematic review (CPASR) of the correlations between 44 highly influential articles out of 473 relevant research studies. Subsequently, we apply a series of statistical analyses, including exploratory factor analysis (EFA), hierarchical cluster analysis (HCA), k-means clustering (KMC) and multidimensional scaling (MDS) to establish the intellectual core. Findings Our findings indicate that there are nine categories in the intellectual core of BIoT: (1) data privacy and security for BIoT systems, (2) models and applications of BIoT, (3) system security theories for BIoT, (4) frameworks for BIoT deployment, (5) the fusion of BIoT with emerging methods and technologies, (6) applied security strategies for using blockchain with the IoT, (7) the design and development of industrial BIoT, (8) establishing trust through BIoT and (9) the BIoT ecosystem. Originality/value We use the CPASR method to examine the intellectual core of BIoT, which is an under-researched and topical area. The paper also provides a structural framework for investigating BIoT research that may be applicable to other knowledge domains.
Y.P. Tsang; C.H. Wu; W.H. Ip; Wen-Lung Shiau. Exploring the intellectual cores of the blockchain–Internet of Things (BIoT). Journal of Enterprise Information Management 2021, ahead-of-p, 1 .
AMA StyleY.P. Tsang, C.H. Wu, W.H. Ip, Wen-Lung Shiau. Exploring the intellectual cores of the blockchain–Internet of Things (BIoT). Journal of Enterprise Information Management. 2021; ahead-of-p (ahead-of-p):1.
Chicago/Turabian StyleY.P. Tsang; C.H. Wu; W.H. Ip; Wen-Lung Shiau. 2021. "Exploring the intellectual cores of the blockchain–Internet of Things (BIoT)." Journal of Enterprise Information Management ahead-of-p, no. ahead-of-p: 1.
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.
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 StyleY.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 StyleY.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.
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.
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 StyleRongxiang 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 StyleRongxiang 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.
With the rapid growth of perishable food e-commerce businesses, there is a definite need for logistics services providers to manage parcel shipments with multi-temperature requirements. E-commerce characteristics, including time-critical delivery, fragmented orders, and high product variety, should be further considered to extend the ontology of multi-temperature joint distribution. However, traditional delivery route planning is insufficient because it merely minimises the cost of travelling between customer locations. Factors related to food quality and arrival time windows should also be considered. In addition, handling dynamic incident management, such as violations of handling requirements during delivery, is lacking. This leads to the likelihood of food deteriorating before it reaches the consumers, thereby impacting customer satisfaction. This paper proposes an Internet of Things–based multi-temperature delivery planning system (IoT-MTDPS), embedding a two-phase multi-objective genetic algorithm optimiser (2PMGAO). The formulation of delivery routing mainly considers product-dependent multi-temperature characteristics, service level, transportation cost, and number of trucks. Once there are unexpected incidents which are detected by Internet of Things technologies, 2PMGAO can optimise the membership functions of fuzzy logic for re-routing the e-commerce delivery plan. With using IoT-MTDPS, the capability of handling e-commerce orders is enhanced, while customer satisfaction can be maintained at a designated level.
Y. P. Tsang; C. H. Wu; H. Y. Lam; K. L. Choy; G. T. S. Ho. Integrating Internet of Things and multi-temperature delivery planning for perishable food E-commerce logistics: a model and application. International Journal of Production Research 2020, 59, 1534 -1556.
AMA StyleY. P. Tsang, C. H. Wu, H. Y. Lam, K. L. Choy, G. T. S. Ho. Integrating Internet of Things and multi-temperature delivery planning for perishable food E-commerce logistics: a model and application. International Journal of Production Research. 2020; 59 (5):1534-1556.
Chicago/Turabian StyleY. P. Tsang; C. H. Wu; H. Y. Lam; K. L. Choy; G. T. S. Ho. 2020. "Integrating Internet of Things and multi-temperature delivery planning for perishable food E-commerce logistics: a model and application." International Journal of Production Research 59, no. 5: 1534-1556.
Nowadays, patients' safety is the top priority for medical services around the world. However, it is believed that many of the adverse events in hospitals are preventable. Type and screen (T&S) procedures require intense practical training by each medical practitioner in each hospital. This study applied an interactive Virtual Reality (VR) technology to supplement the traditional approach to facilitate procedural training. The VR system made use of the Unity3D for application development. To investigate the reliability and validity of the conceptual medical training model, a survey was conducted to measure the content, motivation and enhanced readiness of practitioners. The partial least squares (PLS) modelling was carried out to investigate the correlation between each pair of measured variables. The study results indicated that the learning model has good reliability for each measurement factor and validates the survey study. The PLS modelling also indicated a significant correlation between each pair of measured variables. The project developed a VR training program for training in T&S procedures. The study provides important implications on the development of a practical VR training program for medical practitioners, as well as valuable insights for the development of similar VR training programs in the future.
Yuk Ming Tang; George Wing Yiu Ng; Nam Hung Chia; Eric Hang Kwong So; Chun Ho Wu; Wai Hung Ip. Application of virtual reality ( VR ) technology for medical practitioners in type and screen (T&S) training. Journal of Computer Assisted Learning 2020, 37, 359 -369.
AMA StyleYuk Ming Tang, George Wing Yiu Ng, Nam Hung Chia, Eric Hang Kwong So, Chun Ho Wu, Wai Hung Ip. Application of virtual reality ( VR ) technology for medical practitioners in type and screen (T&S) training. Journal of Computer Assisted Learning. 2020; 37 (2):359-369.
Chicago/Turabian StyleYuk Ming Tang; George Wing Yiu Ng; Nam Hung Chia; Eric Hang Kwong So; Chun Ho Wu; Wai Hung Ip. 2020. "Application of virtual reality ( VR ) technology for medical practitioners in type and screen (T&S) training." Journal of Computer Assisted Learning 37, no. 2: 359-369.
The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system.
Yung Po Tsang; Wai Chi Wong; G. Q. Huang; Chun Ho Wu; Y. H. Kuo; King Lun Choy. A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry. Energies 2020, 13, 3918 .
AMA StyleYung Po Tsang, Wai Chi Wong, G. Q. Huang, Chun Ho Wu, Y. H. Kuo, King Lun Choy. A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry. Energies. 2020; 13 (15):3918.
Chicago/Turabian StyleYung Po Tsang; Wai Chi Wong; G. Q. Huang; Chun Ho Wu; Y. H. Kuo; King Lun Choy. 2020. "A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry." Energies 13, no. 15: 3918.
For companies to gain competitive advantage, an effective customer relationship management (CRM) approach is necessary. Based on customer purchase behaviour and ordering patterns, companies can be classified into different categories in terms of providing customised sales and promotions for customers. However, companies that lack an effective CRM strategy can only offer the same sales and marketing strategies to all customers. Furthermore, the traditional approach to managing customers is control via a centralised method, in which the information regarding customer segmentation is not shared among the customer network. Consequently, valuable customers may be neglected, resulting in the loss of customer loyalty and sales orders, and the weakening of trust in the customer–company relationship. This paper designs an integrated data analytic model (IDAM) in a peer-to-peer cloud, integrating RFM-based k-means clustering algorithm, analytical hierarchy processing and fuzzy logic to divide customers into different segments and hence formulate a customised sales strategy. A pilot study of IDAM is conducted in a trading company specialised in providing advanced manufacturing technology to demonstrate how IDAM can be applied to formulate an effective sales strategy to attract customers. Overall, this study explores the effective deployment of CRM into the peer-to-peer cloud so as to facilitate sales strategy formulation and trust between customers and companies in the network.
H. Y. Lam; Y. P. Tsang; C. H. Wu; Valerie Tang. Data analytics and the P2P cloud: an integrated model for strategy formulation based on customer behaviour. Peer-to-Peer Networking and Applications 2020, 14, 2600 -2617.
AMA StyleH. Y. Lam, Y. P. Tsang, C. H. Wu, Valerie Tang. Data analytics and the P2P cloud: an integrated model for strategy formulation based on customer behaviour. Peer-to-Peer Networking and Applications. 2020; 14 (5):2600-2617.
Chicago/Turabian StyleH. Y. Lam; Y. P. Tsang; C. H. Wu; Valerie Tang. 2020. "Data analytics and the P2P cloud: an integrated model for strategy formulation based on customer behaviour." Peer-to-Peer Networking and Applications 14, no. 5: 2600-2617.
In recent years, conventional artificial method leads to low efficiency in the classification of cervical cell, which requires professional completion. Therefore, the classification process is increasingly dependent on artificial intelligence. The traditional image classification method needs to extract a large number of features. Redundant features cause the recognition speed to be slow, and influence the recognition effect. To address these problems and obtain the highest recognition accuracy with the least number of features, this paper proposes a machine learning method based on feature selection algorithm for cervical cell classification. Firstly, we introduced classification and regression trees (CART) for cell feature selection, which reduces the dimension of input feature attributes. Subsequently, particle swarm optimization (PSO) was used to optimize the hyperparameters of support vector machine (SVM) in this paper, making the SVM model better for classification. Finally, the Herlev dataset was introduced to verify the classification performance. The experimental results show that the proposed algorithm can extract accurate and effective features and obtain high classification accuracy, thus verifying the effectiveness of the proposed algorithm. Moreover, the network structure of the proposed algorithm is relatively simple with a low computation cost, which makes it feasible of further extension to the classification application of other cancer cells.
Na Dong; Meng-Die Zhai; Li Zhao; Chun Ho Wu. Cervical cell classification based on the CART feature selection algorithm. Journal of Ambient Intelligence and Humanized Computing 2020, 12, 1837 -1849.
AMA StyleNa Dong, Meng-Die Zhai, Li Zhao, Chun Ho Wu. Cervical cell classification based on the CART feature selection algorithm. Journal of Ambient Intelligence and Humanized Computing. 2020; 12 (2):1837-1849.
Chicago/Turabian StyleNa Dong; Meng-Die Zhai; Li Zhao; Chun Ho Wu. 2020. "Cervical cell classification based on the CART feature selection algorithm." Journal of Ambient Intelligence and Humanized Computing 12, no. 2: 1837-1849.
Traditional cell classification methods generally extract multiple features of the cell manually. Moreover, the simple use of artificial feature extraction methods has low universality. For example, it is unsuitable for cervical cell recognition because of the complexity of the cervical cell texture and the large individual differences between cells. Using the convolutional neural network classification method is a good way to solve this problem. However, although the cell features can be extracted automatically, the cervical cell domain knowledge will be lost, and the corresponding features of different cell types will be missing; hence, the classification effect is not sufficiently accurate. Aiming at addressing the limitations of the two mentioned classification methods, this paper proposes a cell classification algorithm that combines Inception v3 and artificial features, which effectively improves the accuracy of cervical cell recognition. In addition, to address the under-fitting problem and carry out effective deep learning training with a relatively small amount of medical data, this paper inherits the strong learning ability from transfer learning, and achieves accurate and effective cervical cell image classification based on the Herlev dataset. Using this method, an accuracy of more than 98% is achieved, providing an effective framework for computer aided diagnosis of cervical cancer. The proposed algorithm has good universality, low complexity, and high accuracy, rendering it suitable for further extension and application to the classification of other types of cancer cells.
N. Dong; L. Zhao; C.H. Wu; J.F. Chang. Inception v3 based cervical cell classification combined with artificially extracted features. Applied Soft Computing 2020, 93, 106311 .
AMA StyleN. Dong, L. Zhao, C.H. Wu, J.F. Chang. Inception v3 based cervical cell classification combined with artificially extracted features. Applied Soft Computing. 2020; 93 ():106311.
Chicago/Turabian StyleN. Dong; L. Zhao; C.H. Wu; J.F. Chang. 2020. "Inception v3 based cervical cell classification combined with artificially extracted features." Applied Soft Computing 93, no. : 106311.
PurposeAccurate prediction of order demand across omni-channel supply chains improves the management's decision-making ability at strategic, tactical and operational levels. The paper aims to develop a predictive methodology for forecasting near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better.Design/methodology/approachThe paper proposes a novel machine learning predictive methodology through the integration of the time series data characteristics into the development of an adaptive neuro-fuzzy inference system. A four-stage implementation framework is developed for enabling practitioners to apply the proposed model.FindingsA structured model evaluation framework is constructed for cross-validation of model performance. With the aid of an illustrative case study, forecasting evaluation reveals a high level of accuracy of the proposed machine learning approach in forecasting the arrivals of real e-commerce orders in three different retailers at three-hour intervals.Research limitations/implicationsResults from the case study suggest that real-time prediction of individual retailer's e-order arrival is crucial in order to maximize the value of e-order arrival prediction for daily operational decision-making.Originality/valueEarlier researchers examined supply chain demand, forecasting problem in a broader scope, particularly in dealing with the bullwhip effect. Prediction of real-time, hourly based order arrivals has been lacking. The paper fills this research gap by presenting a novel data-driven predictive methodology.
K.H. Leung; Daniel Y. Mo; G.T.S. Ho; C.H. Wu; G.Q. Huang. Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology. Industrial Management & Data Systems 2020, 120, 1149 -1174.
AMA StyleK.H. Leung, Daniel Y. Mo, G.T.S. Ho, C.H. Wu, G.Q. Huang. Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology. Industrial Management & Data Systems. 2020; 120 (6):1149-1174.
Chicago/Turabian StyleK.H. Leung; Daniel Y. Mo; G.T.S. Ho; C.H. Wu; G.Q. Huang. 2020. "Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology." Industrial Management & Data Systems 120, no. 6: 1149-1174.
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.
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 StyleY. 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 StyleY. 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.
Person re-identification (Re-ID) based on deep learning has made great progress and achieved state-of-the-art performance in recent years. However, the end-to-end properties of deep neural networks allow us to directly feedback the output results based on its input, making the inner working mechanism of the deep person Re-ID model and its decision reasons lack of transparency and explainability. This further impedes improvements to pedestrian recognition performance. As feature visualization has been proven to be an effective method for characterizing the middle layer of a neural network, we propose a novel gradient-based visualization method to interpret the internal features learned by deep person Re-ID. Based on the idea of transfer learning, this model regards the pretrained ResNet-50 on the ImageNet dataset as a basic network for deep person Re-ID. First, the network is fine-tuned on the person Re-ID dataset to achieve pedestrian classification, and then, the gradient-based visualization of the trained network is performed to highlight important regions contributing to image similarity. Experiments conducted on the Market-1501 dataset verify that our model can not only enable the network to identify key features of an individual across different images, but also provide visual interpretation for the pedestrian classification results to improve the reliability of person Re-ID and foster trust from users regarding its decisions.
Heyu Chang; Dongning Zhao; C. H. Wu; Li Li; Nianwen Si; Rongyu He. Visualization of spatial matching features during deep person re-identification. Journal of Ambient Intelligence and Humanized Computing 2020, 1 -13.
AMA StyleHeyu Chang, Dongning Zhao, C. H. Wu, Li Li, Nianwen Si, Rongyu He. Visualization of spatial matching features during deep person re-identification. Journal of Ambient Intelligence and Humanized Computing. 2020; ():1-13.
Chicago/Turabian StyleHeyu Chang; Dongning Zhao; C. H. Wu; Li Li; Nianwen Si; Rongyu He. 2020. "Visualization of spatial matching features during deep person re-identification." Journal of Ambient Intelligence and Humanized Computing , no. : 1-13.
Due to declining fertility rate and increasing life expectancy, population aging has become a growing problem in Hong Kong. Domestic elderly service providers and public health institutions have been suffering from a shortage of experienced staff for elderly healthcare, which has led to a drop in both the quality and efficiency of the local elderly service. With the rising popularity of mobile applications and the betterment of machine vision technology, this paper describes the design of an Intelligent m-Healthcare System (ImHS) for relieving the manpower pressure on local elderly service providers by lowering the technical threshold and simplifying staff training process. Face recognition technology is applied to identify the elderly by searching and tracing the elderly medical record through the FaceAPI service. In addition, the proposed ImHS provides the immediate insight into healthcare knowledge for users, which lowers the occurrence of Adverse Drug Event (ADE) and shortens the duration of pill distribution process. By conducting a case study in a local elderly home, the proposed system allowed the nursing staff to better allocate healthcare resources and to improve the operation effectiveness and efficiency.
H.Y. Lam; Y.M. Tang; Valerie Tang; C.H. Wu. An Intelligent m-Healthcare System for Improving the Service Quality in Domestic Care Industry. IFAC-PapersOnLine 2020, 53, 17439 -17444.
AMA StyleH.Y. Lam, Y.M. Tang, Valerie Tang, C.H. Wu. An Intelligent m-Healthcare System for Improving the Service Quality in Domestic Care Industry. IFAC-PapersOnLine. 2020; 53 (2):17439-17444.
Chicago/Turabian StyleH.Y. Lam; Y.M. Tang; Valerie Tang; C.H. Wu. 2020. "An Intelligent m-Healthcare System for Improving the Service Quality in Domestic Care Industry." IFAC-PapersOnLine 53, no. 2: 17439-17444.
Food traceability has been one of the emerging blockchain applications in recent years, for improving the areas of anti-counterfeiting and quality assurance. Existing food traceability systems do not guarantee a high level of system reliability, scalability, and information accuracy. Moreover, the traceability process is time-consuming and complicated in modern supply chain networks. To alleviate these concerns, blockchain technology is promising to create a new ontology for supply chain traceability. However, most consensus mechanisms and data flow in blockchain are developed for cryptocurrency, not for supply chain traceability; hence, simply applying blockchain technology to food traceability is impractical. In this paper, a blockchain–IoT-based food traceability system (BIFTS) is proposed to integrate the novel deployment of blockchain, IoT technology, and fuzzy logic into a total traceability shelf life management system for managing perishable food. To address the needs for food traceability, lightweight and vaporized characteristics are deployed in the blockchain, while an integrated consensus mechanism that considers shipment transit time, stakeholder assessment, and shipment volume is developed. The data flow of blockchain is then aligned to the deployment of IoT technologies according to the level of traceable resource units. Subsequently, the decision support can be established in the food supply chain by using reliable and accurate data for shelf life adjustment, and by using fuzzy logic for quality decay evaluation.
Y.P. Tsang; King Lun Choy; Chun Ho Wu; George To Sum Ho; H.Y. Lam. Blockchain-Driven IoT for Food Traceability With an Integrated Consensus Mechanism. IEEE Access 2019, 7, 129000 -129017.
AMA StyleY.P. Tsang, King Lun Choy, Chun Ho Wu, George To Sum Ho, H.Y. Lam. Blockchain-Driven IoT for Food Traceability With an Integrated Consensus Mechanism. IEEE Access. 2019; 7 (99):129000-129017.
Chicago/Turabian StyleY.P. Tsang; King Lun Choy; Chun Ho Wu; George To Sum Ho; H.Y. Lam. 2019. "Blockchain-Driven IoT for Food Traceability With an Integrated Consensus Mechanism." IEEE Access 7, no. 99: 129000-129017.
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.
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 StyleGeorge 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 StyleGeorge 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.
C.H. Wu; Polly P.L. Leung; N. Dong; G.T.S. Ho; C.K. Kwong; W.H. Ip. OPTIMIZATION OF TERMINAL SERVICEABILITY BASED ON CHAOTIC GA-BASED METHOD. Malaysian Journal of Computer Science 2019, 32, 62 -82.
AMA StyleC.H. Wu, Polly P.L. Leung, N. Dong, G.T.S. Ho, C.K. Kwong, W.H. Ip. OPTIMIZATION OF TERMINAL SERVICEABILITY BASED ON CHAOTIC GA-BASED METHOD. Malaysian Journal of Computer Science. 2019; 32 (1):62-82.
Chicago/Turabian StyleC.H. Wu; Polly P.L. Leung; N. Dong; G.T.S. Ho; C.K. Kwong; W.H. Ip. 2019. "OPTIMIZATION OF TERMINAL SERVICEABILITY BASED ON CHAOTIC GA-BASED METHOD." Malaysian Journal of Computer Science 32, no. 1: 62-82.