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Radio Frequency Fingerprinting (RFF) refers to the technique for identifying and classifying wireless devices on the basis of their physical characteristics, which appear in the digital signal transmitted in space. Small differences in the radio frequency front-end of the wireless devices are generated across the same wireless device model during the implementation and manufacturing process. These differences create small variations in the transmitted signal, even if the wireless device is still compliant with the wireless standard. By using data analysis and machine-learning algorithms, it is possible to classify different electronic devices on the basis of these variations. This technique has been well proven in the literature, but research is continuing to improve the classification performance, robustness to noise, and computing efficiency. Recently, Deep Learning (DL) has been applied to RFF with considerable success. In particular, the combination of time-frequency representations and Convolutional Neural Networks (CNN) has been particularly effective, but this comes at a great computational cost because of the size of the time-frequency representation and the computing time of CNN. This problem is particularly challenging for wireless standards, where the data to be analyzed is extensive (e.g., long preambles) as in the case of the LoRa (Long Range) wireless standard. This paper proposes a novel approach where two pre-processing steps are adopted to (1) improve the classification performance and (2) to decrease the computing time. The steps are based on the application of Variational Mode Decomposition (VMD) where (in opposition to the known literature) the residual of the VMD application is used instead of the extracted modes. The concept is to remove the modes, which are common among the LoRa devices, and keep with the residuals the unique intrinsic features, which are related to the fingerprints. Then, the spectrogram is applied to the residual component. Even after this step, the computing complexity of applying CNN to the spectrogram is high. This paper proposes a novel step where only segments of the spectrogram are used as input to CNN. The segments are selected using a machine-learning approach applied to the features extracted from the spectrogram using the Local Binary Pattern (LBP). The approach is applied to a recent LoRa radio frequency fingerprinting public data set, where it is shown to significantly outperform the baseline approach based on the full use of the spectrogram of the original signal in terms of both classification performance and computing complexity. [ABSTRACT FROM AUTHOR]

In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in improving cost efficiency, performance, and energy consumption in cloud services. We then discuss various strategies employed in auto-scaling, ranging from threshold-based rules and queuing theory to sophisticated machine learning and time series analysis approaches. After that, we explore the critical issues in auto-scaling practices and review several studies that demonstrate how these challenges can be addressed. We then conclude by offering insights into several promising research directions, emphasizing the development of predictive scaling mechanisms and the integration of advanced machine learning techniques to achieve more effective and efficient auto-scaling solutions. [ABSTRACT FROM AUTHOR]

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematically incorrect. At various phases of the ML pipeline, such as data collection, pre-processing, model selection, and evaluation, these biases appear. Bias reduction methods for ML have been suggested using a variety of techniques. By changing the data or the model itself, adding more fairness constraints, or both, these methods try to lessen bias. The best technique relies on the particular context and application because each technique has advantages and disadvantages. Therefore, in this paper, we present a comprehensive survey of bias mitigation techniques in machine learning (ML) with a focus on in-depth exploration of methods, including adversarial training. We examine the diverse types of bias that can afflict ML systems, elucidate current research trends, and address future challenges. Our discussion encompasses a detailed analysis of pre-processing, in-processing, and post-processing methods, including their respective pros and cons. Moreover, we go beyond qualitative assessments by quantifying the strategies for bias reduction and providing empirical evidence and performance metrics. This paper serves as an invaluable resource for researchers, practitioners, and policymakers seeking to navigate the intricate landscape of bias in ML, offering both a profound understanding of the issue and actionable insights for responsible and effective bias mitigation. [ABSTRACT FROM AUTHOR]

This study delves into the multifaceted nature of cross-validation (CV) techniques in machine learning model evaluation and selection, underscoring the challenge of choosing the most appropriate method due to the plethora of available variants. It aims to clarify and standardize terminology such as sets, groups, folds, and samples pivotal in the CV domain, and introduces an exhaustive compilation of advanced CV methods like leave-one-out, leave-p-out, Monte Carlo, grouped, stratified, and time-split CV within a hold-out CV framework. Through graphical representations, the paper enhances the comprehension of these methodologies, facilitating more informed decision making for practitioners. It further explores the synergy between different CV strategies and advocates for a unified approach to reporting model performance by consolidating essential metrics. The paper culminates in a comprehensive overview of the CV techniques discussed, illustrated with practical examples, offering valuable insights for both novice and experienced researchers in the field. [ABSTRACT FROM AUTHOR]

The height of the F2 peak electron density (hmF2) is an essential parameter in studying ionospheric electrodynamics and high-frequency wireless communication. Based on ionosphere ray propagation theory, the physical relationship between M3000F2 and hmF2 is derived and visualized. Furthermore, based on the above physical theory and the machine learning method, this paper proposes a new model for determining hmF2 using propagation factor at a distance of 3000 km from the ionospheric F2 layer, time, and season. This proposed model is easy to understand and has the characteristics of clear principles, simple structure, and easy application. Furthermore, we used six stations in east Asia to verify this model and compare it with the other three models of the International Reference Ionosphere (IRI) model. The results show that the proposed model (PRO) has minor error and higher accuracy. Specifically the RMSE of the BSE, AMTB, SHU, and the PRO models were 20.35 km, 31.51 km, 13.59 km, and 5.68 km, respectively, and the RRMSE of the BSE, AMTB, SHU, and PRO models were 8.17%, 11.88%, 4.96%, and 2.12%, respectively. In addition, the experimental results show that the PRO model can better predict the trend of the hmF2 inflection point. This method can be further extended to add data sources and provide new ideas for studying the hmF2 over global regions. [ABSTRACT FROM AUTHOR]

Surface integrity is a critical factor that affects the fatigue resistance of materials. A surface mechanical rolling treatment (SMRT) process can effectively improve the surface integrity of the material, thus enhancing the fatigue property. In this paper, an analysis of variance (ANOVA) and signal-to-noise ratio (SNR) are performed by orthogonal experimental design with SMRT parameters as variables and surface integrity indicators as optimization objectives, and the support vector machine-active learning (SVM-AL) model is proposed based on machine learning theory. The entire model includes three rounds of AL processes. In each round of the AL process, the SMRT parameters with relative average deviation and high output values from cross-validation are selected for the additional experimental supplement. The results show that the prediction accuracy and generalization ability of the SVM-AL model are significantly improved compared to the support vector machine (SVM) model. A fatigue test was also carried out, and the fatigue property of the SMRT specimens predicted by the SVM-AL model is also higher than that of the other specimens. [ABSTRACT FROM AUTHOR]

Elevators have become an integral part of modern buildings, and technological advances have enabled the monitoring of their operational status through sensor technology. In response to the development of the elevator industry and the need for practical elevator operation risk evaluation, this paper proposes an elevator risk evaluation method based on fuzzy theory and machine learning methods. The method begins by establishing an elevator operation risk evaluation index system. The traditional fuzzy comprehensive evaluation method is then employed to evaluate the risk levels of the 50 elevators studied. The collected index data and labels (fuzzy comprehensive evaluation results) are used as inputs to train the support vector machine (SVM) model. To optimize the SVM model, the maximum information coefficient method, enhanced by the correlation-based feature selection (MIC-CFS) method, is employed to select features for the index input to the SVM model. The improved gray wolf algorithm (IGWO) method optimizes the SVM. Finally, the model's performance is verified using new index data. The experimental results demonstrate that introducing machine learning methods for elevator risk evaluation saves time and effort while providing good accuracy compared to the traditional expert evaluation method. The optimization of the SVM model by IGWO and feature selection by the MIC-CFS method results in a more concise SVM model that converges faster during training, exhibits better stability, and achieves higher accuracy. [ABSTRACT FROM AUTHOR]

Introduction: A problem that applied researchers and practitioners often face is the fact that different institutions within research consortia use different scales to evaluate the same construct which makes comparison of the results and pooling challenging. In order to meaningfully pool and compare the scores, the scales should be harmonized. The aim of this paper is to use different test equating methods to harmonize the ADHD scores from Child Behavior Checklist (CBCL) and Strengths and Difficulties Questionnaire (SDQ) and to see which method leads to the result. Methods: Sample consists of 1551 parent reports of children aged 10-11.5 years from Raine study on both CBCL and SDQ (common persons design). We used linear equating, kernel equating, Item Response Theory (IRT), and the following machine learning methods: regression (linear and ordinal), random forest (regression and classification) and Support Vector Machine (regression and classification). Efficacy of the methods is operationalized in terms of the root-mean-square error (RMSE) of differences between predicted and observed scores in cross-validation. Results and discussion: Results showed that with single group design, it is the best to use the methods that use item level information and that treat the outcome as interval measurement level (regression approach). [ABSTRACT FROM AUTHOR]

Purpose: This paper presents the Edge Load Management and Optimization through Pseudoflow Prediction (ELMOPP) algorithm, which aims to solve problems detailed in previous algorithms; through machine learning with nested long short-term memory (NLSTM) modules and graph theory, the algorithm attempts to predict the near future using past data and traffic patterns to inform its real-time decisions and better mitigate traffic by predicting future traffic flow based on past flow and using those predictions to both maximize present traffic flow and decrease future traffic congestion. Design/methodology/approach: ELMOPP was tested against the ITLC and OAF traffic management algorithms using a simulation modeled after the one presented in the ITLC paper, a single-intersection simulation. Findings: The collected data supports the conclusion that ELMOPP statistically significantly outperforms both algorithms in throughput rate, a measure of how many vehicles are able to exit inroads every second. Originality/value: Furthermore, while ITLC and OAF require the use of GPS transponders and GPS, speed sensors and radio, respectively, ELMOPP only uses traffic light camera footage, something that is almost always readily available in contrast to GPS and speed sensors. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, digital twin technology is revolutionizing traditional manufacturing paradigms. However, the adoption of this technology in modern manufacturing systems is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing. [ABSTRACT FROM AUTHOR]

We propose a framework that integrates norm entrepreneurship from human society into the dynamics of an agent society. Most work in agent coordination in a distributed environment studies norms that are provided to agents as part of their specification or distributed from centralised agents. Exploring an alternate perspective, we focus on peer-to-peer interaction by providing the agents with the freedom to initiate norm creation in demanding situations like a potential interference. This paper explores the concept of norm entrepreneurship through proactive establishment and emergence of norms by agents. A common approach in prior work focuses on coordination problems that are reduced to simple game theory models involving the simultaneous performance of a single action by each agent. Instead, we define the concept of a local coordination plan (LCP), which is a sequence of actions from each agent to cope with an interference in their normal course of action. We identify LCPs across various scenarios and abstract these plans using coordination state machines (CSMs). A coordination state machine contains a separate state machine for each agent where the states encapsulate the potentially constrained and suboptimal movement options agents have at a given time. We also explore how multiple LCPs lead to a coordination state machine of the same format and how a coordination state machine can abstract across multiple scenarios. [ABSTRACT FROM AUTHOR]

This paper explores the essential dynamics of social networks, specifically examining the phenomenon of information confrontation among users. The goal of the research is the development of a novel simulation methodology that integrates game-theoretic principles with probabilistic techniques to provide a robust model for these interactions. The theoretical framework of the study is founded on the conceptualization of user conflicts as a strategic game between two players. The primary objective for each player in this game is to exert influence and control over as many nodes within the network as possible. To capture the essence of these strategic interactions, we have introduced an innovative algorithm that facilitates dynamic strategy adaptation. This algorithm is pivotal in allowing players to modify their decision-making processes in real-time, based on the continually changing conditions of the network. For practical implementation and validation of the methodology, we used the Facebook Researcher open dataset, with a particular focus on its Kazakhstani segment. This dataset provides a rich source of empirical data, reflecting diverse user interactions and network configurations, which are essential for testing the model. This approach stands out by offering significant improvements in computational efficiency and resource management. By dynamically tracking and updating the network’s status, the proposed method reduces the computational resources required, thereby enhancing the scalability of the simulation. In comparing our methodology with other existing models in the field, it becomes evident that it not only matches but in several respects surpasses these methodologies in terms of flexibility. This study makes substantial contributions to the field of social network analysis by providing a sophisticated tool that can be effectively employed to navigate and analyze the complexities of information confrontation in digital social spaces. [ABSTRACT FROM AUTHOR]

In this article, we consider the correction of metric matrices in quasi-Newton methods (QNM) from the perspective of machine learning theory. Based on training information for estimating the matrix of the second derivatives of a function, we formulate a quality functional and minimize it by using gradient machine learning algorithms. We demonstrate that this approach leads us to the well-known ways of updating metric matrices used in QNM. The learning algorithm for finding metric matrices performs minimization along a system of directions, the orthogonality of which determines the convergence rate of the learning process. The degree of learning vectors' orthogonality can be increased both by choosing a QNM and by using additional orthogonalization methods. It has been shown theoretically that the orthogonality degree of learning vectors in the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is higher than in the Davidon–Fletcher–Powell (DFP) method, which determines the advantage of the BFGS method. In our paper, we discuss some orthogonalization techniques. One of them is to include iterations with orthogonalization or an exact one-dimensional descent. As a result, it is theoretically possible to detect the cumulative effect of reducing the optimization space on quadratic functions. Another way to increase the orthogonality degree of learning vectors at the initial stages of the QNM is a special choice of initial metric matrices. Our computational experiments on problems with a high degree of conditionality have confirmed the stated theoretical assumptions. [ABSTRACT FROM AUTHOR]

In this paper, we explore a novel model for pricing Chinese convertible bonds that seamlessly integrates machine learning techniques with traditional models. The least squares Monte Carlo (LSM) method is effective in handling multiple state variables and complex path dependencies through simple regression analysis. In our approach, we incorporate machine learning techniques, specifically support vector regression (SVR) and random forest (RF). By employing Bayesian optimization to fine-tune the random forest, we achieve improved predictive performance. This integration is designed to enhance the precision and predictive capabilities of convertible bond pricing. Through the use of simulated data and real data from the Chinese convertible bond market, the results demonstrate the superiority of our proposed model over the classic LSM, confirming its effectiveness. The development of a pricing model incorporating machine learning techniques proves particularly effective in addressing the complex pricing system of Chinese convertible bonds. Our study contributes to the body of knowledge on convertible bond pricing and further deepens the application of machine learning in the field in an integrated and supportive manner. [ABSTRACT FROM AUTHOR]

This paper presents data-driven modeling and a results analysis. Group method data handling (GMDH), multivariable regression (MVR), artificial neuron network (ANN), and new proposed GMDH-featured ANN machine learning algorithms were implemented to model a field telemetry equivalent mud circulating density (ECD) dataset based on surface and subsurface drilling parameters. Unlike the standard GMDH-ANN model, the proposed GMDH-featured ANN utilizes a fully connected network. Based on the considered eighteen experimental modeling designs, all the GMDH regression results showed higher R-squared and minimum mean-square error values than the multivariable regression results. In addition, out of the considered eight experimental designs, the GMDH-ANN model predicts about 37.5% of the experiments correctly, while both algorithms have shown similar results for the remaining experiments. However, further testing with diverse datasets is necessary for better evaluation. [ABSTRACT FROM AUTHOR]

News recommender systems (NRS) are crucial for helping users navigate the vast amount of content available online. However, traditional NRS often suffer from biases that lead to a narrow and unfair distribution of exposure across news items. In this paper, we propose a novel approach, the Contextual-Dual Bias Reduction Recommendation System (C-DBRRS), which leverages Long Short-Term Memory (LSTM) networks optimized with a multi-objective function to balance accuracy and diversity. We conducted experiments on two real-world news recommendation datasets and the results indicate that our approach outperforms the baseline methods, and achieves higher accuracy while promoting a fair and balanced distribution of recommendations. This work contributes to the development of a fair and responsible recommendation system. [ABSTRACT FROM AUTHOR]

This paper presents a study guide and an analysis of its use in the computer programming learning process of an 8-year-old elementary school student through the Scratch program. The research's objective is to explore and understand how this individual student approaches learning programming skills and tackles challenges within the Scratch environment. An individual case study approach was adopted at home, combining qualitative and quantitative methods to gain a comprehensive insight into the student's learning process. The study was conducted without grant support, and the researcher actively participated as an educator and observer in the student's learning sessions. Performance was assessed, and a semi-structured interview was conducted to inquire about the student's experiences, motivations, and interests regarding programming in Scratch, as well as their feelings after the training. Additionally, the student's activities during programming sessions were meticulously recorded, and projects created in Scratch were analyzed to assess progress and understanding of concepts. The findings of this research have the potential to contribute to the field of programming education and provide valuable insights into how young elementary school-aged individuals can acquire computer and programming skills in an interactive environment such as Scratch. The results obtained demonstrate that using the proposed guide to introduce elementary school students to programming at home, with parents acting as educators, is feasible. Therefore, it helps facilitate access to this knowledge, which is currently limited for many individuals in an official educational setting. [ABSTRACT FROM AUTHOR]

Variable flux reluctance machines (VFRMs) are increasingly attracting research interest due to their magnetless and robust brushless structure. Under the modulation effect of the airgap permeance, the VFRM operates with a series of field harmonics, distinguishing it from conventional AC synchronous machines. This paper deals with the analysis and preliminary design of the VFRM from the perspective of multiple working airgap field harmonics. Firstly, the spatial and temporal order of the working field harmonics are defined. The systematic winding theory, including the unified star of slots and winding factor calculation method, is established to consider all these working harmonics. Then, an average torque model is built and simplified. The key role of 1st-order rotor permeance, 1st- and 3rd-order polarized stator permeance is deduced. The relationship between key parameters and average torque is computed, providing a guideline for the preliminary design of the VFRM. [ABSTRACT FROM AUTHOR]

Blind spectrum sensing (BSS) is crucial for identifying unknown signals in scenarios with limited prior knowledge. Traditional methods face challenges with unknown and timevarying signals, especially in the presence of noise interference. This paper addresses these issues by introducing a statistical signal processing framework that extends the use of machine learning (ML) features. Our approach improves BSS by incorporating cumulative distribution functions (CDFs) into unsupervised ML, enabling effective clustering of diverse transmission states without assumptions about specific noise distributions. Additionally, we introduce a temporal decomposition technique using shorter Fast Fourier Transforms (FFTs), enhancing the learning process, reducing system inertia, and minimizing data requirements for retraining under dynamic conditions. We evaluate our method, focusing on various features/approaches for incorporating CDFs into ML, including centroid, linear approximation, and low-order statistics. Simulation results demonstrate robust detection in a standard transmission scenario with a Gaussian pulse amidst additive white Gaussian noise, maintaining a consistently low false alarm rate. These findings highlight our BSS approach’s effectiveness and practical potential in handling unknown signals in challenging environments. This research provides valuable insights, laying the groundwork for practical implementation in real-world scenarios. [ABSTRACT FROM AUTHOR]

A Distributed Denial of Service (DDoS) attack is a prevalent issue in the blockchain network layer, causing significant revenue loss for honest mining pools. This paper introduces a novel method, the Repeated Game-based DDoS attack mitigation (RGD), to address this problem. Unlike traditional methods such as game theory and machine learning-based detection, the RGD method can effectively reflect the changes in mining revenue and strategies under different network-strength environments. In particular, we abstract the problem of DDoS mining pool revenue loss into a game revenue model and propose the subgame perfect equilibrium (SPE) approach to solve the optimal payoffs and pool strategies in various network environments. Furthermore, we address the returns of mining pools in an infinitely repeated game environment using the Two-Stage Repeated Game (TSRG) method, where the strategy varies with different network environments. The Matlab experimental simulation results indicate that as the network environment improves, the optimal mining strategies of mining pools are gradually shifting from honest strategies to launching DDoS attacks against each other. The RGD method can effectively represent the impact of changes in the network environment on the mining pool's strategy selection and optimal revenue. Consequently, with the changing network environment, the optimal revenue of the mining pool only increases by 10% of the revenue loss during a DDoS attack. [ABSTRACT FROM AUTHOR]

This research conducts an in-depth investigation into the residual stresses in resin micro-milling processes. Considering that resin is the most crucial matrix material in composites, the construction of a precise machining theory for it is not only key to achieving high-quality- and efficient processing of composite materials but also fundamental to enhancing the overall performance of the materials. This paper meticulously examines the surface integrity and accuracy of epoxy polymers following precision machining, primarily revealing the significance of residual stresses and size effects in extending the lifespan of precision components and promoting their miniaturization. We have adopted an innovative finite element (FE) simulation method, integrated with the Mulliken–Boyce constitutive model, to profoundly analyze the impacts of residual stresses on the surfaces and sub-surfaces of thermosetting polymers. This research further explores the influence of critical machining parameters such as chip thickness, cutting edge radius, feed per tooth, and axial depth on cutting forces, as well as the inherent size effects in polymers. Utilizing X-ray diffraction (XRD) technology, we accurately measured the residual stresses generated during the micro-milling process. The close correlation between FE simulations and experimental results validates the accuracy and effectiveness of our method. This study represents a substantial breakthrough in finite element simulation techniques for high-precision machining of polymer materials, injecting valuable theoretical and practical knowledge into the field. [ABSTRACT FROM AUTHOR]

This paper describes simulations and analysis of flash crash scenarios in an agent-based modelling framework. We design, implement, and assess a novel high-frequency agent-based financial market simulator that generates realistic millisecond-level financial price time series for the E-Mini S&P 500 futures market. Specifically, a microstructure model of a single security traded on a central limit order book is provided, where different types of traders follow different behavioural rules. The model is calibrated using the machine learning surrogate modelling approach. Statistical test and moment coverage ratio results show that the model has excellent capability of reproducing realistic stylised facts in financial markets. By introducing an institutional trader that mimics the real-world Sell Algorithm on May 6th, 2010, the proposed high-frequency agent-based financial market simulator is used to simulate the Flash Crash that took place on that day. We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios. With the help of Monte Carlo simulations, we discover functional relationships between the amplitude of the simulated 2010 Flash Crash and three conditions: the percentage of volume of the Sell Algorithm, the market maker inventory limit, and the trading frequency of fundamental traders. Similar analyses are carried out for mini flash crash events. An innovative "Spiking Trader" is introduced to the model, replicating real-world scenarios that could precipitate mini flash crash events. We analyse the market dynamics during the course of a typical simulated mini flash crash event and study the conditions affecting its characteristics. The proposed model can be used for testing resiliency and robustness of trading algorithms and providing advice for policymakers. [ABSTRACT FROM AUTHOR]

This paper introduced the automata field concept and studied its characteristics. In particular, we define new operations by converting finite automata to finite automata field. Additionally, we examine the properties of finite automata field and their relation to field. We also define substructures of the automata field, including sub-automata field and automata field homomorphism. [ABSTRACT FROM AUTHOR]

The thermal property of the scum layer (soil-rock mixtures) has dominant influence on the heat exchange efficiency between the lower rock layer and the upper environment in the open-pit mines of the cold regions. This paper presents a series of thermal conductivity tests (560 samples) on the scum particle to investigate the coupling effects of ice (moisture) content, temperature, and particle size distribution on the thermal properties. Previously reported models (47 empirical or theoretical models) were adopted to predicate the thermal conductivity of soil-rock mixtures in order to validate the evaluation ability of these models under the wide testing ranges. The comparison results indicate that the theoretical models, normalized model and linear/non-linear models all can not fully predict experimental results under the wide testing conditions. Three machine learning algorithms were used in the assessment presentation for the thermal properties of soil-rock mixtures. The performance of three machine learning algorithms were contrastively examined by using three important indexes (the coefficient of determination (R2), the root mean square error (RMSE) and the relative error (RE)). Based on the evaluation results, the performance ranking of three machine learning algorithms can be listed (GA-BP > SVR > RFR). This investigation is a beneficial attempt for the large data analysis to introduce the machine learning method into the determination of the thermal conductivity of soil-rock mixture under complex conditions. • A series of thermal conductivity tests were conducted to study the coupling effects of moisture content, temperature, and particle size distribution on the thermal properties of soil-rock mixtures. • 47 empirical or theoretical models were adopted to predicate the thermal conductivity of soil-rock mixtures in order to validate the evaluation ability of these models under the wide testing ranges. • Three machine learning algorithms were used in the assessment presentation for the thermal properties of soil-rock mixtures. [ABSTRACT FROM AUTHOR]

The growing urgency for low-carbon lifestyles necessitates developing effective strategies to promote sustainable consumer choices. This study investigates key dimensions of information quality that shape consumer behavior within digital marketing to achieve this goal. Employing a mixed-methods approach that integrates grounded theory and machine learning, this study identifies three core dimensions of low-carbon information quality: matching quality, presentation quality, and interpretability quality. These dimensions underscore the importance of aligning information with consumer needs, ensuring clear and accurate presentation, and fostering transparency for trustworthiness. A Random Forest algorithm-based evaluation model is constructed to assess low-carbon information quality, demonstrating its effectiveness in identifying high-quality, sustainable content. This research provides a practical tool for digital marketers to enhance their strategies, raise consumer awareness of sustainable options, and ultimately contribute to the growth of the low-carbon consumption market. [ABSTRACT FROM AUTHOR]

Online health communities (OHCs) are able to facilitate social support exchange among people with similar health concerns, and the relationship between self-disclosure and social support exchange has been widely discussed. Build upon the previous studies, we extend the seminal theory of reasoned action (TRA) in this research, through the adoption of an salient but understudied construct, user role, which captures user inclination toward providing or seeking social support, and further explore how it impacts OHC users' self-disclosure intention. We adopt a mixed methods approach including manual coding, machine learning algorithms, and econometric analyses, and investigate over 4 million posts over 17 years from a well-developed breast cancer OHC to uncover both direct and indirect effects of user role on OHC users' intention of future self-disclosure intention. The results reveal both the main and moderating effects of user roles regarding users' self-disclosure intention. First, user with a more clear intention of social support seeking or providing are more likely to disclose personal information. Second, the relative tendency of support provision magnifies the positive effects of attitudinal and normative factors toward future disclosure. We also discuss the findings and implications of the extended TRA framework to generate some actionable insights of OHC design. [ABSTRACT FROM AUTHOR]

In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale. [ABSTRACT FROM AUTHOR]

This research article explores Instagram influencer networks using graph theory and machine learning techniques. With the growing impact of social media personalities, understanding their network structures and dynamics is crucial for effective marketing and brand engagement. We model Instagram's influencer ecosystem as a graph and apply several machine learning algorithms, including Node2Vec and Word2Vec, to perform tasks such as link prediction and community detection. Our analysis reveals significant patterns in influencer interactions and network connectivity, providing actionable insights into influencer behavior and the formation of online communities. These findings offer valuable implications for optimizing marketing strategies and enhancing brand collaborations within the social media landscape. [ABSTRACT FROM AUTHOR]

Herein, a comprehensive review of the evolution of intelligent communication anti‐jamming techniques is provided. First, a clear definition of the concept and elaboration on the inherent connotations and capability characteristics of intelligent communication anti‐jamming is provided. Additionally, the initial construction of an intelligent communication anti‐jamming system architecture is outlined. Subsequently, the development of intelligent communication anti‐jamming is delved, tracing its progression from early‐stage adaptive anti‐jamming techniques to the more recent advancements in intelligent anti‐jamming, which are primarily based on game theory and machine learning. Moreover, the latest research findings in this domain are thoroughly examined and the existing challenges and bottlenecks that hinder current research progress are highlighted. Finally, several viable research directions for future studies in the field of intelligent anti‐jamming are proposed. The objective is to facilitate readers in gaining a comprehensive understanding of the concept, characteristics, and system architecture of intelligent communication anti‐jamming. Additionally, it is aimed to provide a comprehensive overview of the developmental process and current state of research, thereby furnishing conceptual and theoretical support for the design of more effective and practical intelligent communication anti‐jamming systems. [ABSTRACT FROM AUTHOR]

We present fully abstract encodings of the call-by-name and call-by-value λ- calculus into HOcore, a minimal higher-order process calculus with no name restriction. We consider several equivalences on the λ-calculus side--normal-form bisimilarity, applicative bisimilarity, and contextual equivalence--that we internalize into abstract machines in order to prove full abstraction of the encodings. We also demonstrate that this technique scales to the λµ-calculus, i.e., a standard extension of the λ-calculus with control operators. [ABSTRACT FROM AUTHOR]

This study addresses a significant gap in the field of time series regression modeling by highlighting the central role of data augmentation in improving model accuracy. The primary objective is to present a detailed methodology for systematic sampling of training datasets through data augmentation to improve the accuracy of time series regression models. Therefore, different augmentation techniques are compared to evaluate their impact on model accuracy across different datasets and model architectures. In addition, this research highlights the need for a standardized approach to creating training datasets using multiple augmentation methods. The lack of a clear framework hinders the easy integration of data augmentation into time series regression pipelines. Our systematic methodology promotes model accuracy while providing a robust foundation for practitioners to seamlessly integrate data augmentation into their modeling practices. The effectiveness of our approach is demonstrated using process data from two milling machines. Experiments show that the optimized training dataset improves the generalization ability of machine learning models in 86.67% of the evaluated scenarios. However, the prediction accuracy of models trained on a sufficient dataset remains largely unaffected. Based on these results, sophisticated sampling strategies such as Quadratic Weighting of multiple augmentation approaches may be beneficial. [ABSTRACT FROM AUTHOR]

In order to effectively solve the problems of the complex growth state of dragon fruit and how the picking process is mostly manual, this study designed a picking and selecting integrated remote-operation-type dragon-fruit-picking device. Based on SOLIDWORKS 2020 software for the three-dimensional digital design and overall assembly of key components, the structure and working theory of the machine are introduced. By improving the high-recognition-rate dragon fruit target detection algorithm based on YOLOv5, better recognition and locating effects were achieved for targets with a small size and high density, as well as those in bright-light scenes. Serial communication, information acquisition, and the precise control of each picking action were realized by building the software and hardware platforms of the picking device control system. By analyzing the working principle of the mechanical system and the mechanics of the machine picking process, the critical factors affecting the net picking rate and damage rate of the dragon-fruit-picking device were confirmed. Based on the force and parameter analysis of the test results, it was confirmed that the machine had an optimal picking influence when the flexible claw closing speed was 0.029 m/s, the electric cylinder extending speed was 0.085 m/s, and the mechanical arm moving speed was 0.15 m/s. The net picking rate of the device reached 90.5%, and the damage rate reached 2.9%. The picking device can complete the picking of a single dragon fruit, as well as a plurality of fruits grown at a growing point, and integrates the integration of picking fruits, removing bad fruits, and sorting fruits, which can improve the efficiency of dragon fruit harvesting and replace manual work. [ABSTRACT FROM AUTHOR]

Modern automotive systems with adaptive control features require rigorous analysis to guarantee correct operation. We report our experience in modeling the automotive case study from the ABZ2020 conference using the ASMETA toolset, based on the Abstract State Machine formal method. We adopted a seamless system engineering method: from an incremental formal specification of high-level requirements to increasingly refined ASMETA models, to the C++ code generation from the model. Along this process, different validation and verification activities were performed. We explored modeling styles and idioms to face the modeling complexity and ensure that the ASMETA models can best capture and reflect specific behavioral patterns. Through this realistic automotive case study, we evaluated the applicability and usability of our formal modeling approach. [ABSTRACT FROM AUTHOR]

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Favipiravir (FP) and ebselen (EB) belong to a diverse class of antiviral drugs known for their significant efficacy in treating various viral infections. Utilizing molecular dynamics (MD) simulations, machine learning, and van der Waals density functional theory, we accurately elucidate the binding properties of these antiviral drugs on a phosphorene single-layer. To further investigate these characteristics, this study employs four distinct machine learning models—Random Forest, Gradient Boosting, XGBoost, and CatBoost. The Hamiltonian of antiviral molecules within a monolayer of phosphorene is appropriately trained. The key aspect of utilizing machine learning (ML) in drug design revolves around training models that are efficient and precise in approximating density functional theory (DFT). Furthermore, the study employs SHAP (SHapley Additive exPlanations) to elucidate model predictions, providing insights into the contribution of each feature. To explore the interaction characteristics and thermodynamic properties of the hybrid drug, we employ molecular dynamics and DFT calculations in a vacuum interface. Our findings suggest that this functionalized 2D complex exhibits robust thermostability, indicating its potential as an effective and enabled entity. The observed variations in free energy at different surface charges and temperatures suggest the adsorption potential of FP and EB molecules from the surrounding environment. [ABSTRACT FROM AUTHOR]

In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the ensemble of FNN and LSTM models, capitalizing on their collective learning. This ensemble approach outperforms individual models, ensuring stable and accurate power output predictions. Additionally, machine learning techniques are applied to detect wind turbine performance deterioration, enabling proactive maintenance strategies and health assessment. Crucially, our analysis reveals the uniqueness of each wind turbine, necessitating tailored models for optimal predictions. These insight underscores the importance of providing automatized customization for different turbines to keep human modeling effort low. Importantly, the methodologies developed in this analysis are not limited to wind turbines; they can be extended to predict and optimize performance in various machinery, highlighting the versatility and applicability of our research across diverse industrial contexts. [ABSTRACT FROM AUTHOR]

Taiwan's auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify data using a Naive Bayes classifier into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring its representativeness. The user-based approach samples data symmetrically around the median of a class as audit evidence. It may be equivalent to a combination of monetary and variable samplings. The item-based approach represents asymmetric sampling based on posterior probabilities for obtaining risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary samplings. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples; handling complex patterns, correlations, and unstructured data; and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities. [ABSTRACT FROM AUTHOR]

Aiming at the problem of high‐speed motors being limited by multiple physical fields, which makes it difficult to achieve ultra‐high‐speed stable operation and develop towards higher speeds and higher power, an ultra‐high‐speed bearingless permanent magnet synchronous motor (UBPMSM) is proposed. Based on the mathematical model of suspension force for the bearingless permanent magnet motor considering the rotor eccentricity and load condition, the electromagnetic design method for UBPMSM is proposed. According to this method, the topology, torque system and suspension force system of a 10 kw 100k r/min UBPMSM are theoretically designed. The finite element method (FEM) is used to analyse the electromagnetic performance, which verifies the correctness of the proposed design method for UBPMSM. Further analysis and selection of winding span combinations are conducted to obtain better torque and suspension performance. [ABSTRACT FROM AUTHOR]

Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand–supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine learning approaches, deep learning models are complex and are often treated as a "black box" that can cause uncertainty regarding how they operate. Explainable artificial intelligence (XAI) refers to methods that explain and interpret machine learning models' inner workings and how they come to decisions, which is especially important in the medical domain to guide healthcare decision-making processes. This review summarizes recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications. [ABSTRACT FROM AUTHOR]

Link prediction aims to anticipate the probability of a future connection between two nodes in a given network based on their previous interactions and the network structure. Link prediction is a rapidly evolving field of research that has attracted interest from physicists and computer scientists. Over the years, numerous methods have been developed for link prediction, encompassing similarity-based indices, machine learning techniques, and more. While existing surveys have covered link prediction research until 2020, there has been a substantial surge in research activities in recent years, particularly between 2021 and 2023. This increased interest underscores the pressing need to comprehensively explore the latest advancements and approaches in link prediction. We analyse and present the most notable research from 2018 to 2023. Our goal is to offer a comprehensive overview of the recent developments in the field. Besides summarizing and presenting previous experimental results, our survey offers a comprehensive analysis highlighting the strengths and limitations of various link prediction methods. [ABSTRACT FROM AUTHOR]

This study aims to develop a reliable safety evaluation model for diverging vehicles and investigates the impact of the surrounding traffic environment on freeway diverging risks. High-resolution trajectory data from three sites in the Netherlands (Delft, Ter-Heide and Zonzeel) were employed for the risk analysis. Linear regression (LR), support vector machine (SVM), random forest (RF), extreme randomize trees (ET), adaptive boosting (Adaboost), extreme gradient boosting (XGboost) and multilayer perceptron (MLP), were developed for safety evaluation. The result showed that MLP outperforms the other models for diverging risk prediction over all the indicators, conflict thresholds and locations. Pairwise matrix, shapely addictive explanation (SHAP), and LR algorithms were further adopted to interpret the influence of the surrounding environment. It indicates that an increase in traffic density, subject vehicle (SV) lateral speed, the distance of SV from ramp nose and SV length would increase the diverging risk. At the same time, an increase in leading vehicle speed and space headway would decrease diverging risk. Finally, spatial analysis was also conducted to explore the stability of identified traffic features regarding the impact on the diverging risk across the sites. [ABSTRACT FROM AUTHOR]

In recent years, the demand for flexible and sustainable strategies in digitization processes has represented a significant challenge for the heritage documentation research community. In particular, the tasks of parametric modelling and AI-based semantic enrichment operations, necessary but traditionally time-consuming, is extremely onerous from a user-oriented perspective. Many efforts of the research community have been dedicated to enhancing efficiency through automation, and one of the possible solutions is represented by the employment of machine learning strategies. This study introduces an innovative methodology that integrates Visual Programming Language platforms and 3D Python libraries, thereby implementing the Scan-to-BIM approach. Two case studies - characterized by varying scales, resolutions, and accuracies - have been analysed to validate the proposed pipeline, demonstrating its flexibility and scalability across architectural objects and archaeological assets belonging to museum collections. The workflow involves several steps, starting from classified 3D and 2D data segmented using machine learning techniques with the aim of managing semantically enriched reality-based data in BIM/HBIM environment without scarifying accuracy criteria. Results highlight the methodology's efficiency and adaptability in diverse contexts, offering a compelling alternative to labour-intensive Scan-to-BIM processes. Ultimately, this methodology contributes to the automation in cultural heritage digitisation, underlining the need for comprehensive standards and protocols in this dynamic domain. [ABSTRACT FROM AUTHOR]

Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamental understanding of their influence on the performance of materials ranging from photovoltaics to advanced nuclear fuels. Here, using fluorite-structured thorium dioxide (ThO2) as an exemplar, we demonstrate how density functional theory and machine learning interatomic potential can be synergistically combined into a powerful tool that enables exhaustive exploration of the large configuration spaces of small point defect clusters. Our study leads to several unexpected discoveries, including defect polymorphism and ground-state structures that defy our physical intuitions. Possible physical origins of these unexpected findings are elucidated using a local cluster expansion model developed in this work. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) are an incredibly diverse group of highly porous hybrid materials, which are interesting for a wide range of possible applications. For a meaningful theoretical description of many of their properties accurate and computationally highly efficient methods are in high demand. These would avoid compromises regarding either the quality of modelling results or the level of complexity of the calculated properties. With the advent of machine learning approaches, it is now possible to generate such approaches with relatively little human effort. Here, we build on existing types of machine-learned force fields belonging to the moment-tensor and kernel-based potential families to develop a recipe for their efficient parametrization. This yields exceptionally accurate and computationally highly efficient force fields. The parametrization relies on reference configurations generated during molecular dynamics based, active learning runs. The performance of the potentials is benchmarked for a representative selection of commonly studied MOFs revealing a close to DFT accuracy in predicting forces and structural parameters for a set of validation structures. The same applies to elastic constants and phonon band structures. Additionally, for MOF-5 the thermal conductivity is obtained with full quantitative agreement to single-crystal experiments. All this is possible while maintaining a very high degree of computational efficiency. The exceptional accuracy of the parameterized force field potentials combined with their computational efficiency has the potential of lifting the computational modelling of MOFs to the next level. [ABSTRACT FROM AUTHOR]

Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization for Minisci and P450-based functionalizations. We validated our model both retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations and outperforms the well-established Fukui-based reactivity indices and other machine learning reactivity-based algorithms. Regioselectivity prediction for many reactions remains a challenging target for a priori prediction. Here, the authors develop a machine learning model that predicts the outcomes of Minisci reactions. [ABSTRACT FROM AUTHOR]

Over recent decades, machine learning, an integral subfield of artificial intelligence, has revolutionized diverse sectors, enabling data-driven decisions with minimal human intervention. In particular, the field of educational assessment emerges as a promising area for machine learning applications, where students can be classified and diagnosed using their performance data. The objectives of Diagnostic Classification Models (DCMs), which provide a suite of methods for diagnosing students' cognitive states in relation to the mastery of necessary cognitive attributes for solving problems in a test, can be effectively addressed through machine learning techniques. However, the challenge lies in the latent nature of cognitive status, which makes it difficult to obtain labels for the training dataset. Consequently, the application of machine learning methods to DCMs often assumes smaller training sets with labels derived either from theoretical considerations or human experts. In this study, the authors propose a supervised diagnostic classification model with data augmentation (SDCM-DA). This method is designed to utilize the augmented data using a data generation model constructed by leveraging the probability of correct responses for each attribute mastery pattern derived from the expert-labeled dataset. To explore the benefits of data augmentation, a simulation study is carried out, contrasting it with classification methods that rely solely on the expert-labeled dataset for training. The findings reveal that utilizing data augmentation with the estimated probabilities of correct responses substantially enhances classification accuracy. This holds true even when the augmentation originates from a small labeled sample with occasional labeling errors, and when the tests contain lower-quality items that may inaccurately measure students' true cognitive status. Moreover, the study demonstrates that leveraging augmented data for learning can enable the successful classification of students, thereby eliminating the necessity for specifying an underlying response model. [ABSTRACT FROM AUTHOR]

Numerous components' complex interrelationships and interconnectedness present a formidable obstacle in developing mix designs for high-performance concrete (HPC) formulation. The effectiveness of machine learning (ML) algorithms in resolving this paradox has been illustrated. However, they are classified as opaque black-box models due to the lack of a discernible correlation between blend ratios and compressive durability. The present study proposes a semi-empirical methodology that integrates various techniques, including non-dimensionalization and optimization, to overcome this constraint. The methodology exhibits a noteworthy level of accuracy when forecasting compressive strength (CS) across a spectrum of divergent datasets, thus evincing its extensive and all-encompassing efficacy. Moreover, the precise relationship that semi-empirical equations convey is of great significance to practitioners and researchers in this field, especially with respect to their predictive abilities. The determination of CS in concrete is a critical facet of the design of HPC. An exhaustive comprehension of the intricate interplay between manifold factors is requisite to attain an ideal blend proportion. The study's findings indicate that RF can accurately predict CS . Moreover, the combination of optimization algorithms significantly enhances the model's effectiveness. Among the three Optimization Algorithms under consideration, the COA optimizer has exhibited superior performance in augmenting the accuracy and precision of the RF prediction model for CS. As a result, RFCO obtained the more suitable value of R2 and RMSE obtained 0.998 and 0.88, alternatively. [ABSTRACT FROM AUTHOR]

Thyroid disease presents a significant health risk, lowering the quality of life and increasing treatment costs. The diagnosis of thyroid disease can be challenging, especially for inexperienced practitioners. Machine learning has been established as one of the methods for disease diagnosis based on previous studies. This research introduces a novel and more effective technique for predicting thyroid disease by utilizing machine learning methodologies, surpassing the performance of previous studies in this field. This study utilizes the UCI thyroid disease dataset, which consists of 9172 samples and 30 features, and exhibits a highly imbalanced target class distribution. However, machine learning algorithms trained on imbalanced thyroid disease data face challenges in reliably detecting minority data and disease. To address this issue, re-sampling is employed, which modifies the ratio between target classes to balance the data. In this study, the down-sampling approach is utilized to achieve a balanced distribution of target classes. A novel RF-based self-stacking classifier is presented in this research for efficient thyroid disease detection. The proposed approach demonstrates the ability to diagnose primary hypothyroidism, increased binding protein, compensated hypothyroidism, and concurrent non-thyroidal illness with an accuracy of 99.5%. The recommended model exhibits state-of-the-art performance, achieving 100% macro precision, 100% macro recall, and 100% macro F1-score. A thorough comparative assessment is conducted to demonstrate the viability of the proposed approach, including several machine learning classifiers, deep neural networks, and ensemble voting classifiers. The results of K-fold cross-validation provide further support for the efficacy of the proposed self-stacking classifier. [ABSTRACT FROM AUTHOR]