Error detection is a critical step in data cleaning. Most traditional error detection methods are based on rules and external information with high cost, especially when dealing with large-scaled data. Recently, with the advances of deep learning, so...
In the present qualitative study, we first synthesize research to clarify the concept of error in science as developed by epistemologists, philosophers, and historians. We also examine the concept of error in educational science, drawing from studies...
Learning from errors (LFE) constitutes a complex, multilevel process whose pedagogical value depends on how errors are elicited, interpreted, and corrected. This narrative review critically synthesizes research on LFE and its implications for science...
Solving algebraic word problems is an essential component of the school mathematics curriculum; nonetheless, many students still make mistakes in solving them. Several studies have largely focused on categorizing errors in solving algebraic word prob...
A machine learning model for correcting errors in Ukrainian texts has been developed. It was established that the neural network has the ability to correct simple sentences written in Ukrainian; however, the development of a full-fledged system requi...
The “Multivariate Ring Learning with Errors” problem was presented as a generalization of Ring Learning with Errors (RLWE), introducing efficiency improvements with respect to the RLWE counterpart thanks to its multivariate structure. Nevertheless, t...
No prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and ligh...
Large-scale quantum computing poses a major threat to classical public-key cryptography. Recently, strong “quantum access” security models have shown that numerous symmetric-key cryptosystems are also vulnerable. In this paper, we conside...
With the advance of Artificial Intelligence, Deep Neural Networks are widely employed in various sensor-based systems to analyze operational conditions. However, due to the inherently nondeterministic and probabilistic natures of neural networks, the...
Generative artificial intelligence (GenAI) is increasingly used to support programming education, but its use in learning-from-errors activities has received limited research attention. This quasi-experimental study compared AI-guided and human-guide...
The swift advancement of quantum computing devices holds the potential to create robust machines that can tackle an extensive array of issues beyond the scope of conventional computers. Consequently, quantum computing machines create new risks at a v...
In practical applications, the hydrophone array has element position errors, which seriously degrade the performance of the direction of arrival estimation. We propose a direction of arrival (DOA) estimation method based on sparse Bayesian learning u...
To prevent eavesdropping and tampering, network security protocols take advantage of asymmetric ciphers to establish session-specific shared keys with which further communication is encrypted using symmetric ciphers. Commonly used asymmetric algorith...
In this paper, we develop a hybrid iterative learning controller (HILC) for a non-holonomic wheeled mobile platform to achieve trajectory tracking with actual complex constraints, such as physical constraints, uncertain parameters, and initial errors...
Ultra-wideband (UWB) technology has been widely adopted for indoor positioning due to its high temporal resolution. However, the accuracy of UWB-based indoor positioning is fundamentally limited by ranging measurement errors, particularly under non-l...
Circular-Scan photoacoustic tomography (PAT) can provide high-resolution images of optical absorption, but its analytical reconstructions, such as delay-and-sum (DAS), are highly sensitive to scanning radius (SR) inaccuracies, which cause severe geom...
This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variabilit...
In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering ve...
The emergence of third-generation single-molecule sequencing (TGS) technology has revolutionized the generation of long reads, which are essential for genome assembly and have been widely employed in sequencing the SARS-CoV-2 virus during the COVID-1...
Heat source-induced thermal error is a primary element influencing the precision of CNC machine tools. A practical and economical approach to mitigating thermal errors is through thermal error compensation. To provide a comprehensive understanding of...
Background: Inborn errors of immunity (IEIs) are a heterogeneous group of rare disorders caused by genetic defects in one or more components of the immune system. The Jeffrey Modell Foundation’s (JMF) Ten Warning Signs are widely used for early...
The activities of the rotary axes of a five-axis machine tool generate heat causing temperature changes within the machine that contribute to tool center point (TCP) deviations. Real time prediction of these thermally induced volumetric errors (TVEs)...
We trained an artificial neural network (ANN) to distinguish between correct and erroneous responses in the perceptual decision-making task using 32 EEG channels. The ANN input took the form of a 2D matrix where the vertical dimension reflected the n...
Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two l...
Objectives: We aimed to explore whether incorporating a deliberate error-based learning activity, adapted from error management training (EMT), could enhance undergraduate dental students’ understanding of common intra-oral radiographic faults...
In view of the problem that traditional dam outlier identification methods mostly rely on single-monitoring-point models and do not fully consider the spatio-temporal correlation characteristics of deformation between monitoring points, which can eas...
Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulate...
Error-based learning theories of language acquisition are highly influential in language development research, yet the predictive learning mechanism they propose has proven difficult to test experimentally. Prime surprisal—the observation that...
Affected by various error factors in the actual environment, the accuracy of the direction of arrival (DOA) estimation algorithm will greatly decrease during an application. To address this issue, in this paper, we propose an intelligent DOA estimati...
The local size of computational grids used in partial differential equation (PDE)-based probabilistic inverse problems can have a tremendous impact on the numerical results. As a consequence, numerical model identification procedures used in structur...
Several attempts have been made to explore the factors influencing teacher emotions, most of which focus on external factors such as student behaviors and classroom teaching. However, research on the links between internal factors and teacher emotion...
Artificial neural networks are widely applied for prediction, function simulation, and data classification. Among these applications, the wavelet neural network is widely used in image classification problems due to its advantages of high approximati...
In this paper, a parameter optimal gain-arguable iterative learning control algorithm is proposed for a class of linear discrete-time systems with quantized error. Based on the lifting model description for ILC systems, the iteration time-variable de...
To address the critical demand for high-precision forecasts in flood management, real-time error correction techniques are increasingly implemented to improve the accuracy and operational reliability of the hydrological prediction framework. However,...
Current mainstream for Chinese grammatical error correction methods rely on deep neural network models, which require a large amount of high-quality data for training. However, existing Chinese grammatical error correction corpora have a low annotati...
Designing an optimal iterative learning control is a huge challenge for linear and nonlinear dynamic systems. For such complex systems, standard Norm optimal iterative learning control (NOILC) is an important consideration. This paper presents a nove...
The segmented mirror co-phase error identification technique based on supervised learning methods has the advantages of simple application conditions, no dependence on custom sensors, a fast calculation speed, and low computing power requirements com...
Deep-learning post-processing can correct systematic errors in numerical wave forecasts, but its reliability under high-wave conditions remains unclear. We develop an error-correction framework in which deep networks predict the full future error tra...
For metagenomics datasets, datasets of complex polyploid genomes, and other high-variation genomics datasets, there are difficulties with the analysis, error detection and variant calling, stemming from the challenges of discerning sequencing errors...
Thermal error is one of the main sources of machining error of machine tools. Being a key component of the machine tool, the spindle will generate a lot of heat in the machining process and thereby result in a thermal error of itself. Real-time measu...
Current transformer measurement errors exhibit strong non-stationarity and multi-scale temporal dynamics, which make accurate prediction challenging for conventional deep learning models. This paper presents a hybrid signal processing and temporal le...
Quantum error-correcting codes are essential for achieving fault-tolerant quantum computing. Heavy hexagonal code is a type of topological code that leverages the arrangement of qubits to find and correct errors. The heavy hexagonal code is suitable...
A data-driven nonlinear control approach, called error dynamics-based dual heuristic dynamic programming (ED-DHP), is proposed for air vehicle attitude control. To solve the optimal tracking control problem, the augmented system is defined by the der...
Large compressors have complex structures and constantly changing operating conditions. It is challenging to build physical models of compressors to analyse their performance parameters. An improved error-based stacked ensemble learning prediction mo...
Predicting complex nonlinear turbulent dynamical systems is an important and practical topic. However, due to the lack of a complete understanding of nature, the ubiquitous model error may greatly affect the prediction performance. Machine learning a...
Recent advances in Machine Learning have significantly improved anomaly detection in industrial screw driving operations. However, most existing approaches focus on binary classification of normal versus anomalous operations or employ unsupervised me...
The spiking neural network (SNN) is regarded as a promising candidate to deal with the great challenges presented by current machine learning techniques, including the high energy consumption induced by deep neural networks. However, there is still a...
Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically use...
In the context of quantum computing, error correction remains a pivotal challenge, primarily due to imperfect gate operations and environmental interactions. This study introduces a machine learning-based method to simulate and analyze these errors....
Deep learning is crucial in marine logistics and container crane error detection, diagnosis, and prediction. A novel deep learning technique using Long Short-Term Memory (LSTM) detected and anticipated errors in a system with imbalanced data. The LST...
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