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In this work, several machine learning (ML) algorithms, both classical ML and modern deep learning, were investigated for their ability to improve the performance of a pipeline for the segmentation and classification of prostate lesions using MRI data. The algorithms were used to perform a binary classification of benign and malignant tissue visible in MRI sequences. The model choices include support vector machines (SVMs), random decision forests (RDFs), and multi-layer perceptrons (MLPs), along with radiomic features that are reduced by applying PCA or mRMR feature selection. Modern CNN-based architectures, such as ConvNeXt, ConvNet, and ResNet, were also evaluated in various setups, including transfer learning. To optimize the performance, different approaches were compared and applied to whole images, as well as gland, peripheral zone (PZ), and lesion segmentations. The contribution of this study is an investigation of several ML approaches regarding their performance in prostate cancer (PCa) diagnosis algorithms. This work delivers insights into the applicability of different approaches for this context based on an exhaustive examination. The outcome is a recommendation or preference for which machine learning model or family of models is best suited to optimize an existing pipeline when the model is applied as an upstream filter. [ABSTRACT FROM AUTHOR]

This article presents the current state and development directions of the tire industry. One of the main requirements that a tire must meet before it can leave the factory is achieving values of quantities describing uniformity at a defined level. Of particular importance areconicity and the components of the tire with the greatest impact on its value. This research is based on the possibility of using an ANN to meet contemporary challenges faced by tire manufacturers. In order to achieve a satisfactory level of prediction, we compared the use of a multi-layer perceptron and decision trees XGBoost, LightGbmRegression, and FastTreeRegression. Based on data analysis and similar examples from the literature, metrics were selected to evaluate the models' ability to solve regression problems in relation to the described problem. We selected the best possible solution, standing at the top of the features covered by the criterion analysis. The proposed solutions can be the basis for acquiring new knowledge and contributions in the field of the computational analysis of industrial data in tire production. These solutions are characterized by the required accuracy and efficiency for online work, and they also contribute to the creation of the best fit elements of complex systems (including computational models). The results of this study will contribute to reducing the volume of waste in the tire industry by eliminating defective tire parts in the early stages of the production process. [ABSTRACT FROM AUTHOR]

The assessment of scour depth downstream of weirs holds paramount importance in ensuring the structural stability of these hydraulic structures. This study presents groundbreaking experimental investigations highlighting the innovative use of baffles to enhance energy dissipation and mitigate scour in the downstream beds of rectangular piano key weirs (RPKWs) and trapezoidal piano key weirs (TPKWs). By leveraging three state-of-the-art supervised machine learning algorithms—multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and support vector regression (SVR)—to estimate scour hole parameters, this research showcases significant advancements in predictive modeling for scour analysis. Experimental results reveal that the incorporation of baffles leads to a remarkable 18–22% increase in energy dissipation and an 11–14% reduction in scour depth for both RPKWs and TPKWs. Specifically, introducing baffles in RPKWs resulted in a noteworthy 26.7% reduction in scour hole area and a 30.3% decrease in scour volume compared to RPKWs without baffles. Moreover, novel empirical equations were developed to estimate scour parameters, achieving impressive performance metrics with an average R2 = 0.951, RMSE = 0.145, and MRPE = 4.429%. The MLP models demonstrate superior performance in predicting maximum scour depth across all scenarios with an average R2 = 0.988, RMSE = 0.035, and MRPE = 1.036%. However, the predictive capabilities varied when estimating weir toe scour depth under diverse circumstances, with the XGBoost model proving more accurate in scenarios involving baffled TPKWs with R2 = 0.965, RMSE = 0.048, and MRPE = 2.798% than the MLP and SVR models. This research underscores the significant role of baffles in minimizing scouring effects in TPKWs compared to RPKWs, showcasing the potential for improved design and efficiency in water-management systems. [ABSTRACT FROM AUTHOR]

A neural network method has been developed for helicopter turboshaft engine residual life determination, the basis of which is a hierarchical system, which is represented in neural network model form, consisting of four layers, which determines the numerical value of the residual life. To implement a hierarchical system, a justified multilayer perceptron is used. A multilayer perceptron training algorithm has been developed, which, by introducing an initial parameter to the output layer, yields a prediction accuracy of up to 99.3%, and the adaptive Adam training rate ensures an accuracy of up to 99.4% in helicopter turboshaft engine residual life determination. A method for constructing a degradation curve has been developed that takes into account both the parameter predictions and similarities with past patterns, allowing you to determine the range of possible values of the residual life estimate, with a probability of up to 95%. The article considers an example of solving the task of determining the thermally stressed state of helicopter turboshaft engine compressor turbine blades and assessing their residual life. A computational experiment was carried out to determine the residual life of helicopter turboshaft engine compressor turbine blades, and the results, with 160 training epochs, recorded an accuracy of 99.3%, with a reduction in losses from 2.5% to 0.5% thanks to training process optimization by applying an adaptive training rate. The comparative analysis results showed that use of the multilayer perceptron as a hierarchical system gives better results than the classical RBF network and the least squares method. The first and second types of error were reduced by 2.23 times compared to the RBF network and by 4.74 times compared to the least squares method. [ABSTRACT FROM AUTHOR]

Accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for maintaining the safe and stable operation of rechargeable battery management systems. However, this task is often challenging due to the complex temporal dynamics. Recently, attention-based networks, such as Transformers and Informer, have been the popular architecture in time series forecasting. Despite their effectiveness, these models with abundant parameters necessitate substantial training time to unravel temporal patterns. To tackle these challenges, we propose a straightforward MLP-Mixer-based architecture named "Intra–Inter Patch Mixer" (IIP-Mixer), which leverages the strengths of multilayer perceptron (MLP) models to capture both local and global temporal patterns in time series data. Specifically, it extracts information using an MLP and performs mixing operations along both intra-patch and inter-patch dimensions for battery RUL prediction. The proposed IIP-Mixer comprises parallel dual-head mixer layers: the intra-patch mixing MLP, capturing local temporal patterns in the short-term period, and the inter-patch mixing MLP, capturing global temporal patterns in the long-term period. Notably, to address the varying importance of features in RUL prediction, we introduce a weighted loss function in the MLP-Mixer-based architecture, marking the first time such an approach has been employed. Our experiments demonstrate that IIP-Mixer achieves competitive performance in battery RUL prediction, outperforming other popular time series frameworks, such as Informer and DLinear, with relative reductions in mean absolute error (MAE) of 24% and 10%, respectively. [ABSTRACT FROM AUTHOR]

The carbon content as received (Car) of coal is essential for the emission factor method in IPCC methodology. The traditional carbon measurement mechanism relies on detection equipment, resulting in significant detection costs. To reduce detection costs and provide precise predictions of Cars even in the absence of measurements, this paper proposes a neural network combining MLP with an attention mechanism (MSA-Net). In this model, the Attention Module is proposed to extract important and potential features. The Skip-Connections are utilized for feature reuse. The Huber loss is used to reduce the error between predicted Car values and actual values. The experimental results show that when the input includes eight measured parameters, the MAPE of MSA-Net is only 0.83%, which is better than the state-of-the-art Gaussian Process Regression (GPR) method. MSA-Net exhibits better predictive performance compared to MLP, RNN, LSTM, and Transformer. Moreover, this article provides two measurement solutions for thermal power enterprises to reduce detection costs. [ABSTRACT FROM AUTHOR]

High-quality printing is a longstanding objective in the printing and replication industry. However, the methods used to evaluate print quality suffer from subjectivity and multidimensionality, relying on personal preferences and subjective perceptions to assess the quality of printed images, which poses significant limitations. To address these issues, a set of evaluation metrics aimed at assessing the quality of digital printing products is proposed to achieve evaluation results consistent with human visual perception. Given the differing imaging principles of pre-press digital images and post-scan images, these images are first preprocessed to standardize them for comparison. Next, features are extracted in both spatial and frequency domains, and similarity metrics are used to quantify the differences in features between pre-press digital images and post-scan images. Finally, a multilayer perceptron (MLP) neural network regression model is trained to predict the final objective quality scores. Experimental results on two standard databases demonstrate that this metric exhibits high consistency in both subjective and objective quality evaluation metrics for printed image quality assessment and outperforms other metrics in terms of accuracy. [ABSTRACT FROM AUTHOR]

(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) during exercise and recovery periods and the levels of anxiety and depression among college students. Additionally, the study assesses the accuracy of a multilayer perceptron-based HRV analysis in predicting these emotional states. (2) Methods: A total of 845 healthy college students, aged between 18 and 22, participated in the study. Participants completed self-assessment scales for anxiety and depression (SAS and PHQ-9). HRV data were collected during exercise and for a 5-min period post-exercise. The multilayer perceptron neural network model, which included several branches with identical configurations, was employed for data processing. (3) Results: Through a 5-fold cross-validation approach, the average accuracy of HRV in predicting anxiety levels was 89.3% for no anxiety, 83.6% for mild anxiety, and 74.9% for moderate to severe anxiety. For depression levels, the average accuracy was 90.1% for no depression, 84.2% for mild depression, and 82.1% for moderate to severe depression. The predictive R-squared values for anxiety and depression scores were 0.62 and 0.41, respectively. (4) Conclusions: The study demonstrated that HRV during exercise and recovery in college students can effectively predict levels of anxiety and depression. However, the accuracy of score prediction requires further improvement. HRV related to exercise can serve as a non-invasive biomarker for assessing psychological health. [ABSTRACT FROM AUTHOR]

In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein sequences as input, utilizes SCRATCH-1D for protein secondary structure prediction, and employs feature extraction techniques to derive distinctive features from both sequence and structural data. Subsequently, a Siamese network is employed to foster a contrastive learning environment, enhancing the model's ability to effectively represent the data. Finally, a multi-layer perceptron (MLP) integrates and processes sequence embeddings alongside predicted secondary structure embeddings to forecast ARG presence. To evaluate our approach, we curated a pioneering open dataset termed ARSS (Antibiotic Resistance Sequence Statistics). Comprehensive comparative experiments demonstrate that our method surpasses current state-of-the-art methodologies. Additionally, through detailed case studies, we illustrate the efficacy of our approach in predicting potential ARGs. [ABSTRACT FROM AUTHOR]

Background: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM). Methods: A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro–Wilk test for normality, the Kruskal–Wallis H test for group differences, and the Mann–Whitney U test with Bonferroni correction for post-hoc comparisons. Results: The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR. Conclusion: The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management. [ABSTRACT FROM AUTHOR]

The objective of basic oxygen furnace (BOF) steelmaking is to achieve molten steel with final carbon content, temperature, and phosphorus content meeting the requirements. Accurate prediction of the above properties is crucial for end-point control in BOF steelmaking. Traditional prediction models typically use multi-variable input and single-variable output approaches, neglecting the coupling relationships between different property indicators, making it difficult to predict multiple outputs simultaneously. Consequently, a multi-output prediction model based on the fusion of deep convolution and attention mechanism networks (FDCAN) is proposed. The model inputs include scalar data, such as the properties of raw materials and target molten steel, and time series data, such as lance height, oxygen supply intensity, and bottom air supply intensity during the blowing process. The FDCAN model utilizes a fully connected module to extract nonlinear features from scalar data and a deep convolution module to process time series data, capturing high-dimensional feature representations. The attention mechanism then assigns greater weight to significant features. Finally, multiple multi-layer perceptron modules predict the outputs—final carbon content, temperature, and phosphorus content. This structure allows FDCAN to learn complex relationships within the input data and between input and output variables. The effectiveness of the FDCAN model is validated using actual BOF steelmaking data, achieving hit rates of 95.14% for final carbon content within ±0.015 wt%, 84.72% for final temperature within ±15 °C, and 88.89% for final phosphorus content within ±0.005 wt%. [ABSTRACT FROM AUTHOR]

Morse code recognition plays a very important role in the application of human–machine interaction. In this paper, based on the carbon nanotube (CNT) and polyurethane sponge (PUS) composite material, a flexible tactile CNT/PUS sensor with great piezoresistive characteristic is developed for detecting Morse code precisely. Thirty-six types of Morse code, including 26 letters (A–Z) and 10 numbers (0–9), are applied to the sensor. Each Morse code was repeated 60 times, and 2160 (36 × 60) groups of voltage time-sequential signals were collected to construct the dataset. Then, smoothing and normalization methods are used to preprocess and optimize the raw data. Based on that, the long short-term memory (LSTM) model with excellent feature extraction and self-adaptive ability is constructed to precisely recognize different types of Morse code detected by the sensor. The recognition accuracies of the 10-number Morse code, the 26-letter Morse code, and the whole 36-type Morse code are 99.17%, 95.37%, and 93.98%, respectively. Meanwhile, the Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) models are built to distinguish the 36-type Morse code (letters of A–Z and numbers of 0–9) based on the same dataset and achieve the accuracies of 91.37%, 88.88%, 87.04%, and 90.97%, respectively, which are all lower than the accuracy of 93.98% based on the LSTM model. All the experimental results show that the CNT/PUS sensor can detect the Morse code's tactile feature precisely, and the LSTM model has a very efficient property in recognizing Morse code detected by the CNT/PUS sensor. [ABSTRACT FROM AUTHOR]

The objective and automatic detection of the sleep–wake cycle (SWC) stages is essential for the investigation of its physiology and dysfunction. Here, we propose a machine learning model for the classification of SWC stages based on the measurement of synchronization between neural oscillations of different frequencies. Publicly available electrophysiological recordings of mice were analyzed for the computation of phase–amplitude couplings, which were then supplied to a multilayer perceptron (MLP). Firstly, we assessed the performance of several architectures, varying among different input choices and numbers of neurons in the hidden layer. The top performing architecture was then tested using distinct extrapolation strategies that would simulate applications in a real lab setting. Although all the different choices of input data displayed high AUC values (>0.85) for all the stages, the ones using larger input datasets performed significantly better. The top performing architecture displayed high AUC values (>0.95) for all the extrapolation strategies, even in the worst-case scenario in which the training with a single day and single animal was used to classify the rest of the data. Overall, the results using multiple performance metrics indicate that the usage of a basic MLP fed with highly descriptive features such as neural synchronization is enough to efficiently classify SWC stages. [ABSTRACT FROM AUTHOR]

The acoustic performance of automotive micro-motors directly impacts the comfort and driving experience of both drivers and passengers. However, various motor production and testing uncertainties can lead to noise fluctuations during operation. Thus, predicting the operational noise range of motors on the production line in advance becomes crucial for timely adjustments to production parameters and process optimization. This paper introduces a prediction model based on a Multi-Branch Channel–Spatial Adaptive Weighting Strategy (MCSAWS). The model includes a multi-branch feature extraction (MFE) network and a channel–spatial attention module (CSAM). It uses the vibration and noise data from micro-motors' idle operations on the production line as input to efficiently predict the operational noise uncertainty interval of automotive micro-motors. The model employs the VAE-GAN approach for data augmentation (DA) and uses Gammatone filters to emphasize the noise at the commutation frequency of the motor. The model was compared with Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). Experimental results demonstrate that the MCSAWS method is superior to conventional methods in prediction accuracy and reliability, confirming the feasibility of the proposed approach. This research can help control noise uncertainty in micro-motors' production and manufacturing processes in advance. [ABSTRACT FROM AUTHOR]

Resonant controllers are widely used in power electronics to track sinusoidal references. According to the internal model principle (IMP), these controllers should embed the poles of the Laplace or Z transform of the reference for the closed-loop system to track the reference asymptotically. Thus, tracking a sinusoidal reference is difficult as the controller should adapt its structure to embed the poles of the sinusoidal reference with variable frequency, as those poles depend on that variable frequency. On the other hand, Generalized Predictive Control (GPC) is widespread in industry applications due to its fast response, robustness and capability to include constraints. Resonant controllers based on GPC, which satisfy IMP, have been developed. However, these controllers consider the sinusoidal frequency to be constant. This paper presents a new GPC-based resonant controller with an adaptive and simple control law to track references with variable frequencies. A PLL estimates the frequency of the reference. A multi-layer perceptron uses the estimated frequency to define the gain matrix required to calculate the GPC control action. The GPC control action and the estimated frequency define the control law, which satisfies IMP in steady-state conditions. The authors did not find in the literature the proposed mathematical development of an adaptive GPC resonant controller with a discrete-time augmented model whose control law satisfies IMP. Thus, the proposed approach is helpful to develop other adaptive predictive controllers. Experimental results show that the proposed controller can track sinusoidal references whose frequencies vary in time. [ABSTRACT FROM AUTHOR]

The economic time series prediction literature has seen an increase in research leveraging artificial neural networks (ANNs), particularly the multilayer perceptron (MLP) and, more recently, transformer networks. These ANN models have shown superior accuracy compared to traditional techniques such as autoregressive integrated moving average (ARIMA) models. The most recent models in the prediction of this type of neural network, such as recurrent or Transformers models, are composed of complex architectures that require sufficient processing capacity to address the problems, while MLP is based on densely connected layers and supervised learning. A deep understanding of the limitations is necessary to appropriately choose the ideal model for each of the prediction tasks. In this article, we show how a simple architecture such as the MLP allows a better adjustment than other models, including a shorter prediction time. This research is based on the premise that the use of the most recent models will not always allow better results. [ABSTRACT FROM AUTHOR]

The importance of early detection in the management of skin lesions, such as skin cancer, cannot be overstated due to its critical role in enhancing treatment outcomes. This study presents an innovative multi-input model that fuses image and tabular data to improve the accuracy of diagnoses. The model incorporates a dual-input architecture, combining a ResNet-152 for image processing with a multilayer perceptron (MLP) for tabular data analysis. To optimize the handling of tabular data, Principal Component Analysis (PCA) is employed to reduce dimensionality, facilitating more focused and efficient model training. The model's effectiveness is confirmed through rigorous testing, yielding impressive metrics with an F1 score of 98.91%, a recall of 99.19%, and a precision of 98.76%. These results underscore the potential of combining multiple data inputs to provide a nuanced analysis that outperforms single-modality approaches in skin lesion diagnostics. [ABSTRACT FROM AUTHOR]

In this study, a novel method for retrieving atmospheric temperature profiles with tree-structured Parzen estimator (TPE) and multilayer perceptron (MLP) algorithms was proposed, using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) and ERA5 data. Firstly, by adding solar altitude angle, satellite zenith angle, 2m temperature, and surface temperature to the input layer of MLP, there is an improvement in retrieval accuracy. Secondly, TPE is effective in optimizing the hyper-parameters of MLP, and a set of optimized hyper-parameters is obtained through iterative optimization. Thirdly, comparing the retrieved temperature profiles with ERA5 data, we found that retrieval accuracy is influenced by detector, signal-to-noise ratio, terrain, solar altitude angle, satellite zenith angle, and the horizontal temperature gradient. The mean biases of the two adjacent detectors show significant differences, and the retrieval accuracy of the center detectors is greater than that of the north and south sides. The retrieval accuracy is relatively poor in areas with high terrain and large satellite zenith angle. There is a monthly variation in the retrieval accuracy due to the horizontal temperature gradient and signal-to-noise ratio and a significant diurnal variation due to solar altitude angle and signal-to-noise ratio. Compared to in situ sounding data, the mean biases vary from −0.56 K to 0.60 K, and the standard deviations vary from 1.26 K to 2.17 K. The analysis of factors influencing retrieval accuracy provides important insights into improving the ability to retrieve atmospheric temperatures from geostationary hyperspectral IR sounder observations for near real-time (NRT) applications. [ABSTRACT FROM AUTHOR]

Price prediction tools play a significant role in small investors' behavior. As such, this study aims to propose a method to more effectively predict stock prices in North America. Chiefly, the study addresses crucial questions related to the relevance of news and tweets in stock-price prediction and highlights the potential value of considering such parameters in algorithmic trading strategies—particularly during times of market panic. To this end, we develop innovative multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to investigate the influence of Twitter count (TC), and news count (NC) variables on stock-price prediction under both normal and market-panic conditions. To capture the impact of these variables, we integrate technical variables with TC and NC and evaluate the prediction accuracy across different model types. We use Bloomberg Twitter count and news publication count variables in North American stock-price prediction and integrate them into MLP and LSTM neural networks to evaluate their impact during the market pandemic. The results showcase improved prediction accuracy, promising significant benefits for traders and investors. This strategic integration reflects a nuanced understanding of the market sentiment derived from public opinion on platforms like Twitter. [ABSTRACT FROM AUTHOR]

This paper presents a feature fusion and sparse transformer-based anomalous traffic detection system (FSTDS). FSTDS utilizes a feature fusion network to encode the traffic data sequences and extracting features, fusing them into coding vectors through shallow and deep convolutional networks, followed by deep coding using a sparse transformer to capture the complex relationships between network flows; finally, a multilayer perceptron is used to classify the traffic and achieve anomaly traffic detection. The feature fusion network of FSTDS improves feature extraction from small sample data, the deep encoder enhances the understanding of complex traffic patterns, and the sparse transformer reduces the computational and storage overhead and improves the scalability of the model. Experiments demonstrate that the number of FSTDS parameters is reduced by up to nearly half compared to the baseline, and the success rate of anomalous flow detection is close to 100%. [ABSTRACT FROM AUTHOR]

Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than a fully connected multi-layer perceptrons (MLPs) without compromising the accuracy of classification. However, the concept of preserving data locality is usually overlooked in the existing quantum counterparts of CNNs, particularly for extracting multifeatures in multidimensional data. In this paper, we present an multidimensional quantum convolutional classifier (MQCC) that performs multidimensional and multifeature quantum convolution with average and Euclidean pooling, thus adapting the CNN structure to a variational quantum algorithm (VQA). The experimental work was conducted using multidimensional data to validate the correctness and demonstrate the scalability of the proposed method utilizing both noisy and noise-free quantum simulations. We evaluated the MQCC model with reference to reported work on state-of-the-art quantum simulators from IBM Quantum and Xanadu using a variety of standard ML datasets. The experimental results show the favorable characteristics of our proposed techniques compared with existing work with respect to a number of quantitative metrics, such as the number of training parameters, cross-entropy loss, classification accuracy, circuit depth, and quantum gate count. [ABSTRACT FROM AUTHOR]

Governments and energy providers all over the world are moving towards the use of renewable energy sources. Solar photovoltaic (PV) energy is one of the providers' favourite options because it is comparatively cheaper, clean, available, abundant, and comparatively maintenance-free. Although the PV energy source has many benefits, its output power is dependent on continuously changing weather and environmental factors, so there is a need to forecast the PV output power. Many techniques have been employed to predict the PV output power. This work focuses on the short-term forecast horizon of PV output power. Multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with success. The performances of these three algorithms have been compared with other algorithms singly or in a hybrid (with other methods) but not with themselves. This study aims to compare the predictive performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions and showcase their robustness in making predictions in this regard. The performance of MLPNN, CNN, and kNN are compared using solar PV (hourly) data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in South Africa that may be prone to extreme weather conditions. Our data does not have lots of missing data and many data spikes. The kNN algorithm was found to have an RMSE value of 4.95%, an MAE value of 2.74% at its worst performance, an RMSE value of 1.49%, and an MAE value of 0.85% at its best performance. It outperformed the others by a good margin, and kNN could serve as a fast, easy, and accurate tool for forecasting solar PV output power. Considering the performance of the kNN algorithm across the different seasons, this study shows that kNN is a reliable and robust algorithm for forecasting solar PV output power. [ABSTRACT FROM AUTHOR]

In the healthcare field, diagnosing disease is the most concerning issue. Various diseases including cardiovascular diseases (CVDs) significantly influence illness or death. On the other hand, early and precise diagnosis of CVDs can decrease chances of death, resulting in a better and healthier life for patients. Researchers have used traditional machine learning (ML) techniques for CVD prediction and classification. However, many of them are inaccurate and time-consuming due to the unavailability of quality data including imbalanced samples, inefficient data preprocessing, and the existing selection criteria. These factors lead to an overfitting or bias issue towards a certain class label in the prediction model. Therefore, an intelligent system is needed which can accurately diagnose CVDs. We proposed an automated ML model for various kinds of CVD prediction and classification. Our prediction model consists of multiple steps. Firstly, a benchmark dataset is preprocessed using filter techniques. Secondly, a novel arithmetic optimization algorithm is implemented as a feature selection technique to select the best subset of features that influence the accuracy of the prediction model. Thirdly, a classification task is implemented using a multilayer perceptron neural network to classify the instances of the dataset into two class labels, determining whether they have a CVD or not. The proposed ML model is trained on the preprocessed data and then tested and validated. Furthermore, for the comparative analysis of the model, various performance evaluation metrics are calculated including overall accuracy, precision, recall, and F1-score. As a result, it has been observed that the proposed prediction model can achieve 88.89% accuracy, which is the highest in a comparison with the traditional ML techniques. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution has transformed the industry, with information technology playing a crucial role in this shift. The increasing digitization of industrial systems demands efficient sensing and control methods, giving rise to soft sensors that have the potential to replace traditional physical sensors in order to reduce costs and enhance efficiency. This study explores the implementation of an artificial neural network (ANN) based soft sensor model in a water supply system to predict flow rates within the system. The soft sensor is centered on a Long Short-Term Memory (LSTM) artificial neural network model using Monte Carlo dropout to reduce uncertainty and improve estimation performance. Based on the results of this work, it is concluded that the proposed soft sensor (with Monte Carlo dropout) can predict flow rates more precisely, contributing to the reduction in water losses, as well as cost savings. This approach offers a valuable solution for minimizing water losses and ensuring the efficient use of this vital resource. Regarding the use of soft sensors based on LSTM neural networks with a careful choice of Monte Carlo dropout parameters, when compared to the multilayer perceptron model, the LSTM model with Monte Carlo dropout showed better mean absolute error, root mean square error, and coefficient of determination: 0.2450, 0.3121, and 0.996437 versus 0.2556, 0.3522, and 0.9954. Furthermore, this choice of Monte Carlo dropout parameters allowed us to achieve an LSTM network model capable of reducing uncertainty to 1.8290, keeping the error metrics also at low levels. [ABSTRACT FROM AUTHOR]

This study proposes a method for classifying economic activity descriptors to match Nomenclature of Economic Activities (NACE) codes, employing a blend of machine learning techniques and expert evaluation. By leveraging natural language processing (NLP) methods to vectorize activity descriptors and utilizing genetic algorithm (GA) optimization to fine-tune hyperparameters in multi-class classifiers like Naive Bayes, Decision Trees, Random Forests, and Multilayer Perceptrons, our aim is to boost the accuracy and reliability of an economic classification system. This system faces challenges due to the absence of precise target labels in the dataset. Hence, it is essential to initially check the accuracy of utilized methods based on expert evaluations using a small dataset before generalizing to a larger one. [ABSTRACT FROM AUTHOR]

Parameters such as the leaf area index (LAI), canopy chlorophyll content (CCH), and canopy carotenoid content (CCA) are important indicators for evaluating the ecological functions of forests. Currently, rapidly developing unmanned aerial vehicles (UAV) equipped with hyperspectral technology provide advanced technical means for the real-time dynamic acquisition of regional vegetation canopy parameters. In this study, a hyperspectral sensor mounted on a UAV was used to acquire the data in the study area, and the canopy parameter estimation model of moso bamboo forests (MBF) was developed by combining the PROSAIL radiative transfer model and the machine learning regression algorithm (MLRA), inverted the canopy parameters such as LAI, CCH, and CCA. The method first utilized the extended Fourier amplitude sensitivity test (EFAST) method to optimize the global sensitivity analysis and parameters of the PROSAIL model, and the successive projections algorithm (SPA) was used to screen the characteristic wavebands for the inversion of MBF canopy parameter inversion. Then, the optimized PROSAIL model was used to construct the 'LAI-CCH-CCA-canopy reflectance' simulation dataset for the MBF; multilayer perceptron regressor (MLPR), extra tree regressor (ETR), and extreme gradient boosting regressor (XGBR) employed used to construct PROSAIL_MLPR, PROSAIL_ETR, and PROSAIL_XGBR, respectively, as the three hybrid models. Finally, the best hybrid model was selected and used to invert the spatial distribution of the MBF canopy parameters. The following results were obtained: Waveband sensitivity analysis reveals 400–490 and 710–1000 nm as critical for LAI, 540–650 nm for chlorophyll, and 490–540 nm for carotenoids. SPA narrows down the feature bands to 43 for LAI, 19 for CCH, and 9 for CCA. The three constructed hybrid models were able to achieve high-precision inversion of the three parameters of the MBF, the model fitting accuracy of PROSAIL_MLRA reached more than 95%, with lower RMSE values, and the PROSAIL_XGBR model yielded the best fitting results. Our study provides a novel method for the inversion of forest canopy parameters based on UAV hyperspectral data. [ABSTRACT FROM AUTHOR]

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for "measuring carbon through electricity", in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Power quality improvement and Power quality disturbance (PQD) detection are two significant concerns that must be addressed to ensure an efficient power distribution within the utility grid. When the process to analyze PQD is migrated to real-time platforms, the possible occurrence of a phase mismatch can affect the algorithm's accuracy; this paper evaluates phase shifting as an additional stage in signal acquisition for detecting and classifying eight types of single power quality disturbances. According to their mathematical models, a set of disturbances was generated using an arbitrary waveform generator BK Precision 4064. The acquisition, detection, and classification stages were embedded into a BeagleBone Black. The detection stage was performed using multiresolution analysis. The feature vectors of the acquired signals were obtained from the combination of Shannon entropy and log-energy entropy. For classification purposes, four types of classifiers were trained: multilayer perceptron, K-nearest neighbors, probabilistic neural network, and decision tree. The results show that incorporating a phase-shifting stage as a preprocessing stage significantly improves the classification accuracy in all cases. [ABSTRACT FROM AUTHOR]

In recent years, the occurrence of high-voltage cable buffer layer ablation faults has become frequent, posing a serious threat to the safe and stable operation of cables. Failure to promptly detect and address such faults may lead to cable breakdowns, impacting the normal operation of the power system. To overcome the limitations of existing methods for identifying buffer layer ablation faults in high-voltage cables, a method for identifying buffer layer ablation faults based on frequency domain impedance spectroscopy and artificial intelligence is proposed. Firstly, based on the cable distributed parameter model and frequency domain impedance spectroscopy, a mathematical model of the input impedance of a cable containing buffer layer ablation faults is derived. Through a simulation, the input impedance spectroscopy at the first end of the cables under normal conditions, buffer layer ablation, local aging, and inductive faults is performed, enabling the identification of inductive and capacitive faults through a comparative analysis. Secondly, the frequency domain amplitude spectroscopy of the buffer layer ablation and local aging faults are used as datasets and are input into a neural network model for training and validation to identify buffer layer ablation and local aging faults. Finally, using multiple evaluation metrics to assess the neural network model validates the superiority of the MLP neural network in cable fault identification models and experimentally confirms the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, under some suitable assumptions, using the Taylor expansion, Borel–Cantelli lemma and the almost sure central limit theorem for independent random variables, the almost sure central limit theorem for error variance estimator in the pth-order nonlinear autoregressive processes with independent and identical distributed errors was established. Four examples, first-order autoregressive processes, self-exciting threshold autoregressive processes, threshold-exponential AR progresses and multilayer perceptrons progress, are given to verify the results. [ABSTRACT FROM AUTHOR]

This study deals with the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONFs) and Recurrences of Faults (RFs). To perform the predictions, they used predictive models based on machine learning, more specifically on MLP (Multi-Layer Perceptron) neural networks. The investigation unveiled that through the analysis of historical failure data and the consideration of variables including altitude, number of failures on the same street, and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes significantly to the field of water infrastructure management by proposing the applicability of a data-driven approach in diverse urban settings and across various types of infrastructure networks, including those pertaining to energy or communication. These conclusions underscore the paramount importance of systematic data collection and analysis in both averting failures and optimizing the allocation of resources within water utilities. [ABSTRACT FROM AUTHOR]

Various types of energetic fuel combustion processes emit dangerous pollutants into the air, including aerosol particles, marked as PM10. Routine air quality monitoring includes determining the PM10 concentration as one of the basic measurements. At some air monitoring stations, the PM10 measurement is supplemented by the simultaneous determination of the concentration of PM2.5 as a finer fraction of suspended particles. Since the PM2.5 fraction has a significant share in the PM10 fraction, the concentrations of both types of particles should be strongly correlated, and the concentrations of one of these fractions can be used to model the concentrations of the other fraction. The aim of the study was to assess the error of predicting PM2.5 concentration using PM10 concentration as the main predictor. The analyzed daily concentrations were measured at 11 different monitoring stations in Poland and covered the period 2010–2021. MLP (multilayer perceptron) artificial neural networks were used to approximate the daily PM2.5 concentrations. PM10 concentrations and time variables were tested as predictors in neural networks. Several different prediction errors were taken as measures of modeling quality. Depending on the monitoring station, in models with one PM10 predictor, the RMSE error values were in the range of 2.31–6.86 μg/m3. After taking into account the second predictor D (date), the corresponding RMSE errors were lower and were in the range of 2.06–5.54 μg/m3. Our research aimed to find models that were as simple and universal as possible. In our models, the main predictor is the PM10 concentration; therefore, the only condition to be met is monitoring the measurement of PM10 concentrations. We showed that models trained at other air monitoring stations, so-called foreign models, can be successfully used to approximate PM2.5 concentrations at another station. [ABSTRACT FROM AUTHOR]

We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of computational efficiency and resource constraints in analyzing data from the upcoming High Luminosity Large Hadron Collider, presenting the architecture as a potential solution. In particular, we evaluate our method by applying the model to multi-detector jet images from CMS Open Data. The goal is to distinguish quark-initiated from gluon-initiated jets. We successfully train the quantum model and evaluate it via numerical simulations. Using this approach, we achieve classification performance almost on par with the one obtained with the completely classical architecture, considering a similar number of parameters. [ABSTRACT FROM AUTHOR]

To solve the problem of environmental pollution caused by road traffic, alternatives to vehicles with internal combustion engines are often proposed. As such, eco-mobility microvehicles have significant potential in the fight against environmental pollution, but only on the condition that they are widely accepted and that they replace the vehicles that predominantly pollute the environment. With this in mind, this study aims to elucidate the main variables that influence the acceptability of these vehicles, using prediction models based on binary logistic regression and a multilayer artificial neural network—a multilayer perceptron (ANN). The data of a random sample obtained via an online questionnaire, answered by 503 inhabitants of Belgrade (Serbia), were used for training and testing the model. A multilayer perceptron with 9 and 7 neurons in two hidden layers, a hyperbolic tangent activation function in the hidden layer, and an identity function in the output layer performed slightly better than the binary logistic regression model. With an accuracy of 85%, a precision of 79%, a recall of 81%, and an area under the ROC curve of 0.9, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that a respondent's relationship to their current environmental pollution, the frequency of their use of modes of transport such as bicycles and motorcycles, their mileage for commuting, and their personal income have the greatest influence on the acceptability of using eco-mobility vehicles. [ABSTRACT FROM AUTHOR]

Forecasting electricity spot prices holds paramount significance for informed decision-making among energy market stakeholders. This study introduces a methodology utilizing a multilayer perceptron (MLP) neural network for multivariate electricity spot price prediction. The model underwent a feature selection process to identify the most influential predictors. In the validation phase, the model's performance was evaluated using key metrics, including trend accuracy percentage index (TAPI), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE). The results were obtained for a four-week forecast horizon in order to serve as an auxiliary tool to facilitate decision-making processes in the short-term energy market. The relevance of short-term electricity spot price forecasting lies in its direct impact on pricing strategies during energy contract negotiations, which allows for the making of assertive decisions in the energy trading landscape. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm–convolutional neural network–long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research. [ABSTRACT FROM AUTHOR]

The accurate prediction of highway passenger volume is very important for China's transportation planning and economic development. Based on a neural network, this paper establishes a prediction model by using historical road passenger traffic and related influencing factor data, aiming to provide an accurate road passenger traffic prediction. Firstly, the historical highway passenger volume data and the factor data affecting passenger volume are collected. Then, a multilayer perceptron neural network is established by using SPSS software (PASW Statistics 18) to analyze the significant relationship between highway passenger volume and influencing factors. Then, through the training and verification of the model by MATLAB software (R2021a), the reliability of the prediction model is proved. Finally, the model is used to predict the passenger traffic volume in 2020–2022, and the actual passenger traffic volume is compared and analyzed. It is concluded that the highway passenger traffic volume decreased significantly in 2020–2022 due to various factors such as the epidemic situation and policies, which have had an impact on China's economic development. [ABSTRACT FROM AUTHOR]

In this paper, the issue of detecting a user's position in relation to the antenna geometry in ultra-wideband (UWB) off-body wireless body area network (WBAN) communication using deep learning methods is presented. To measure the impulse response of the channel, a measurement stand consisting of EVB1000 devices and DW1000 radio modules was developed and indoor static measurement scenarios were performed. It was proven that for the binary classification of user orientation, neural networks achieved accuracy that was more than 9% higher than that for the well-known threshold method. In addition, the classification of user position angles relative to the reference node was analyzed. It was proven that, using the proposed deep learning approach and the channel impulse response, it was possible to estimate the angle of the user's position in relation to the antenna geometry. Absolute user orientation angle errors of about 4–7 ° for convolutional neural networks and of about 14–15 ° for multilayer perceptrons were achieved in approximately 85% of the cases in both tested scenarios. [ABSTRACT FROM AUTHOR]

International students play a crucial role in China's talent development strategy. Thus, predicting overseas talent mobility is essential for formulating scientifically reasonable talent introduction policies, optimizing talent cultivation systems, and fostering international talent cooperation. In this study, we proposed a novel fractional-order grey model based on the Multi-Layer Perceptron (MLP) and Grey Wolf Optimizer (GWO) algorithm to forecast the movement of overseas talent, namely MGDFGM(1,1). Compared to the traditional grey model FGM(1,1), which utilizes the same fractional order at all time points, the proposed MGDFGM(1,1) model dynamically adjusts the fractional-order values based on the time point. This dynamic adjustment enables our model to better capture the changing trends in the data, thereby enhancing the model's fitting capability. To validate the effectiveness of the MGDFGM(1,1) model, we primarily utilize Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as the evaluation criteria for the prediction accuracy, as well as standard deviation (STD) as an indicator of the model stability. Furthermore, we perform experimental analysis to evaluate the predictive performance of the MGDFGM(1,1) model in comparison to NAÏVE, ARIMA, GM(1,1), FGM(1,1), LSSVR, MLP, and LSTM. The research findings demonstrate that the MGDFGM(1,1) model achieves a remarkably high level of prediction accuracy and stability for forecasting overseas talent mobility in China. The implications of this study offer valuable insights and assistance to government departments involved in overseas talent management. [ABSTRACT FROM AUTHOR]

Presently, active detectors are widely used to detect mines, providing high accuracy. However, the principle of the operation of active detectors can lead to the explosion of hidden mines. The novelty of this work is the development of the morphology of a neural network for the classification of mines made of different materials (metallic, semi-metallic, plastic) with high accuracy (99.23%), based on a vector of input features with the following components: the value of the output voltage of the FLC-100 magnetic field sensor, which measures magnetic field anomalies in the vicinity of mines with an accuracy of 10−10–10−4 Tesla; six different soil types, depending on the humidity; and the height at which the magnetic field sensor is located above the mine. Due to the fact that mines, when made of different materials (metallic, semi-metallic, plastic), have different magnetic properties, the neural network method of mine classification, based on the sensor data regarding anomalies of the magnetic field in the vicinity of mines, allows the classification of mines made of different materials. The accuracy of mine classification was assessed with two-layer and three-layer neural networks on various metrics (confusion matrix, ROC curves, accuracy–loss curves), using ADAM, RMSprop, and SGD optimisers, and analyses and comparisons were then carried out. The impact of asymmetry in the neuron number and the types of activation functions in the first and second hidden layers on the values of the accuracy and loss metrics was studied. In particular, it was established that the asymmetry of the number of neurons in the first and second hidden layers relative to the plane of symmetry between the hidden layers has a significant effect on the accuracy of the model (decrease in accuracy by 25%), while the loss function, when the symmetry of the neurons number in the hidden layers is violated, increases to a maximum of 50%. [ABSTRACT FROM AUTHOR]

Motor power models are a key tool in robotics for modeling and simulations related to control and optimization. The authors collect the dataset of motor power using the ABB IRB 120 industrial robot. This paper applies a multilayer perceptron (MLP) model to the collected dataset. Before the training of MLP models, each of the variables in the dataset is evaluated using the random forest (RF) model, observing two metrics-mean decrease in impurity (MDI) and feature permutation score difference (FP). Pearson's correlation coefficient was also applied Based on the scores of these values, a total of 15 variables, mainly static variables connected with the position and orientation of the robot, are eliminated from the dataset. The scores demonstrate that while both MLPs achieve good scores, the model trained on the pruned dataset performs better. With the model trained on the pruned dataset achieving R ¯ 2 = 0.99924 , σ = 0.00007 and M A ¯ P E = 0.33589 , σ = 0.00955 , the model trained on the original, non-pruned, data achieves R ¯ 2 = 0.98796 , σ = 0.00081 and M A ¯ P E = 0.46895 , σ = 0.05636 . These scores show that by eliminating the variables with a low influence from the dataset, a higher scoring model is achieved, and the created model achieves a better generalization performance across five folds used for evaluation. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder is known to cause difficulties in social interaction and communication, as well as repetitive patterns of behavior, interests, or hobbies. These challenges can significantly affect the individual's daily life. Therefore, it is crucial to identify and assess children with Autism Spectrum Disorder early to significantly benefit the long-term health of children. Unfortunately, many children are not diagnosed or are misdiagnosed, which means they miss out on the necessary interventions. Clinicians and other experts face various challenges during the diagnostic process. Digital tools can facilitate early diagnosis effectively. This study aimed to explore the use of machine learning techniques on a dataset collected from a serious game designed for children with autism to investigate how these techniques can assist in classification and make the clinical process more efficient. The responses were gathered from children who participated in interactive games deployed on mobile devices, and the data were analyzed using various types of neural networks, such as multilayer perceptrons and constructed neural networks. The performance metrics of these models, including error rate, precision, and recall, were reported, and the comparative experiments revealed that the constructed neural network using the integer rule-based neural networks approach was superior. Based on the evaluation metrics, this method showed the lowest error rate of 11.77%, a high accuracy of 0.75, and a good recall of 0.66. Thus, it can be an effective way to classify both typically developed children and children with Autism Spectrum Disorder. Additionally, it can be used for automatic screening procedures in an intelligent system. The results indicate that clinicians could use these techniques to enhance conventional screening methods and contribute to providing better care for individuals with autism. [ABSTRACT FROM AUTHOR]

Collaborative robots face barriers to widespread adoption due to the complexity of programming them to achieve human-like movement. Learning from demonstration (LfD) has emerged as a crucial solution, allowing robots to learn tasks directly from expert demonstrations, offering versatility and an intuitive programming approach. However, many existing LfD methods encounter issues such as convergence failure and lack of generalization ability. In this paper, we propose: (1) a generative adversarial network (GAN)-based model with multilayer perceptron (MLP) architecture, coupled with a novel loss function designed to mitigate convergence issues; (2) an affine transformation-based generalization method aimed at enhancing LfD tasks by improving their generalization performance; (3) a data preprocessing method tailored to facilitate deployment on robotics platforms. We conduct experiments on a UR5 robotic platform tasked with handwritten digit recognition. Our results demonstrate that our proposed method significantly accelerates generation speed, achieving a remarkable processing time of 23 ms, which is five times faster than movement primitives (MPs), while preserving key features from demonstrations. This leads to outstanding convergence and generalization performance. [ABSTRACT FROM AUTHOR]

This paper provides a comparative analysis between a force sensor and an eddy current sensor, focusing on their usability to determine the position of a circular levitating permanent magnet (PM) mover within an axially symmetric magnetic levitation platform (MLP) with an exceptionally large air gap. The sensors enable closed-loop control, which is essential for accurately and stably maintaining the mover's radial position. For the considered MLP, a change in radial position in principle results in a tilting of the mover, i.e., a deviation from the parallel alignment relative to the stator. As both the radial position and the tilting angle affect the sensors' (force and eddy current) output voltage, an observer must deduce the radial position from the output sensor's voltage, requiring a comprehensive MLP dynamic model and calibration of the models for both sensing approaches. The paper discusses the advantages and weaknesses of each sensor concept, exploring operational principles and performance in levitation tests. The force sensor exhibits versatility, proving functional across various application scenarios, such as when the mover is sealed in a conductive, non-magnetic chamber. In contrast, due to its high-frequency operation, the eddy current sensor is more straightforward to characterize, simplifying its behavior relative to the mover's slower dynamics. Measurements are conducted to validate the models, showing the eddy current sensor's robustness against disturbances and imperfections in the MLPs and its immunity to cross-axis interference. Conclusively, in levitation experiments where the mover is vertically distanced at 104 mm from the stator, the eddy current sensor achieves a position tracking precision about ten times better and a signal-to-noise ratio (SNR) ten times higher compared to the off-the-shelf force sensor, confirming its better performance and reliability; however, it cannot be used in applications where conductive objects are present in the air gap. Furthermore, additional experiments are conducted on the MLP using the eddy current sensor to show the controller's robustness and dynamic reference tracking capability, with and without a payload. [ABSTRACT FROM AUTHOR]

This document is a preface to a special issue on "Advances in Machine Learning, Optimization, and Control Applications." It highlights the connection between machine learning and control theory as a popular research topic that can improve control systems. The special issue includes eight research articles and three reviews that cover various topics such as action recognition, person re-identification, petroleum product identification, vessel prediction, fractured elbow classification, task scheduling in cloud computing, metaheuristic algorithms, and machine learning methods. The authors express their gratitude to the contributors and hope that the research papers will inspire further exploration in these areas. [Extracted from the article]

Camera traps play an important role in biodiversity monitoring. An increasing number of studies have been conducted to automatically recognize wildlife in camera trap images through deep learning. However, wildlife recognition by camera trap images alone is often limited by the size and quality of the dataset. To address the above issues, we propose the Temporal-SE-ResNet50 network, which aims to improve wildlife recognition accuracy by exploiting the temporal information attached to camera trap images. First, we constructed the SE-ResNet50 network to extract image features. Second, we obtained temporal metadata from camera trap images, and after cyclical encoding, we used a residual multilayer perceptron (MLP) network to obtain temporal features. Finally, the image features and temporal features were fused in wildlife identification by a dynamic MLP module. The experimental results on the Camdeboo dataset show that the accuracy of wildlife recognition after fusing the image and temporal information is about 93.10%, which is an improvement of 0.53%, 0.94%, 1.35%, 2.93%, and 5.98%, respectively, compared with the ResNet50, VGG19, ShuffleNetV2-2.0x, MobileNetV3-L, and ConvNeXt-B models. Furthermore, we demonstrate the effectiveness of the proposed method on different national park camera trap datasets. Our method provides a new idea for fusing animal domain knowledge to further improve the accuracy of wildlife recognition, which can better serve wildlife conservation and ecological research. [ABSTRACT FROM AUTHOR]

As a core component of the smart grid, capacitive equipment plays a critical role in modern power systems. When defects occur, they pose a significant threat to the safety of both other equipment and personnel. Hence, it is of great significance to predict whether defects occur in capacitive equipment in advance. To achieve this goal, we propose a novel method that integrates the weight of evidence (WOE) feature encoding with machine learning (ML). Five models, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and linear classification, are employed with WOE features for defect prediction. Furthermore, based on the prediction of equipment with defects, an additional prediction is conducted to determine the potential defect level of the equipment. Experimental results demonstrate that the performance of each algorithm significantly improves with WOE encoding features. Particularly, the RF model with WOE encoding features exhibits optimal performance. In conclusion, the proposed method offers a promising solution for predicting the occurrence of defects and the corresponding defect levels of capacitive equipment. It enables relevant personnel to focus on and inspect equipment predicted to be at risk of defects, thereby preventing major malfunctions. [ABSTRACT FROM AUTHOR]

Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners. [ABSTRACT FROM AUTHOR]

Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical spatio-temporal Automatic Identification System (AIS) data. However, environmental factors such as sea wind and visibility also affect ship navigation in real-world maritime shipping. Therefore, developing reliable models utilizing multimodal data, such as AIS and environmental data, is challenging. In this research, we design an adaptive multimodal vessel trajectory data prediction model (termed AMD) based on satellite AIS and environmental data. The AMD model mainly consists of an AIS-based extraction network, an environmental-based extraction network, and a fusion block. In particular, this work considers multimodal data such as historical spatio-temporal information and environmental factors. Time stamps and distances are correlated with AIS and environmental data, and a multilayer perceptron and gated recurrent unit networks are used to design multimodal feature extraction networks. Finally, the fusion block realizes the fusion output of multimodal features to improve the reliability of the AMD model. Several quantitative and qualitative experiments are conducted using real-world AIS and multimodal environmental datasets. Numerous experimental results prove that prediction performance using multimodal data can ensure satisfactory accuracy and reliability while exhibiting a positive impact on improving maritime transport services. [ABSTRACT FROM AUTHOR]

In this study, a quantitative detection method of pipeline cracks based on a one-dimensional convolutional neural network (1D-CNN) was developed using the time-domain signal of ultrasonic guided waves and the crack size of the pipeline as the input and output, respectively. Pipeline ultrasonic guided wave detection signals under different crack defect conditions were obtained via numerical simulations and experiments, and these signals were input as features into a multi-layer perceptron and one-dimensional convolutional neural network (1D-CNN) for training. The results revealed that the 1D-CNN performed better in the quantitative analysis of pipeline crack defects, with an error of less than 2% in the simulated and experimental data, and it could effectively evaluate the size of crack defects from the echo signals under different frequency excitations. Thus, by combining the ultrasonic guided wave detection technology and CNN, a quantitative analysis of pipeline crack defects can be effectively realized. [ABSTRACT FROM AUTHOR]