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Promoting social information disclosure can develop sustainable banking. This paper aims to evaluate the social sustainability of banks by constructing a new interval-based composite indicator using the Thomson Reuters database. In this paper, to measure the social sustainability of European banks, we propose an approach to construct interval-based composite indicators that usefully extends the construction of the composite indicator and allows us to measure the uncertainty generated by the choices made in the construction of the composite indicator. The methodological approach is based on Monte Carlo simulation and allows us to improve the information provided by the composite indicators. Thus, we measure the value of the social indicator and its subcomponents and the uncertainty of the value due to the different possible weightings. The results show that the best international ESG practices in European banks are found in French and British banks, especially in Italian ones. Finally, we analyze innovative perspectives and propose policy recommendations to support sustainable banking ecosystems in light of the increasing attention to ESG indicator disclosure and its correspondence to reality. • The best practices are found in French and U.K. banks. • Skandinavian banks face non-excellent results. • The work is a first attempt to elaborate on a specific social disclosure index. • Results are a valuable baseline to understand the actions to take. [ABSTRACT FROM AUTHOR]

• Trapping indices stabilization is lagged as injection period is active. • Greater CO 2 cumulative volume occurs as reservoir pressure is far from caprock fracture pressure. • Larger closures will require pressure management-based brine production to reach theoretical storage capacity. • ML-based CO 2 storage modelling preliminary de-risked saline aquifers lacking numerical models. This paper presents a machine learning (ML) model designed to speed up the appraisal of geologic CO 2 storage sites by predicting the effectiveness in trapping and accommodating CO 2 in saline aquifers. Considering the urgency of de-risking as much geologic CO 2 storage resources as possible to help with CO 2 emission reduction Paris' goal, ML-based reservoir modelling has been documented as proper tool when a faster, good approximate, and less expensive approach is needed to surrogate multiple assessments of storage sites traditionally performed by long-timeframe and multi-stage geologic CO 2 storage numerical modelling approach. In this paper, a case study is presented. It consisted of a dataset comprised of six geologic aquifer parameters (CO 2 residual saturation, horizontal permeability, vertical to horizontal permeability ratio, porosity, brine salinity, and CO 2 flow rate) and elapsed time as input data, and as output data the CO 2 trapping mechanism indices (Solubility Trapping Index, Residual Trapping Index, and Structural Trapping Index) along with the dynamic storage capacity (CO 2 injected volume). Such dataset was used to train and test the artificial neural network (ANN) model. The dataset was generated from thousands of post-processed numerical realizations at several injection periods by applying design of experiment using a synthetic aquifer model derived from the Bunter Sandstone Closure 36 aquifer numerical model, from the Southern North Sea. The ANN architecture designed in Python consisted of 3 hidden layers and 40 nodes and its performance was assessed using the coefficient of determination (R2) and root mean squared error (RMSE). The ANN performance showed accuracies (R2) for training and testing with 96% and 95% of precision respectively. Practical application of the ANN model was successfully implemented to CO 2 storage aquifer sites selected from CO2Stored® database which lacking numerical models (Bunter Closure 3, 9, 35, and 40), obtaining at the end of 100-years injection case a Structural, Residual, and Solubility Trapping Index averaging 83%, 11%, and 6% respectively, with low variation coefficient indicating that trapping indices were predicted properly because aquifers selected for ANN model application have similar structures (dome-like shape) and reservoir properties. In addition, CO 2 injected volume predictions for 100-years injection case were ranging from 397 to 456 million ton (Mt) totalling 2.1 giga ton (Gt) of potential storage capacity which represents 70% of total theoretical volumetric capacity. These results show the significant impact to accelerate geologic CO 2 storage sites assessment by implementing ML-based modelling to preliminary de-risking groups of saline aquifers and reasonably consider them technically feasible CO 2 storage sites in UK. [ABSTRACT FROM AUTHOR]

Automatic grading is not a new approach but the need to adapt the latest technology to automatic grading has become very important. As the technology has rapidly became more powerful on scoring exams and essays, especially from the 1990s onwards, partially or wholly automated grading systems using computational methods have evolved and have become a major area of research. In particular, the demand of scoring of natural language responses has created a need for tools that can be applied to automatically grade these responses. In this paper, we focus on the concept of automatic grading of short answer questions such as are typical in the UK GCSE system, and providing useful feedback on their answers to students. We present experimental results on a dataset provided from the introductory computer science class in the University of North Texas. We first apply standard data mining techniques to the corpus of student answers for the purpose of measuring similarity between the student answers and the model answer. This is based on the number of common words. We then evaluate the relation between these similarities and marks awarded by scorers. We consider an approach that groups student answers into clusters. Each cluster would be awarded the same mark, and the same feedback given to each answer in a cluster. In this manner, we demonstrate that clusters indicate the groups of students who are awarded the same or the similar scores. Words in each cluster are compared to show that clusters are constructed based on how many and which words of the model answer have been used. The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer. We argue that computational methods be used to enhance the reliability of human scoring, and not replace it. Humans are required to calibrate the system, and to deal with situations that are challenging. Computational methods can provide insight into which student answers will be found challenging and thus be a place human judgement is required. [ABSTRACT FROM AUTHOR]

This paper aims to introduce an artificial intelligence approach to enhance short-term daily streamflow forecasts through the integration of machine learning (ML) techniques with meta -heuristic (MH) algorithms. To maximize performance, a comprehensive analysis involving the autocorrelation and partial autocorrelation functions of the observed streamflow was conducted to derive ten distinct input combinations, from which the most suitable set was selected for investigation. Subsequently, six distinct ML methods, namely, decision trees, support vector regression, long short-term memory, gated recurrent unit, multi-layer perceptron, and radial basis function (as the optimal input combination), were leveraged to predict one-day-ahead streamflow. The most promising outcomes from these six explored methods were synergistically amalgamated to enhance predictive precision. This amalgamation was then optimized by utilizing parameter values hyper-tuned by four different MH algorithms. The algorithms included the shuffled frog leaping algorithm, particle swarm optimization, ant colony optimization (ACO), and gray wolf optimization (GWO). In culmination, a case study centered around the "Wick" river in the UK was conducted to rigorously evaluate the performance of the proposed method, utilizing a range of statistical evaluation indices. The results underscored the efficacy of the proposed combined method in substantially refining streamflow predictions, exhibiting an impressive level of accuracy. Additionally, the outcomes emphasized that ACO and GWO stand out as the optimal algorithms for calibrating the parameters of the amalgamated model. For instance, the R2 values attained by the combined models optimized through the ACO and GWO algorithms were 0.69422 and 0.69444, respectively. These values notably surpass those observed in the initial models, substantiating the superiority of the applied optimization approaches. [ABSTRACT FROM AUTHOR]

Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long-short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease. [ABSTRACT FROM AUTHOR]

Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation provided by these algorithms enables transparency and explainability, which is necessary for decision making in critical real-world problems. Yet, causal ML has had limited impact in practice compared to associational ML. This paper investigates the challenges of causal ML with application to COVID-19 UK pandemic data. We collate data from various public sources and investigate what the various structure learning algorithms learn from these data. We explore the impact of different data formats on algorithms spanning different classes of learning, and assess the results produced by each algorithm, and groups of algorithms, in terms of graphical structure, model dimensionality, sensitivity analysis, confounding variables, predictive and interventional inference. We use these results to highlight open problems in causal structure learning and directions for future research. To facilitate future work, we make all graphs, models, data sets, and source code publicly available online. [ABSTRACT FROM AUTHOR]

• We studied 72,363 cyclist crashes that occurred in Great Britain in the period 2016–2018. • We investigated factors contributing to higher proportion of severe and fatal crashes. • We used both a machine learning method as well as an econometric model. • We found unobserved heterogeneity in the data. • The models resulted complementary in identifying factors associated with higher crash severity. Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016–2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55–64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended. [ABSTRACT FROM AUTHOR]

• The performance of on-line vis-NIR for estimating soil organic carbon was evaluated. • Predictions for three datasets single field, UK with spiking, and UK were compared. • Random forest (RF) regression and spiking technique were used. • The best results obtained with the spiked UK dataset. Accurate on-line visible and near infrared (vis-NIR) spectroscopy prediction of soil organic carbon (OC) is essential for food security and environmental management. This paper aims at using on-line vis-NIR spectra coupled with random forest (RF) modelling approach for the prediction of soil organic carbon (OC), comparing between single field (SF), non-spiked UK multiple-field (NSUK) and spiked UK multiple-field (SUK) calibration models. Fresh soil samples collected from 6 fields in the UK (including two target fields) were scanned with a fibre-type vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305–2200 nm. After dividing spectra into calibration and independent validation sets, RF was run on the calibration set to develop calibration models for OC for the three studied datasets. Results showed that the model prediction performance depends on the dataset used and varies between fields. Less accurate prediction performance was obtained for the on-line prediction compared to laboratory (samples scanned in the laboratory under non-mobile measurement) prediction, and for non-spiked models compared to spiked models. The best model performance in both laboratory and on-line predictions was obtained when samples from the SF were spiked into the UK samples, with coefficients of determination (R2) values of 0.80 to 0.84 and 0.74 to 0.75, root mean square error of prediction (RMSEP) values of 0.14% and 0.17 to 0.18%, and ratio of prediction deviation (RPD) values of 2.30 to 2.5 and 1.98 to 2.04, respectively. Therefore, these results suggest that RF modelling approach when coupled with spiking provides high prediction performance of OC under both non-mobile laboratory and on-line field scanning conditions. [ABSTRACT FROM AUTHOR]

• Injury severity in single-bicycle crashes (SBCs) is analyzed. • A machine learning-based method termed Ordered Forest is employed. • ORF performs better than the traditional random forest in terms of predictions. • Factors associated with the injury severity in SBCs are investigated. • Practical guidance for the implementation of ORF is presented. This paper investigates the injury severity of cyclists in single-bicycle crashes (SBCs) in the UK. The data for analysis is constructed from the STATS19 road traffic casualty database, covering the period of 2016–2019. A machine learning-based ordered choice model termed Ordered Forest (ORF) is used. In our empirical analysis, ORF is found to produce more accurate class predictions of the SBC injury severity than the traditional random forest algorithm. Moreover, the factors associated with the injury severity are revealed, including the time and location of occurrence, the age of cyclists, roadway conditions, and crash-related factors. Specifically, old cyclists are more likely to be seriously injured in SBCs. Rural areas, higher speed limits, run-off crashes, and hitting objects are also related to an increased probability of serious injuries. While SBCs occurring at junctions, and/or during peak hours (i.e., 6:30–9:30 and 16:00–19:00) are less severe. To achieve the ambition of a step change in cycling and walking put forward by the UK Department for Transport, SBCs deserve more public attention. Lastly, regarding the implementation of ORF in crash injury severity analysis, we provide some practical guidance based on a series of simulation experiments. [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]

The evolution towards Smart Grids (SGs) represents an important opportunity for modernization of the energy industry. It is characterized by a bidirectional flow of information and energy between consumers and suppliers. However, the rapid increase of energy demands in residential areas is becoming a challenging problem. In order to address this issue, Demand-Side Management (DSM) has proven to be an effective solution. In this paper, we propose LOSISH, a price-based Demand Response (DR) system for load scheduling in residential Smart Homes (SHs) that achieves a trade-off between electricity payments and consumer's discomfort. Our proposed system considers Renewable Energy Sources (RESs), Battery Energy Storage System (BESS) and Plug-in Electric Vehicle (PEV). We formulate our scheduling as a constrained optimization problem and we propose a new hybrid algorithm to solve it. The latter combines two well known heuristic algorithms: Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO). Moreover, we propose a new clustering algorithm based on Machine Learning (ML) to extract consumer's preferences from a real dataset that contains the historical consumption patterns of his smart appliances. We test our approach on real data traces obtained from a SH and we set up an experiment to evaluate our algorithm on a Raspberry Pi and measure its energy consumption. To prove the effectiveness of our approach, we compare our results with another approach from the literature in terms of electricity bill, Peak-to-Average Ratio (PAR), energy consumption, and execution time. Numerical results show that LOSISH outperforms the other approach in terms of electricity bill (up to 52.92% cheaper), PAR (up to 44% decrease in peak demands), energy consumption (up to 69.44% less consumption), and execution time (up to 63.15% faster). • The load scheduling problem in residential smart homes is studied. • A constrained problem is formulated to make a trade-off between costs and comfort. • A hybrid heuristic algorithm and a Machine Learning clustering model are proposed. • Real data collected from a smart home in the UK was used to validate our approach. • Simulation and experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Figure. Graphical abstract (LCS = low-cost sensors; Ref = reference; AQ = air quality; Met = meteorology; GAM = generalised additive model; RF = random forest; QRM = quantile regression model; NO 2 = nitrogen dioxide). Air quality is monitored before intervention, then the intervention is implemented, and finally air quality is monitored again, which is analysed and modelled employing different machine learning approaches. [Display omitted] In this paper, the impact of smart traffic interventions on air quality was assessed in Thatcham, West Berkshire, UK. The intervention linked NO 2 levels with the cycle time of the traffic lights. When NO 2 levels exceeded a certain threshold, the strategy was triggered, which reduced the traffic congestion by turning the traffic lights green. Eight Earthsense Zephyrs air quality sensors and nine inductive-loop traffic detectors were installed in Thatcham to simultaneously monitor the air quality and traffic flows, respectively. Compared to the pre-intervention period, the observed NO 2 concentrations decreased in June, July and August and increased in September 2021, however, this does not reveal the true effect of smart traffic intervention. Using the observed data on the days with- and without-exceedances, we developed two machine learning models to predict the Business-as-usual (BAU) air quality level, i.e., a generalised additive model for average concentration and a quantile regression model for peak concentration. Our results demonstrated that average predicted concentrations (BAU) were lower than the observed concentrations (with intervention) by 12.45 %. However, we found that peak concentrations decreased by 20.54 %. [ABSTRACT FROM AUTHOR]

• Both spatial and temporal transferability of real-time crash risk models are assessed. • Wasserstein Generative Adversarial Network (WGAN) is introduced to address the extreme data imbalance issue. • Deep Neural Networks (DNN) and Transfer Learning (TL) are applied for predicting crashes. • Transfer learning improves transferability where direct transfer is not feasible. Real-time crash prediction is a heavily studied area given their potential applications in proactive traffic safety management in which a plethora of statistical and machine learning (ML) models have been developed to predict traffic crashes in real-time. However, one of the fundamental issues relating to the application of these models is spatio-temporal transferability. The present paper attempts to address this gap of knowledge by combining Generative Adversarial Network (GAN) and transfer learning to examine the transferability of real-time crash prediction models under an extremely imbalanced data setting. Initially, a baseline model was developed using Deep Neural Network (DNN) with crash and microscopic traffic data collected from M1 Motorway in the UK in 2017. The dataset utilised in the baseline model is naturally imbalanced with 257 crash cases and 16,359,163 non-crash cases. To overcome data imbalance issue, Wasserstein GAN (WGAN) was utilised to generate synthetic crash data. Non-crash data were randomly undersampled due to computational limitations. The calibrated model was then applied to predict traffic crashes for five other datasets obtained from M1 (2018), M4 (2017 & 2018 separately) and M6 Motorway (2017 & 2018 separately) by using transfer learning. Model transferability was compared with standalone models and direct transfer from the baseline model. The study revealed that direct transfer is not feasible. However, models become transferable temporally, spatially, and spatio-temporally if transfer learning is applied. The predictability of the transferred models outperformed existing studies by achieving high Area Under Curve (AUC) values ranging between 0.69 and 0.95. The best transferred model can predict nearly 95% crashes with only a 5% false alarm rate by tuning thresholds. Furthermore, the performances of transferred models are on par with or better than the standalone model. The findings of this study proves that transfer learning can improve model transferability under extremely imbalanced settings which helps traffic engineers in developing highly transferable models in future. [ABSTRACT FROM AUTHOR]

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT's predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation. • A deep learning and digital twins (DT) based scheduling method is proposed. • Physical IES performance is addressed by its virtual replica's predictive capability. • A machine learning method is proposed to address multiple uncertainty challenges. • Historical U.K. datasets are used for performance evaluations. • Results demonstrate the Digital Twin model reduces the extra operating cost by 63.5%. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has provided psych challenges for many in society. One such challenge is the anxiety that is created in many people faced with the risk of death from the disease. Another issue is understanding how individuals cope psychologically with the threat of death from the disease. In this study we examine the manifestation of death anxiety and various coping mechanisms through the lens of terror management theory (TMT) and online platforms. We take a novel approach to testing the theory using big data analytics and machine learning, focusing on the user-generated content of Twitter users. Based on a sample of all tweets in the UK mentioning COVID-19 terms over a 5-month period, we evaluate dictionary mentions of anxiety and death, and various TMT defense mechanisms, and calculate the pattern of latent death anxiety or 'terror' states of Twitter users via Hidden Markov Models. The research identifies four online 'terror' states, with high death and anxiety mentions during the peak of the pandemic. Further we examine various TMT defense mechanisms that have been proposed in the literature for coping with death anxiety and find that online social connection, achievement and religion all play important roles in improving the model and explaining movement between states. The paper concludes with various implications of the study for future research and practice. • Extends terror management theory in an online social media context during the pandemic. • Examines death anxiety and online coping mechanisms from terror management theory. • Combines text analytics and hidden Markov models using a big data set. • Results support online social connection, achievement and religion coping mechanisms. • Contributes to the understanding of online individual behavior during pandemics. [ABSTRACT FROM AUTHOR]

Increasing the accuracy and intelligence of short-term load forecasting system can improve modern power systems management and economic power generation. In recent decades, the optimized machine learning methods have been widely used in load forecasting problems because of their predictability with higher accuracy and robustness. However, most related researches only use evolutionary algorithms for parameters fine-tuning and ignore the evolutionary algorithm based decision-making support and the matching relation between the used evolutionary algorithm and machine learning method, which greatly limit the improvement of forecasting system. To dissolve the above issues, a data-driven evolutionary ensemble learning forecasting model is proposed in this paper. Firstly, a novel multimodal evolutionary algorithm based on comprehensive weighted vector angle and shift-based density estimation is proposed. Secondly, based on the proposed multimodal evolutionary algorithm, an intelligent decision-making support scheme including predictive performance evaluation, model properties analysis, structure and fusion strategy optimization, and optimal model preference selection is designed to improve the random vector functional link network based ensemble learning model and boost the forecasting accuracy. Thirdly, experimental studies on 15 test problems with up to 6000 decision variables are conducted to validate the excellent optimization ability of the proposed evolutionary algorithm. Finally, the proposed evolutionary ensemble learning method is compared with 6 other representative forecast methods on real-world short-term load forecasting datasets from Australia, Great Britain, and Norway. The experiment results verify the superiority and applicability of the proposed method. • A new multimodal evolutionary algorithm (MMOWVA) is being proposed. • MMOWVA is validated by comparison experiments on 15 multimodal problems. • An expandable intelligent scheme is offered to make decision-makers convenient. • An evolutionary ensemble learning model is designed to improve forecast accuracy. • Experiment results demonstrate a higher forecast accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Geographic variation in digital inequality manifests as a result of a range of demographic, attitudinal, behavioural and locational factors. To better understand this multidimensional geography, our paper develops a new geodemographic classification for the spatial extent of Great Britain. In this model, we integrate a range of new small area measures that are drawn from multiple new forms of data including consumer purchasing data, survey and open data sources. Our analytical approach innovatively provides an integration of machine learning into a small-area estimation technique to obtain Lower Super Output Area / Data Zone estimates of Internet use, alongside a range of online engagement and consumption measures. Following the collation of a range of input measures, we implemented a more standard geodemographic framework that utilises the unsupervised clustering algorithm k-means to produce a map of the multidimensional characteristics of digital inequality for Great Britain; creating the Internet User Classification (IUC). Our outputs provide a new and nuanced understanding of the contemporary salient characteristics of digital inequality in Great Britain, which we evaluate both internally and externally within the context of preparations for the 2021 UK Census of the Population, exploring the geodemographic patterns of Census test response rates and the prevalence to complete the survey online. Our innovative work illustrates the strength of a geodemographic approach in mapping spatial patterns of digital inequality, and through the presented application concerning Census response rates and characteristics we demonstrate how the IUC can be operationalised within such settings for local intervention or benchmarking. • Great Britain can be differentiated into ten groups of salient digital inclusion / exclusion. • Supervised machine learning can provide methodological enhancement to small area estimation. • Census response rates have a relationship to patterns of digital inclusion / exclusion. [ABSTRACT FROM AUTHOR]

• Proposed an intelligent diagnosis framework for skin lesion classification. • Designed a multi-class multi-level algorithm to enhance the accuracy. • Proposed improved techniques for noise removal from the images. • Deep learning and other machine learning approaches developed and compared. • So far, the best multi-class result (∼96.5% accuracy) achieved using deep learning. Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level (MCML) classification algorithm inspired by the "divide and conquer" rule is explored to address the research challenges. The MCML classification algorithm is implemented using traditional machine learning and advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the main algorithm used in most of the existing literature. The results also indicate that the MCML classification algorithm is capable of enhancing the classification performance of multiple skin lesions. [ABSTRACT FROM AUTHOR]

• A novel algorithm named LWGF (Local Warning integrated with Global Feature) was proposed for FAIMS data analysis. • The LWGF identified 26 clinical patients (6 uninfected, 20 E. coli -infected) with the best average AUC of 0.98, and recognition rate of 96.15%. • For FAIMS data analysis, we have proven that it is better to use dynamic spectra rather than several stable spectra. Infections have long been a thorny problem that severely threatened public health and resulted in tremendous economic losses worldwide. Current detection methods for wound infection do not fully meet the requirements of preventing and treating this disease. Therefore, people are looking for better alternatives, wherein FAIMS (Field Asymmetric Ion Mobility Spectrometry) technology, by virtue of its high sensitivity, rapid response and noninvasive operation, is a promising candidate. This paper aims to investigate the possibility of FAIMS technology in detecting wound infections quickly and accurately. For this purpose, we gathered an odor dataset of clinical wound samples with the employment of a FAIMS instrument, the Lonestar (Owlstone, UK) analyzer. To enhance detectability, we proposed a novel algorithm framework, i.e., Local Warning integrated with Global Feature (LWGF), which is verified on distinguishment between twenty patients with single or mixed infection of Escherichia coli (E. coli) and six wounded patients without infection. Experimental results showed that the LWGF successfully identified the patients with the best average AUC of 0.98, and the best recognition rate of 96.15%, which are much higher than the conventional methods. [ABSTRACT FROM AUTHOR]