Search

In this paper, 1,538 papers retrieved with the keywords "sports artificial intelligence (AI)" on the Web of Science database since 2007 were taken as the data source, and the Cite Space V software was used to visualize and analyze them. A visual knowledge graph was used to streamline the countries, institutions and authors conducting sports AI research, discipline distribution, research hotspots and development trends in the past 15 years. Subsequently, its development direction and research progress were discussed. Sports AI was widely distributed, with the US, China and the UK leading the way. The most prolific authors and teams in research on sports AI were concentrated in American universities. Their main research direction is to develop and improve smart wearable devices based on machine learning and deep learning technologies for different groups of people. Research on sports AI involved multiple disciplines, which mainly applied and referred to research methodologies and theories on engineering, computer science and sports science. It could be seen from the frequency and centrality of keywords that in the current field of sports AI, machine learning is the main direction, artificial neural networks is the main algorithm, and practical and empirical research based on data mining is the focus. The research hotspots were divided into three major clusters: physical health promotion, sports injury prevention and control, and athletic performance enhancement. How to introduce intelligent technology into sports for a perfect integration still has an arduous and long way to go. Future development requires joint efforts and participation of scientific researchers, professionals and common people. [ABSTRACT FROM AUTHOR]

This study reviewed the status quo of research on machine learning (ML), including deep learning, in the rail industry. This study conducted a scientometric analysis and critical review of 640 papers selected from 12,675 web-crawled papers. The extensive and complex networks of topics, researchers, and countries were analyzed using the Louvain method, a co-occurrence keyword analysis, a degree centrality analysis, and other network analysis methods. The results indicate that the majority of studies of ML in the rail industry focused on maintenance activities and traffic management, and mainly targeted rolling stock, rails, and passengers. Overhead contact systems, including catenaries, are a high-demand objective for ML-based maintenance. Although analyses of tunnels and stations remain rare, passenger flow prediction, station air quality estimation, shield tunneling performance improvement, and ground settlement are areas of high importance. Geographically, China, the US, and the United Kingdom lead ML studies in the rail industry, and the level of collaboration is higher among European countries than among countries on other continents. Future challenges include ensuring the security and stability of ML, along with considering novel mindsets, the black-box effect, improvements in ML techniques, and resource overload when introducing ML technologies. [ABSTRACT FROM AUTHOR]

Purpose: Artificial intelligence (AI), machine learning (ML) and deep learning (DL) are having a major impact on banking (FinTech), health (HealthTech), law (RegTech) and other sectors such as charitable fundraising (CharityTech). The pace of technological innovation and the ability of AI systems to think like human beings (simulate human intelligence), perform tasks independently, develop intelligence based on its own experiences and process layers of information to learn ever-complex representations of data (ML/DL) means that improvements in the rates at which this technology can undertake complex, technical and time-consuming tasks, identify people, objects, voices, patterns, etc., screen for 'problems' earlier, and provide solutions, provide astounding benefit in economic, political and social terms. The purpose of this paper is to explore advents in AI, ML and DL in the context of the regulatory compliance challenge faced by financial institutions in the United Kingdom (UK). Design/methodology/approach: The subject is explored through the analysis of data and domestic and international published literature. The first part of the paper summarises the context of current regulatory issues, the advents in deep learning, how financial institutions are currently using AI, and how AI could provide further technological solutions to regulatory compliance as of February 2023. Findings: It is suggested that UK financial institutions can further utilise AI, ML and DL as part of an armoury of solutions that ease the regulatory burden and achieve high levels of compliance success. Originality/value: To the best of the author's knowledge, this is the first study to specifically explore how AI, ML and DL can continue to assist UK financial institutions in meeting the regulatory compliance challenge and the opportunities provided for financial institutions by the metaverse. [ABSTRACT FROM AUTHOR]

Background: Surgical waiting lists have risen dramatically across the UK as a result of the COVID-19 pandemic. The effective use of operating theatres by optimal scheduling could help mitigate this, but this requires accurate case duration predictions. Current standards for predicting the duration of surgery are inaccurate. Artificial intelligence (AI) offers the potential for greater accuracy in predicting surgical case duration. This study aimed to investigate whether there is evidence to support that AI is more accurate than current industry standards at predicting surgical case duration, with a secondary aim of analysing whether the implementation of the models used produced efficiency savings. Method: PubMed, Embase, and MEDLINE libraries were searched through to July 2023 to identify appropriate articles. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Study quality was assessed using a modified version of the reporting guidelines for surgical AI papers by Farrow et al. Algorithm performance was reported using evaluation metrics. Results: The search identified 2593 articles: 14 were suitable for inclusion and 13 reported on the accuracy of AI algorithms against industry standards, with seven demonstrating a statistically significant improvement in prediction accuracy (P < 0.05). The larger studies demonstrated the superiority of neural networks over other machine learning techniques. Efficiency savings were identified in a RCT. Significant methodological limitations were identified across most studies. Conclusion: The studies suggest that machine learning and deep learning models are more accurate at predicting the duration of surgery; however, further research is required to determine the best way to implement this technology. Waiting lists for elective surgeries are rising in the UK. We conducted a scoping review with the aim to understand if artificial intelligence has the potential to improve the utilization of operating theatre resources through improved scheduling. [ABSTRACT FROM AUTHOR]

Predicting the status of train delays, a complex and dynamic problem, is crucial for railway enterprises and passengers. This paper proposes a novel hybrid deep learning model composed of convolutional neural networks (CNN) and temporal convolutional networks (TCN), named the CNN + TCN model, for predicting train delays in railway systems. First, we construct 3D data containing the spatiotemporal characteristics of real-world train data. Then, the CNN + TCN model employs a 3D CNN component, which is fed into the constructed 3D data to mine the spatiotemporal characteristics, and a TCN component that captures the temporal characteristics in railway operation data. Furthermore, the characteristic variables corresponding to the two components are selected. Finally, the model is evaluated by leveraging data from two railway lines in the United Kingdom. Numerical results show that the CNN + TCN model has greater accuracy and convergence performance in train delay prediction. [ABSTRACT FROM AUTHOR]

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions. [ABSTRACT FROM AUTHOR]

For efficient operation, solar power operators often require generation forecasts for multiple sites with varying data availability. Many proposed methods for forecasting solar irradiance / solar power production formulate the problem as a time-series, using current observations to generate forecasts. This necessitates a real-time data stream and enough historical observations at every location for these methods to be deployed. In this paper, we propose the use of Global methods to train generalised models. Using data from 20 locations distributed throughout the UK, we show that it is possible to learn models without access to data for all locations, enabling them to generate forecasts for unseen locations. We show a single Global model trained on multiple locations can produce more consistent and accurate results across locations. Furthermore, by leveraging weather observations and measurements from other locations we show it is possible to create models capable of accurately forecasting irradiance at locations without any real-time data. We apply our approaches to both classical and state-of-the-art Machine Learning methods, including a Transformer architecture. We compare models using satellite imagery or point observations (temperature, pressure, etc.) as weather data. These methods could facilitate planning and optimisation for both newly deployed solar farms and domestic installations from the moment they come online. [ABSTRACT FROM AUTHOR]

Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependent on the quality of the image that is being segmented. A commonly neglected point in the medical image analysis community is the vast amount of clinical images that have severe image artefacts due to organ motion, movement of the patient and/or image acquisition related issues. In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity. The method is based on our recently developed joint artefact detection and reconstruction method, which reconstructs high quality MR images from k-space using a joint loss function and essentially converts the artefact correction task to an under-sampled image reconstruction task by enforcing a data consistency term. In this paper, we propose to use a segmentation network coupled with this in an end-to-end framework. Our training optimises three different tasks: 1) image artefact detection, 2) artefact correction and 3) image segmentation. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted cardiac MR k-space data and uncorrected reconstructed images. Using a test set of 500 2D+time cine MR acquisitions from the UK Biobank data set, we achieve demonstrably good image quality and high segmentation accuracy in the presence of synthetic motion artefacts. We showcase better performance compared to various image correction architectures. [ABSTRACT FROM AUTHOR]

Introduction: Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been shown to decrease the associated mortality and morbidity. Combining deep neural networks and genomics with the already established clinical predictors may hold promise for improvement. Methods: The UK Biobank was utilized to build linear and deep learning models for the prediction of surgery relevant outcomes. An initial GWAS for the relevant outcomes was initially conducted to select the Single Nucleotide Polymorphisms for inclusion in the models. Model performance was assessed with Receiver Operator Characteristics of the Area Under the Curve and optimum precision and recall. Feature importance was assessed with SHapley Additive exPlanations. Results: Models were generated for atrial fibrillation, venous thromboembolism and pneumonia as genetics only, clinical features only and a combined model. For venous thromboembolism, the ROC-AUCs were 60.1% [59.6%-60.4%], 63.4% [63.2%-63.4%] and 66.6% [66.2%-66.9%] for the linear models and 51.5% [49.4%-53.4%], 63.2% [61.2%-65.0%] and 62.6% [60.7%-64.5%] for the deep learning SNP, clinical and combined models, respectively. For atrial fibrillation, the ROC-AUCs were 60.3% [60.0%-60.4%], 78.7% [78.7%-78.7%] and 80.0% [79.9%-80.0%] for the linear models and 59.4% [58.2%-60.9%], 78.8% [77.8%-79.8%] and 79.8% [78.8%-80.9%] for the deep learning SNP, clinical and combined models, respectively. For pneumonia, the ROC-AUCs were 50.1% [49.6%-50.6%], 69.2% [69.1%-69.2%] and 68.4% [68.0%-68.5%] for the linear models and 51.0% [49.7%-52.4%], 69.7% [.5%-70.8%] and 69.7% [68.6%-70.8%] for the deep learning SNP, clinical and combined models, respectively. Conclusion: In this report we presented linear and deep learning predictive models for surgery relevant outcomes. Overall, predictability was similar between linear and deep learning models and inclusion of genetics seemed to improve accuracy. [ABSTRACT FROM AUTHOR]

Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However, incorrect mammography diagnoses are common and may harm patients through unnecessary treatments and operations (or a lack of treatment). Therefore, systems that can learn to detect breast cancer on their own could help reduce the number of incorrect interpretations and missed cases. Various deep learning techniques, which can be used to implement a system that learns how to detect instances of breast cancer in mammograms, are explored throughout this paper. Convolution Neural Networks (CNNs) are used as part of a pipeline based on deep learning techniques. A divide and conquer approach is followed to analyse the effects on performance and efficiency when utilising diverse deep learning techniques such as varying network architectures (VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2), class weights, input sizes, image ratios, pre-processing techniques, transfer learning, dropout rates, and types of mammogram projections. This approach serves as a starting point for model development of mammography classification tasks. Practitioners can benefit from this work by using the divide and conquer results to select the most suitable deep learning techniques for their case out-of-the-box, thus reducing the need for extensive exploratory experimentation. Multiple techniques are found to provide accuracy gains relative to a general baseline (VGG19 model using uncropped 512 × 512 pixels input images with a dropout rate of 0.2 and a learning rate of 1 × 10−3) on the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) dataset. These techniques involve transfer learning pre-trained ImagetNet weights to a MobileNetV2 architecture, with pre-trained weights from a binarised version of the mini Mammography Image Analysis Society (mini-MIAS) dataset applied to the fully connected layers of the model, coupled with using weights to alleviate class imbalance, and splitting CBIS-DDSM samples between images of masses and calcifications. Using these techniques, a 5.6% gain in accuracy over the baseline model was accomplished. Other deep learning techniques from the divide and conquer approach, such as larger image sizes, do not yield increased accuracies without the use of image pre-processing techniques such as Gaussian filtering, histogram equalisation and input cropping. [ABSTRACT FROM AUTHOR]

Building on the richness of recent contributions in the field, this paper presents a state-of-the-art CNN analysis method for automating the recognition of standardised building components in modern heritage buildings. At the turn of the twentieth century manufactured building components became widely advertised for specification by architects. Consequently, a form of standardisation across various typologies began to take place. During this era of rapid economic and industrialised growth, many forms of public building were erected. This paper seeks to demonstrate a method for informing the recognition of such elements using deep learning to recognise 'families' of elements across a range of buildings in order to retrieve and recognise their technical specifications from the contemporary trade literature. The method is illustrated through the case of Carnegie Public Libraries in the UK, which provides a unique but ubiquitous platform from which to explore the potential for the automated recognition of manufactured standard architectural components. The aim of enhancing this knowledge base is to use the degree to which these were standardised originally as a means to inform and so support their ongoing care but also that of many other contemporary buildings. Although these libraries are numerous, they are maintained at a local level and as such, their shared challenges for maintenance remain unknown to one another. Additionally, this paper presents a methodology to indirectly retrieve useful indicators and semantics, relating to emerging HBIM families, by applying deep learning to a varied range of architectural imagery. [ABSTRACT FROM AUTHOR]

Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration of equilibrium optimizer (EO) and extreme learning machine (ELM)) and (ii) a deep neural network (DNN) for one day-ahead R-R modelling. The proposed R-R models are validated at two different benchmark stations of the catchments, namely river Teifi at Glanteifi and river Fal at Tregony in the UK. Firstly, a partial autocorrelation function (PACF) is used for optimal number of lag inputs to deploy the proposed models. Six other well-known machine learning models, called ELM, kernel ELM (KELM), and particle swarm optimization-based ELM (PSO-ELM), support vector regression (SVR), artificial neural network (ANN) and gradient boosting machine (GBM) are utilized to validate the two proposed models in terms of prediction efficiency. Furthermore, to increase the performance of the proposed models, paper utilizes a discrete wavelet-based data pre-processing technique is applied in rainfall and runoff data. The performance of wavelet-based EO-ELM and DNN are compared with wavelet-based ELM (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) and GBM (WGBM). An uncertainty analysis and two-tailed t-test are carried out to ensure the trustworthiness and efficacy of the proposed models. The experimental results for two different time series datasets show that the EO-ELM performs better in an optimal number of lags than the others. In the case of wavelet-based daily R-R modelling, proposed models performed better and showed robustness compared to other models used. Therefore, this paper shows the efficient applicability of EO-ELM and DNN in R-R modelling that may be used in the hydrological modelling field. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel approach to address the dynamic prediction of customer activity in electronic payment transactions for individual clients. Our approach is founded on customer online payment transaction records from registered UK-based online retailers between 01/12/2009 and 09/12/2011. These retailers primarily specialize in unique gift items for various occasions, catering to a wide range of clients, including wholesalers. We used classification analysis based on the correlation coefficient to measure and describe a customer's electronic payment capability based on the quality of products they purchase. Furthermore, we trained multi-layered models (linear model, deep learning, random forest, and support vector machines (SVM)) to capture the dynamics of e-bank transaction reinforcement for retail customers using machine learning. Real transaction data from a UK online retailer was employed in our study. The experimental results consistently demonstrated the effectiveness of our proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, we explore the learning and teaching of a maritime simulation programme to understand its deep learning elements. We followed the mixed methods approach and collected student perception data from a maritime school, situated within a UK university, using reflection-based survey (n = 112) and three focus groups with eleven students. Findings include the needs for defining clear learning outcomes, improving the learning content to enable exploration and second-chance learning, minimising theory–practice gaps by ensuring skills-knowledge balance and in-depth scholarship building, facilitating tasks for learning preparation and learning extension, and repositioning simulation components and their assessment schemes across the academic programme. Overall, the paper provides evidence on the importance of deep learning activities in maritime simulation and suggests guidelines on improving the existing practice. Although the findings are derived from a maritime education programme, they can be considered and applied in other academic disciplines which use simulation in their teaching and learning. [ABSTRACT FROM AUTHOR]

Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) has been broadly applied to solve big data problems in biomedical fields, which is most successful in image processing. Recently, many DL methods have been applied to analyze genomic studies. However, genomic data usually has too small a sample size to fit a complex network. They do not have common structural patterns like images to utilize pre-trained networks or take advantage of convolution layers. The concern of overusing DL methods motivates us to evaluate DL methods' performance versus popular non-deep Machine Learning (ML) methods for analyzing genomic data with a wide range of sample sizes. In this paper, we conduct a benchmark study using the UK Biobank data and its many random subsets with different sample sizes. The original UK Biobank data has about 500k participants. Each patient has comprehensive patient characteristics, disease histories, and genomic information, i.e., the genotypes of millions of Single-Nucleotide Polymorphism (SNPs). We are interested in predicting the risk of three lung diseases: asthma, COPD, and lung cancer. There are 205,238 participants have recorded disease outcomes for these three diseases. Five prediction models are investigated in this benchmark study, including three non-deep machine learning methods (Elastic Net, XGBoost, and SVM) and two deep learning methods (DNN and LSTM). Besides the most popular performance metrics, such as the F1-score, we promote the hit curve, a visual tool to describe the performance of predicting rare events. We discovered that DL methods frequently fail to outperform non-deep ML in analyzing genomic data, even in large datasets with over 200k samples. The experiment results suggest not overusing DL methods in genomic studies, even with biobank-level sample sizes. The performance differences between DL and non-deep ML decrease as the sample size of data increases. This suggests when the sample size of data is significant, further increasing sample sizes leads to more performance gain in DL methods. Hence, DL methods could be better if we analyze genomic data bigger than this study. [ABSTRACT FROM AUTHOR]

• This paper proposed a comprehensive framework for parametric semi-automatic BIM and bridge member segmentation. • The parametric algorithm accounted for diverse types of bridge members and produced BIM bridge models using shape information automatically extracted per parameter. • The parametrically generated bridge BIM model was converted into PCD, serving as deep learning train data for automatic segmentation of bridge members. • The trained deep learning model showed high accuracy for segmenting superstructures, indicating promise for automated segmentation of bridge members. • The segmentation accuracy increased when accounting for the point density of members corresponding to the different sizes of bridge members. This paper proposes a comprehensive framework for parametric semi-automatic BIM and bridge member segmentation. For this, a parametric algorithm was developed to accommodate diverse superstructures, piers, abutments, and bearings. Each structural member in a bridge was classified to designate its shape information as parameters. The range of each parameter was defined to account for various dimensions of the shape of the bridge member. Therefore, libraries for each member were established to produce BIM bridge models using shape information automatically extracted per parameter. Then, the framework includes an algorithm that is developed to convert bridge BIM models generated by the parametric algorithm into PCD data. The virtually generated PCD is used as deep learning train data for automatic member segmentation based on the Point-Net algorithm. The trained algorithm using the virtual PCD was applied to a real bridge PCD. The segmentation of the bridge members showed high accuracy for the superstructure but low accuracy for the bearing. The segmentation accuracy of the algorithm including the bearing member could increase by modifying the density of PCD to account for the different sizes of bridge members. Furthermore, the proposed framework was applied to a UK bridge PCD and showed its applicability for use in bridges in different countries. [ABSTRACT FROM AUTHOR]

National Mapping agencies (NMA) are frequently tasked with providing highly accurate geospatial data for a range of customers. Traditionally, this challenge has been met by combining the collection of remote sensing data with extensive field work, and the manual interpretation and processing of the combined data. Consequently, this task is a significant logistical undertaking which benefits the production of high quality output, but which is extremely expensive to deliver. Therefore, novel approaches that can automate feature extraction and classification from remotely sensed data, are of great potential interest to NMAs across the entire sector. Using research undertaken at Great Britain's NMA; Ordnance Survey (OS) as an example, this paper provides an overview of the recent advances at an NMA in the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) based applications. Examples of these approaches are in automating the process of feature extraction and classification from remotely sensed aerial imagery. In addition, recent OS research in applying deep (convolutional) neural network architectures to image classification are also described. This overview is intended to be useful to other NMAs who may be considering the adoption of similar approaches within their workflows. [ABSTRACT FROM AUTHOR]

This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory (RAL) site at Harwell near Oxford. Such 'Big Scientific Data' comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility and the UK's Central Laser Facility. Increasingly, scientists are now required to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now used the deep learning technology to develop their AlphaFold tool to make predictions for protein folding. Remarkably, it has been able to achieve some spectacular results for this specific scientific problem. Can deep learning be similarly transformative for other scientific problems? After a brief review of some initial applications of machine learning at the RAL, we focus on challenges and opportunities for AI in advancing materials science. Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from several different scientific domains. We conclude with some initial examples of our 'scientific machine learning' benchmark suite and of the research challenges these benchmarks will enable. [ABSTRACT FROM AUTHOR]

Currently available risk prediction methods are limited in their ability to deal with complex, heterogeneous, and longitudinal data such as that available in primary care records, or in their ability to deal with multiple competing risks. This paper develops a novel deep learning approach that is able to successfully address current limitations of standard statistical approaches such as landmarking and joint modeling. Our approach, which we call Dynamic-DeepHit, flexibly incorporates the available longitudinal data comprising various repeated measurements (rather than only the last available measurements) in order to issue dynamically updated survival predictions for one or multiple competing risk(s). Dynamic-DeepHit learns the time-to-event distributions without the need to make any assumptions about the underlying stochastic models for the longitudinal and the time-to-event processes. Thus, unlike existing works in statistics, our method is able to learn data-driven associations between the longitudinal data and the various associated risks without underlying model specifications. We demonstrate the power of our approach by applying it to a real-world longitudinal dataset from the U.K. Cystic Fibrosis Registry, which includes a heterogeneous cohort of 5883 adult patients with annual follow-ups between 2009 to 2015. The results show that Dynamic-DeepHit provides a drastic improvement in discriminating individual risks of different forms of failures due to cystic fibrosis. Furthermore, our analysis utilizes post-processing statistics that provide clinical insight by measuring the influence of each covariate on risk predictions and the temporal importance of longitudinal measurements, thereby enabling us to identify covariates that are influential for different competing risks. [ABSTRACT FROM AUTHOR]

Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive control system for coordinated metro operations with flexible train composition by using a multi-agent deep reinforcement learning (MADRL) approach. The control problem is formulated as a Markov decision process (MDP) with multiple agents regulating different service lines in a metro network with passenger transfer. To ensure the overall computational effectiveness and stability of the control system, we adopt an actor–critic reinforcement learning framework in which each control agent is associated with a critic function for estimating future system states and an actor function deriving local operational decisions. The critics and actors in the MADRL are represented by multi-layer artificial neural networks (ANNs). A multi-agent deep deterministic policy gradient (MADDPG) algorithm is developed for training the actor and critic ANNs through successive simulated transitions over the entire metro network. The developed framework is tested with a real-world scenario in Bakerloo and Victoria Lines of London Underground, UK. Experiment results demonstrate that the proposed method can outperform previous centralized optimization and distributed control approaches in terms of solution quality and performance achieved. Further analysis shows the merits of MADRL for coordinated service regulation with flexible train composition. This study contributes to real-time coordinated metro network services with flexible train composition and advanced optimization techniques. • An adaptive rail transit control system with passengers' transfers and flexible train composition. • A novel modeling and optimization framework based on multi-agent deep reinforcement learning. • A computational framework with 'decentralized execution and centralized training' for effectiveness and stability. • Case study demonstrating the system efficiency and computational effectiveness of proposed algorithm over previous methods. [ABSTRACT FROM AUTHOR]

This paper presents findings from a doctoral study, which investigated effective methods for teaching social sustainability within product design courses in British and Irish universities. This paper explores approaches for encouraging students to explore the social aspects of sustainable product design through workshops specifically designed to foster deep learning through collaboration, discovery and critical reflection. The importance of deep learning is reflected in both the sustainable design education (O'Rafferty et al., 2008, Griffith and Bamford, 2007) and education for sustainability literature (Warburton, 2003) as important to an understanding of the holistic and complex nature of sustainability. Three 'Rethinking Design' workshops were designed and developed as part of the doctoral main study to introduce students to the wider social aspects of sustainability and these were conducted in five universities in Britain and Ireland. The workshops were developed to foster principles that encourage students to adopt deep learning methods, taking into account the specific learning preferences of the current generation of students to enhance motivational factors such as relevance, appropriate teaching materials and opportunities for collaborative learning. The workshops were tested amongst 150 undergraduate and postgraduate students and found to be successful in fostering deep learning by facilitating learning through discovery, critical reflection, peer learning and creativity leading to an exploration of design thinking solutions. [ABSTRACT FROM AUTHOR]

Previous research indicates that discipline identification — the level of integration of one's discipline of study into the self-concept — is associated with deep approaches to learning and academic self-efficacy. However, it is not known whether these relationships would hold in the context of learning during the COVID-19 pandemic. The present study investigates this, as well as exploring whether belonging, identity incompatibility, and virtual and in-person time spent with fellow students and with faculty act as potential antecedents of discipline identification. A cross-sectional online survey with n = 385 students in the UK was conducted during the spring term (February–May) of the 2019–2020 academic year, when UK universities were under COVID-19 restrictions. Of those students, n = 109 completed a follow-up survey during the autumn term (September–December) of the 2020–2021 academic year. Due to pandemic-related changes, the two waves of data were analysed separately using path analysis. During the spring term, identity incompatibility and discipline identification were significantly associated with both deep approaches to learning and academic self-efficacy. There were also significant indirect effects of belonging on both deep approaches to learning and academic self-efficacy via discipline identification. During the autumn term, discipline identification was again significantly associated with both academic outcomes. Identity incompatibility was significantly associated with academic self-efficacy. There were also indirect effects of identity incompatibility and belonging on deep approaches to learning via discipline identification. These results add to the literature on the importance of discipline identification and associated social-psychological processes in predicting important higher education outcomes during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Social anthropology in the U.K. is largely absent from the pre-university curriculum, contributing to the discipline's marginal status within higher education. My paper reports a small-scale empirical study of the transition to undergraduate anthropology as a socializing process that begins with the choice of discipline, continues as a learning experience and enables students to acquire elements of the discipline's 'culture'. The study identified 'chance' factors, serendipity and opportunism as important influences on choice of degree. These factors reflected the availability to applicants of cultural and economic capital. Students demonstrated varying degrees of socialization in identifying with anthropology's epistemological and social norms and values. My findings justify current attempts to increase the visibility of anthropology among pre-university students. They also support teaching initiatives that promote deep learning at undergraduate level. Both developments are necessary to sustain anthropology as a university discipline. [ABSTRACT FROM AUTHOR]

[Display omitted] The environment where we live and recreate can have a significant effect on our well-being. More beautiful landscapes have considerable benefits to both health and quality of life. When we chose where to live or our next holiday destination, we do so according to some perception of the environment around us. In a way, we value nature and assign an ecosystem service to it. Landscape aesthetics, or scenicness, is one such service, which we consider in this paper as a collective perceived quality. We present a deep learning model called ScenicNet for the large-scale inventorisation of landscape scenicness from satellite imagery. We model scenicness with an interpretable deep learning model and learn a landscape beauty estimator based on crowdsourced scores derived from more than two hundred thousand landscape images in the United Kingdom. Our ScenicNet model learns the relationship between land cover types and scenicness by using land cover prediction as an interpretable intermediate task to scenicness regression. It predicts landscape scenicness and land cover from the Corine Land Cover product concurrently, without compromising the accuracy of either task. In addition, our proposed model is interpretable in the sense that it learns to express preferences for certain types of land covers in a manner that is easily understandable by an end-user. Our semantic bottleneck also allows us to further our understanding of crowd preferences for landscape aesthetics. [ABSTRACT FROM AUTHOR]

High-frequency financial data is more difficult to predict than low-frequency data because it possesses nonlinearity, nonstationarity, higher volatility, and long memory and is frequently accompanied by the jump phenomena. In this paper, the nonparametric regression (NR) model based on kernel function is used to fit the nonlinear relationship between the nonstationary series Y t and its lagging series to model the trend of high frequency financial time series. Furthermore, the deep learning LSTM (long short-term memory) model is applied to capture the high volatility and frequent jumps of high frequency financial data and to improve the forecasting accuracy of the residual series. The results demonstrate that the hybrid NR and LSTM model has greatly improved the forecasting accuracy in several evaluation criteria. In comparison to NR, support vector machine (SVM), LSTM, ARIMA and NR-SVM models, the mean absolute error (MAE) of NR-LSTM has reduced by 89.78%, 97.85%, 86.48%, 32.47% and 89%, respectively. In addition, we have constructed the trading strategy for the Shanghai-Shenzhen 300 index by using the NR-LSTM model. The NR-LSTM model can continue to provide good returns even during a bear market, which can serve as a guide for investors. Furthermore, the NR-LSTM model also exhibits the best forecasting effect when we model the high-frequency data of Ping An bank in China, the FTSE 100 index in the UK, and the S&P 500 index in the US. [ABSTRACT FROM AUTHOR]

Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk. In this study, a deep learning-based model of left atrial size in UK Biobank enabled genome-wide association studies in 35,049 healthy participants. Several lines of evidence, including the PITX2 locus, linked left atrial dysfunction to atrial fibrillation risk. [ABSTRACT FROM AUTHOR]

In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results has been widely recognised. Yet, these models face limitations due to overfitting and the potential overestimation of effect sizes in correlated variants. To surmount these obstacles, we devised the Stacked Neural Network Polygenic Risk Score (SNPRS). This novel approach synthesises outputs from multiple neural network models, each calibrated using genetic variants chosen based on diverse p-value thresholds. By doing so, SNPRS captures a broader array of genetic variants, enabling a more nuanced interpretation of the combined effects of these variants. We assessed the efficacy of SNPRS using the UK Biobank data, focusing on the genetic risks associated with breast and prostate cancers, as well as quantitative traits like height and BMI. We also extended our analysis to the Korea Genome and Epidemiology Study (KoGES) dataset. Impressively, our results indicate that SNPRS surpasses traditional PRS models and an isolated deep neural network in terms of accuracy, highlighting its promise in refining the efficacy and relevance of PRS in genetic studies. [ABSTRACT FROM AUTHOR]

Objectives: There is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre study aims to develop an accurate CTPA lung segmentation model, with evaluation of outputs in two diverse patient cohorts with pulmonary hypertension (PH) and interstitial lung disease (ILD). Methods: This retrospective study develops an nnU-Net-based segmentation model using data from two specialist centres (UK and USA). Model was trained (n = 37), tested (n = 12), and clinically evaluated (n = 176) on a diverse 'real-world' cohort of 225 PH patients with volumetric CTPAs. Dice score coefficient (DSC) and normalised surface distance (NSD) were used for testing. Clinical evaluation of outputs was performed by two radiologists who assessed clinical significance of errors. External validation was performed on heterogenous contrast and non-contrast scans from 28 ILD patients. Results: A total of 225 PH and 28 ILD patients with diverse demographic and clinical characteristics were evaluated. Mean accuracy, DSC, and NSD scores were 0.998 (95% CI 0.9976, 0.9989), 0.990 (0.9840, 0.9962), and 0.983 (0.9686, 0.9972) respectively. There were no segmentation failures. On radiological review, 82% and 71% of internal and external cases respectively had no errors. Eighteen percent and 25% respectively had clinically insignificant errors. Peripheral atelectasis and consolidation were common causes for suboptimal segmentation. One external case (0.5%) with patulous oesophagus had a clinically significant error. Conclusion: State-of-the-art CTPA lung segmentation model provides accurate outputs with minimal clinical errors on evaluation across two diverse cohorts with PH and ILD. Clinical relevance: Clinical translation of artificial intelligence models requires radiological review and understanding of model limitations. This study develops an externally validated state-of-the-art model with robust radiological review. Intended clinical use is in techniques such as lung volume or parenchymal disease quantification. Key Points: • Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease). • No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error. • Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis. [ABSTRACT FROM AUTHOR]

This paper explores disciplinary patterns of assessment and feedback, using data from the Transforming the Experience of Students through Assessment project. Its central research question concerns the effect of disciplinary assessment patterns on student learning. Audit data from 18 degree programmes at 8 UK universities showed variations in assessment patterns across three disciplinary fields: Humanities, Professional and Science courses. There were variations in assessment demands; in the quantity of feedback and in the proportion of examinations. Statistical analysis of Assessment Experience Questionnaire data (n = 762) explored whether these differences influenced students' perceptions of learning across the disciplines. Findings showed that there were no significant differences in students' perceptions of learning from examinations. Humanities students evaluated the appropriateness of their assessment lower than other discipline groups; professional students were less clear about goals and standards. The researchers propose explanations for these findings and suggest avenues for further research. [ABSTRACT FROM PUBLISHER]

• A novel model of loop metro service driven by stochastic demand of general distribution. • A Markov decision process for optimal schedule with circulation of limited rolling stock. • An actor-critic deep reinforcement learning framework with an off-policy training algorithm. • A case study demonstrating the efficiency and robustness of the proposed control system. This paper presents a novel actor-critic deep reinforcement learning approach for metro train scheduling with circulation of limited rolling stock. The scheduling problem is modeled as a Markov decision process driven by stochastic passenger demand. As in most dynamic optimization problems, the complexity of the scheduling process grows exponentially with the amount of states, decisions, and uncertainties involved. This study aims to address this 'curses of dimensionality' issue by adopting an actor-critic deep reinforcement learning solution framework. The framework simplifies the evaluation and searching process for potential optimal solutions by parameterizing the original state and decision spaces with the use of artificial neural networks. A deep deterministic policy gradient algorithm is developed for training the artificial neural networks via simulated system transitions before the actor-critic agent can be applied for online schedule control. The proposed approach is tested with a real-world scenario configured with data collected from the Victoria Line of London Underground, UK. Experiment results illustrate the advantages of the proposed method over a range of established meta-heuristics in terms of computing time, system efficiency, and robustness under different stochastic environments. This study innovates urban transit operations with state-of-the-art computer science and dynamic optimization techniques. [ABSTRACT FROM AUTHOR]

Multiphase flowmeters are important to monitor oil wells, as they allow operators to obtain a real-time estimate of the production. However, due to its high installation cost, uncertainty, and reading errors, the oil and gas industry started to invest in virtual meters, mathematical models that can obtain the oil and gas flow forecast using the instrumentation already available in the well, such as pressure and temperature sensors. The purpose of this article is to develop a virtual multiphase flow predictor using artificial intelligence models such as Long Short-Term Memory (LSTM), Gate Recurrent Units (GRU), Multi-Layer Perceptron (MLP) neural networks and deep learning. As input data, this paper uses a public database available on the Oil and Gas Authority (UK) to train the models and demonstrate the possibility of predicting multiphase flow with a reasonable error margin, proving that the method is efficient in estimating well production rates. • In the oil industry, it is important to measure the production from each well. • A Virtual Meter is a low-cost alternative. It can measure multiphase flow without any additional equipment. • This paper proposes a Virtual Meter using neural networks such as MLP, LSTM, GRU and deep learning combinations. • Prediction performance was evaluated using the Oil and Gas Authority (OGA) public database and it shows promising results. • Experiments using a combination of multiple layers shows this type of prediction takes advantage of deep learning techniques. [ABSTRACT FROM AUTHOR]

Highway safety researchers focus on crash injury severity, utilizing deep learning--specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)--as the preferred method for modeling accident severity. Deep learning's strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent. The framework optimizes hidden layers and integrates data augmentation, Gaussian noise, and dropout regularization for improved generalization. Sensitivity and factor contribution analyses identify influential predictors. Evaluated on three diverse crash record databases--NCDB 2018-2019, UK 2015-2020, and US 2016-2021--the D-RNN model excels with an ACC score of 89.0281%, a Roc Area of 0.751, an F-estimate of 0.941, and a Kappa score of 0.0629 over the NCDB dataset. The proposed framework consistently outperforms traditional methods, existing machine learning, and deep learning techniques. [ABSTRACT FROM AUTHOR]

Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank (currently 29,035 subjects). In our previous work, it was demonstrated that using a few 2D projections (mean and standard deviation along three axes) instead of each full 3D volume leads to much faster training at the cost of a reduction in prediction accuracy. Here, we investigated if another set of 2D projections, based on higher-order statistical central moments and eigenslices, leads to a higher accuracy. Our results show that higher-order moments do not lead to a higher accuracy, but that eigenslices provide a small improvement. We also show that an ensemble of such models provides further improvement. [ABSTRACT FROM AUTHOR]

Researchers and engineers employ machine learning (ML) tools to detect pipe bursts and prevent significant non-revenue water losses in water distribution systems (WDS). Nonetheless, many approaches developed so far consider a fixed number of sensors, which requires the ML model redevelopment and collection of sufficient data with the new sensor configuration for training. To overcome these issues, this study presents a novel approach based on Long Short-Term Memory neural networks (NNs) that leverages transfer learning to manage a varying number of sensors and retain good detection performance with limited training data. The proposed detection model first learns to reproduce the normal behavior of the system on a dataset obtained in burst-free conditions. The training process involves predicting flow and pressure one-time step ahead using historical data and time-related features as inputs. During testing, a post-prediction step flags potential bursts based on the comparison between the observations and model predictions using a time-varied error threshold. When adding new sensors, we implement transfer learning by replicating the weights of existing channels and then fine-tune the augmented NN. We evaluate the robustness of the methodology on simulated fire hydrant bursts and real-bursts in 10 district metered areas (DMAs) of the UK. For real bursts, we perform a sensitivity analysis to understand the impact of data resolution and error threshold on burst detection performance. The results obtained demonstrate that this ML-based methodology can achieve Precision of up to 98.1% in real-life settings and can identify bursts, even in data scarce conditions. [ABSTRACT FROM AUTHOR]

Population imaging studies generate data for developing and implementing personalised health strategies to prevent, or more effectively treat disease. Large prospective epidemiological studies acquire imaging for pre-symptomatic populations. These studies enable the early discovery of alterations due to impending disease, and enable early identification of individuals at risk. Such studies pose new challenges requiring automatic image analysis. To date, few large-scale population-level cardiac imaging studies have been conducted. One such study stands out for its sheer size, careful implementation, and availability of top quality expert annotation; the UK Biobank (UKB). The resulting massive imaging datasets (targeting ca. 100,000 subjects) has put published approaches for cardiac image quantification to the test. In this paper, we present and evaluate a cardiac magnetic resonance (CMR) image analysis pipeline that properly scales up and can provide a fully automatic analysis of the UKB CMR study. Without manual user interactions, our pipeline performs end-to-end image analytics from multi-view cine CMR images all the way to anatomical and functional bi-ventricular quantification. All this, while maintaining relevant quality controls of the CMR input images, and resulting image segmentations. To the best of our knowledge, this is the first published attempt to fully automate the extraction of global and regional reference ranges of all key functional cardiovascular indexes, from both left and right cardiac ventricles, for a population of 20,000 subjects imaged at 50 time frames per subject, for a total of one million CMR volumes. In addition, our pipeline provides 3D anatomical bi-ventricular models of the heart. These models enable the extraction of detailed information of the morphodynamics of the two ventricles for subsequent association to genetic, omics, lifestyle habits, exposure information, and other information provided in population imaging studies. We validated our proposed CMR analytics pipeline against manual expert readings on a reference cohort of 4620 subjects with contour delineations and corresponding clinical indexes. Our results show broad significant agreement between the manually obtained reference indexes, and those automatically computed via our framework. 80.67% of subjects were processed with mean contour distance of less than 1 pixel, and 17.50% with mean contour distance between 1 and 2 pixels. Finally, we compare our pipeline with a recently published approach reporting on UKB data, and based on deep learning. Our comparison shows similar performance in terms of segmentation accuracy with respect to human experts. [ABSTRACT FROM AUTHOR]

• An algorithm to automatically detect motion artefacts in cardiac MR short axis images. • Use of k-space corruption to generate realistic motion artefacts to address data imbalance. • Training of a spatio-temporal convolutional neural network using curriculum learning. • Detailed comparison against a range of machine learning methods. • An area under ROC curve of 0.89 is achieved. Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89. [ABSTRACT FROM AUTHOR]

This paper documents the impact of a study visit to south India in 2007 on students following courses in initial teacher education at Canterbury Christ Church University, UK. The research was conducted by the two course leaders and employed a varied methodology, involving open-ended questionnaires, self-reporting of emotions, video diaries, informal conversations and follow-up interviews. The main finding was that the study visit was a powerful learning experience which transformed students' thinking by creating dissonance on a cognitive, emotional and existential level. These results are placed alongside current research in psychology and neuroscience to argue that a sense of hope and personal flourishing can be generated through experiences of this kind. This is supported by a model of transformational learning which highlights personal growth and development. We conclude by proposing that the study visit provides an example of how intercultural understanding can reinvigorate teacher education with a sense of meaning and hope. [ABSTRACT FROM AUTHOR]

• A framework for LRV detection, segmentation in large CMR studies. • Automatic initialization of bi-ventricles for the first time frame without any user intervention. • Automatic End-diastole and End-systole phase assessments. • Supervised deep neural network employed to train and segment the heart. • 3D-ASM image search procedure improved by combining image intensity models with derived distance maps for LV and RV edge localization. The sheer volume of data generated by population imaging studies is unparalleled by current capabilities to extract objective and quantitative cardiac phenotypes; subjective and time-consuming manual image analysis remains the gold standard. Automated image analytics to compute quantitative imaging biomarkers of cardiac function are desperately needed. Data volumes and their variability pose a challenge to most state-of-the-art methods for endo- and epicardial contours, which lack robustness when applied to very large datasets. Our aim is to develop an analysis pipeline for the automatic quantification of cardiac function from cine magnetic resonance imaging data. This work adopt 4,638 cardiac MRI cases coming from UK Biobank with ground truth available for left and RV contours. A hybrid and robust algorithm is proposed to improve the accuracy of automatic left and right ventricle segmentation by harnessing the localization accuracy of deep learning and the morphological accuracy of 3D-ASM (three-dimensional active shape models). The contributions of this paper are three-fold. First, a fully automatic method is proposed for left and right ventricle initialization and cardiac MRI segmentation by taking full advantage of spatiotemporal constraint. Second, a deeply supervised network is introduced to train and segment the heart. Third, the 3D-ASM image search procedure is improved by combining image intensity models with convolutional neural network (CNN) derived distance maps improving endo- and epicardial edge localization. The proposed architecture outperformed the state of the art for cardiac MRI segmentation from UK Biobank. The statistics of RV landmarks detection errors for Triscuspid valve and RV apex are 4.17 mm and 5.58 mm separately. The overlap metric, mean contour distance, Hausdorff distance and cardiac functional parameters are calculated for the LV (Left Ventricle) and RV (Right Ventricle) contour segmentation. Bland–Altman analysis for clinical parameters shows that the results from our automated image analysis pipelines are in good agreement with results from expert manual analysis. Our hybrid scheme combines deep learning and statistical shape modeling for automatic segmentation of the LV/RV from cardiac MRI datasets is effective and robust and can compute cardiac functional indexes from population imaging. [ABSTRACT FROM AUTHOR]

Deep learning (DL) models have shown promise in detecting chronic kidney disease (CKD) from fundus photographs. However, previous studies have utilized a serum creatinine-only estimated glomerular rate (eGFR) equation to measure kidney function despite the development of more up-to-date methods. In this study, we developed two sets of DL models using fundus images from the UK Biobank to ascertain the effects of using a creatinine and cystatin-C eGFR equation over the baseline creatinine-only eGFR equation on fundus image-based DL CKD predictors. Our results show that a creatinine and cystatin-C eGFR significantly improved classification performance over the baseline creatinine-only eGFR when the models were evaluated conventionally. However, these differences were no longer significant when the models were assessed on clinical labels based on ICD10. Furthermore, we also observed variations in model performance and systemic condition incidence between our study and the ones conducted previously. We hypothesize that limitations in existing eGFR equations and the paucity of retinal features uniquely indicative of CKD may contribute to these inconsistencies. These findings emphasize the need for developing more transparent models to facilitate a better understanding of the mechanisms underpinning the ability of DL models to detect CKD from fundus images. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the deadly cancer types that causes high mortality among women globally. Meanwhile, Deep Learning (DL) emerges as the most frequently utilized and rapidly developing branch of classical machine learning. The study examines a modern Computer-Aided Diagnosis (CAD) framework that uses DL to extract features and classify them for aiding radiologists in breast cancer diagnosis. This is accomplished through four distinct experimentations aimed at identifying the most optimal method of effective classification. Here, the first uses Deep CNNs that are pre-trained, such as AlexNet, GoogleNet, ResNet50, and Dense-Net121. The second is based on experiments using Deep CNNs to extract features and applying them onto a Support Vector Machine algorithm using three different kernels. The next one involves the fusion of different deep features for demonstrating the classification improvement by fusion of these deep features. The final experiment involves Principal Component Analysis (PCA) for reducing the computational cost and for decreasing the larger feature vectors created during fusion. The abovesaid experimentations are carried out in two different mammogram datasets namely MIAS and INbreast. The classification accuracy attained for both datasets through the fuzing of deep features (97.93% for MIAS and 96.646% for INbreast) is the highest compared with the state-of-the-art frameworks. In contrast, the classification performance did not enhance while applying the PCA on combined deep features; but the decrease in execution time provides a reduced computational cost. Abbreviations: CAD: Computer Aided Diagnosis; CNN: Convolution Neural Network; CSI: Classification Success Index; DCNN: Deep Convolution Neural Network; DICOM: Digital Imaging and Communications in Medicine; DL: Deep Learning; FC layer: Fully Connected layer; FFDM: Full-Field Digital Mammograms; FN: False Negative; FP: False Positive; ICSI: Individual Classification Success Index; MIAS: Mammographic Image Analysis Society; ML: Machine Learning; MLO: Medio-Lateral Oblique; PCA: Principal Component Analysis; PGM: Portable Gray Map; PPV: Positive Predictive Value; RBF: Radial Basis Function; SGDM: Stochastic Gradient Descent with Momentum; SVM: Support Vector Machine; TN: True Negative; TP: True Positive; TPR: True Positive Rate; UK: United Kingdom [ABSTRACT FROM AUTHOR]

Objective: The objective of the study is to demonstrate that with the use of artificial intelligence (AI) in computed tomography (CT), radiation doses of CT kidney-ureter-bladder (KUB) and CT urogram (CTU) can be reduced to less than that of X-ray KUB and CT KUB, respectively, while maintaining the good image quality. Materials and Methods: We reviewed all CT KUBs (n = 121) performed in September 2019 and all CTUs (n = 74) performed in December 2019 at our institution. The dose length product (DLP) of all CT KUBs and each individual phase of CTU were recorded. DLP of each scan done with new scanner (Canon Aquilion One Genesis with AiCE [CAOG]) which uses AI and deep learning reconstruction (DLR) were compared against traditional non-AI scanner (GE OPTIMA 660 [GEO-660]). We also compared DLPs of both scanners against the United Kingdom, National Diagnostic Reference Levels (NDRL) for CT. Results: One hundred and twenty-one patient's CT KUBs and 74 patient's CTUs were reviewed. For CT KUB group, the mean DLP of 81/121 scans done using AI/DLR scanner (CAOG) was 77.8 mGy cm (1.16 mSv), while the mean DLP of 40/121 CT KUB done with GEO-660 was 317.1 mGy cm (4.75 mSv). For CTU group, the mean DLP for 46/74 scans done using AI/DLR scanner (CAOG) was 401.9 mGy cm (6 mSv), compared to mean DLP of 1352.6 mGy cm (20.2 mSv) from GEO-660. Conclusion: We propose that CT scanners using AI/DLR method have the potential of reducing radiation doses of CT KUB and CTU to such an extent that it heralds the extinction of plain film XR KUB for follow-up of urinary tract stones. To the best of our knowledge, this is the first study comparing CT KUB and CTU doses from new scanners utilizing AI/DLR technology with traditional scanners using hybrid iterative reconstruction technology. Moreover, we have shown that this technology can markedly reduce the cumulative radiation burden in all urological patients undergoing CT examinations, whether this is CT KUB or CTU. [ABSTRACT FROM AUTHOR]

Traffic cameras are a widely available source of open data that offer tremendous value to public authorities by providing real-time statistics to understand and monitor the activity levels of local populations and their responses to policy interventions such as those seen during the COrona VIrus Disease 2019 (COVID-19) pandemic. This paper presents an end-to-end solution based on the Google Cloud Platform with scalable processing capability to deal with large volumes of traffic camera data across the UK in a cost-efficient manner. It describes a deep learning pipeline to detect pedestrians and vehicles and to generate mobility statistics from these. It includes novel methods for data cleaning and post-processing using a Structure SImilarity Measure (SSIM)-based static mask that improves reliability and accuracy in classifying people and vehicles from traffic camera images. The solution resulted in statistics describing trends in the 'busyness' of various towns and cities in the UK. We validated time series against Automatic Number Plate Recognition (ANPR) cameras across North East England, showing a close correlation between our statistical output and the ANPR source. Trends were also favorably compared against traffic flow statistics from the UK's Department of Transport. The results of this work have been adopted as an experimental faster indicator of the impact of COVID-19 on the UK economy and society by the Office for National Statistics (ONS). [ABSTRACT FROM AUTHOR]

The wellbore stability is an important issue in the drilling operation. Wellbore instability can interrupt the drilling and waste lots of time and money. The drilling mud is used to keep up the stability of the wellbore. Therefore, selecting the proper mud weight is an important issue in the drilling industry. The goal of this research is presenting an efficient mud weight estimator using deep learning techniques. To obtain this goal, a relatively big dataset (contained more than half-million samples) has been compiled from 116 wells of two fields in the United Kingdom and Norway. Our main contributions are assembling this large dataset, and applying the deep learning techniques to obtain efficient mud weight estimators. Our estimator is an artificial neural network with five hidden layers and 256 nodes in each layer that is able to estimate the mud weight for new wells and depths with the mean absolute error (MAE) of less than ±0.04 pound per gallon (ppg). In various experiments, the presented model has been challenged and the real-world conditions have been simulated. The results have shown that our model can be reliable and efficient in the real world. [ABSTRACT FROM AUTHOR]

Social media has created new opportunities to map cultural ecosystem services (CES) related to biodiversity at large scales. However, using these novel data to understand people's preferences in relation to these CES remains a challenge.To address this, we trained a deep learning model to capture people's interactions with selected flora and fauna on Flickr as a cultural service related to biodiversity and compared this with citizen science data on iNaturalist, with photos of individual species considered as human–species interactions.After mapping the distribution of people's interactions in Great Britain on Flickr and iNaturalist, we find significant spatial differences in people's preferences on the two platforms.Using a second, pretrained deep learning model, we were also able to identify different preferences for species groups such as birds on social media versus citizen science.To better understand people's preferences, we also compared people's interactions with species richness and abundance for a group of 36 bird species, sometimes finding large differences between people's interactions and these ecological measures.Our findings demonstrate that social media can be used to include a wider range of preferences in CES assessments along‐side citizen science data. However, these preferences reflect only a limited first‐hand experience of biodiversity. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

• A transfer learning architecture is proposed to improve the transferability of traffic prediction models. • A novel insight is provided to deal with data insufficient problems in traffic prediction. • The data selection criteria during model transfer are tested and discussed. Short-term traffic flow forecasting is a key element in Intelligent Transport Systems (ITS) to provide proactive traffic state information to road network operators. A variety of methods to predict traffic variables in the short-term can be found in the literature, ranging from time-series algorithms, machine learning tools and deep learning methods to a selective hybrid of these approaches. Despite the advances in prediction techniques, a challenging problem that affects the application of such methods in the real world is the prevalence of insufficient data across an entire network. It is rare that extensive historical training data required for model training are available for all the links in a city. In order to address this data insufficiency problem, this paper applies transfer learning techniques to machine learning methods in short-term traffic prediction. All the traffic data used in this paper were collected from Highways England road networks in the UK. The results show that through improving the transferability of machine learning-based models, the computational burden due to the model training process can be significantly reduced and the prediction accuracy under data deficient scenarios can be improved for one-step ahead prediction. However, the prediction accuracy gradually decreases in multi-step ahead prediction. It is also found that the accuracy of the proposed hybrid method is highly dependent upon consistency between datasets but less dependent on geographical attributes of links. [ABSTRACT FROM AUTHOR]

Cybersecurity mainly prevents the hardware, software, and data present in the system that has an active internet connection from external attacks. Organizations mainly deploy cybersecurity for their databases and systems to prevent it from unauthorized access. Different forms of attacks like phishing, spear-phishing, a drive-by attack, a password attack, denial of service, etc. are responsible for these security problems In this survey, we analyzed and reviewed the usage of deep learning algorithms for Cybersecurity applications. Deep learning which is also known as Deep Neural Networks includes machine learning techniques that enable the network to learn from unsupervised data and solve complex problems. Here, 80 papers from 2014 to 2019 have been used and successfully analyzed. Deep learning approaches such as Convolutional Neural Network (CNN), Auto Encoder (AE), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Generative Adversal Network (GAN) and Deep Reinforcement Learning (DIL) are used to categorize the papers referred. Each specific technique is effectively discussed with its algorithms, platforms, dataset, and potential benefits. The paper related to deep learning with cybersecurity is mainly published in the year 2018 in a large number and 18% of published articles originate from the UK. In addition, the papers are selected from a variety of journals, and 30% of papers used are from the Elsevier journal. From the experimental analysis, it is clear that the deep learning model improved the accuracy, scalability, reliability, and performance of the cybersecurity applications when applied in realtime. [ABSTRACT FROM AUTHOR]

Most existing deep learning based methods for vessel segmentation neglect two important aspects of retinal vessels: The orientation information of vessels and the contextual information of the whole fundus region. In this paper, we propose a robust orientation and context entangled network (OCE-Net), which can extract complex orientation and context information from blood vessels. To achieve complex orientation-aware convolution, we propose a dynamic complex orientation-aware convolution (DCOA Conv) to extract complex vessels with multiple orientations for improving vessel continuity. To simultaneously capture the global context information and emphasize the important local information, we propose a global and local fusion module (GLFM) to simultaneously model the long-range dependency of vessels and give sufficient attention to local thin vessels. A novel orientation and context entangled nonlocal (OCE-NL) module is also proposed to entangle the orientation and context information together. In addition, an unbalanced attention refining module (UARM) is proposed to deal with the unbalanced pixel numbers of the background and thick and thin vessels. Extensive experiments were performed on several commonly used datasets (DRIVE, STARE, and CHASEDB1) and some more challenging datasets (AV-WIDE, UoA-DR, RFMiD, and UK Biobank). The ablation study results show the proposed method's good performance in maintaining the continuity of thin vessels, and the comparative experimental results show OCE-Net's good performance in retinal vessel segmentation. Thus, the proposed framework can effectively carry out retinal vessel segmentation. • An Orientation and Context Entangled Network is proposed for vessel segmentation. • A DCOA convolution is proposed to capture the orientation of vessels. • A Global and Local Fusion Module is proposed to modeling the global dependencies. • An OCE-DNL is proposed to entangle the orientation and context. • An Unbalanced Attention Refine Module is designed to refine the output features. [ABSTRACT FROM AUTHOR]

An ongoing outbreak of monkeypox virus (MPXV) was first reported in the United Kingdom on 6 May 2022. As of 17 November, there had been a total of 80 221 confirmed MPXV cases in over 110 countries. Based on data reported between 6 May and 30 June 2022 in the United Kingdom, Spain, and Germany, we applied a deep learning approach using convolutional neural networks to evaluate the parameters of the 2022 MPXV outbreak. The basic reproduction number (R0) of MPXV was estimated to be 2.32 in the United Kingdom, which indicates the active diffusion of MPXV since the beginning of the outbreak. The data from Spain and Germany produced higher median R0 values of 2.42 and 2.88, respectively. Importantly, the estimated R0 of MPXV in the three countries tends to the previously calculated R0 of smallpox (3.50 to 6.00). Furthermore, the incubation (1/ε) and infectious (1/γ) period was predicted between 9 and 10 days and 4−5 days, respectively. The R0 value derived from MPXV is consistent with the significantly increasing number of cases, indicating the risk of a rapid spread of MPXV worldwide, which would provide important insights for the prevention and control of MPXV epidemic. [ABSTRACT FROM AUTHOR]