Search Results (11,068)

The four basic language skills, listening, speaking, reading, and writing play, a crucial role in the development of an individual’s skills in other disciplines. The current study aims to underpin the relationship between language skills and mathematics skills by focusing on the language and mathematics curricula of two consistently high-achieving countries, Hong Kong and Singapore, in the Program for International Student Assessment (PISA) rankings. In the current study, the convergent parallel mixed method was utilized that qualitative and quantitative data were composed together. Primarily, the outcomes of four language skills were determined in the native language teaching curricula of the two countries. The topics and themes related to four basic language skills were determined from the two mathematics curricula. The curricula were examined by document analysis from qualitative research methods. The analysis was conducted by examining the native language teaching and the mathematics curricula of both countries by the content analysis method. Later, the findings of the document analysis were used to develop machine learning models to find a possible positive relationship between language skills and the PISA scores. Although a number of previous studies have found a reasonable relationship between language skills and mathematics skills, the current study results were contradictory to the ones performed previously in the literature, and considering the curricula no positive relationship between the language and mathematics skills was found. The findings of the current study were further supported by the artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) model performance metrics. Compared to an acceptable level of 0.80, significantly low R² values of 0.35 and 0.39 for the ANN and ANFIS models, respectively, indicated very little relationship between the language and mathematics skills. Full article

The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data from a universal robot to detect anomalies or early-stage faults using test data from designed anomalous conditions and artificial-intelligence-based anomaly detection techniques called autoencoders. The performance of three autoencoders, namely, a multi-layer-perceptron-based autoencoder, convolutional-neural-network-based autoencoder, and sparse autoencoder, was compared in detecting anomalies. The results indicate that the autoencoders effectively detected anomalies in the examined complex and noisy datasets with more than 93% overall accuracy and an F1 score exceeding 96% for the considered anomalous cases. Moreover, the integration of trajectory change detection and anomaly detection algorithms (i.e., the dynamic time warping algorithm and sparse autoencoder, respectively) was proposed for the local implementation of online condition monitoring. This integrated approach to anomaly detection and trajectory change provides a practical, adaptive, and economical solution for enhancing the reliability and safety of collaborative robots in smart manufacturing environments. Full article

(1) Background: Cervical screening and additional diagnostic procedures often lead to depression. This research aimed to develop a prediction model for depression in women who received an abnormal Papanicolaou screening test, prior to and following the diagnostic procedures. (2) Methods: The study included women who had a positive Papanicolaou screening test (N = 172) and attended the Clinical Center of Kragujevac in Serbia for additional diagnostic procedures (colposcopy/biopsy/endocervical curettage). Women filled out a sociodemographic survey and the Center for Epidemiologic Studies Depression questionnaire (CES-D scale) before and after diagnostic procedures. A prediction model was built with multilayer perceptron neural networks. (3) Results: A correlation-based filter method of feature selection indicated four variables that correlated with depression both prior to and following the diagnostic procedures—anxiety, depression, worry, and concern about health consequences. In addition, the use of sedatives and a history of both induced and spontaneous abortion correlated with pre-diagnostic depression. Important attributes for predicting post-diagnostic depression were scores for the domains ‘Tension/discomfort’ and ‘Embarrassment’ and depression in personal medical history. The accuracy of the pre-diagnostic procedures model was 70.6%, and the area under the receiver operating characteristic curve (AUROC) was 0.668. The model for post-diagnostic depression prediction showed an accuracy of 70.6%, and an AUROC = 0.836. (4) Conclusions: This study helps provide means to predict the occurrence of depression in women with an abnormal Papanicolaou screening result prior to and following diagnostic procedures, which can aid healthcare professionals in successfully providing timely psychological support to those women who are referred to further diagnostics. Full article

We compare three machine learning models—artificial neural network (ANN), random forest (RF), and convolutional neural network (CNN)—for spatial downscaling of temperature at 2 m above ground (T2M) from a 9 km ERA5-Land reanalysis to 1 km in a complex terrain area, including the Non Valley and the Adige Valley in the Italian Alps. The results suggest that CNN performs better than the other methods across all seasons. RF performs similar to CNN, particularly in spring and summer, but its performance is reduced in winter and autumn. The best performance was observed in summer for CNN (R² = 0.94, RMSE = 1 °C, MAE = 0.78 °C) and the lowest in winter for ANN (R² = 0.79, RMSE = 1.6 °C, MAE = 1.3 °C). Elevation is an important predictor for ANN and RF, whereas it does not play a significant role for CNN. Additionally, CNN outperforms others even without elevation as an additional feature. Furthermore, MAE increases with higher elevation for ANN across all seasons. Conversely, MAE decreases with increased elevation for RF and CNN, particularly for summer, and remains mostly stable for other seasons. Full article

Building energy consumption prediction models are powerful tools for optimizing energy management. Among various methods, artificial neural networks (ANNs) have become increasingly popular. This paper reviews studies since 2015 on using ANNs to predict building energy use and demand, focusing on the characteristics of different ANN structures and their applications across building phases—design, operation, and retrofitting. It also provides guidance on selecting the most appropriate ANN structures for each phase. Finally, this paper explores future developments in ANN-based predictions, including improving data processing techniques for greater accuracy, refining parameterization to better capture building features, optimizing algorithms for faster computation, and integrating ANNs with other machine learning methods, such as ensemble learning and hybrid models, to enhance predictive performance. Full article

This paper studies an artificial neural network (ANN) for multi-asset European options. Firstly, a simple three-layer ANN-3 is established with undetermined weights and bias. Secondly, the time–space discrete PDE of the multi-asset option is given and the corresponding discrete data are fed into the ANN-3. Then, using least squares error as the objective function, the weights and bias of ANN-3 are trained well. Numerical examples are carried out to confirm the stability, accuracy and efficiency. Experiments show the ANN’s relative error is about $0.8\%$. This method can be extended into multi-layer ANN-$q(q>3)$ and extended into American options. Full article

A neural network was developed using the Multilayer Perceptron (MLP) model to predict the darkness value of the night sky in rural areas. For data collection, a photometer was placed in three different rural locations in the province of Cáceres, Spain, recording darkness values over a period of 23 months. The recorded data were processed, debugged, and used as a training set (75%) and validation set (25%) in the development of an MLP capable of predicting the darkness level for a given date. The network had a single hidden layer of 10 neurons and hyperbolic activation function, obtaining a coefficient of determination (R²) of 0.85 and a mean absolute percentage error (MAPE) of 6.8%. The developed model could be employed in unpopulated rural areas for the promotion of sustainable astronomical tourism. Full article

In this study, the static E₅₀ and υ parameters of rock materials were investigated using P-S wave velocities and Shore hardness (SH), using non-destructive measurement methods. In this study, the multiple linear regression (MLR), multiple non-linear regression (MNLR), and artificial neural network (ANN) models were used to estimate and determine the static E₅₀ and υ parameters. When comparing the models defined by MLR, MNLR, and ANN to the R² values, it was found that the ANN models, which estimate the E₅₀ and υ parameters of rock materials using non-destructive methods (V_p, V_s, V_p/V_s, ρ_d, and SH), achieved higher accuracy than the MLR and MNLR models. The originality of this study is rooted in the fact that ores such as galena, chromite, and barite were studied for the first time from a rock mechanics perspective, providing an innovative viewpoint. In addition, the use of all non-destructive measurement methods, V_p, V_s, and Shore hardness tests, also increases the importance of the study findings. Full article

This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations. Full article

This study presents a soft-computing-based method for determining polymer pipelines’ creep function parameters (CFPs) and pressure wave speeds (PWSs) through transient flow analysis. To this end, first, a numerical model for transient flow in polymer pipes was developed in the time domain. Then, by considering a pipeline with a specific geometry, 2000 transient flow signals were generated for different CFPs and PWSs. The amplitudes obtained by transforming the time-domain pressure signals to the frequency domain using the fast Fourier transform algorithm are the inputs for an artificial neural network model. The results showed that the proposed approach accurately estimated the creep function of the polymer pipes. Full article

In this paper, an innovative hybrid technique is proposed for the efficient training of artificial neural networks, which are used both in class learning problems and in data fitting problems. This hybrid technique combines the well-tested technique of Genetic Algorithms with an innovative variant of Simulated Annealing, in order to achieve high learning rates for the neural networks. This variant was applied periodically to randomly selected chromosomes from the population of the Genetic Algorithm in order to reduce the training error associated with these chromosomes. The proposed method was tested on a wide series of classification and data fitting problems from the relevant literature and the results were compared against other methods. The comparison with other neural network training techniques as well as the statistical comparison revealed that the proposed method is significantly superior, as it managed to significantly reduce the neural network training error in the majority of the used datasets. Full article

Hellenic Open University has been developing Onlabs, a virtual biology laboratory simulating its on-site laboratory, for its students to be trained before the on-site learning activities. The evaluation of user performance in Onlabs is based on a scoring algorithm, which admits some optimization by means of Genetic Algorithms and Artificial Neural Networks. Moreover, for a particular experimental procedure (microscoping), we have experimented with incorporating into it some background knowledge about the procedure, which allows one to break it down in a series of conceptually linked steps in a hierarchical fashion. In this work, we review the flat and hierarchical modes used for the calibration of the automated assessment mechanism and offer an experimental comparison of both approaches with the aim of devising automated scoring schemes which are fit for training in an at-a-distance learning context. Overall, the genetic algorithm fails to deliver good convergence results in the non-hierarchical setting but performs better in the hierarchical one. On the other hand, the neural network most of the time converges, with the non-hierarchical network achieving a slightly better convergence than the hierarchical one, with the latter, however, delivering a smoother and more realistic assessment mechanism. Full article

As an extension of the transformer architecture, the BERT model has introduced a new paradigm for natural language processing, achieving impressive results in various downstream tasks. However, high-performance BERT-based models—such as ELECTRA, ALBERT, and RoBERTa—suffer from limitations such as poor continuous learning capability and insufficient understanding of domain-specific documents. To address these issues, we propose the use of an attention mechanism to combine BERT-based models with neural topic models. Unlike traditional stochastic topic modeling, neural topic modeling employs artificial neural networks to learn topic representations. Furthermore, neural topic models can be integrated with other neural models and trained to identify latent variables in documents, thereby enabling BERT-based models to sufficiently comprehend the contexts of specific fields. We conducted experiments on three datasets—Movie Review Dataset (MRD), 20Newsgroups, and YELP—to evaluate our model’s performance. Compared to the vanilla model, the proposed model achieved an accuracy improvement of 1–2% for the ALBERT model in multiclassification tasks across all three datasets, while the ELECTRA model showed an accuracy improvement of less than 1%. Full article

The design of the high-speed on/off valve is challenging due to the interrelated structural parameters of its driving actuator. Hence, this study proposes a multi-objective optimization approach that integrates a backpropagation neural network and artificial fish swarm algorithm optimization techniques to accurately model the electromagnetic solenoid structure. The backpropagation neural network is fitted and trained using simulation data to obtain a reduced-order model of the system, enabling the precise prediction of the system’s output based on the input structural parameters. By employing the artificial fish swarm algorithms, with optimization objectives focusing on the valve’s opening and closing times, a Pareto optimal solution set comprising 30 solutions is generated. Utilizing the optimized structural parameters, a prototype is manufactured and an experimental setup is constructed to verify the dynamic characteristics and flow pressure drop. The high-speed on/off valve achieves an approximate opening and closing time of 3 ms. Notably, the system output predicted using the backpropagation neural network (BPNN) exhibits consistency with the experimental findings, providing a reliable alternative to mathematical modeling. Full article

Parkinson’s disease (PD), the second most prevalent neurodegenerative disorder worldwide, frequently presents with early-stage speech impairments. Recent advancements in Artificial Intelligence (AI), particularly deep learning (DL), have significantly enhanced PD diagnosis through the analysis of speech data. Nevertheless, the progress of research is restricted by the limited availability of publicly accessible speech-based PD datasets, primarily due to privacy concerns. The goal of this systematic review is to explore the current landscape of speech-based DL approaches for PD classification, based on 33 scientific works published between January 2020 and March 2024. We discuss their available resources, capabilities, and potential limitations, and issues related to bias, explainability, and privacy. Furthermore, this review provides an overview of publicly accessible speech-based datasets and open-source material for PD. The DL approaches identified are categorized into end-to-end (E2E) learning, transfer learning (TL), and deep acoustic feature extraction (DAFE). Among E2E approaches, Convolutional Neural Networks (CNNs) are prevalent, though Transformers are increasingly popular. E2E approaches face challenges such as limited data and computational resources, especially with Transformers. TL addresses these issues by providing more robust PD diagnosis and better generalizability across languages. DAFE aims to improve the explainability and interpretability of results by examining the specific effects of deep features on both other DL approaches and more traditional machine learning (ML) methods. However, it often underperforms compared to E2E and TL approaches. Full article