• Data mining in real-world traditional Chinese medicine clinical data warehouse

      Zhou, X.; Liu, B.; Zhang, X.; Xie, Q.; Zhang, R.; Wang, Y.; Peng, Yonghong (2014)
      Real-world clinical setting is the major arena of traditional Chinese medicine (TCM) as it has experienced long-term practical clinical activities, and developed established theoretical knowledge and clinical solutions suitable for personalized treatment. Clinical phenotypes have been the most important features captured by TCM for diagnoses and treatment, which are diverse and dynamically changeable in real-world clinical settings. Together with clinical prescription with multiple herbal ingredients for treatment, TCM clinical activities embody immense valuable data with high dimensionalities for knowledge distilling and hypothesis generation. In China, with the curation of large-scale real-world clinical data from regular clinical activities, transforming the data to clinical insightful knowledge has increasingly been a hot topic in TCM field. This chapter introduces the application of data warehouse techniques and data mining approaches for utilizing real-world TCM clinical data, which is mainly from electronic medical records. The main framework of clinical data mining applications in TCM field is also introduced with emphasizing on related work in this field. The key points and issues to improve the research quality are discussed and future directions are proposed.
    • Data mining: concepts and techniques

      Han, J.; Kamber, M.; Pei, J. (2012-06-22)
    • Data quality and governance in a UK social housing initiative: Implications for smart sustainable cities

      Duvier, Caroline; Anand, Prathivadi B.; Oltean-Dumbrava, Crina (2018-05)
      Smart Sustainable Cities (SSC) consist of multiple stakeholders, who must cooperate in order for SSCs to be successful. Housing is an important challenge and in many cities, therefore, a key stakeholder are social housing organisations. This paper introduces a qualitative case study of a social housing provider in the UK who implemented a business intelligence project (a method to assess data networks within an organisation) to increase data quality and data interoperability. Our analysis suggests that creating pathways for different information systems within an organisation to ‘talk to’ each other is the first step. Some of the issues during the project implementation include the lack of training and development, organisational reluctance to change, and the lack of a project plan. The challenges faced by the organisation during this project can be helpful for those implementing SSCs. Currently, many SSC frameworks and models exist, yet most seem to neglect localised challenges faced by the different stak
    • Data quality and governance in a UK social housing initiative: Implications for smart sustainable cities

      Duvier, Caroline; Anand, Prathivadi B.; Oltean-Dumbrava, Crina (2018)
      Smart Sustainable Cities (SSC) consist of multiple stakeholders, who must cooperate in order for SSCs to be successful. Housing is an important challenge and in many cities, therefore, a key stakeholder are social housing organisations. This paper introduces a qualitative case study of a social housing provider in the UK who implemented a business intelligence project (a method to assess data networks within an organisation) to increase data quality and data interoperability. Our analysis suggests that creating pathways for different information systems within an organisation to ‘talk to’ each other is the first step. Some of the issues during the project implementation include the lack of training and development, organisational reluctance to change, and the lack of a project plan. The challenges faced by the organisation during this project can be helpful for those implementing SSCs. Currently, many SSC frameworks and models exist, yet most seem to neglect localised challenges faced by the different stakeholders. This paper hopes to help bridge this gap in the SSC research agenda.
    • Data quality challenges in the UK social housing sector

      Duvier, Caroline; Neagu, Daniel; Oltean-Dumbrava, Crina; Dickens, D. (2018)
      The social housing sector has yet to realise the potential of high data quality. While other businesses, mainly in the private sector, reap the benefits of data quality, the social housing sector seems paralysed, as it is still struggling with recent government regulations and steep revenue reduction. This paper offers a succinct review of relevant literature on data quality and how it relates to social housing. The Housing and Development Board in Singapore offers a great example on how to integrate data quality initiatives in the social housing sector. Taking this example, the research presented in this paper is extrapolating cross-disciplinarily recommendations on how to implement data quality initiatives in social housing providers in the UK.
    • Data-flow vs control-flow for extreme level computing

      Evripidou, P.; Kyriacou, Costas (2013)
      This paper challenges the current thinking for building High Performance Computing (HPC) Systems, which is currently based on the sequential computing also known as the von Neumann model, by proposing the use of Novel systems based on the Dynamic Data-Flow model of computation. The switch to Multi-core chips has brought the Parallel Processing into the mainstream. The computing industry and research community were forced to do this switch because they hit the Power and Memory walls. Will the same happen with HPC? The United States through its DARPA agency commissioned a study in 2007 to determine what kind of technologies will be needed to build an Exaflop computer. The head of the study was very pessimistic about the possibility of having an Exaflop computer in the foreseeable future. We believe that many of the findings that caused the pessimistic outlook were due to the limitations of the sequential model. A paradigm shift might be needed in order to achieve the affordable Exascale class Supercomputers.
    • DDI: A Novel Technology And Innovation Model for Dependable, Collaborative and Autonomous Systems

      Armengaud, E.; Schneider, D.; Reich, J.; Sorokos, I.; Papadopoulos, Y.; Zeller, M.; Regan, G.; Macher, G.; Veledar, O.; Thalmann, S.; et al. (2021-02)
      Digital transformation fundamentally changes established practices in public and private sector. Hence, it represents an opportunity to improve the value creation processes (e.g., “industry 4.0”) and to rethink how to address customers’ needs such as “data-driven business models” and “Mobility-as-a-Service”. Dependable, collaborative and autono-mous systems are playing a central role in this transformation process. Furthermore, the emergence of data-driven approaches combined with autonomous systems will lead to new business models and market dynamics. Innovative approaches to re-organise the value creation ecosystem, to enable distributed engineering of dependable systems and to answer urgent questions such as liability will be required. Consequently, digital transformation requires a comprehensive multi-stakeholder approach which properly balances technology, ecosystem and business innovation. Targets of this paper are (a) to introduce digital transformation and the role of / opportunities provided by autonomous systems, (b) to introduce Digital Depednability Identities (DDI) - a technology for dependability engineering of collaborative, autonomous CPS, and (c) to propose an appropriate agile approach for innovation management based on business model innovation and co-entrepreneurship.
    • De-smokeGCN: Generative Cooperative Networks for joint surgical smoke detection and removal

      Chen, L.; Tang, W.; John, N.W.; Wan, Tao Ruan; Zhang, J.J. (IEEE, 2020-05)
      Surgical smoke removal algorithms can improve the quality of intra-operative imaging and reduce hazards in image-guided surgery, a highly desirable post-process for many clinical applications. These algorithms also enable effective computer vision tasks for future robotic surgery. In this paper, we present a new unsupervised learning framework for high-quality pixel-wise smoke detection and removal. One of the well recognized grand challenges in using convolutional neural networks (CNNs) for medical image processing is to obtain intra-operative medical imaging datasets for network training and validation, but availability and quality of these datasets are scarce. Our novel training framework does not require ground-truth image pairs. Instead, it learns purely from computer-generated simulation images. This approach opens up new avenues and bridges a substantial gap between conventional non-learning based methods and which requiring prior knowledge gained from extensive training datasets. Inspired by the Generative Adversarial Network (GAN), we have developed a novel generative-collaborative learning scheme that decomposes the de-smoke process into two separate tasks: smoke detection and smoke removal. The detection network is used as prior knowledge, and also as a loss function to maximize its support for training of the smoke removal network. Quantitative and qualitative studies show that the proposed training framework outperforms the state-of-the-art de-smoking approaches including the latest GAN framework (such as PIX2PIX). Although trained on synthetic images, experimental results on clinical images have proved the effectiveness of the proposed network for detecting and removing surgical smoke on both simulated and real-world laparoscopic images.
    • Decision support for coordinated road traffic control actions

      Dahal, Keshav P.; Almejalli, Khaled A.; Hossain, M. Alamgir (2013)
      Selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task, which requires significant expert knowledge and experience. Also, the application of a control action for solving a local traffic problem could create traffic congestion at different locations in the network because of the strong interrelations between traffic situations at different locations of a road network. Therefore, coordination of control strategies is required to make sure that all available control actions serve the same objective. In this paper, an Intelligent Traffic Control System (ITCS) based on a coordinated-agent approach is proposed to assist the human operator of a road traffic control centre to manage the current traffic state. In the proposed system, the network is divided into sub-networks, each of which has its own associated agent. The agent of the sub-network with an incident reacts with other affected agents in order to select the optimal traffic control action, so that a globally acceptable solution is found. The agent uses an effective way of calculating the control action fitness locally and globally. The capability of the proposed ITCS has been tested for a case study of a part of the traffic network in the Riyadh city of Saudi Arabia. The obtained results show its ability to identify the optimal global control action. (C) 2012 Elsevier B.V. All rights reserved.
    • Decision-making model for supply chain risk management in the petroleum industry

      Aroge, Olatunde O.; Rahmanian, Nejat; Munive-Hernandez, J. Eduardo; Abdi, Reza (2020)
      The purpose of this paper is to develop a decision-making model for supporting the management of risks in supply chain. This proposed model is applied to the case of the oil industry in Nigeria. A Partial Least Square Structural Equation Model (PLS-SEM) is developed to measure the significance of the influence of risk management strategy on mitigating disruption risks and their correlations with the performance of activities in the supply chain and relevance of key performance measures in the organisation. The model considered seven aspects: behavioural-based management strategy, buffer based oriented management strategy, exploration and production risks, environmental and regulatory compliance risks, geopolitical risks, supply chain performance, and organisational performance measures. A survey questionnaire was applied to collect data to populate the model, with 187 participants from the oil industry. Based on the PLS-SEM methodology, an optimised risk management decision-making method was developed and accomplished. The results show that behavioural-based mechanism predicts the capacity of the organisation to manage risks successfully in its supply chain. The approach proposed provides a new and practical methodology to manage disruption risks in supply chains. Further, the behavioural-based mechanism can help to formulate risk management strategies in the oil industry.
    • Decomposition of manufacturing processes: a review

      Mohamed, N.M.Z.Nik; Khan, M. Khurshid (2012)
      Manufacturing is a global activity that started during the industrial revolution in the late 19th century to cater for the large-scale production of products. Since then, manufacturing has changed tremendously through the innovations of technology, processes, materials, communication and transportation. The major challenge facing manufacturing is to produce more products using less material, less energy and less involvement of labour. To face these challenges, manufacturing companies must have a strategy and competitive priority in order for them to compete in a dynamic market. A review of the literature on the decomposition of manufacturing processes outlines three main processes, namely: high volume, medium volume and low volume. The decomposition shows that each sub process has its own characteristics and depends on the nature of the firm’s business. Two extreme processes are continuous line production (fast extreme) and project shop (slow extreme). Other processes are in between these two extremes of the manufacturing spectrum. Process flow patterns become less complex with cellular, line and continuous flow compared with jobbing and project. The review also indicates that when the product is high variety and low volume, project or functional production is applied.
    • Decoupling control in statistical sense: minimised mutual information algorithm

      Zhang, Qichun; Wang, A. (2016-01)
      This paper presents a novel concept to describe the couplings among the outputs of the stochastic systems which are represented by NARMA models. Compared with the traditional coupling description, the presented concept can be considered as an extension using statistical independence theory. Based on this concept, the decoupling control in statistical sense is established with the necessary and sufficient conditions for complete decoupling. Since the complete decoupling is difficult to achieve, a control algorithm has been developed using the Cauchy-Schwarz mutual information criterion. Without modifying the existing control loop, this algorithm supplies a compensative controller to minimise the statistical couplings of the system outputs and the local stability has been analysed. In addition, a further discussion illustrates the combination of the presented control algorithm and data-based mutual information estimation. Finally, a numerical example is given to show the feasibility and efficiency of the proposed algorithm.
    • Decreased brain venous vasculature visibility on susceptibility-weighted imaging venography in patients with multiple sclerosis is related to chronic cerebrospinal venous insufficiency

      Zivadinov, R.; Poloni, G.U.; Marr, K.; Schirda, C.V.; Magnano, C.R.; Carl, E.; Bergsland, N.; Hojnacki, D.; Kennedy, C.; Beggs, Clive B.; et al. (2011)
      BACKGROUND: The potential pathogenesis between the presence and severity of chronic cerebrospinal venous insufficiency (CCSVI) and its relation to clinical and imaging outcomes in brain parenchyma of multiple sclerosis (MS) patients has not yet been elucidated. The aim of the study was to investigate the relationship between CCSVI, and altered brain parenchyma venous vasculature visibility (VVV) on susceptibility-weighted imaging (SWI) in patients with MS and in sex- and age-matched healthy controls (HC). METHODS: 59 MS patients, 41 relapsing-remitting and 18 secondary-progressive, and 33 HC were imaged on a 3T GE scanner using pre- and post-contrast SWI venography. The presence and severity of CCSVI was determined using extra-cranial and trans-cranial Doppler criteria. Apparent total venous volume (ATVV), venous intracranial fraction (VIF) and average distance-from-vein (DFV) were calculated for various vein mean diameter categories: < .3 mm, .3-.6 mm, .6-.9 mm and > .9 mm. RESULTS: CCSVI criteria were fulfilled in 79.7% of MS patients and 18.2% of HC (p < .0001). Patients with MS showed decreased overall ATVV, ATVV of veins with a diameter < .3 mm, and increased DFV compared to HC (all p < .0001). Subjects diagnosed with CCSVI had significantly increased DFV (p < .0001), decreased overall ATVV and ATVV of veins with a diameter < .3 mm (p < .003) compared to subjects without CCSVI. The severity of CCSVI was significantly related to decreased VVV in MS (p < .0001) on pre- and post-contrast SWI, but not in HC. CONCLUSIONS: MS patients with higher number of venous stenoses, indicative of CCSVI severity, showed significantly decreased venous vasculature in the brain parenchyma. The pathogenesis of these findings has to be further investigated, but they suggest that reduced metabolism and morphological changes of venous vasculature may be taking place in patients with MS.
    • A deep artificial neural network architecture for mesh free solutions of nonlinear boundary value problems

      Aggarwal, R.; Ugail, Hassan; Jha, R.K. (2022-01)
      Seeking efficient solutions to nonlinear boundary value problems is a crucial challenge in the mathematical modelling of many physical phenomena. A well-known example of this is solving the Biharmonic equation relating to numerous problems in fluid and solid mechanics. One must note that, in general, it is challenging to solve such boundary value problems due to the higher-order partial derivatives in the differential operators. An artificial neural network is thought to be an intelligent system that learns by example. Therefore, a well-posed mathematical problem can be solved using such a system. This paper describes a mesh free method based on a suitably crafted deep neural network architecture to solve a class of well-posed nonlinear boundary value problems. We show how a suitable deep neural network architecture can be constructed and trained to satisfy the associated differential operators and the boundary conditions of the nonlinear problem. To show the accuracy of our method, we have tested the solutions arising from our method against known solutions of selected boundary value problems, e.g., comparison of the solution of Biharmonic equation arising from our convolutional neural network subject to the chosen boundary conditions with the corresponding analytical/numerical solutions. Furthermore, we demonstrate the accuracy, efficiency, and applicability of our method by solving the well known thin plate problem and the Navier-Stokes equation.
    • Deep face recognition using imperfect facial data

      Elmahmudi, Ali A.M.; Ugail, Hassan (2019-10)
      Today, computer based face recognition is a mature and reliable mechanism which is being practically utilised for many access control scenarios. As such, face recognition or authentication is predominantly performed using ‘perfect’ data of full frontal facial images. Though that may be the case, in reality, there are numerous situations where full frontal faces may not be available — the imperfect face images that often come from CCTV cameras do demonstrate the case in point. Hence, the problem of computer based face recognition using partial facial data as probes is still largely an unexplored area of research. Given that humans and computers perform face recognition and authentication inherently differently, it must be interesting as well as intriguing to understand how a computer favours various parts of the face when presented to the challenges of face recognition. In this work, we explore the question that surrounds the idea of face recognition using partial facial data. We explore it by applying novel experiments to test the performance of machine learning using partial faces and other manipulations on face images such as rotation and zooming, which we use as training and recognition cues. In particular, we study the rate of recognition subject to the various parts of the face such as the eyes, mouth, nose and the cheek. We also study the effect of face recognition subject to facial rotation as well as the effect of recognition subject to zooming out of the facial images. Our experiments are based on using the state of the art convolutional neural network based architecture along with the pre-trained VGG-Face model through which we extract features for machine learning. We then use two classifiers namely the cosine similarity and the linear support vector machines to test the recognition rates. We ran our experiments on two publicly available datasets namely, the controlled Brazilian FEI and the uncontrolled LFW dataset. Our results show that individual parts of the face such as the eyes, nose and the cheeks have low recognition rates though the rate of recognition quickly goes up when individual parts of the face in combined form are presented as probes.
    • Deep learning technology for predicting solar flares from (Geostationary Operational Environmental Satellite) data

      Nagem, Tarek A.M.; Qahwaji, Rami S.R.; Ipson, Stanley S.; Wang, Z.; Al-Waisy, Alaa S. (2018)
      Solar activity, particularly solar flares can have significant detrimental effects on both space-borne and grounds based systems and industries leading to subsequent impacts on our lives. As a consequence, there is much current interest in creating systems which can make accurate solar flare predictions. This paper aims to develop a novel framework to predict solar flares by making use of the Geostationary Operational Environmental Satellite (GOES) X-ray flux 1-minute time series data. This data is fed to three integrated neural networks to deliver these predictions. The first neural network (NN) is used to convert GOES X-ray flux 1-minute data to Markov Transition Field (MTF) images. The second neural network uses an unsupervised feature learning algorithm to learn the MTF image features. The third neural network uses both the learned features and the MTF images, which are then processed using a Deep Convolutional Neural Network to generate the flares predictions. To the best of our knowledge, this work is the first flare prediction system that is based entirely on the analysis of pre-flare GOES X-ray flux data. The results are evaluated using several performance measurement criteria that are presented in this paper.
    • Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series

      Tambuwal, Ahmad I.; Neagu, Daniel (Springer, 2021-09-30)
      Time-series anomaly detection receives increasing research interest given the growing number of data-rich application domains. Recent additions to anomaly detection methods in research literature include deep neural networks (DNNs: e.g., RNN, CNN, and Autoencoder). The nature and performance of these algorithms in sequence analysis enable them to learn hierarchical discriminative features and time-series temporal nature. However, their performance is affected by usually assuming a Gaussian distribution on the prediction error, which is either ranked, or threshold to label data instances as anomalous or not. An exact parametric distribution is often not directly relevant in many applications though. This will potentially produce faulty decisions from false anomaly predictions due to high variations in data interpretation. The expectations are to produce outputs characterized by a level of confidence. Thus, implementations need the Prediction Interval (PI) that quantify the level of uncertainty associated with the DNN point forecasts, which helps in making better-informed decision and mitigates against false anomaly alerts. An effort has been made in reducing false anomaly alerts through the use of quantile regression for identification of anomalies, but it is limited to the use of quantile interval to identify uncertainties in the data. In this paper, an improve time-series anomaly detection method called deep quantile regression anomaly detection (DQR-AD) is proposed. The proposed method go further to used quantile interval (QI) as anomaly score and compare it with threshold to identify anomalous points in time-series data. The tests run of the proposed method on publicly available anomaly benchmark datasets demonstrate its effective performance over other methods that assumed Gaussian distribution on the prediction or reconstruction cost for detection of anomalies. This shows that our method is potentially less sensitive to data distribution than existing approaches.
    • Deep YOLO-Based Detection of Breast Cancer Mitotic-Cells in Histopathological Images

      Maisun Mohamed, Al Zorgani,; Irfan, Mehmood,; Hassan, Ugail,; Al Zorgani, Maisun M.; Mehmood, Irfan; Ugail, Hassan (2021-08-15)
      Coinciding with advances in whole-slide imaging scanners, it is become essential to automate the conventional image-processing techniques to assist pathologists with some tasks such as mitotic-cells detection. In histopathological images analysing, the mitotic-cells counting is a significant biomarker in the prognosis of the breast cancer grade and its aggressiveness. However, counting task of mitotic-cells is tiresome, tedious and time-consuming due to difficulty distinguishing between mitotic cells and normal cells. To tackle this challenge, several deep learning-based approaches of Computer-Aided Diagnosis (CAD) have been lately advanced to perform counting task of mitotic-cells in the histopathological images. Such CAD systems achieve outstanding performance, hence histopathologists can utilise them as a second-opinion system. However, improvement of CAD systems is an important with the progress of deep learning networks architectures. In this work, we investigate deep YOLO (You Only Look Once) v2 network for mitotic-cells detection on ICPR (International Conference on Pattern Recognition) 2012 dataset of breast cancer histopathology. The obtained results showed that proposed architecture achieves good result of 0.839 F1-measure.
    • Defect prediction on production line

      Khalfaoui, S.; Manouvrier, E.; Briot, A.; Delaux, D.; Butel, S.; Ibrahim, Jesutofunmi; Kanyere, Tatenda; Orimogunje, Bola; Abdullatif, Amr A.A.; Neagu, Daniel (Springer, 2022)
      Quality control has long been one of the most challenging fields of manufacturing. The development of advanced sensors and the easier collection of high amounts of data designate the machine learning techniques as a timely natural step forward to leverage quality decision support and manufacturing challenges. This paper introduces an original dataset provided by the automotive supplier company VALEO, coming from a production line, and hosted by the École Normale Supérieure (ENS) Data Challenge to predict defects using non-anonymised features, without access to final test results, to validate the part status (defective or not). We propose in this paper a complete workflow from data exploration to the modelling phase while addressing at each stage challenges and techniques to solve them, as a benchmark reference. The proposed workflow is validated in series of experiments that demonstrate the benefits, challenges and impact of data science adoption in manufacturing.