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The paper "Consumer choices under new payment methods" suggests a payment portfolio model that includes new payment methods that have emerged from the development of cryptocurrency markets and central bank digital currencies (CBDCs). The 35th issue of Financial Innovation (FIN), Volume 8, No.5 (2022) presents 19 papers contributed by authors and co-authors from sixteen countries and areas: Australia, Brazil, Canada, China, Hong Kong, Iran, Italy, Japan, Korea, Netherlands, Saudi Arabia, South Africa, Taiwan, Türkiye, Tunisia and USA. The paper "Predicting cash holdings using supervised machine learning algorithms" implies that with more advanced algorithms, considerable improvements are observed with a maximum of 42% improvement in RMSE values. [Extracted from the article]

Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. The comparison result of the proposed algorithm with other algorithms proves that EDOLSCA also has advantages in heat exchanger optimal design. Finally, the experimental results confirm the efficiency of the proposed scheme in real-world urban scenarios; The last paper by Lidong Zhang et al. on "Elite and dynamic opposite learning enhanced sine cosine algorithm for application to plat-fin heat exchangers design problem" proposes a novel variant of sine and cosine algorithm named EDOLSCA enhanced by dynamic opposite learning algorithm and the elite strategy. [Extracted from the article]

Accurate identification of gas-liquid two-phase flow patterns during oil and gas drilling is critical to analyzing bottom hole pressure, detecting overflows in time, and preventing blowout accidents. Since the gas-liquid two-phase flow has deformable interfaces, resulting in complex gas-liquid two-phase flow patterns, the existing gas-liquid two-phase flow patterns are limited in width in terms of pipe diameter and incline, leading to adaptation problems in experimental flow patterns and mechanistic models. Machine learning methods provide potential tools for solving gas-liquid two-phase flow pattern identification. In this paper, a sample database with 5879 data points was established by reviewing and organizing existing literature focusing on normal pressure and temperature, and air-water experimental conditions to provide a data-preparation for the relationship between gas and liquid velocities, pipe diameter and incline characteristics and flow pattern objectives. Four machine learning models, including K-Nearest Neighbor, Naïve Bayes, Decision Tree and Random Forest, were investigated, and each model was trained and tested using a sample database to reveal the performance of four types of supervised machine learning methods, representing similarity, probability, inductive inference and ensemble-learning principles, for gas-liquid two-phase flow pattern recognition, and the prediction accuracy was 0.86, Naïve Bayes is 0.56, Decision Tree is 0.89 and Random Forest 0.97. Comprehensive analysis of each model confusion matrix shows that the machine learning method has the best recognition of dispersed bubble flow, better recognition of slug flow, and the worst recognition of churn flow among the nine flow patterns which proves the controversial nature of the mechanism model in the transition from slug flow to churn flow. This paper uses experimental data as model input features, making the machine learning-based gas-liquid two-phase flow pattern identification model meaningful for practical engineering applications, and also demonstrating the feasibility of using supervised machine learning methods for gas-liquid two-phase flow pattern identification at normal pressure and temperature, wide-range of pipe diameter and incline. [ABSTRACT FROM AUTHOR]

Looking at the rapidity the social media has gained ascendancy in the society, coupled with considerable shortage of addressing the health of the social media users, there is a pressing need for employing mechanized systems to help identify individuals at risk. In this study, we investigated potential of people's social media language in order to predict their vulnerability towards the future episode of mental distress. This work aims to (a) explore the most frequent affective expressions used by online users which reflect their mental health condition and (b) develop predictive models to detect users with risk of psychological distress. In this paper, dominant sentiment extraction techniques were employed to quantify the affective expressions and classify and predict the incident of psychological distress. We trained a set of seven supervised machine learning classifiers on logs crowd-sourced from 2500 Indian Social Networking Sites (SNS) users and validated with 3149 tweets collected from Indian Twitter. We test the model on these two different SNS datasets with different scales and ground truth labeling method and discuss the relationship between key factors and mental health. Performance of classifiers is evaluated at all classification thresholds; accuracy, precision, recall, F1-score. and experimental results show a better traction of accuracies ranging from ~ 82 to ~ 99% as compared to the models of relevant existing studies. Thus, this paper presents a mechanized decision support system to detect users' susceptibility towards mental distress and provides several evidences that it can be utilized as an efficient tool to preserve the psychological health of the social media users. [ABSTRACT FROM AUTHOR]

Forecasting of load is essential for operating power systems. India recently witnessed one of the worst power crisis with the highest ever power demand of 207 GW on April 29, 2022. The demand in the month of May and June 2022 was estimated to reach 215 GW. The peak demand this year 2023, according to the electricity ministry, is predicted to be around 230 GW from April to June. The inability to meet certain fundamental issues as power can take a toll on any country's economy. Proper prediction helps in proper decision making and planning. The main objective of this paper is to predict day ahead electrical load demand for Assam. Statistical and Machine Learning Algorithms has been studied. The study has been carried out using real-time data for the years 2016, 2017 and 2018. The paper presents a detailed analysis of the different hyper parameters of the deep learning models and their effect is seen on the learning efficiency. A novel stacked forecasting model is proposed using neural networks as base learners and CatBoost as the meta-learner. The performance of the proposed model has been evaluated and compared with individual models in terms of training time and accuracy using different error metrics namely MAE, MSE, RMSE, MAPE and R2 score. A comparison of the proposed prediction model with the prediction models available in literature has been presented. The conclusion states that both the statistical and machine learning algorithms used in this study act as useful tools for daily load forecasting with considerable accuracy; yet machine learning algorithm outperforms the statistical methods. The entire work has been done in Google Colaboratory using Python as the programming language. [ABSTRACT FROM AUTHOR]

This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects. The methodology proposed in this paper determines the material composition of space objects from single pixel images. Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra. From this information, a supervised machine learning algorithm is used to classify the object into one of several categories based on the detection of materials on the object. The behaviour of the material classification methods is investigated under non-ideal circumstances, to determine the effect of weathered materials, and the behaviour when the training library is missing a material that is present in the object being observed. Finally the paper will present some preliminary results on the identification and classification of space objects. [ABSTRACT FROM AUTHOR]

We welcome you to this special issue of Machine Learning Journal (MLJ), comprising of papers accepted to the journal track of the 11th Asian Conference on Machine Learning (ACML 2019), held in Nagoya, Japan, from 17 to 19 November 2019. This year's ACML journal track received 52 submissions and 9 papers have been accepted for this special issue. [Extracted from the article]

Since the traditional ship stability failure probability assessment method has many input parameters and cumbersome intermediate calculation process, this paper proposes a joint multi-model machine learning prediction method based on confidence. The method calculates the confidence of each machine learning model for the current prediction result, selects the top n models among them, and takes the average of their prediction results as the output. The confidence is calculated by a method inspired by semi-supervised learning. To reduce the number of ship features that need to be input for assessment, a sensitivity analysis is used to reject irrelevant features. Eight machine learning models that have good performance and are widely used in other fields, including Radial Basis Function Neural Network, Random Forest, eXtreme Gradient Boosting, and so on, are used as component learners in this study. The hyperparameters of each model are obtained by cross-validation and grid search. The advancedness of the proposed method is verified by comparing it with other models on a small self-built ship stability failure probability dataset. By conducting experiments that simply average the results of the component learners, it is confirmed that simple superposition different models does not necessarily improve the accuracy. At the same time, after pre-processing the input features in different ways, the comparison of the prediction performance was conducted, and the experimental results showed that the proposed method is not affected by the way the input features are preprocessed and therefore has some robustness. [ABSTRACT FROM AUTHOR]

Lithofacies identification is critical to energy exploration and reservoir evaluation. Machine learning provides a way to use logging data for lithofacies intelligence identification. However, labeled logging data are usually scarce, which makes the currently used supervised algorithms less effective, so semi-supervised methods have received attention from researchers. In this paper, we propose to apply Tri-Training to the field of lithofacies recognition. The framework used Random Forest (RF), Gradient-Boosted Decision Trees (GBDT), and Support Vector Machine (SVM), as the baseline supervised classifiers, and based on the idea of inductive semi-supervised methods and ensemble learning. Baseline classifiers are trained and iterated using unlabeled data to obtain effect improvement. The final results are output in an ensemble paradigm. We used seven logging parameters from two wells as input and divide the data randomly 10 times for training and testing. With only five samples of each lithology, the prediction accuracy improved by the average of 2.1% and 14.5% in both wells compared to the baseline methods. In addition, we also compared two commonly used semi-supervised methods, label propagation algorithm (LPA) and Co-Training. The experimental results also confirm that Tri-training has the better and more stable performance. The Tri-training method in this paper can be effectively applied to lithofacies identification under scarce labeled logging data. [ABSTRACT FROM AUTHOR]

The number of deployed web applications and the number of web-based attacks in the last decade are constantly increasing. One group of tools that gained the attention of cyber security specialists are Dynamic Application Security Testing (DAST) tools, which is used to assess the security posture of web applications. DAST tools have similar purpose for web applications as network scanners and mappers have for local networks and computers—to scan web applications, enumerate as much as possible information from them and this way potentially reveal existing vulnerabilities. The tools are not only used by security analysts but also by the attackers in the reconnaissance and enumeration phases of the attack. This paper analyses DAST tools' network behaviour patterns, characteristic features that distinguish them from other traffic and methods to detect their operation using classical supervised machine learning methods. Unlike most of the work related to web application security and web application attack detection, which relies on HTTP logs, the research presented here is based on network traffic traces and flow statistics. This allows malicious scanning detection on the network traffic path even in the case of encrypted web traffic. Experimental results show that an accurate and reliable detection of four analysed DAST tools, ZAP, Nikto, Vega and Arachni, is possible. Flow classification of the existing DAST tools has high precision because DAST tools still do not deploy any mechanisms to hide their operation and mimic web application browsing by human users. Additionally, the paper contains an analysis of fast malicious behaviour detection through an analysis of the detection of malicious behaviour, while the flows are still active. The experimental results show that it is possible to detect malicious behaviour with a relatively high accuracy after only 15 packets in a flow. [ABSTRACT FROM AUTHOR]

Reinforcement learning is one of the promising approaches for algorithmic trading in financial markets. However, in certain situations, buy or sell orders issued by an algorithmic trading program may not be fulfilled entirely. By considering the actual scenarios from the financial markets, in this paper, we propose a novel framework named Supervised Actor-Critic Reinforcement Learning with Action Feedback (SACRL-AF) for solving this problem. The action feedback mechanism of SACRL-AF notifies the actor about the dealt positions and corrects the transitions of the replay buffer. Meanwhile, the dealt positions are used as the labels for the supervised learning. Recent studies have shown that Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) are more stable and superior to other actor-critic algorithms. Against this background, based on the proposed SACRL-AF framework, two reinforcement learning algorithms henceforth referred to as Supervised Deep Deterministic Policy Gradient with Action Feedback (SDDPG-AF) and Supervised Twin Delayed Deep Deterministic Policy Gradient with Action Feedback (STD3-AF) are proposed in this paper. Experimental results show that SDDPG-AF and STD3-AF achieve the state-of-art performance in profitability. [ABSTRACT FROM AUTHOR]

Machine learning (ML), a transformational technology, has been successfully applied to forecasting events down the road. This paper demonstrates that supervised ML techniques can be used in recession and stock market crash (more than 20% drawdown) forecasting. After learning from strictly past monthly data, ML algorithms detected the Covid-19 recession by December 2019, six months before the official NBER announcement. Moreover, ML algorithms foresaw the March 2020 S&P500 crash two months before it happened. The current labor market and housing are harbingers of a future U.S. recession (in 3 months). Financial factors have a bigger role to play in stock market crashes than economic factors. The labor market appears as a top-two feature in predicting both recessions and crashes. ML algorithms detect that the U.S. exited recession before December 2020, even though the official NBER announcement has not yet been made. They also do not anticipate a U.S. stock market crash before March 2021. ML methods have three times higher false discovery rates of recessions compared to crashes. [ABSTRACT FROM AUTHOR]

For detecting and resolving the various types of malware, novel techniques are proposed, among which deep learning algorithms play a crucial role. Although there has been a lot of research on the development of DL-based mobile malware detection approaches, they were not reviewed in detail yet. This paper aims to identify, assess, and synthesize the reported articles related to the application of DL techniques for mobile malware detection. A Systematic Literature Review is performed in which we selected 40 journal articles for in-depth analysis. This SLR presents and categorizes these articles based on machine learning categories, data sources, DL algorithms, evaluation parameters & approaches, feature selection techniques, datasets, and DL implementation platforms. The study also highlights the challenges, proposed solutions, and future research directions on the use of DL in mobile malware detection. This study showed that Convolutional Neural Networks and Deep Neural Networks algorithms are the most used DL algorithms. API calls, Permissions, and System Calls are the most dominant features utilized. Keras and Tensorflow are the most popular platforms. Drebin and VirusShare are the most widely used datasets. Supervised learning and static features are the most preferred machine learning and data source categories. [ABSTRACT FROM AUTHOR]

This document is an introduction to a special issue of the journal "Language Resources & Evaluation" focused on language resources for clinical linguistics. Clinical linguistics is an interdisciplinary field that studies the linguistic characteristics associated with medical conditions. The special issue covers a range of topics, including corpus-based research in speech-language pathology, the identification and use of language resources for clinical applications, the design of annotation tools for analyzing atypical verbal productions, and legal and ethical aspects of language technology. The issue includes papers on topics such as dementia, schizophrenia, pragmatic skills, breast cancer diagnosis, depressive symptoms detection on Twitter, and robotic-assisted surgery. The authors provide a brief overview of each paper and express gratitude to the authors and reviewers involved in the special issue. [Extracted from the article]

With the rapid development of the Internet, there are a large number of high-dimensional multi-label data to be processed in real life. To save resources and time, semi-supervised multi-label feature selection, as a dimension reduction method, has been widely used in many machine learning and data mining. In this paper, we design a new semi-supervised multi-label feature selection algorithm. First, we construct an initial similarity matrix with supervised information by considering the similarity between labels, so as to learn a more ideal similarity matrix, which can better guide feature selection. By combining latent representation with semi-supervised information, a more ideal pseudo-label matrix is learned. Second, the local manifold structure of the original data space is preserved by the manifold regularization term based on the graph. Finally, an effective alternating iterative updating algorithm is applied to optimize the proposed model, and the experimental results on several datasets prove the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

The scaling methodology described in this paper to find the geometry and working parameters of Hall Thrusters is based on algorithms of supervised Machine Learning. The approach considers the determination of the geometrical sizes, propellant mass flow rate and discharge voltage taking thrust and specific impulse as requirements. The magnetic field is also considered. The Gradient Boosting Regression is found as the most suitable algorithm for our purpose. Scaling relies on a specific database of 54 thrusters for the determination of all parameters. The database includes measurements carried out with xenon, krypton and argon as propellant. A unique analytical approach based on the GBR algorithm has been developed and validated to determine the suitable design for a Hall thruster according to space mission requirements. [ABSTRACT FROM AUTHOR]

In the digital era, organizations belonging to the same or different market segments come together in digital platforms that allow them to exchange. These organizations are unified within a Digital Business Ecosystem. However, the rapid growth of the number of these organizations accentuates the complexity of finding economic partners, customers, suppliers, or other organizations that can share economic interests. In our research, we propose a recommendation system that is implemented on such a digital platform, and which is based on matchmaking and hybrid supervised machine learning algorithms. In this paper, we provide a detailed analysis of the functioning of this system, the challenge encountered when processing the data which made it possible to highlight the similarities between the organizations that can be associated. Thus, we seek to improve the understanding and analysis of the data for the identification of partners in an optimal way. [ABSTRACT FROM AUTHOR]

Rating prediction is a crucial element of business analytics as it enables decision-makers to assess service performance based on expressive customer feedback. Enhancing rating score predictions and demand forecasting through incorporating performance features from verbatim text fields, particularly in service quality measurement and customer satisfaction modelling is a key objective in various areas of analytics. A range of methods has been identified in the literature for improving the predictability of customer feedback, including simple bag-of-words-based approaches and advanced supervised machine learning models, which are designed to work with response variables such as Likert-based rating scores. This paper presents a dynamic model that incorporates values from topic membership, an outcome variable from Latent Dirichlet Allocation, with sentiment analysis in an Extreme Gradient Boosting (XGBoost) model used for rating prediction. The results show that, by incorporating features from simple unsupervised machine learning approaches (LDA-based), an 86% prediction accuracy (AUC based) can be achieved on objective rating values. At the same time, a combination of polarity and single-topic membership can yield an even higher accuracy when compared with sentiment text detection tasks both at the document and sentence levels. This study carries significant practical implications since sentiment analysis tasks often require dictionary coverage and domain-specific adjustments depending on the task at hand. To further investigate this result, we used Shapley Additive Values to determine the additive predictability of topic membership values in combination with sentiment-based methods using a dataset of customer reviews from food delivery services. [ABSTRACT FROM AUTHOR]

Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care. [ABSTRACT FROM AUTHOR]

Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic (α -pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of α -pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and α -pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering. [ABSTRACT FROM AUTHOR]

Intrusion Detection Systems (IDSs) are crucial for safeguarding modern IoT communication networks against cyberattacks. IDSs must exhibit exceptional performance, low false positive rates, and significant flexibility in constructing attack patterns to efficiently identify and neutralize these attacks. This research paper discusses the use of an Extreme Learning Machine (ELM) as a new technique to enhance the performance of IDSs. The study utilizes two standard IDS-based IoT network datasets: NSL-KDD 2009 via Distilled-Kitsune 2021. Both datasets are used to assess the effectiveness of ELM in a conventional supervised learning setting. The study investigates the capacity of the ELM algorithm to handle high-dimensional and unbalanced data, indicating the potential to enhance IDS accuracy and efficiency. The research also examines the setup of ELM for both NSL_KDD and Kitsune using Python and Google COLAB to do binary and multi-class classification. The experimental evaluation revealed the proficient performance of the proposed ELM-based IDS among other implemented supervised learning-based IDSs and other state-of-the-art models in the same study area. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are the future of networking. They represent one of the best technologies for automation due to their characteristics, such as cost-effectiveness and wireless nature. However, WSNs require explicit programming to perform certain tasks, lacking adaptability. To address this limitation, machine learning (ML) can be highly beneficial as it grants networks the ability to make decisions automatically. This capability can refine the accuracy and efficiency of WSNs. This paper aims to provide a detailed classification of machine learning algorithms, discussing various problems of traditional wireless sensor networks and the solutions that machine learning can offer. It also includes a brief discussion of problems associated with machine learning in WSNs. This review paper is written with the hope of being useful for researchers in a future era where the amalgamation of WSNs and machine learning algorithms will influence many aspects of human life. [ABSTRACT FROM AUTHOR]

In the paper titled "A prediction-based cycle life test optimization method for cross-formula batteries using instance transfer and variable-length-input", Ma et al. deal with the prediction of the useful life of lithium-ion power-grade batteries. The practice of maintenance and asset management has been among the key beneficiaries of the industrial transition towards digitalisation. Data-driven machine learning approaches for critical assets can rarely benefit from the availability of data which cover a sufficient range of operating conditions and faults. [Extracted from the article]

Mobile apps for healthcare (mHealth apps for short) have been increasingly adapted to help users manage their health or to get healthcare services. User feedback analysis is a pertinent method that can be used to improve the quality of mHealth apps. The objective of this paper is to use supervised machine learning algorithms to evaluate the quality of mHealth apps according to the ISO/IEC 25010 quality model based on user feedback. For this purpose, a total of 1682 user reviews have been collected from 86 mHealth apps provided by Google Play Store. Those reviews have been classified initially into the ISO/IEC 25010 eight quality characteristics, and further into Negative, Positive, and Neutral opinions. This analysis has been performed using machine learning and natural language processing techniques. The best performances were provided by the Stochastic Gradient Descent (SGD) classifier with an accuracy of 82.00% in classifying user reviews according to the ISO/IEC 25010 quality characteristics. Moreover, Support Vector Machine (SVM) classified the collected user reviews into Negative, Positive, and Neutral with an accuracy of 90.50%. Finally, for each quality characteristic, we classified the collected reviews according to the sentiment polarity. The best performance results were obtained for the Usability, Security, and Compatibility quality characteristics using SGD classifier with an accuracy equal to 98.00%, 97.50%, and 96.00%, respectively. The results of this paper will be effective to assist developers in improving the quality of mHealth apps. [ABSTRACT FROM AUTHOR]

The domain of Machine learning has experienced Substantial advancement and development. Recently, showcasing a Broad spectrum of uses like Computational linguistics, image identification, and autonomous systems. With the increasing demand for intelligent systems, it has become crucial to comprehend the different categories of machine acquiring knowledge systems along with their applications in the present world. This paper presents actual use cases of machine learning, including cancer classification, and how machine learning algorithms have been implemented on medical data to categorize diverse forms of cancer and anticipate their outcomes. The paper also discusses supervised, unsupervised, and reinforcement learning, highlighting the benefits and disadvantages of each category of Computational intelligence system. The conclusions of this systematic study on machine learning methods and applications in cancer classification have numerous implications. The main lesson is that through accurate classification of cancer kinds, patient outcome prediction, and identification of possible therapeutic targets, machine learning holds enormous potential for improving cancer diagnosis and therapy. This review offers readers with a broad understanding as of the present advancements in machine learning applied to cancer classification today, empowering them to decide for themselves whether to use these methods in clinical settings. Lastly, the paper wraps up by engaging in a discussion on the future of machine learning, including the potential for new types of systems to be developed as the field advances. Overall, the information included in this survey article is useful for scholars, practitioners, and individuals interested in gaining knowledge about the fundamentals of machine learning and its various applications in different areas of activities. [ABSTRACT FROM AUTHOR]

This paper explores the potential benefits of quantum coherence and quantum discord in the non-universal quantum computing model called deterministic quantum computing with one qubit (DQC1) in supervised machine learning. We show that the DQC1 model can be leveraged to develop an efficient method for estimating complex kernel functions. We demonstrate a simple relationship between coherence consumption and the kernel function, a crucial element in machine learning. The paper presents an implementation of a binary classification problem on IBM hardware using the DQC1 model and analyzes the impact of quantum coherence and hardware noise. The advantage of our proposal lies in its utilization of quantum discord, which is more resilient to noise than entanglement. [ABSTRACT FROM AUTHOR]

The importance and growth of the Internet of Things (IoT) in computer networks and applications have been increasing. Additionally, many of these applications generate large volumes of data, which are critical and require protection against attacks. Various techniques have been proposed to identify and counteract these threats. In this paper, we offer a hybrid machine learning approach (using the k-nearest neighbors and random forests as supervised classifiers) to enhance the accuracy of intrusion detection systems and minimize the risk of potential attacks. Also, we employ backward elimination and linear discriminant analysis algorithms for feature reduction and to lower computational costs. Following the training phase, when discrepancies arose between the decisions of the classifiers, the ultimate determination was supported by ISO/IEC 27001 regulations. The performance of the proposed model was assessed within a Python programming framework, utilizing the CICIDS 2017, NSL-KDD, and TON-IoT datasets. The outcomes illustrated that the proposed approach attained a noteworthy accuracy of 99.96% in the multi-class classification of CICIDS 2017, 99.37% in the binary classification of the NSL-KDD dataset, and 99.96% in the multi-class classification of TON-IoT dataset. Furthermore, the attack success rate for each dataset stands at 0.05%, 0.24%, and 0% respectively, demonstrating a significant reduction compared to other methods. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification (RSISC) greatly benefits from the use of few-shot learning, as it enables the recognition of novel scenes with only a small amount of labeled data. Most previous works focused on learning representations of prior knowledge with scarce labeled data while ignoring the feasibility of using potential information with large amounts of unlabeled data. In this paper, we introduce a novel semi-supervised few-shot pseudo-label propagation method through the introduction of unlabeled knowledge. This approach utilizes the pseudo-loss property generated by the classifier to indirectly reflect the credibility of pseudo-labeled samples. Therefore, we propose a semi-supervised pseudo-loss confidence metric-based method called a pseudolabel meta-learner (PLML) for RSISC. Specifically, we adopt a pseudoloss estimation module to map the pseudo-labeled data obtained from different tasks to a unified pseudo-loss metric space. Then, the distributions of the pseudolosses with both correct and incorrect pseudolabels are fitted by a semi-supervised beta mixture model (ss-BMM). This model can iteratively select high-quality unlabeled data to enhance the self-training effect of the classifier. Finally, to address the problem of shifting pseudo-loss distributions in remote sensing images, a progressive self-training strategy is proposed to mitigate the cumulative error induced by the classifier. Experimental results demonstrate that our proposed PLML approach outperforms the existing alternatives on the NWPU-RESISC45, AID, and UC Merced datasets. [ABSTRACT FROM AUTHOR]

Although the imbalanced learning problem is best known in the context of classification tasks, it also affects other areas of learning algorithms, such as regression. For regression, the problem is characterized by the existence of a continuous target variable domain and the need for models capable of making accurate predictions about rare events. Furthermore, such rare events with a real-value target are often the ones with greater interest in having models that can predict them. In this paper, we propose the novel approach WSMOTER (Weighting SMOTE for Regression) to tackle the imbalanced regression problem, which, according to the experimental work we present, outperforms currently available solutions to the problem. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are model-based, utilizing satellite geometry and the known properties of satellite signals. However, model-based methods have limitations in challenging environments and often lack adaptability to uncertain noise models. This paper highlights recent advances in machine learning (ML) and its potential to address these limitations. It covers a broad range of ML methods, including supervised learning, unsupervised learning, deep learning, and hybrid approaches. The survey provides insights into positioning applications related to GNSS, such as signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement using ML. It discusses the strengths, limitations, and challenges of current ML-based approaches for GNSS positioning, providing a comprehensive overview of the field. [ABSTRACT FROM AUTHOR]

Label noise poses a significant challenge to supervised learning algorithms. Extensive research has been conducted on classification and regression tasks, but label noise filtering methods specifically designed for ordinal regression are lacking. In this paper, we propose a set of ordinal label noise filtering frameworks by theoretically exploring the generalization error bound in noisy environments. Besides, we present a robust label noise estimation method voted by inter-class distance. It takes into account the nonuniformity of ordinal labels and the reliability of the base model. This estimator is integrated into our framework in the proposed Inter-Class Distance-based Filtering (ICDF) algorithm. We empirically demonstrate the effectiveness of ICDF in identifying label noise and achieving improved generalization performance. Our experiments conducted on benchmark and real age estimation datasets show the superiority of ICDF over the existing filters in ordinal label noise cleaning. [ABSTRACT FROM AUTHOR]

User reviews can play a big part in deciding a company's income in the e-commerce industry. Before making selections regarding any product or service, online users rely on reviews. As a result, the trustworthiness of online evaluations is vital for organisations and can directly impact their reputation and revenue. Because of this, some firms pay spammers to publish false reviews. Most recent studies to detect fake reviews utilise supervised learning. However, neural network techniques, a recent form of advanced technology, have been utilised extensively to detect fake reviews and have demonstrated their ability to do so. Thus, this paper first provides a benchmark study to analyse the performance of various machine learning algorithms with different feature extraction methods on five fake review datasets to present our results. Second, we propose three advanced language models for embedding reviews into the classifiers. Third, we conduct an exhaustive feature set evaluation study to find the best features in detecting fake reviews. Fourth, we analyse the performance of traditional machine learning, deep learning, and advanced deep learning models using different feature extraction methods on five fake review datasets. Finally, we integrate the ELECTRA model with CNN which can identify real or fake reviews. Our proposed technique utilises accuracy, precision, recall, and F1 score as assessment criteria to determine the leniency of the proposed model. For deep contextualised representation and neural classification, we integrate Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNN) following the embedding layer of unique pre-trained models like ELMo, ELECTRA, and GPT2. The experimental results indicate that our proposed model outperforms state-of-the-art methods with improvements ranging from 1 to 7% in terms of the accuracy, F1 score. To the best of our knowledge, no prior work has evaluated such advanced pre-trained models' efficiency in detecting fake reviews. Further, this research comprehensively evaluates several machine-learning approaches and feature extraction strategies for fake online review detection. [ABSTRACT FROM AUTHOR]

Landmarks are defined in the ARIA—Accessible Rich Internet Applications specification, and are important regions of web applications that blind users access through keyboard shortcuts. However, ARIA landmarks are not always included in web applications, given developer's lack of knowledge of accessibility guidelines and specifications. The primary goal of this study was to investigate the efficacy in using supervised machine learning for identifying ARIA landmarks in web applications. In this paper, we present an approach for identifying landmarks based on classification of elements in web applications. Our approach uses a classification model with features extracted from the structure of web applications. Then, we cluster elements of the web application based on their landmarks class probability, position and size in the web application, reporting only the element with the highest probability as a landmark for each cluster. Our goal was to use landmarks correctly implemented in web applications to fit a classifier and then use this classifier to identify landmarks in web applications which did not implement them. Our evaluation reported different accuracy results for each landmark, identifying multiple landmarks in web applications with precision values higher than 0.75 to the landmarks: banner, contentinfo, navigation, region and search. The conducted evaluation showed the potential of the proposed approach in the identification of specific types of landmarks and quantifies the amount of elements that could be automatically identified and adapted, enhancing the accessibility of web applications. Furthermore, the results set a baseline accuracy for future studies in the area. [ABSTRACT FROM AUTHOR]

In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell's mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited. While machine learning platforms can improve the assessment of Hematoxylin & Eosin (H&E) stained-tumour tissue images, current models typically require manual cell-type annotations in training. Here, the authors develop VOLTA, a self-supervised machine learning framework to improve cell representation learning in H&E images based on the cells environment [ABSTRACT FROM AUTHOR]

Wireless link scheduling in device-to-device (D2D) networks is an NP-hard problem. As a solution, multiple supervised deep learning (DL) models have been recently proposed, which depend on the geographical information of D2D pairs. However, such DL models require labeled training data. In this paper, we focus on unsupervised learning of scheduling. More specifically, this paper proposes using a Density-Based anti-Clustering for Scheduling D2D Communications (DBSCHedule). The proposed algorithm is a two-step approach that consists of clustering and anti-clustering. First, clustering aims at identifying the non-interfering groups of D2D pairs. Then, anti-clustering aims at identifying the maximally separated sub-groups to minimize the interference. The clustering step uses a fully-automated unsupervised density-based spectral-clustering of applications with noise (DBSCAN) and the anti-clustering uses the inverse of the objective function of the k-means clustering. Results show comparable performance with the optimal FPLinQ scheduler yet without requiring any channel information nor is there a requirement to solve a complex optimization problem. Moreover, a comparable performance to the previous attempts using DL and modified clustering is achieved while being completely adaptive and easily accommodating to changes in the network layout. [ABSTRACT FROM AUTHOR]

Portfolio optimisation is a core problem in quantitative finance and scenario generation techniques play a crucial role in simulating the future behaviour of the assets that can be used in allocation strategies. In the literature, there are different approaches to generating scenarios, from historical observations to models that predict the volatility of assets. In this paper, we propose a new methodology to generate one-day-ahead discrete scenarios, which are then used as input in choosing the portfolio that optimises the conditional value at risk (CVaR). Our approach uses machine learning supervised algorithms as forecasting models to predict the realized variance and intraday Kendall correlation of assets. With the predictions, we use an EVT-Copula approach to simulate the multivariate probability distribution of the assets. Our computational experiments suggest that our approach could produce more accurate volatility and correlation forecasts, and lower risk portfolios than traditional literature baselines. [ABSTRACT FROM AUTHOR]

The paper proposes the use of supervised machine learning (ML) methods for quickly predicting the seismic response of rocking systems when subjected to seismic excitations. Different supervised ML algorithms are discussed, while a relatively simple and a more sophisticated algorithm are examined in detail. Specifically, the two algorithms compared are the k-Nearest Neighbor (k-NN) and the Support Vector Machine (SVM). The performance of the ML models is demonstrated considering both sine pulses and different sets of natural ground motion records. The results are practically perfect for sine pulses, while accurate results were also obtained for the case of natural ground motions. The proposed ML-based tool allows to quickly assess the risk of damage for rocking systems, while it is also very important when a large number of rocking blocks have to be studied, e.g. in the case of a building's inventory. [ABSTRACT FROM AUTHOR]

Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future. [ABSTRACT FROM AUTHOR]

Treatment effect estimation (TEE) is widely adopted in various domains such as machine learning, dvertising and marketing, and medicine. During the TEE, there normally exist selection bias on counterfactual prediction, which results in different distributions of covariates between the treated and control groups. One important challenge in TEE is to mitigate the impact of selection bias, which has attracted a lot of research in recent years. To address this challenge, existing neural network-based methods generally aim to minimize the distribution differences using integral probability metrics. However, minimizing the distribution differences may inadvertently remove outcome-related information during the balancing procedure, which has negative impact on the accuracy of TEE. In this paper, we propose a novel self-supervised learning approach to conduct TEE. Rather than minimizing the distribution differences, we first introduce the concept of virtual samples which have identical covariates as observed samples but with different treatments. In this way, we aim to simulate the scenario where each sample receives both treatment and control. Next, we propose a self-supervised domain embedding learning (SDEL) approach to conduct TEE. In SDEL, we propose to learn both treated and control embeddings for observed and virtual samples, thereby learning the effects of different treatments. To the best of our knowledge, we are the first to introduce the concept of virtual samples and the first to conduct embedding learning in TEE. Building upon SDEL, we propose a feature extraction counterfactual regression network (FE-CFR), in which we propose a feature extraction module (FEM) to estimate the importance of different covariates. Compared with existing TEE methods, our proposed self-supervised learning approach to could improve the accuracy of TEE. Extensive experiments have been conducted on benchmark datasets for TEE, and the results demonstrate that our proposed approach outperforms the compared baseline approaches. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach for performing package classification and sizing using Radio-Frequency Identification (RFID) systems. This technique is applicable when packages are labeled with or contain multiple RFID-tagged items. During the interrogation of the tags, received signal strength (RSS) statistics and other information, such as the frame count or the reading time, are collected by the reader and used to predict the package type from a set of candidate classes using an Artificial Neural Network (ANN). The primary challenge lies in acquiring sufficient training data for a target scenario to ensure reliable predictions. To address this, a two-phase training process based on transfer learning is adopted. Initially, a base model is developed using synthetic data generated from a detailed RFID simulator, designed to suit diverse scenarios, establish detailed link budgets, and comprehensively simulate the communication protocols. This model is then refined using a small dataset collected experimentally in the actual scenario. This method was validated in a real testbed with four different package types. The base model was trained using 1000 synthetic samples per package type (4000 in total), whereas the refined model was trained with a dataset consisting of only 25 real interrogation traces (samples) per package type (100 in total). The experimental samples were obtained using a software-defined radio unit, the Ettus B210 Universal Software Radio Peripheral (USRP) platform. This experiment achieved an accuracy of over 92%. In summary, this approach introduces a new feature to existing RFID setups, demonstrating potential for advanced package handling and cost optimization in the logistics sector. [ABSTRACT FROM AUTHOR]

Purpose: This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools. Methods: This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included. Results: A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data. Conclusion: AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice. [ABSTRACT FROM AUTHOR]

In this paper, a novel data-centric approach is proposed for solving the 3D mesh segmentation problem. The method uses node2vec, a semi-supervised learning algorithm, to create vector embedding representations for each node in a 3D mesh graph. This makes the mesh data more compact and easier to process which is important for reducing computation costs. K-Means clustering is then used to cluster each node according to their node embedding information. This data-centric approach is more computationally efficient than other complex models such as CNN and RNN. The main contribution of this study is the development of a data-centric AI framework that combines node2vec embedding, machine learning, and deep learning techniques. The use of cosine similarity is also adapted to compare and evaluate the trained node embedding vectors with different hyperparameters. Additionally, a new algorithm is developed to determine the optimal cluster number using geodesic distance on the 3D mesh. Overall, this approach provides competitive results compared to existing mesh segmentation methods. [ABSTRACT FROM AUTHOR]

Although cryptocurrency trading can be highly profitable, it carries significant risks due to extreme price fluctuations and high degree of market noise. To increase profits and minimize risks, traders typically use various forecasting methods, such as technical analysis and Machine Learning (ML), but developing effective trading strategies in noisy markets still remains a challenging task. Recently, Deep Reinforcement Learning (DRL) agents have achieved high performance on challenging tasks, including algorithmic trading, however it requires significant amount of time and high-quality data to train effectively. Additionally, DRL agents lack explainability, making them a less popular option for traders. The purpose of this paper is to address these challenges by proposing a reliable trading framework. Our framework, named UNSURE, generates high-quality features from candlestick data using technical analysis along with a novel parameterization method, and then exploits high price fluctuations by combining three ML components: A) Unsupervised component, which further improves feature quality by clustering market data; B) DRL component, which is responsible for training agents that open Buy or Short positions; C) Supervised component, which estimates price fluctuations in order to open and close positions efficiently, while reducing trading uncertainty. We demonstrate the effectiveness of this approach on nine cryptocurrency markets using several risk-adjusted performance metrics. [ABSTRACT FROM AUTHOR]

A convenient, accurate and well-known way toward any supervised task is using Nearest Neighbor approach or its variants. However, there has been little attempt toward improving interpretability by human and providing a classical optimization of Synthetic Reduced Nearest Neighbor. To tackle these issues, this paper provides a novel optimization of Synthetic Reduced Nearest Neighbor based on Expectation Maximization (EM-SRNN). Reduced Nearest Neighbor model consists of a subset of the samples from the trainset that has similar accuracy to Nearest Neighbor. Synthetic Reduced Nearest Neighbor relaxes the model to learn K synthetic samples (or prototypes/centroids) in the space of dataset. Therefore, inspired by EM algorithm for K-means, we propose a novel optimization based on EM algorithm to learn EM-SRNN by iterating over the centroids of the model and assignment of training samples to the centroids. The first step consists of optimizing the position of each centroid based on the assignment of the samples to the centroid and the second step consists finding optimal assignments and labels of the centroids. The EM-SRNN is interpretable since the centroids exist inside the space of training samples. Additionally, the centroids represent the multiple modalities (or sub-clusters) of the classes that are interpretable by human. These properties make this type of interpretability unique, hence, making this model suitable for various studies that are related to interpretability by human, such as image processing and epidemiological studies. In this paper, analytical aspects of problem are explored and it is shown that computational complexity of proposed optimization is linear over size of the trainset. Finally, EM-SRNN shows superior or similar performance when compared with several other interpretable and similar state-of-the-art models, such as trees and kernel SVMs. [ABSTRACT FROM AUTHOR]

Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed. [ABSTRACT FROM AUTHOR]

Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur. [ABSTRACT FROM AUTHOR]

Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early detection of COVID-19 is important for the prevention and treatment of pandemic. Numerous sources of medical images (e.g., Chest X-Rays (CXR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI)) are regarded as a desirable technique for diagnosing COVID-19 cases. Medical images of coronavirus patients show that the lungs are filled with sticky mucus that prevents them from inhaling. Today, Artificial Intelligence (AI) based algorithms have made a significant shift in the computer aided diagnosis due to their effective feature extraction capabilities. In this survey, a complete and systematic review of the application of Machine Learning (ML) methods for the detection of COVID-19 is presented, focused on works that used medical images. We aimed to evaluate various ML-based techniques in detecting COVID-19 using medical imaging. A total of 26 papers were extracted from ACM, ScienceDirect, Springerlink, Tech Science Press, and IEEExplore. Five different ML categories to review these mechanisms are considered, which are supervised learning-based, deep learning-based, active learning-based, transfer learning-based, and evolutionary learning-based mechanisms. A number of articles are investigated in each group. Also, some directions for further research are discussed to improve the detection of COVID-19 using ML techniques in the future. In most articles, deep learning is used as the ML method. Also, most of the researchers used CXR images to diagnose COVID-19. Most articles reported accuracy of the models to evaluate model performance. The accuracy of the studied models ranged from 0.84 to 0.99. The studies demonstrated the current status of AI techniques in using AI potentials in the fight against COVID-19. [ABSTRACT FROM AUTHOR]

Monte-Carlo based valuation in life insurance involves the simulation of various components of the balance sheet: portfolios, guarantees, assets mix, market conditions and many other specific risk factors; which can be time-consuming. In view of the time needed to achieve this task, insurers are then facing a trade-off of balancing the number of simulations against the uncertainty surrounding the estimated quantity. In the current paper, we propose a variance-reduction methodology using a machine learning technique. It roots from the unsupervised learning literature in conjunction with the quantization of random processes. The goal is to reduce the number of simulated Brownian paths, using auxiliary scenarios that can be seen as path clusters, which efficiently and accurately approximate the initial ones with regards to an adequate measure of distance. Moreover, we introduce penalty to accommodate for various inputs of the initial conditions of risk factors. By doing so, we implicitly assign labels to the scenarios and thus advocate using a semi-supervised learning to enhance the performance of the scenarios reduction by imposing an additional impurity condition on the clusters based on these labels. This is made possible thanks to a decomposition property of the insurers cash-flows, which allows to disentangle the initial conditions of risk factors from the Brownian motions driving their dynamics. The training of the proposed learning algorithm is based on an adaptation of the well-known k-means algorithm. An intensive numerical study is carried out over a range of simulation setups to compare the performances of the proposed methodology. We show, numerically, that the proposed methodology outperforms some classical variance reduction approach. Also, using a real-life dataset, we show that our methodology outperforms some conventional variance reduction used by life insurance practitioners. [ABSTRACT FROM AUTHOR]

Through hypothetical scenarios, this paper analyses whether machine learning (ML) could resolve one of the main shortcomings present in Christopher Boorse's Biostatistical Theory of health (BST). In doing so, it foregrounds the boundaries and challenges of employing ML in formulating a naturalist (i.e., prima facie value-free) definition of health. The paper argues that a sweeping dataist approach cannot fully make the BST truly naturalistic, as prior theories and values persist. It also points out that supervised learning introduces circularity, rendering it incompatible with a naturalistic perspective. Additionally, it underscores the need for pre-existing auxiliary theories to assess results from unsupervised learning. It emphasizes the importance of understanding the epistemological entanglements between data and data processing methods to manage expectations about what data patterns can predict. In conclusion, the paper argues against delegating the final authority for defining complex concepts like health to AI systems, as it necessitates ethical judgment and capacities for deliberation that AI currently lacks. It also warns against granting creators and deployers of AI systems the discretionary authority to determine these definitions outside the wider social discussion, advocating for ongoing public engagement on normative notions. Failure to do so risks limiting individuals and collectives' ability to shape a just digital future and diminishes their fundamental epistemic agency. [ABSTRACT FROM AUTHOR]