• Web-based visual analytics of lifestyle data in MyHealthAvatar

      Zhao, Youbing; Parvinzamir, Farzad; Zhao, Xia; Deng, Zhikun; Ersotelos, Nikolaos; Dong, Feng; Clapworthy, Gordon J. (ICST, 2015-12-22)
      MyHealthAvatar is a project designed to collect lifestyle and health data to promote citizen's wellbeing. As a lifetime companion of citizens the amount of data to be collected is large. It is almost impossible for citizens, patients and doctors to view, utilise and understand these data without proper visual presentation and user interaction. Visual analytics of lifestyle data is one of the key features of MyHealthAvatar. This paper presents the visual analytics components in MyHealthAvatar to facilitate health and lifestyle data presentation and analysis, including 3D avatar, dashboard, diary, timeline, clock view and map. These components can be used cooperatively to achieve flexible visual analysis of spatial temporal lifestyle and health data.
    • MyHealthAvatar: a case study of web-based interactive visual analytics of lifestyle data

      Parvinzamir, Farzad; Zhao, Youbing; Deng, Zhikun; Zhao, Xia; Ersotelos, Nikolaos; Dong, Feng; Liu, Enjie; Clapworthy, Gordon J.; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2015-12-28)
      MyHealthAvatar is a project designed to collect and track lifestyle and health data to promote citizen wellbeing. As a lifetime companion of citizens, the amount of data collected will be huge. It is almost impossible for citizen, patients and doctors to view, utilise and understand these data without proper visual presentation and user interaction. Interactive visual analytics of lifestyle data is one of the key features of MyHealthAvatar. This paper presents the interactive visual analytics components in MyHealthAvatar to facilitate health and lifestyle data presentation and analysis, including 3d avatar, dashboard, diary, timeline, clock view and map. These components can be integrated to achieve flexible visual analysis of spatio-temporal lifestyle data.
    • Visual analytics for health monitoring and risk management in CARRE

      Zhao, Youbing; Parvinzamir, Farzad; Wei, Hui; Liu, Enjie; Deng, Zhikun; Dong, Feng; Third, Allan; Lukoševičius, Arūnas; Marozas, Vaidotas; Kaldoudi, Eleni; et al. (Springer Verlag, 2016-12-31)
      With the rise of wearable sensor technologies, an increasing number of wearable health and medical sensors are available on the market, which enables not only people but also doctors to utilise them to monitor people’s health in such a consistent way that the sensors may become people’s lifetime companion. The consistent measurements from a variety of wearable sensors implies that a huge amount of data needs to be processed, which cannot be achieved by traditional processing methods. Visual analytics is designed to promote knowledge discovery and utilisation of big data via mature visual paradigms with well-designed user interactions and has become indispensable in big data analysis. In this paper we introduce the role of visual analytics for health monitoring and risk management in the European Commission funded project CARRE which aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The visual analytics components of timeline and parallel coordinates for health monitoring and of node-link diagrams, chord diagrams and sankey diagrams for risk analysis are presented to achieve ubiquitous and lifelong health and risk monitoring to promote people’s health.
    • MyHealthAvatar: a lifetime visual analytics companion for citizen well-being

      Deng, Zhikun; Zhao, Youbing; Parvinzamir, Farzad; Zhao, Xia; Wei, Hui; Liu, Mu; Zhang, Xu; Dong, Feng; Liu, Enjie; Clapworthy, Gordon J.; et al. (Springer Verlag, 2016-12-31)
      MyHealthAvatar is a European Commission funded project aimed to design a lifetime companion for citizens to collect, track and store lifestyle and health data to promote citizen well-being. MyHealthAvatar collects and aggregates life-logging data from wearable devices and mobile apps by integrating a variety of life-logging resources, such as Fitbit, Moves, Withings, etc. As a lifelong companion, the data collected will be too large for citizens, patients and doctors to understand and utilise without proper visual presentation and user interaction. This paper presents the key interactive visual analytics components in MyHealthAvatar to facilitate health and lifestyle data presentation and analysis, including 3D avatar, dashboard, diary, timeline, clockview and map to achieve flexible spatio-temporal lifestyle visual analysis to promote citizen well-being.
    • Data mining, management and visualization in large scientific corpuses

      Wei, Hui; Wu, Shaopeng; Zhao, Youbing; Deng, Zhikun; Ersotelos, Nikolaos; Parvinzamir, Farzad; Liu, Baoquan; Liu, Enjie; Dong, Feng; University of Bedfordshire (Springer Verlag, 2016-12-31)
      Organizing scientific papers helps efficiently derive meaningful insights of the published scientific resources, enables researchers grasp rapid technological change and hence assists new scientific discovery. In this paper, we experiment text mining and data management of scientific publications for collecting and presenting useful information to support research. For efficient data management and fast information retrieval, four data storages are employed: a semantic repository, an index and search repository, a document repository and a graph repository, taking full advantage of their features and strength. The results show that the combination of these four repositories can effectively store and index the publication data with reliability and efficiency and hence supply meaningful information to support scientific research.
    • Management of scientific documents and visualization of citation relationships using weighted key scientific terms

      Wei, Hui; Zhao, Youbing; Liu, Enjie; Wu, Shaopeng; Deng, Zhikun; Parvinzamir, Farzad; Dong, Feng; Clapworthy, Gordon J. (SciTePress, 2016-12-31)
      Effective management and visualization of scientific and research documents can greatly assist researchers by improving understanding of relationships (e.g. citations) between the documents. This paper presents work on the management and visualization of large corpuses of scientific papers in order to help researchers explore their citation relationships. Term selection and weighting are used for mining citation relationships by identifying the most relevant. To this end, we present a variation of the TF-IDF scheme, which uses external domain resources as references to calculate the term weighting in a particular domain; document weighting is taken into account in the calculation of term weighting from a group of citations. A simple hierarchical word weighting method is also presented. The work is supported by an underlying architecture for document management using NoSQL databases and employs a simple visualization interface.
    • Topic-aware visual citation tracing via enhanced term weighting for efficient literature retrieval

      Zhao, Youbing; Wei, Hui; Wu, Shaopeng; Parvinzamir, Farzad; Deng, Zhikun; Zhao, Xia; Ersotelos, Nikolaos; Dong, Feng; Clapworthy, Gordon J.; Liu, Enjie; et al. (Springer Verlag, 2017-12-31)
      Efficient retrieval of scientific literature related to a certain topic plays a key role in research work. While little has been done on topic-enabled citation filtering in traditional citation tracing, this paper presents visual citation tracing of scientific papers with document topics taken into consideration. Improved term selection and weighting are employed for mining the most relevant citations. A variation of the TF-IDF scheme, which uses external domain resources as references is proposed to calculate the term weighting in a particular domain. Moreover document weight is also incorporated in the calculation of term weight from a group of citations. A simple hierarchical word weighting method is also presented to handle keyword phrases. A visual interface is designed and implemented to interactively present the citation tracks in chord diagram and Sankey diagram.
    • Quantitative imaging for early detection of osteoarthritis

      Schetinin, Vitaly; University of Bedfordshire (University of Bedfordshire, 2020-07-09)
      The project supported by European Regional Development Fund is related to Quantitative Imaging for Early Detection of Osteoarthritis. The developed method has been tested on high resolution X-Ray images of knees at early stage when the pathological changes in patient's bones cannot be reliably quantified by using the standard radiologic tests. At early stage the pathology is latently developing and so being diagnosed later becomes untreatable. The proposed method has been developed in collaboration with Fusion Radiology (UK) and with Stavropol regional hospital (Russia). The Fusion Radiology (led by Mr Azizul Ambia) is a contractor of the NHS, providing radiology opinions for multiple UK hospitals. The regional hospital (the Deputy MD Anna Sadovaya) has verified the developed method on 160 patient cases. The new method has provided a statistically significant improvement of diagnostic accuracy on the anonymised patient records. The improvements were between 7% and 9%. The results achieved in the studies will allow radiologists to minimise false negative rate which is critically important for early diagnostics.
    • Application of Error-Correcting Codes (ECCs) for efficient message transmission in Vehicular Ad Hoc Networks (VANETs)

      Muhammad, Shehu Jabaka; Zhang, Sijing; Dyo, Vladimir; University of Bedfordshire (Springer, 2018-09-29)
      In this paper, we presented an adaptive application of forward error code (FEC) for efficient message transmission in vehicular ad hoc networks (VANETs). Our solution is a combination of automatic retransmission request (ARQ) with FEC at the MAC layer. The proposed scheme used the existing channel condition, an estimate of the maximum number of transmissions before the message deadline elapses and message type as an index in code lookup ensemble (CLE) to get an optimum code (optCode) for the current transmission. Furthermore, the system also set the transmission timeout delay RTT , encode the message with the optCode and transmit. However, if the transmission timeout delay elapses before receiving an ACK/NAK, the scheme will return to the initial stage for feasible retransmission of the message. We evaluated the scheme and compared it with the static FEC for reliable and timely safety message transmission; our system outperformed the static FEC in all cases that we have considered.
    • Using autoregressive modelling and machine learning for stock market prediction and trading

      Hushani, Phillip; University of Bedfordshire (Springer, 2018-09-29)
      Investors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. In this paper, we compare four forecasting models: autoregressive integrated moving average (ARIMA), vector autoregression (VAR), long short-term memory (LSTM) and nonlinear autoregressive Exogenous (NARX). The results of predictive modelling are analysed and compared in terms of prediction accuracy. The research aims to develop a new profitable trading strategy. Our findings are: (i) the NARX model has provided accurate short-term predictions but failed long forecasts, and (ii) the VAR model can form a good trend line required for trading. Thus, the profitable trading strategy can combine the machine learning predictive modelling and technical analysis.
    • Extraction of texture features from x-ray images: case of osteoarthritis detection

      Akter, Mukti; Jakaite, Livija; University of Bedfordshire (Springer, 2018-09-29)
      Texture features quantitatively represent patterns of interest in image analysis and interpretation. Texture features can vary so largely that the analysis leads to interpretation errors and undesirable consequences. In such cases, finding of informative features becomes problematic. In medical imaging, the texture features were found useful for representing variations in patterns of pixel intensity, which were correlated with pathological changes. In this paper, we describe a new approach to extracting the texture features which are represented on the basis of Zernike orthogonal polynomials. We report the preliminary results which were obtained for a case of osteoarthritis detection in X-ray images using a deep learning paradigm known as group method of data handling.
    • Nephroblastoma analysis in MRI images

      Kaba, Djibril; McFarlane, Nigel J.B.; Dong, Feng; Graf, Norbert; Ye, Xujiong; University of Bedfordshire; Saarland University Hospital; University of Lincoln (International Society for Stereology, 2019-12-31)
      The annotation of the tumour from medical scans is a crucial step in nephroblastoma treatment. Therefore, an accurate and reliable segmentation method is needed to facilitate the evaluation and the treatments of the tumour. The proposed method serves this purpose by performing the segmentation of nephroblastoma in MRI scans. The segmentation is performed by adapting and a 2D free hand drawing tool to select a region of interest in the scan slices. Results from 24 patients show a mean root-mean-square error of 0.0481 ± 0.0309, an average Dice coefficient of 0.9060 ± 0.0549 and an average accuracy of 99.59% ± 0.0039. Thus the proposed method demonstrated an effective agreement with manual annotations.
    • Enhancing user fairness in OFDMA radio access networks through machine learning

      Comşa, Ioan-Sorin; Zhang, Sijing; Aydin, Mehmet Emin; Kuonen, Pierre; Trestian, Ramona; Ghinea, Gheorghiţă; Brunel University; University of Bedfordshire; University of the West of England; HEIA-FR; et al. (IEEE, 2019-06-13)
      The problem of radio resource scheduling subject to fairness satisfaction is very challenging even in future radio access networks. Standard fairness criteria aim to find the best trade-off between overall throughput maximization and user fairness satisfaction under various types of network conditions. However, at the Radio Resource Management (RRM) level, the existing schedulers are rather static being unable to react according to the momentary networking conditions so that the user fairness measure is maximized all time. This paper proposes a dynamic scheduler framework able to parameterize the proportional fair scheduling rule at each Transmission Time Interval (TTI) to improve the user fairness. To deal with the framework complexity, the parameterization decisions are approximated by using the neural networks as non-linear functions. The actor-critic Reinforcement Learning (RL) algorithm is used to learn the best set of non-linear functions that approximate the best fairness parameters to be applied in each momentary state. Simulations results reveal that the proposed framework outperforms the existing fairness adaptation techniques as well as other types of RL-based schedulers.
    • IEEE Access special section: advances in interference mitigation techniques for device-to-device communications

      Ur-Rehman, Masood; Gao, Yue; Chaudhry, Mohammad Asad Rehman; Safdar, Ghazanfar Ali; Xu, Yanli; University of Essex; Queen Mary University of London; University of Toronto; University of Bedfordshire; Shanghai Maritime University (IEEE, 2019-12-17)
    • Displacement error analysis of 6-DoF virtual reality

      Aksu, Ridvan; Chakareski, Jacob; Velisavljevic, Vladan; University of Alabama; University of Bedfordshire (ACM, 2019-12-31)
      Virtual view synthesis is a critical step in enabling Six-Degrees of Freedom (DoF) immersion experiences in Virtual Reality (VR). It comprises synthesis of virtual viewpoints for a user navigating the immersion environment, based on a small subset of captured viewpoints featuring texture and depth maps. We investigate the extreme values of the displacement error in view synthesis caused by depth map quantization, for a given 6DoF VR video dataset, particularly based on the camera settings, scene properties, and the depth map quantization error. We establish a linear relationship between the displacement error and the quantization error, scaled by the sine of the angle between the location of the object and the virtual view in the 3D scene, formed at the reference camera location. In the majority of cases the horizontal and vertical displacement errors induced at a pixel location of a reconstructed 360° viewpoint comprising the immersion environment are respectively proportional to 3/5 and 1/5 of the respective quantization error. Also, the distance between the reference view and the synthesized view severely increases the displacement error. Following these observations: displacement error values can be predicted for given pixel coordinates and quantization error, and this can serve as a first step towards modeling the relationship between the encoding rate of reference views and the quality of synthesized views.
    • Growth of nerve cells induced by diverse nanopillar arrays

      Liu, Mengnan; Dong, Litong; Yang, Xueying; Guo, Xuan; Wang, Xuan; Xie, Chenchen; Song, Zhengxun; Wang, Zuobin; Li, Dayou; Changchun University of Science and Technology; et al. (IEEE, 2020-01-02)
      The nanotopographies can induce the growth of nerve cells and the growth of their synapses. Studying the anisotropic structures for the guidance of neuronal synapses is beneficial to the in vitro repair of neurons and the development of regenerative medicine. Thus, studying how diverse nanopillar arrays affect the growth of nerve cells is essential. This paper employed the technology of laser interference lithography (LIL) to fabricate different nanopillar arrays with the same and different size gaps between the X and Y directions, and observe how the structures induce the growth of nerve cells and their synapses.
    • Direct imaging of IgE on the mica surface by tapping-mode atomic force microscopy

      Hu, Jing; Wang, Ying; Gao, Mingyan; Song, Zhengxun; Chen, Yujuan; Wang, Zuobin; Changchun University of Science and Technology; University of Bedfordshire (IEEE, 2020-02-02)
      Immunoglobulin E (IgE) antibody is essential in the functioning of the immune system, so the study of IgE has its practical and profound significance. Herein, the effect of protein concentration and adsorption time on IgE morphology of mica surface was investigated. For this purpose, atomic force microscopy (AFM) has been performed for monitoring protein morphology at different concentrations and adsorption times. In addition, the height and average roughness of IgE were also obtained. The changes of IgE molecule morphology including the shape, height and average roughness indicated that the interactions of protein-surface and protein-protein were varying with the protein concentration and adsorption time.
    • Imaging quality assessment of different AFM working modes on living cancer cells

      Wang, Guoliang; Sun, Baishun; Wu, Xiaomin; Zhang, Wenxiao; Qu, Yingmin; Song, Zhengxun; Wang, Zuobin; Li, Dayou; Changchun University of Science and Technology; University of Bedfordshire (IEEE, 2020-02-02)
      Since the invention of atomic force microscope (AFM) in 1986, its capabilities in biophysical research, such as living cell imaging, molecule imaging and recognition and drug treatment analysis, have been deeply investigated. Various types of working modes of atomic force microscopy have been employed for imaging and analyzing living cells. The physical properties of living cells can be directly illustrated by its good resolution images. In this paper, the applications of three AFM working modes including contact, tapping and quantitative imaging (QI) modes for the investigation of living lung cancer cells (A549) are presented. Meanwhile, the quality of images of the cells obtained by different working modes is compared through the image quality assessment (IQA) methods.
    • A disulfiram-loaded fibers scaffold fabricated via electrospinning method

      Xie, Chenchen; Ding, Ran; Wang, Xinyue; Yan, Jin; Wang, Ying; Zhang, Wenxiao; Qu, Yingmin; Wang, Zuobin; Changchun University of Science and Technology; University of Bedfordshire (IEEE, 2020-01-02)
      In this study, we developed a disulfiram-loaded fibers scaffold via the electrospinning method for enhancing the stability of disulfiram and facilitating the appropriate distribution in tumor tissues. The drug release profile of the disulfiram-loaded fibers scaffold was examined by high-performance liquid chromatography. The results showed that both the initial burst release and the subsequent sustainable release of the drug were suitable for cancer treatments. The results of an MTT assay, which tested the therapeutic efficacy of electrospun fibers in vitro, showed that the DSF-PVDF fibers exhibited their anticancer activity due to the incorporation of DSF. It indicates that DSF is successfully incorporated into the electrospun fibers and the resultant electrospun fibers are highly promising for cancer treatments.
    • Information foraging for enhancing implicit feedback in content-based image recommendation

      Jaiswal, Amit Kumar; Liu, Haiming; Frommholz, Ingo; University of Bedfordshire (ACM, 2019-12-31)
      User implicit feedback plays an important role in recommender systems. However, finding implicit features is a tedious task. This paper aims to identify users' preferences through implicit behavioural signals for image recommendation based on the Information Scent Model of Information Foraging Theory. In the first part, we hypothesise that the users' perception is improved with visual cues in the images as behavioural signals that provide users' information scent during information seeking. We designed a content-based image recommendation system to explore which image attributes (i.e., visual cues or bookmarks) help users find their desired image. We found that users prefer recommendations predicated by visual cues and therefore consider the visual cues as good information scent for their information seeking. In the second part, we investigated if visual cues in the images together with the images itself can be better perceived by the users than each of them on its own. We evaluated the information scent artifacts in image recommendation on the Pinterest image collection and the WikiArt dataset. We find our proposed image recommendation system supports the implicit signals through Information Foraging explanation of the information scent model.