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Abstract: This article explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. From 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the paper sorting demand. Still, in many countries including Malaysia, waste papers are sorted into different grades using a manual sorting system. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using first-order features. To construct a template database, a statistical approach with intra-class and inter-class variation techniques are applied to the feature selection process. Finally, the K-nearest neighbor (KNN) algorithm is applied for paper object grade identification. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques. [Copyright &y& Elsevier]

Digital twins belong to ten of the strategic technology trends according to the Gartner list from 2019, and have encountered a big expansion, especially with the introduction of Industry 4.0. Sport, on the other hand, has become a constant companion of the modern human suffering a lack of a healthy way of life. The application of digital twins in sport has brought dramatic changes not only in the domain of sport training, but also in managing athletes during competitions, searching for strategical solutions before and tactical solutions during the games by coaches. In this paper, the domain of digital twins in sport is reviewed based on papers which have emerged in this area. At first, the concept of a digital twin is discussed in general. Then, taxonomies of digital twins are appointed. According to these taxonomies, the collection of relevant papers is analyzed, and some real examples of digital twins are exposed. The review finishes with a discussion about how the digital twins affect changes in the modern sport disciplines, and what challenges and opportunities await the digital twins in the future. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic exposed the importance of research on the spread of epidemic diseases. In this paper, we apply Artificial Intelligence and statistics techniques to build prediction models to estimate the SARS-CoV-2 seroprevalence in the United States, using multiple estimates of COVID-19 prevalence and other explanatory variables. We propose the use of stacking techniques based on multiple model building techniques (Linear and Beta Regression, Genetic Programming and Neural Networks) to obtain Predictive Ensemble Models. There has been extensive research on this field, but there has not been in-depth research on the application of stacking methods to estimate and forecast seroprevalence in the USA specifically. This paper provides a novel comparison of the behaviour and performance of different building techniques for stacking ensemble models and presents which methods are better for different scenarios. We find that Genetic Programming and Neural Networks are the best models with trained data within single states, and when multiple states are considered Genetic Programming is still better than the Regression models, but Neural Networks fail to estimate the seroprevalence accurately. Another novelty of our work is the use of cross-state validation to evaluate the models with new data, as well as temporal forecasting. Depending on how the data is processed, Linear Regression performs very well with cross-state validation and temporal forecasting, and Genetic Programming is very accurate with the former while Neural Networks work better with the latter. • COVID-19 seropositivity modelling in the USA. • Use of stacking and ensemble ML models. • Comparison of ML methods (linear regression, beta regression, genetic programming, neural network). • Trade-off between explainability and performance. [ABSTRACT FROM AUTHOR]

As diabetes management becomes more complicated, there is an increasing interest in understanding how to manage diabetes with physical activity. Our study aimed to investigate the role of wearable, non-invasive technologies in collecting data related to physical activity to model them via artificial intelligence methods for efficient diabetes management. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (also known as PRISMA) protocol and searched three databases, namely PubMed, Scopus, and Web of Science. Out of 960 titles, we included 32 in the full-text analysis. Results showed two main methods were used for the analysis, i.e., statistical and classification modeling. Results indicate among the employed regression methods, linear regression was used more than other methods, and the most common classification-based method for analyzing data was the Artificial Neural Network method. Assessing the quality of papers that used the classification method was done through Prediction model Risk Of Bias Assessment Tool (also known as PROBAST) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (also known as TRIPOD) tools. Based on PROBAST outcomes, although the risk of bias was low in most of the works, explaining the analyzing method specifically, the method of handling missing data needs more attention. Upon evaluating papers using the TRIPOD, it realized that there is a need to place emphasis on improving the quality of the presentation and explanation of the result. According to our review, the conjunction of non-invasive technologies and artificial intelligence is promising in managing diabetic risk factors for real-time monitoring of physical activities, enabling regular clinical intervention and optimized medical treatment. [ABSTRACT FROM AUTHOR]

• One-step abductive multi-target learning with diverse noisy samples (OSAMTL-DiNS) is proposed. • OSAMTL-DiNS is applied to tumour segmentation for breast cancer (TSfBC). • OSAMTL-DiNS enables various approaches for noisy labels achieving more rational results in TSfBC. • A predictive model pre-trained with OSAMTL-DiNS for TSfBC is released. Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced technologies for different artificial intelligence applications. One-step abductive multi-target learning (OSAMTL), an approach inspired by abductive learning, via simply combining machine learning and logical reasoning in a one-step balanced multi-target learning way, has as well shown its effectiveness in handling complex noisy labels of a single noisy sample in medical histopathology whole slide image analysis (MHWSIA). However, OSAMTL is not suitable for the situation where diverse noisy samples (DiNS) are provided for a learning task. In this paper, giving definition of DiNS, we propose one-step abductive multi-target learning with DiNS (OSAMTL-DiNS) to expand the original OSAMTL to handle complex noisy labels of DiNS. Applying OSAMTL-DiNS to tumour segmentation for breast cancer in MHWSIA, we show that OSAMTL-DiNS is able to enable various state-of-the-art approaches for learning from noisy labels to achieve more rational predictions. We released a model pre-trained with OSAMTL-DiNS for tumour segmentation in HE-stained pre-treatment biopsy images in breast cancer, which has been successfully applied as a pre-processing tool to extract tumour-associated stroma compartment for predicting the pathological complete response to neoadjuvant chemotherapy in breast cancer. [ABSTRACT FROM AUTHOR]

• The paper explores AI decision-making to enhance transparency of CIN diagnosis using colposcopy images. • The study presents interpretable insights for clinical validation using different XAI methods. • Saliency method showed best result, using gradients to reveal likely malignancy traits. • Expert Clinic Validation of results conducted, along with statistical analyses. Although artificial intelligence techniques have performed well in analysing medical images, it is essential to consider the degree of understanding or interpretation in evaluations. This is especially important when diagnostic support is involved, as the trustworthiness of machine learning predictions needs to be clarified and evaluated. For deep learning algorithms to be translated into clinical practice, they must be made less opaque. To examine the classification of cervical dysplasia-diagnosed patients, we will explore explainable artificial intelligence (XAI) approaches, by implementing seven different interpretation techniques (Saliency, XRAI, Integrated Gradients, Smooth Gradients, Smooth Integrated Gradients, Grad-CAM, Smoothgrad-CAM). These XAI techniques shall be explored in order to assess the performance of four Convolutional Neural Network models (AlexNet, VGG16, MobileNet, and InceptionNet). Our results show that saliency qualities most closely match expert annotations, and there is a moderate to strong association between a model's sensitivity and the agreement between humans and computers. We also discovered a relevant relationship between expert insights and computational learning. This highlights the potential role of human expertise in influencing effective computer learning. [ABSTRACT FROM AUTHOR]

Intelligent Decision Support Systems (DSS) have gained considerable interest within the field of construction engineering through the utilization of Artificial Intelligence (AI) and Machine Learning (ML) methodologies. These systems possess the capacity to enhance decision-making processes and optimize the results of projects. Nevertheless, it is imperative to conduct a thorough and inclusive examination that incorporates and evaluates the various methodologies employed within this particular discipline, thereby bridging the existing void in research. The current state of research in the field of AI and ML in construction engineering is characterized by a dearth of comprehensive reviews that encompass the wide array of methodologies utilized. Although certain studies have delved into specific facets of this subject, there remains a novelty and research gap in terms of a holistic examination of the topic. The objective of this review is to conduct a comprehensive analysis of AI and ML methodologies in the field of construction engineering, with a specific emphasis on their practical applications, advantages, and constraints. The study also ascertains potential areas for future research and improvement in the domain of Intelligent DSS. This study employed a rigorous approach that involved an extensive review of scholarly literature and empirical investigations from 2001 to June 2023. The findings of the analysis indicate a noticeable increase in the number of research papers focusing on Intelligent DSS within the field of construction engineering during the last twenty years. The field has witnessed the emergence of key research areas, namely the utilization of Internet of Things (IoT), explainable AI, supply chain optimization, sustainable construction practices, and human–machine collaboration. The results of this study hold significant implications for the construction industry. The incorporation of AI and ML techniques within Intelligent DSS can effectively tackle various issues such as cost overruns, project delays, and safety concerns. [ABSTRACT FROM AUTHOR]

Fairness is, along with explainability, a key requirement to assess the trustworthiness of Artificial Intelligence methods that are employed for credit rating. While explainability is concerned with the capability to identify the main drivers of credit ratings, fairness evaluates how similar are credit ratings assigned to different population groups. Although explainability and fairness are strictly connected, there are no research papers that consider them jointly. We aim to fill this gap. To this purpose, we propose a general post-processing methodology for credit ratings based on machine learning models, which measures explainability, by means of the Shapley–Lorenz values of the explanatory variables, and fairness, by comparing the Shapley–Lorenz values calculated in each population group, conditionally on the explanatory variables. We experiment our approach on a credit rating model applied to a panel dataset of 119,857 credit records for approximately 20,000 small and medium-sized enterprises (SMEs), in four European countries and eleven industry sectors for the period 2015 to 2020. The obtained results indicate which are the most explainable variables and show that the credit rating model is fair, across countries and industrial sectors. However, when subsamples of smaller size are considered, the model is not fair, indicating that fairness depends on the available data. • We propose a new fairness metrics for AI credit scoring. • The metrics compares variable Shapley–Lorenz values in different population groups. • The usefulness of the metrics is illustrated on a real database of SMEs. • When all data are considered, the credit scoring model is fair. • When small samples are considered, unfairness arises. [ABSTRACT FROM AUTHOR]

In recent years, many studies have explored artificial intelligence (AI) in quantitative trading and financial prediction. Among financial products, options are highlighted for their risk management capabilities and defined trading cycles, yet they pose challenges due to their complexity. This paper proposes HAPPY (Heatmap Analysis via PON/POD Yield), an options trading system designed to predict expected winning rates (EW) by integrating actual profits, losses, and risk factors, thus enhancing traditional winning rate metrics. HAPPY employs heatmap analysis to address the risk of overfitting by smoothing isolated low EW values and incorporates machine learning (ML) models like random forest, extreme gradient boosting (XGBoost), and light gradient boosted machine (LGBM) for improved prediction accuracy. Employing TAIEX weekly options, the study evaluates EW and backtests trading performance, comparing empirical statistics and ML models. Findings indicate that ML models excel in accuracy and precision, though empirical statistics perform better in backtesting, especially as options near expiration. This research offers a robust options trading system that can be applied to other options markets or predictive models. • Options trading system development with heatmap analysis. • Winning rate refinement using comprehensive risk assessments. • Trading framework validated against empirical experiments and ML models. • Empirical testing and backtesting with TAIEX weekly options. [ABSTRACT FROM AUTHOR]

Glaucoma, a leading cause of irreversible blindness worldwide, poses significant diagnostic challenges due to its reliance on subjective evaluation. Recent advances in computer vision and deep learning have demonstrated the potential for automated assessment. This paper provides a comprehensive survey of studies on AI-based glaucoma diagnosis using fundus, optical coherence tomography, and visual field images, with a focus on deep learning-based methods. We searched Web of Science, PubMed, IEEE Xplore, and Google Scholar, applying specific selection criteria to identify relevant studies published from 2017 to 2023. Our analysis provides a structured overview of architectural paradigms, including convolutional neural networks, autoencoders, attention networks, generative adversarial networks, and geometric deep learning models. Additionally, we discuss approaches for extracting informative features, such as structural, statistical, and hybrid techniques. Furthermore, we outline key research challenges and future directions, emphasizing the need for larger, more diverse datasets, strategies for early disease detection, multi-modal data integration, model explainability, and clinical translation. This survey is expected to be useful for Artificial Intelligence (AI) researchers seeking to translate advances into practice and ophthalmologists aiming to improve clinical workflows and diagnosis using the latest AI outcomes. • Surveyed deep learning techniques (2017–2023) for glaucoma diagnosis. • Categorized glaucoma diagnosis feature extraction methods. • Studied datasets, architectures, and metrics for glaucoma diagnosis. • Outlined challenges and future directions in glaucoma diagnosis. [ABSTRACT FROM AUTHOR]

The vast amount of now available genomic, biomolecular, and clinical patient data may revolutionize personalized medicine. Yet, this availability poses significant challenges to artificial intelligence as the major obstacle is that retrospective patients had only one treatment, and there is no way to determine what would have happened if they had other treatments. In this paper, we propose a novel way to use historical retrospective patient data to build systems that rank different treatments according to the possible benefit for individuals with specific tumor characteristics. This novel approach – our Drug Rankers over Historical Cases (DRHC) – solves the major obstacle for using retrospective patients by proposing a way to rank treatments in training and by introducing two novel measures (average ranking score and soft average ranking score) to evaluate these novel DRHCs Additionally, we categorize these novel DRHC methods into predictor-based and selector-based families. The latter leverages our novel measures, making it more innovative. Both families were implemented with various learning algorithms. Experiments demonstrate that the selector-based family outperforms the predictor-based family in terms of Average Ranking Score (ASR) and Average Soft Ranking Score (ASRS), showcasing superior drug ranking capabilities. The selector-based family achieves a maximum ASR of 0.642 and ASRS of 0.641, compared to 0.638 ASR and 0.634 ASRS for the predictor-based family. • Rethinking historical patient data for drug ranking in Personalized Medicine with Machine Learning. • Two new metrics to evaluate drug ranking models over historical patients: ASR and ASRS. • The classification of drug ranking models into predictor-based and selector-based. • An experimental analysis of different drug ranking models over historical patients. • A selector-based model on Neural Networks that outperforms all the other models. [ABSTRACT FROM AUTHOR]

The conventional standardized theoretical models (such as Webster, Alcelik, Indo-HCM) used for the delay estimation revolve around the mathematical hypothesis and assumptions, making them static with limitations in accommodating the dynamic traffic behaviors. Recent advances in artificial intelligence make machine learning techniques suitable for estimating vehicle control delay compared to conventional methods. This paper demonstrates the application of several machine learning models developed by focusing on the fluctuations of traffic observed at an intersection having heterogeneous traffic conditions in Ahmedabad city for delay estimation. Several parameters are extracted from on-field and video surveys. However, not all parameters are relevant for accurately estimating vehicle control delay. Hence, a feature selection process consisting of several feature-scoring techniques from filter, wrapper, and embedded methods is applied to all the parameters. This process gave insights into the most statistically relevant independent parameters, and out of all the parameters, cycle time was found to be insignificant, with a feature score of 0 from almost all techniques. Hence, it was removed, and then the prominent parameters were then used to build a vehicle control delay model using support vector regression (SVR), K-nearest neighbor (KNN), artificial neural network (ANN), random forest regression (RF), and decision tree regression (DT). Error distribution, standard deviation of errors, coefficient of Determination (R2), and Root Mean Squared Error (RMSE) are the parameters used for evaluating the performance of the machine learning models. RF outperformed them all with a standard deviation of errors, R2, and RMSE of 11.065, 0.926, and 11.081 on testing data. But ANN, KNN, and DT also performed satisfactorily. Compared with conventional standardized theoretical models, all the machine learning models except SVR performed better. [ABSTRACT FROM AUTHOR]

In recent years, the enormous development of Machine Learning, especially Deep Learning, has led to the widespread adoption of Artificial Intelligence (AI) systems in a large variety of contexts. Many of these systems provide excellent results but act as black-boxes. This can be accepted in various contexts, but there are others (e.g., medical ones) where a result returned by a system cannot be accepted without an explanation on how it was obtained. Explainable AI (XAI) is an area of AI well suited to explain the behavior of AI systems that act as black-boxes. In this paper, we propose a model-agnostic XAI framework to explain the behavior of classifiers. Our framework is based on network theory; thus, it is able to make use of the enormous amount of results that researchers in this area have discovered over time. Being network-based, our framework is completely different from the other model-agnostic XAI approaches. Furthermore, it is parameter-free and is able to handle heterogeneous features that may not even be independent of each other. Finally, it introduces the notion of dyscrasia that allows us to detect not only which features are important in a particular task but also how they interact with each other. • Performing model-agnostic Explainable AI on classifiers. • Introducing the concept of dyscrasia of feature occurrences. • Assessing the relevance of a feature during classification. [ABSTRACT FROM AUTHOR]

Highlights • Ensemble classifier system is applied to biometric verification. • For each base classifier, the classifier's competence is separately calculated. • A lip geometrical features measurements are proposed. • Experiments confirmed our method outperforms other state-of-the-art methods. Abstract Machine learning approaches are largely focused on pattern or object classification, where a combination of several classifier systems can be integrated to help generate an optimal or suboptimal classification decision. Multiple classification systems have been extensively developed because a committee of classifiers, also known as an ensemble, can outperform the ensemble's individual members. In this paper, a classification method based on an ensemble of binary classifiers is proposed. Our strategy consists of two phases: (1) the competence of the base heterogeneous classifiers in a pool is determined, and (2) an ensemble is formed by combining those base classifiers with the greatest competences for the given input data. We have shown that the competence of the base classifiers can be successfully calculated even if the number of their learning examples was limited. Such a situation is particularly observed with biometric data. In this paper, we propose a new biometric data structure, the Sim coefficients, along with an efficient data processing technique involving a pool of competent classifiers chosen by dynamic selection. [ABSTRACT FROM AUTHOR]

Highlights • An integrated scheme is proposed for prognostics of Lithium-ion batteries. • The prognostic scheme contains pre-processing and prediction modules. • Novel imputation techniques handle battery data with missing observations. • The proposed multiple imputation technique reveals the uncertainty of estimations. • Extreme learning machines predict the remaining useful life over a long horizon. Abstract This paper focuses on the development of a prognostic scheme for estimating the remaining useful life (RUL) of Lithium-ion batteries with missing observations. The scheme has two main modules based on extreme learning machines: pre-processing and prediction. The pre-processing module uses novel single and multiple imputation techniques to estimate the missing observations. The prediction module aims to obtain precise predictions even in the presence of missing observations and with the related uncertainty. The pre-processing module sends imputed subsets of samples to the prediction module, which makes use of extreme learning machines for one-step and multi-steps predictions. The prediction module contains various multi-steps prediction strategies including iterative, direct and DirRec, which use the constant-current experimental capacity data for the long-term prediction of the remaining useful life. Accurate prediction of RUL requires continuity in the time-series dataset. The proposed scheme is designed to build an intelligent prediction system with the ability to handle time-series data containing missing values and is robust enough to generate a complete time-series dataset and, then, make short or long term predictions. The experimental results confirm that the proposed framework can be beneficial for intelligent diagnostic and prognostic systems related to battery as well as other wide range of applications. The main focus of the paper is the development of an integrated imputation-prediction scheme and not the evaluation of individual performances of the imputation or prediction techniques. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technologies are increasingly applied to traffic flow prediction (TFP) to enhance prediction accuracy. This study utilizes bibliometric methods and network analysis measures to gain insights into the research status, development process, opportunities, and challenges of AI-based TFP research based on the literature data retrieved from the Web of Science core collection. The study first conducts basic statistical analysis of all papers. Subsequently, cooperation network analysis is conducted to identify the most productive countries/territories, institutions, and authors, the cooperative relationships, and the formed research communities. Co-citation network analysis is then employed to identify publications that have made outstanding contributions to the AI-based TFP field. Finally, the main path analysis of the paper citation network is used to analyze the knowledge diffusion process, while the keyword co-occurrence analysis is conducted to reveal the evolution characteristics of the research topics. Based on the bibliometric analysis results, we gain insights into the opportunities and challenges in this field from the perspectives of data, models, and applications, and provide pertinent suggestions for future research. Overall, this study can assist researchers in capturing the state-of-the-art and research directions in AI-based TFP. [ABSTRACT FROM AUTHOR]

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at decisions. Assuming an already trained Supervised Classification model, post-hoc explanations can be obtained via so-called counterfactual analysis: a counterfactual explanation of an instance indicates how this instance should be minimally modified so that the perturbed instance is classified in the desired class by the given Machine Learning classification model. Most of the Counterfactual Analysis literature focuses on the single-instance single-counterfactual setting, in which the analysis is done for one single instance to provide one single counterfactual explanation. Taking a stakeholder's perspective, in this paper we introduce the so-called collective counterfactual explanations. By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints. Making the process of constructing counterfactuals collective instead of individual enables us to detect the features that are critical to the entire dataset to have the individuals classified in the desired class. Our methodology allows for some instances to be treated individually, as in the single-instance single-counterfactual case, performing the collective counterfactual analysis for a fraction of records of the group of interest. This way, outliers are identified and handled appropriately. Under some assumptions on the classifier and the space in which counterfactuals are sought, finding collective counterfactual explanations is reduced to solving a convex quadratic linearly constrained mixed integer optimization problem, which, for datasets of moderate size, can be solved to optimality using existing solvers. The performance of our approach is illustrated on real-world datasets, demonstrating its usefulness. • A new model for Explainable Artificial Intelligence is proposed. • Counterfactual Explanations for a full group of individuals are sought. • Sparsity both at individual and group level is achieved. [ABSTRACT FROM AUTHOR]

Optimal path planning is a fundamental problem in artificial intelligence (AI) and has wide applications in areas such as robotics, transportation, and logistics. In this paper, we propose a novel approach for ordering algorithms in pathfinding problems using the concept of shell layers. Our approach aims to improve the computational efficiency and distance cost of ordering algorithms. To evaluate the effectiveness of our approach, we compare it with five state-of-the-art techniques commonly used in the field of ordering algorithms on eight different scenarios with varying configurations. Our results show that our proposed approach outperformed the state-of-the-art techniques in terms of time-computational complexity and distance cost in most of the scenarios, demonstrating its potential as a new state-of-the-art technique for ordering algorithms. Specifically, our approach outperformed the nearest neighbor, A*, branch and bound, Christofides, and genetics ordering algorithms. However, we also identified two specific situations where our approach was outperformed by two state-of-the-art algorithms due to the specific distribution of the goal nodes. These findings highlight the importance of evaluating and comparing ordering algorithms in various scenarios. Our approach has significant implications for AI research and development, as it has the potential to improve the performance of pathfinding algorithms in various applications. We also discuss the limitations of our approach and potential areas for further research, such as investigating the effectiveness of our approach in different types of graphs and exploring the potential use of machine learning techniques to optimize the shell layer construction. The code for this study is available at https://github.com/abdullah1aloush1/MuGONA. • Equally-spaced nodes formed over workspace with obstacles, optimizing search process. • Efficient neighboring graph built for equally-spaced nodes, reducing complexity. • Path optimization for navigation by ordering goal nodes, reducing time and distance. • Path smoothing for navigation via interpolation to yield a trajectory on path nodes. [ABSTRACT FROM AUTHOR]

The Computational Fluid Dynamics (CFD) simulations at different fidelity levels are a common tool for predicting the aerodynamic performance in many engineering applications such as; automotive and aerospace to cite a few. However, the associated computational costs with these simulations are not trivial, and using the CFD simulations might easily become unpractical specially at the early design stages. The number of the required CFD simulations could be reduced immensely by using Artificial Intelligence (AI) methods which can predict, for example, the aerodynamic coefficients based on previous and limited CFD simulations. Having discussed the AI potential to minimize the CFD usage, the objective of this work is to investigate and compare the ability of different AI methods to predict the aerodynamic coefficients using the same CFD database from the literature. In this study, three different AI methods, namely; machine learning (e.g. Extreme Gradient Boosting (XGB), CatBoost, Bagging, Light Gradient Boosting Machine (Light-GBM), Random Forest (RFR), Gradient Boosting), deep learning (e.g. One Dimensional Convolutional Neural Network (Conv1D)) and surrogate models (e.g. Gaussian Process Regression (GPR)) have been utilized to estimate a sample CFD database (e.g. NACA0012, RAE2822) from the existing literature, and the prediction performance of these methods have been compared. The results show that, the aerodynamic coefficients predicted using CatBoost, XGB and Bagging are in good agreement with the reference CFD results. Furthermore, the proposed Conv1D method with LeakyReLU activation function showed a promising results in the development of aerodynamic models which can be used in the early stages of the air vehicles design. The obtained results from this work indicate that the CatBoost and the XGB models required much smaller training time compared to the Conv1D method, moreover, the proposed regression trees; CatBoost, Bagging and XGB can reduce the required number of CFD simulations significantly. • Various artificial intelligence methods were studied to predict aerodynamic data. • Regression tree models such as XGB, Catboost and LightGBM were used. • Surrogate models and deep learning models (i.e. Conv1D) also were used. • Detailed comparison were presented between the aforementioned models. • The paper suggests the suitability of XGB and Catboost for aerodynamics cases. [ABSTRACT FROM AUTHOR]

Finding a more efficient trading strategy has always been one of the main concerns in financial market trading. In order to create trading strategies that lead to higher profits, historical data must be used. Due to a large amount of financial data and various factors affecting them, algorithmic trading and, more recently, artificial intelligence are employed to overcome the decision-making complexity. This paper aims to introduce a new approach using Twin-Delayed DDPG (TD3) and the daily close price to create a trading strategy. As a continuous action space deep reinforcement learning algorithm, in contrast to the discrete ones, the TD3 provides us with both the number of trading shares and the trading positions. In order to evaluate the performance of the proposed algorithm, the comparison results of our approach and other commonly-used algorithms such as technical analysis, reinforcement learning, supervised learning, stochastic strategies, and deterministic strategies are reported. By employing both position and the number of trading shares, we show that the performance of a trading strategy can be improved in terms of Return and Sharpe ratio. • Proposing an autonomous trading algorithm. • Providing smart trading capability in stock markets. • Employing a continuous action space DRL model. • Facilitating Money management. [ABSTRACT FROM AUTHOR]

In recent days, the research on student's intelligence level modelling is a challenging Artificial Intelligence (AI) task, which gains more attraction because it provides actionable insights to the tutor by analysing the intelligence level of the learners. Each learner's knowledge, comprehension, and intellectual capacities are unique. It is critical to identify these capacities and provide learners, particularly slow learners, with the necessary knowledge. Cognitive Performance Test (CPT) is an essential component for assessing the knowledge level of students. The reasoning level or coefficient deals with the analysis of the thinking capability in a logical way. It also reflects the child's learning potential. The main aim of the proposed system is to design a Cognitive Knowledge Representation Model (CKRM), which fuses Cognitive Performance Metrics (CPM) calculation and Reasoning Coefficient Calculation (RCC) algorithms to assess the student's intelligence level. The result of the proposed system is stratification of students to three different ranges of reasoning coefficient. The CKRM consists of the following phases: data collection, statistical Exploratory Data Analysis (EDA), model building and analysis, which involve the assessment of the knowledge level using CPT and calculation of reasoning coefficient using First Order Logic (FOL), and finally model evaluation using cognitive evaluation metrics. CPM and RCC algorithms have been proposed in this paper to calculate the student's reasoning coefficient by using the forward chaining FOL inference engine. The dataset is a real time data which consists of the academic and cognitive performance details of school students from classes 1 to 6 for the year 2019 to 2020. The academic data are collected from the Educational Management Information System (EMIS) maintained by the school. The cognitive performance data are collected by conducting the tests for the students using the memory training application called Lumosity. The proposed system's performance is evaluated using ten Machine Learning (ML) algorithms in which the Quadratic Discriminant Analysis achieved an accuracy of 0.97 for classes 1, 2, and 3. For classes 4, 5, and 6, nearly twelve ML algorithms are evaluated in which Random Forest (RF) Classifier achieved an accuracy of 0.98. Six math expert committee teachers concluded that the reasoning coefficient value was acceptable with an average accuracy of 0.92 for classes 1, 2, 3 and 0.9 for classes 4, 5, 6. In comparison to the pre-existing models employed in the prior research, it was determined that the created CKRM (academic and cognitive) was superior. The cognitive metrics such as taskability, Response Time (RT), knowledge capacity and utilization has also been evaluated. The average values of taskability, RT, knowledge capacity and knowledge utilization are 0.85, 0.81, 0.55, and 0.44. The ultimate goal is to make customized teaching easier; hence, this article involves determining a student's cognitive level by estimating their reasoning coefficient. The suggested approach analyses and categorizes students' cognitive abilities, such as memory, reasoning, problem solving, thinking, and logical reasoning, using three different reasoning coefficients. This approach assists teachers in determining the degree of intelligence of their students. • Cognitive Knowledge Representation Model (CKRM) to assess students' reasoning level. • Knowledge level assessment of students based on Cognitive Performance Test. • Computation of Cognitive Performance Metrics and Reasoning Coefficient. • Performance analysis using expert judgements and cognitive evaluation metrics. • Average accuracy score using expert judgemental analysis is 0.914. [ABSTRACT FROM AUTHOR]

Joint inference is a fundamental issue in the field of artificial intelligence. The greatest advantage of the joint inference is demonstrated by its capability of avoiding errors from cascading and accumulating on a pipeline of multiple chained sub-tasks. Markov Logic Network(MLN) is the most common joint inference model that provides a flexible representation and handles uncertainty. It has been applied successfully to joint inference on many natural language processing tasks to avoid error propagation. However, due to the great expressiveness of first-order logic, the representation for it in MLN generates rather complicated graph structures, which makes the learning and inference on large scale data intractably. In this paper, we present an ensemble learning approach to deal with the challenges in MLNs. Firstly, we give a proof within the probably approximately correct (PAC) framework. The proof points out what conditions are necessary for successful applying the ensemble learning approach to MLN. Secondly, the paper explains how to combine the learners. Finally, in order to illustrate the working mechanism of the ensemble joint inference model, we present an Ensemble Markov Logic Networks (EMLNs) method and use it to extract knowledge from a large scale corpus published by Google. 1 1 code.google.com/p/relation-extraction-corpus/. Experiments suggest that significant speedup can be gained by the EMLNs. Meanwhile, it show that this approach leads to a higher precision and recall than that of those pipeline approaches. [ABSTRACT FROM AUTHOR]

The use of robots in our daily lives is increasing. As we rely more on robots, thus it becomes more important for us that the robots will continue on with their mission successfully. Unfortunately, these sophisticated, and sometimes very expensive, machines are susceptible to different kinds of faults. It becomes important to apply a Fault Detection (FD) mechanism which is suitable for the domain of robots. Two important requirements of such a mechanism are: high accuracy and low computational-load during operation (online). Supervised learning can potentially produce very accurate FD models, and if the learning takes place offline then the online computational-load can be reduced. Yet, the domain of robots is characterized with the absence of labeled data (e.g., “faulty”, “normal”) required by supervised approaches, and consequently, unsupervised approaches are being used. In this paper we propose a hybrid approach - an unsupervised approach can label a data set, with a low degree of inaccuracy, and then the labeled data set is used offline by a supervised approach to produce an online FD model. Now, we are faced with a choice – should we use the unsupervised or the hybrid fault detector? Seemingly, there is no way to validate the choice due to the absence of (a priori) labeled data. In this paper we give an insight to why, and a tool to predict when, the hybrid approach is more accurate. In particular, the main impacts of our work are (1) we theoretically analyze the conditions under which the hybrid approach is expected to be more accurate. (2) Our theoretical findings are backed with empirical analysis. We use data sets of three different robotic domains: a high fidelity flight simulator, a laboratory robot, and a commercial Unmanned Arial Vehicle (UAV). (3) We analyze how different unsupervised FD approaches are improved by the hybrid technique and (4) how well this improvement fits our prediction tool. The significance of the hybrid approach and the prediction tool is the potential benefit to expert and intelligent systems in which labeled data is absent or expensive to create. [ABSTRACT FROM AUTHOR]

This paper considers the two-stage capacitated facility location problem (TSCFLP) in which products manufactured in plants are delivered to customers via storage depots. Customer demands are satisfied subject to limited plant production and limited depot storage capacity. The objective is to determine the locations of plants and depots in order to minimize the total cost including the fixed cost and transportation cost. However, the problem is known to be NP-hard. A practicable exact algorithm is impossible to be developed. In order to solve large-sized problems encountered in the practical decision process, an efficient alternative approximate method becomes more valuable. This paper aims to propose a hybrid evolutionary algorithm framework with machine learning fitness approximation for delivering better solutions in a reasonable amount of computational time. In our study, genetic operators are adopted to perform the search process and a local search strategy is used to refine the best solution found in the population. To avoid the expensive consumption of computational time during the fitness evaluating process, the framework uses extreme machine learning to approximate the fitness of most individuals. Moreover, two heuristics based on the characteristics of the problem is incorporated to generate a good initial population. Computational experiments are performed on two sets of test instances from the recent literature. The performance of the proposed algorithm is evaluated and analyzed. Compared with other algorithms in the literature, the proposed algorithm can find the optimal or near-optimal solutions in a reasonable amount of computational time. By employing the proposed algorithm, facilities can be positioned more efficiently, which means the fixed cost and the transportation cost can be decreased significantly, and organizations can enhance competitiveness by using the optimized facility location scheme. [ABSTRACT FROM AUTHOR]

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation algorithms (also known as nature inspired algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed. [ABSTRACT FROM AUTHOR]

To deal with increasing amounts of data, decision and policymakers frequently turn to advances in machine learning and artificial intelligence to capitalise on the potential reward. But there is also a reluctance to trust black-box models, especially when such models are used to support decisions and policies that affect people directly, like those associated with transport and people's mobility. Recent developments focus on explainable artificial intelligence to bolster models' trustworthiness. In this paper, we demonstrate the use of an explainable-by-design model, Bayesian Networks, on travel behaviour. The model incorporates various demographic and socioeconomic variables to describe full day activity chains: activity and mode choice, as well as the activity and trip durations. More importantly, this paper shows how the model can be used to provide the most relevant explanation for people's observed travel behaviour. The overall goal is to show that model explanations can be quantified and, therefore, assist policymakers to truly make evidence-based decisions. This goal is achieved through two case studies to explain people's vulnerability as it pertains to their total trip duration. • Bayesian network approach to demonstrate explainable artificial intelligence (XAI). • Network structure includes demographic, travel and temporal variables. • Bayesian networks is both behaviourally rich and intuitively interpretable. • Quantify vulnerability explanations for travel behaviour. [ABSTRACT FROM AUTHOR]

The past decade has witnessed the rapid development of Artificial Intelligence (AI), especially the explosion of deep learning-related connectionist approaches. This study combines traditional literature review, bibliometric methods, and the Science of Science (SciSci) theory to scrutinize the development context of AI in the last decade on AMiner. 4 4 www.aminer.cn , AMiner is an academic mining system, which will be introduced in the following section. With the assistance of AMiner tools and datasets, this paper aims to describe a further explicit context and evolution of AI in the past decade from the development of connectionist approaches. Five aspects of the past decade are highlighted: self-learning and self-coding algorithms, Recurrent Neural Networks (RNN) algorithms, reinforcement learning, pre-trained models, and other typical deep learning algorithms, which represent the significant progress of this field. By combining these critical parts, we then summarize the current limitations and corresponding future of AI trends in the next decade and discuss some topics about the next generation of AI. Discoveries in this paper will benefit AI research in promoting understanding of the current critical stage and future trends of AI development and the AI industry in the dramatic ascendant for the academic research results transformation and its industrial layout. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is fast becoming a general purpose technology with outstanding impacts in industries worldwide, thus supporting the Industry 4.0 revolution. In particular, the energy sector is one of those that has taken more advantages from the implementation of AI approaches, especially Machine Learning models, for several applications, including energy performance benchmarking of buildings. However, the black-box approach could lead to a lack of result interpretability thus preventing the effective application of AI in some real-world scenarios. For this reason, eXplainable Artificial Intelligence (XAI) tools can be effectively embedded within an AI-based Energy Analytics methodology in order to enhance the explainability of the model results. In this paper, we propose an explainable AI-based benchmarking framework for estimating the membership to specific energy performance classes of a large set of Energy Performance Certificates (EPCs) of flats. The classification is obtained by leveraging different black-box classifiers characterized by high accuracy, yet their inference mechanism is not human-readable. Therefore, a generalizable XAI methodology, based on the combination of a local explainer together with a clustering algorithm, is employed to explain the model results and causal effects between the predictors and target variable to better understand the model behaviour, and the motivations behind correct and wrong performed classifications. The paper provides a general methodological approach capable to exploit a limited number of instances to extract, explain and interpret inference mechanisms learnt by the model that are useful for the end-user. The framework was tested on about 100,000 EPCs of flats located in Italy. • A data-driven benchmarking process of building energy performance is proposed. • A set of about 100,000 energy performance certificates of buildings is analysed. • Five classification algorithms are considered for developing the benchmarking tool. • Clustering analysis and XAI are coupled for extracting human-readable patterns. • Classification results are explained for understanding the model behaviour. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent human trajectory destination detection system from video. The system assumes a passive collection of video from a wide scene used by humans in their daily motion activities such as walking towards a door. The proposed system includes three main modules, namely human blob detection, star skeleton detection and destination area prediction, and it works directly with raw video, producing motion features for destination prediction system, such as position, velocity and acceleration from detected human skeletons, resulting in several input features that are used to train a machine learning classifier. We adopted a university campus exterior scene for the experimental study, which includes 348 pedestrian trajectories from 171 videos and five destination areas: A, B, C, D and E. A total of six data processing combinations and four machine learning classifiers were compared, under a realistic growing window evaluation. Overall, high quality results were achieved by the best model, which uses 37 skeleton motion inputs, undersampling on training data and a random forest. The global discrimination, in terms of area of the receiver operating characteristic curve is around 87%. Furthermore, the best model can predict in advance the five destination classes, obtaining a very good ahead discrimination for classes A, B, C and D, and a reasonable ahead discrimination for class E. [ABSTRACT FROM AUTHOR]

Data-driven intelligent systems provide a possible solution to condition-based maintenance of wind turbines without experts' knowledge or mechanism models. However, the accuracy of fault diagnosis results is easily impaired by the data imbalance in real-world applications. In this paper, a novel method is proposed to address the mentioned problem. Specifically, spatial and temporal information of supervisory control and data acquisition (SCADA) data is extracted, and a contrastive learning strategy is developed to obtain the data representations correlated with the health conditions. Additionally, the bollards of the data distributions are determined in the representation space, and they are matched with the data centers of each health state. Under this circumstance, the unexpected effects of data imbalance on data representations are relieved, leading to effective decision boundaries regardless of the sample numbers. Then, a classifier is trained on the learned representations to recognize the faults in wind turbines. Compared with several baselines and state-of-the-art approaches, the impressive performance of the proposed method is demonstrated with simulated data and actual measurements from a utility-scale wind turbine. This research provides useful insights into the efficient development of wind energy with data-driven intelligent systems. [ABSTRACT FROM AUTHOR]

• Showed the feasibility of identifying health condition in drivers by monitoring their actions. • Built model of driver behavior through machine learning from observation. • Falconet system employs Neuroevolution and Particle Swarm Optimization to build model. This paper describes our investigation to determine whether undesirable health conditions of an automobile driver can be identified in real time solely by monitoring and assessing his/her driving behavior. The concept has great potential to reduce the accident rate on roadways, especially for young inexperienced drivers who may be suffering from chronic health conditions that when uncontrolled, can result in dangerous driving actions. Our approach involves building models of "normal" and "abnormal" driving by an individual through machine learning from observation (MLfO, or simply LfO). Conceptually, discrepancies between actual driving actions taken by a driver in real time and the actions prescribed by a model of her/his normal driving, and/or similarities to a model of his/her abnormal driving, could indicate a dangerous medical condition. If appropriate, the system could alert the driver and/or the appropriate authorities (e.g., EMTs, police, or parents if a minor) of the potential for danger. More specifically, our research created models of human driving through the use of an LfO system developed previously in our laboratory called Force-feedback Approach to Learning from Coaching and Observation with Natural and Experiential Training (Falconet). Time-stamped traces of actions taken by 12 human test subjects in a driving simulator were collected and used to create the models of human driving behavior through Falconet. Then the overall actions prescribed by the models (called the agents) were compared to the original traces to ascertain whether similarities and/or differences between the human test subject behaviors and the agent behaviors could be indicative of the target conditions. In our use case presented here, the target condition was Attention Deficit/Hyperactivity Disorder (ADHD), a condition that afflicts many driving age teenagers and which can be detrimental to safe driving when not under control through medication. The work described in this paper is exploratory in nature, with the objective of showing scientific feasibility. The results of extensive testing indicate that the agents created with the Falconet system produced promising results, being able to correctly characterize traces in up to nearly 82% of the test cases presented. Nevertheless, as is typical in such exploratory works, we found that much further work remains to be done before this concept becomes ready for commercial application. In this paper we describe the approach taken, the agents created and the extensive quantitative experiments conducted, as well as any insights learned. Areas of further research are also identified and discussed. [ABSTRACT FROM AUTHOR]

Urban traffic as one of the most important challenges in modern city life needs practically effective and efficient solutions. Artificial intelligence methods have gained popularity for optimal traffic light control. In this paper, a review of most important works in the field of controlling traffic signal timing, in particular studies focusing on Q -learning, neural network, and fuzzy logic system are presented. As per existing literature, the intelligent methods show a higher performance compared to traditional controlling methods. However, a study that compares the performance of different learning methods is not published yet. In this paper, the aforementioned computational intelligence methods and a fixed-time method are implemented to set signals times and minimize total delays for an isolated intersection. These methods are developed and compared on a same platform. The intersection is treated as an intelligent agent that learns to propose an appropriate green time for each phase. The appropriate green time for all the intelligent controllers are estimated based on the received traffic information. A comprehensive comparison is made between the performance of Q -learning, neural network, and fuzzy logic system controller for two different scenarios. The three intelligent learning controllers present close performances with multiple replication orders in two scenarios. On average Q -learning has 66%, neural network 71%, and fuzzy logic has 74% higher performance compared to the fixed-time controller. [ABSTRACT FROM AUTHOR]

The growing number of e-commerce orders is leading to increased risk management to prevent default in payment. Default in payment is the failure of a customer to settle a bill within 90 days upon receipt. Frequently, credit scoring (CS) is employed to identify customers’ default probability. CS has been widely studied, and many computational methods have been proposed. The primary aim of this work is to develop a CS model to replace the pre-risk check of the e-commerce risk management system Risk Solution Services (RSS), which is currently one of the most used systems to estimate customers’ default probability. The pre-risk check uses data from the order process and includes exclusion rules and a generic CS model. The new model is supposed to replace the whole pre-risk check and has to work both in isolation and in integration with the RSS main risk check. An application of genetic programming (GP) to CS is presented in this paper. The model was developed on a real-world dataset provided by a well-known German financial solutions company. The dataset contains order requests processed by RSS. The results show that GP outperforms the generic CS model of the pre-risk check in both classification accuracy and profit. GP achieved competitive classificatory accuracy with several state-of-the-art machine learning methods, such as logistic regression, support vector machines and boosted trees. Furthermore, the GP model can be used in combination with the RSS main risk check to create a model with even higher discriminatory power. [ABSTRACT FROM AUTHOR]

Traditional topic modeling has been widely studied and popularly employed in expert systems and information systems. However, traditional topic models cannot discover structural relations among topics, thus losing the chance to explore the data more deeply. Hierarchical topic modeling has the capability of learning topics, as well as discovering the hierarchical topic structure from text data. But purely unsupervised models tend to generate weak topic hierarchies. To solve this problem, we propose a novel knowledge-based hierarchical topic model (KHTM), which can incorporate prior knowledge into topic hierarchy building. A key novelty of this model is that it can mine prior knowledge automatically from the topic hierarchies of multiple domains corpora. In this paper, the knowledge is represented as the word pairs which satisfy the requirement of frequent co-occurrence, and knowledge is organized in form of hierarchical structure. We also propose an iterative learning algorithm. For evaluation, we crawled two new multi-domain datasets and conducted comprehensive experiments. The experimental results show that our algorithm and model can generate more coherent topics, and more reasonable hierarchical structure. [ABSTRACT FROM AUTHOR]

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE ( S mart A daptation through C ontextual RE quirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems’ contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems’ engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains. [ABSTRACT FROM AUTHOR]

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics. [ABSTRACT FROM AUTHOR]

Many intelligent systems, such as assistive robots, augmented reality trainers or unmanned vehicles, need to know their physical location in the environment in order to fulfill their task. While relying exclusively on natural landmarks for that task is the preferred option, their use is somewhat limited because the proposed methods are complex, require high computational power, and are not reliable in all environments. On the other hand, artificial landmarks can be placed in order to alleviate these problems. In particular, square fiducial markers are one of the most popular tools for camera pose estimation due to their high performance and precision. However, the state-of-the-art methods still perform poorly under difficult image conditions, such as camera defocus, motion blur, small scale or non-uniform lighting. This paper proposes a method to robustly detect this type of landmarks under challenging image conditions present in realistic scenarios. To do so, we re-define the marker identification problem as a classification one based on state-of-the-art machine learning techniques. Second, we propose a procedure to create a training dataset of synthetically generated images affected by several challenging transformations. Third, we show that, in this problem, a classifier can be trained using exclusively synthetic data, performing well in real and challenging conditions. Different types of classifiers have been tested to prove the validity of our proposal (namely, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Support Vector Machine (SVM)), and statistical analyses have been performed in order to determine the best approach for our problem. Finally, the obtained classifiers have been compared to the ArUco and AprilTags fiducial marker systems in challenging video sequences. The results obtained show that the proposed method performs significantly better than previous approaches, making the use of this technology more reliable in a wider range of realistic scenarios such as outdoor scenes or fast moving cameras. [ABSTRACT FROM AUTHOR]

Extractive text summarization is an effective way to automatically reduce a text to a summary by selecting a subset of the text. The performance of a summarization system is usually evaluated by comparing with human-constructed extractive summaries that are created in annotated text datasets. However, for datasets where an abstract is written for reader purpose, the performance of a summarization system is evaluated by comparing with an abstract that is created by human who uses his own words. This makes it difficult to determine how far the state-of-the-art extractive methods are away from the upper bound that an ideal extractive method might achieve. In addition, the performance of an extractive method is always different in each domain, which make it difficult to benchmark. Previous studies construct an ideal sentence-based extract of a document that provides the best score of a given metric by exhaustive search of all possible sentence combinations of a given length. They then use the performance of the extract as the sentence-based upper-bound. However, this only applies to short texts. For long texts and multiple documents, previous studies rely on manual effort, which is expensive and time consuming. In this paper, we propose nine fast heuristic methods to generate the near ideal sentence-based extracts for long texts and multiple documents. Furthermore, we propose an n-gram construction method to construct the word-based upper-bound. A percentage ranking method is used to benchmark different extractive methods across different corpora. In the experiments, five different corpora are used. The results show that the near upper bounds constructed by the proposed methods are close to that using exhaustive search, but the proposed methods are much faster. Six general extractive summarization methods were also assessed to demonstrate the difference between the performance of the methods and the near upper bounds. [ABSTRACT FROM AUTHOR]

This paper shows the development of a tool with which to solve the most critical aspect of the porthole die design problem using a predictive model based on machine learning (ML). The model relies on a large amount of geometrical data regarding successful porthole die designs, information on which was obtained thanks to a collaboration with a leading extrusion company. In all cases, the dies were made of H-13 hot work steel and the billet material was 6063 aluminium alloy. The predictive model was chosen from a series of probes with different algorithms belonging to various ML families, which were applied to the analysis of geometrical data corresponding to 596 ports from 88 first trial dies. Algorithms based on the generation of multiple decision trees together with the boosting technique obtained the most promising results, the best by far being the CatBoost algorithm. The explainability of this model is based on a post-hoc approach using the SHAP (SHapley Additive exPlanations) tool. The results obtained with this ML-based model are notably better than those of a previous model based on linear regression as regards both the R2 metric and the results obtained with the application examples. An additional practical advantage is its explainability, which is a great help when deciding the best way in which to adjust an initial design to the predictive model. This ML-based model is, therefore, an optimal means to integrate the experience and know-how accumulated through many designs over time in order to apply it to new designs. It will also provide an aid in generating the starting point for the design of high-difficulty dies, in order to minimise the number of FEM (finite element method) simulation/correction iterations required until an optimal solution is achieved. It is not aimed to eliminate FEM simulation from the design tasks, but rather to help improve and accelerate the whole process of designing porthole dies. The work presented herein addresses a validation model for a very common porthole die typology: four cavity and four port per cavity dies for 6xxx series aluminium alloys. However, a wide range of research regarding the generalisation of this model or its extension to other porthole die typologies must still be carried out. [ABSTRACT FROM AUTHOR]

In the railway domain, rolling stock maintenance affects service operation time and efficiency. Minimizing train unavailability is essential for reducing capital loss and operational costs. To this aim, prediction of failures of rolling stock equipment is crucial to proactively trigger proper maintenance activities. Indeed, predictive maintenance is a golden example of the digital transformation within Industry 4.0, which affects several engineering processes in the railway domain. Nowadays, it may leverage artificial intelligence and machine learning algorithms to forecast failures and schedule the optimal time for maintenance actions. Generally, rail systems deteriorate gradually over time or fail directly, leading to data that vary extremely slowly. Indeed, ML approaches for predictive maintenance should consider this type of data to accurately predict and forecast failures. This paper proposes a methodology based on Long Short-Term Memory deep learning algorithms for predictive maintenance of railway rolling stock equipment. The methodology allows us to properly learn long-term dependencies for gradually changing data, and both predicting and forecasting failures of rail equipment. In the framework of an academic-industrial partnership, the methodology is experimented on a train traction converter cooling system, demonstrating its applicability and benefits. The results show that it outperforms state-of-the-art methods, reaching a failure prediction and forecasting accuracy over 99%, with a false alarm rate of ∼ 0. 4 % and a mean absolute error in the order of 1 0 − 4 , respectively. [ABSTRACT FROM AUTHOR]

Every day the innovative IoT technology is expanding further and further in our environment, with applications deployed in various contexts including cities. Communities can indeed address problems linked to urbanization thanks to this technology through the Smart City concept and thus support a sustainable development of their cities. Artificial intelligence and namely its machine learning branch is expected to reinforce this trend by making smart cities even smarter. However, smart cities can only be successful if they can be trusted and, with this in mind, machine learning can potentially be an efficient tool to mitigate cyberattacks. This paper can be divided into two main parts. In the first part the ability of Gradient Boosting and Deep Learning to make long-term predictions of noise level is studied based on noise data collected in the suburb of an English city. In the second part, we proposed an approach for detecting noise levels anomalies based on predictions. Two types of injections were taken into consideration namely punctual noise level attacks and gradual noise level attacks. Specifically, for the punctual attacks, when the difference between the actual sensed and predicted noise levels is greater than a given threshold, we considered that there is an anomaly in the data. For the gradual attacks, we used a criterion comparing the mean absolute error of predictions in the attacked set of data to statistics of the absolute error in the training set. The obtained results show that our approach, which uses a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) hybrid network for the noise level prediction, can effectively be used to detect the aforementioned types of anomalies. In the case of punctual attacks an increase in sound intensity of 5 dB was detected, while for gradual attacks, smaller changes can be detected. • Urban noise data forecasting is performed using deep learning models and gradient boosting. • Noise data from parking meters deployed in an English city. • Several deep learning models (LSTM, CNN-LSTM, Transformer, TFT) are studied. • Accurate short-term (one hour) and long-term (6 days) forecasts are obtained. • Detection of anomalous data. [ABSTRACT FROM AUTHOR]

• Presented an extensive overview on different stages of electrocardiogram analysis. • Summarized the most notable developments of smart health monitoring systems. • Discussed the latest hardware implementations of real-time ECG monitoring systems. • Identified challenges and limitations for various automatic ECG diagnosis systems. • Outlines the future vision of next generation ECG monitoring systems for healthcare. Over the last decade, cardiovascular diseases (CVD's) are the leading cause of death globally. Early prediction of CVD's can help in reducing the complications of high-risk patients. The electrocardiogram (ECG) is an efficient aiding tool, provides accurate information about various cardiac conditions of the human heart. Evaluation and interpretation of ECG signal has become the major goal in current research to identify and mitigate risky cardiovascular conditions. The ECG signal is efficiently analysed and classified using signal processing techniques, ranging from traditional to machine learning approaches and its subbranches, such as deep learning are used for the early detection and diagnosis of cardiac conditions and arrhythmias. The development of novel types of body sensors increases the need for automated, low-cost, real-time, and efficient ECG monitoring systems that can be used at home or in ambulatory settings. IoT in healthcare industry enables the doctors and specialists to diagnose the patient status remotely in a smart and efficient manner. Motivated with all the research done in this area for early diagnosis of cardiac abnormalities and save the life of high-risk patients, we surveyed numerous research works reported in the literature and provide a glance on several aspects of ECG signal monitoring systems. We first introduce the step-by-step framework of ECG signal analysis starting data acquisition to classification, describes each stage analysis of both traditional and advanced machine learning models that have been reported in the literature. We provide a deep discussion on ECG signal acquisition, pre-recorded clinical ECG data taken from the databases, signal processing and denoising, detection of feature based on feature engineering, and signal classification along with the comparative assessment amongst reviewed studies. This work also presents the detailed analysis of smart health care system that employs portable and wearable devices, as well as the Internet of Things (IoT) and wireless technologies, as an innovative medium for data transmission that allows for remote access to a patient's health status at lower costs and with greater efficiency. Moreover, this work provides an extensive knowledge on various hardware platforms that have been adopted for the development of biomedical processors including microcontrollers, FPGA and ASIC. Additionally, the challenges and limitations are discussed in this research field and directions for future work are suggested. This is the first review paper of its kind to present a comprehensive discussion on all aspects of cardiovascular disease monitoring systems in one place. [ABSTRACT FROM AUTHOR]

Progress in biomechanical modelling of human soft tissue is the basis for the development of new clinical applications capable of improving the diagnosis and treatment of some diseases (e.g. cancer), as well as the surgical planning and guidance of some interventions. The finite element method (FEM) is one of the most popular techniques used to predict the deformation of the human soft tissue due to its high accuracy. However, FEM has an associated high computational cost, which makes it difficult its integration in real-time computer-aided surgery systems. An alternative for simulating the mechanical behaviour of human organs in real time comes from the use of machine learning (ML) techniques, which are much faster than FEM. This paper assesses the feasibility of ML methods for modelling the biomechanical behaviour of the human liver during the breathing process, which is crucial for guiding surgeons during interventions where it is critical to track this deformation (e.g. some specific kind of biopsies) or for the accurate application of radiotherapy dose to liver tumours. For this purpose, different ML regression models were investigated, including three tree-based methods (decision trees, random forests and extremely randomised trees) and other two simpler regression techniques (dummy model and linear regression). In order to build and validate the ML models, a labelled data set was constructed from modelling the deformation of eight ex-vivo human livers using FEM. The best prediction performance was obtained using extremely randomised trees, with a mean error of 0.07 mm and all the samples with an error under 1 mm. The achieved results lay the foundation for the future development of some real-time software capable of simulating the human liver deformation during the breathing process during clinical interventions. [ABSTRACT FROM AUTHOR]

While artificial intelligence is widely employed in many applications, it is vulnerable to bias and unethical use. Therefore, fairness evaluation tools and bias mitigation algorithms have drawn considerable attention. The main concern arises when an intelligent system is developed to make life-changing decisions in a social application. The ethical aspects of the decisions are vital because they significantly affect human lives. Therefore, numerous techniques have been developed to prevent discrimination in the learning algorithm output. This survey paper focuses on the application of fairness techniques in healthcare, organizational justice, and finance. Different kinds of discrimination are identified and comprehensively discussed in each application, and state-of-the-art fairness methods in each category are reviewed. • Fairness algorithms in healthcare, organizational justice, and finance. • Discrimination in accessing medical services and insurance. • Unfair AI model has destructive effects on employee engagement and motivation. • Lending and credit-scoring model in AI fair models. [ABSTRACT FROM AUTHOR]

In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the users wait. In this paper, we propose Accelerated Model-agnostic Explanations (AcME), an interpretability approach that quickly provides feature importance scores both at the global and the local level. AcME can be applied a posteriori to each regression or classification model based on tabular data. Not only AcME computes feature ranking, but it also provides a what-if analysis tool to assess how changes in features values would affect model predictions. We evaluated the proposed approach on synthetic and real-world datasets, also in comparison with SHapley Additive exPlanations (SHAP), the approach we drew inspiration from, which is currently one of the state-of-the-art model-agnostic interpretability approaches. We achieved comparable results in terms of quality of produced explanations while reducing dramatically the computational time and providing consistent visualization for global and local interpretations. To foster research in this field, and for the sake of reproducibility, we also provide a repository with the code used for the experiments. • AcME, a new interpretability approach for Machine Learning, is proposed. • AcME is a model-agnostic, global and local approach. • AcME allows fast computational time w.r.t. to state-of-the-art approaches. • The effectiveness of AcME is demonstrated on both synthetic and real data. [ABSTRACT FROM AUTHOR]

In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods. • We propose a framework for defining different types of explanations of AI systems. • We contextualize current XAI discourses within the proposed framework. • We highlight two broad perspectives for defining quality criteria for explainability. • We discuss the relevance of our framework in light of current and upcoming AI regulation. • We confer fundamental aspects for future research of XAI scholars. [ABSTRACT FROM AUTHOR]

• A significant contribution of artificial emotional intelligence in recent years. • Comparative study of conventional and deep learning techniques. • Limitations and issues in existing techniques and how to overcome them. • Research gap and challenges to obtain better emotion recognition performance. • Research opportunities and future direction in artificial emotional intelligence. Artificial intelligence substantially changes the global world, influencing technologies, machines, and objects in various encouraging aspects nowadays; emotion recognition is also one of them. This paper describes a significant contribution of emotion recognition by applying conventional and deep learning methodologies by focusing on limitations and demanding challenges. It also intends to explore the comparative study on recently applied machine learning and deep learning-based algorithms, which provide the best accuracy rates to recognize emotions. This Comparative study consists of different feature extractions, classifier models, and datasets that recognize the emotions within a facial image, speech, and non-verbal communication and describes their features and principles for future research work. We have shown the balancing accuracy, and efficiency of using hybrid classification techniques briefly explained in Speech emotion recognition. This review study would be more beneficial in enhancing automated decision-making services in various customer-based industries and observing patients in the health care sector, industries, public sectors, private sectors, and production firms. [ABSTRACT FROM AUTHOR]

Data streams classification is an important approach to get useful knowledge from massive and dynamic data. Because of concept drift, traditional data mining techniques cannot be directly applied in data streams environment. Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN), comparing with the traditional neural network (e.g. BP network), ELM has a faster speed, so it is very suitable for real-time data processing. In order to deal with the challenge in data streams classification, a new approach based on extreme learning machine is proposed in this paper. The approach utilizes ELMs as base classifiers and adaptively decides the number of the neurons in hidden layer, in addition, activation functions are also randomly selected from a series of functions to improve the performance of the approach. Finally, the algorithm trains a series of classifiers and the decision results for unlabeled data are made by weighted voting strategy. When the concept in data streams keeps stable, every classifier is incrementally updated by using new data; if concept drift is detected, the classifiers with weak performance will be cleared away. In the experiment, we used 7 artificial data sets and 9 real data sets from UCI repository to evaluate the performance of the proposed approach. The testing results showed, comparing with the conventional classification methods for data streams such as ELM, BP, AUE2 and Learn++.MF, on most data sets, the new approach could not only be simplest in the structure, but also get a higher and more stable accuracy with lower time consuming. [ABSTRACT FROM AUTHOR]

Abstract: Computer programming is a high-level thinking activity. In the educational area, using learning styles to understand how students learn is a significant issue. The electronic Portfolio (ePortfolio) is a popular educational management and assessment tool. Unfortunately, few researchers investigate programming learning style diagnosis. This paper addresses this gap in research: this study constructs an ePortfolio-based programming learning style diagnosis to detect students’ styles. The fusion of multiple fuzzy-based diagnosis knowledge is the main contribution of this work. This paper built a heuristic optimization method to integrate multiple diagnosis knowledge bases. Performance evaluations and empirical studies were implemented to verify the proposed algorithm and fusion solution. Experimental results showed that the proposed heuristic optimization firms the validity and stability of a diagnostic system, and the ePortfolio-based programming learning style diagnosis is highly accepted by students. Furthermore, teachers agreed that the knowledge fusion mechanism and diagnosis system were usable. [Copyright &y& Elsevier]

Abstract: The automatic generation of individualized plans in specific domains is an open problem that combines aspects related to automated planning, machine learning or recommendation systems technology. In this paper, we focus on a specific instance of that task; that of generating e-learning courses adapted to students’ profiles, within the automated planning paradigm. One of the open problems in this type of automated planning application relates to what is known as oversubscription: given a set of goals, each one with a utility, obtain a plan that achieves some (or all) the goals, maximizing the utility, as well as minimizing the cost of achieving those goals. In the generation of e-learning designs there is only one goal: generating a course design for a given student. However, in order to achieve the goal, the course design can include many different kinds of activities, each one with a utility (that depend on the student profile) and cost. Furthermore, these activities are usually grouped into clusters, so that at least one of the activities in each cluster is needed, though many more can be used. Finally, there is also an overall cost threshold (usually in terms of student time). In this paper, we present our work on building an individualized e-learning design. We pose each course design as a variation of the oversubscription problem, that we call the clustered-oversubscription problem, and we use linear programming for assisting a planner to generate the design that better adapts to the student. [Copyright &y& Elsevier]