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Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf's healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn't show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB. [ABSTRACT FROM AUTHOR]

Precise diagnosis of benign and malignant breast cancer plays an important role in the effective treatment of breast cancer patients. Several classification models with different characteristics have been developed and used in a wide range of breast cancer domains to improve classification accuracy. Although the classification models differ in different aspects, they all have the same logic in their learning processes and use a continuous distance-based cost function. However, using a continuous distance-based function as a cost function in the learning processes of the traditional classification models is unreasonable or at least insufficient; since the goal function of the classification, is discrete. Hence, developing a discrete cost function for learning the classification problems, due to more consistency, may improve the classification rate; but, it has been neglected in the literature. In this paper, in contrast to all traditional continuous distance-based learning processes, a novel discrete learning-based process is proposed and implemented on a multilayer perceptron to yield a more consistent intelligent classifier. Then, the proposed discrete learning-based multilayer perceptron (DIMLP) is used for breast cancer classification. Empirical results of the breast cancer datasets indicate that the proposed DIMLP model can averagely achieve the classification rate of 94.70%, while the classification rate for the traditional MLP model is only equal to 88.54%. Therefore, the proposed DIMLP can be an appropriate and efficient alternative model for intelligent breast cancer classification, especially when more accurate results and/or a more reasonable model are required. [ABSTRACT FROM AUTHOR]

Background: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. Materials and methods: In this research, we propose a new ViT Gradient‐Weighted Class Activation Mapping (GradCAM) based architecture named ViT‐GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. Result: The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT‐GradCAM obtains better and more accurate detection and classification than other state‐of‐the‐art deep learning‐based skin lesion detection models. The architecture of ViT‐GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin‐specific pathologies. Conclusion: This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models. [ABSTRACT FROM AUTHOR]

The achievement of consistent molding quality, which is critical in injection molding, is heavily reliant on the reasonable control of processing materials, molds, machines, process parameters, and environmental conditions. Notably, new molds usually require a trial molding process before being delivered to relevant machines for online production. However, performance differences between machines make it challenging to maintain consistent molding quality, and suitable adjustments must be made to machine parameters to compensate for these differences. Therefore, cross-machine product quality prediction is critical for accurately forecasting product quality across different machines in a manufacturing process and thus for ensuring consistent quality, few defects, and optimized production. To avoid the considerable time and high cost required for quality inspection and to improve production efficiency, this study developed a multilayer perceptron (MLP) model combined with quality indices to predict molding quality. This paper describes how the developed model predicts product quality for the same mold in different machines. The procedure of the proposed MLP model involves four steps. First, data are prepared, features are extracted (extraction of quality indices), and the model is trained on an actual injection molding machine (machine A). Second, the developed MLP model establishes the relationships between the process parameters, quality indices, and product quality for machine A. Third, Moldex3D Studio, which is a software program for simulating injection molding, is employed to generate production data for a virtual injection molding machine (machine B). Finally, a transfer model is used to fit the quality indices of machines A and B so that the MLP model can directly predict the product quality (in terms of weight and geometric dimensions) for machine B on the basis of the quality indices generated using the process parameters of machine B. Experimental results indicate that the developed MLP model can accurately predict the weight and dimensions of products manufactured using different injection molding machines. In particular, the average error in predicting the product quality for machine B was found to be smaller than 0.5%, which indicates the feasibility of the developed model for cross-machine product quality prediction. [ABSTRACT FROM AUTHOR]

Working with pushbroom imagery in photogrammetry and remote sensing presents a fundamental challenge in object-to-image space transformation. For this transformation, accurate estimation of Exterior Orientation Parameters (EOPs) for each scanline is required. To tackle this challenge, Best Scanline Search or Determination (BSS/BSD) methods have been developed. However, the current BSS/BSD methods are not efficient for real-time applications due to their complex procedures and interpolations. This paper introduces a new non-iterative BSD method specifically designed for line-type pushbroom images. The method involves simulating a pair of sets of points, Simulated Control Points (SCOPs), and Simulated Check Points (SCPs), to train and test a Multilayer Perceptron (MLP) model. The model establishes a strong relationship between object and image spaces, enabling a direct transformation and determination of best scanlines. This proposed method does not rely on the Collinearity Equation (CE) or iterative search. After training, the MLP model is applied to the SCPs for accuracy assessment. The proposed method is tested on ten images with diverse landscapes captured by eight sensors, exploiting five million SCPs per image for statistical assessments. The Root Mean Square Error (RMSE) values range between 0.001 and 0.015 pixels across ten images, demonstrating the capability of achieving the desired sub-pixel accuracy within a few seconds. The proposed method is compared with conventional and state-of-the-art BSS/BSD methods, indicating its higher applicability regarding accuracy and computational efficiency. These results position the proposed BSD method as a practical solution for transforming object-to-image space, especially for real-time applications. [ABSTRACT FROM AUTHOR]

Measurement inaccuracies and the absence of precise parameters value in conceptual and analytical models pose challenges in simulating the rainfall–runoff modeling (RRM). Accurate prediction of water resources, especially in water scarcity conditions, plays a distinctive and pivotal role in decision-making within water resource management. The significance of machine learning models (MLMs) has become pronounced in addressing these issues. In this context, the forthcoming research endeavors to model the RRM utilizing four MLMs: Support Vector Machine, Gene Expression Programming (GEP), Multilayer Perceptron, and Multivariate Adaptive Regression Splines (MARS). The simulation was conducted within the Malwathu Oya watershed, employing a dataset comprising 4,765 daily observations spanning from July 18, 2005, to September 30, 2018, gathered from rainfall stations, and Kappachichiya hydrometric station. Of all input combinations, the model incorporating the input parameters Qt−1, Qt−2, and R̄t was identified as the optimal configuration among the considered alternatives. The models' performance was assessed through root mean square error (RMSE), mean average error (MAE), coefficient of determination (R2), and developed discrepancy ratio (DDR). The GEP model emerged as the superior choice, with corresponding index values (RMSE, MAE, R2, DDRmax) of (43.028, 9.991, 0.909, 0.736) during the training process and (40.561, 10.565, 0.832, 1.038) during the testing process. HIGHLIGHTS ML models for forecasting streamflow in the Malwathu Oya River basin were evaluated.; Rainfall for several stations was used in model development.; The GEP model showcased the best predictability of streamflow.; Research findings help the proposed Malwathu Oya development scheme.;

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]

Artificial neural networks (ANNs) are powerful data-oriented "black-box" algorithms capable of assessing and delineating linear and multifaceted non-linear correlations between the dependent and explanatory variables. Through the years, neural networks have proven to be effective and robust analytical techniques for establishing artificial intelligence-based tools for modelling, estimating, and projecting spatial and temporal variations in water bodies. Accordingly, ANN-based algorithms gained increased attention and have emerged as practical alternatives to traditional approaches for hydro-chemical analysis. ANNs are among the widely used computer systems for modelling surface water quality. Considering their wide recognition, resilience, flexibility, and accuracy, the current study employs a neural network-based methodology to construct a novel water quality index (WQI) model suitable for analysing South African rivers. The feed-forward, back-propagated multilayered perceptron model has three parallel-distributed neuron layers interconnected with seventy weighted links orientated laterally from left to right. First, the input layer includes thirteen neuro-nodes symbolising thirteen explanatory variables, including NH3, Ca, Cl, Chl-a, EC, F, CaCO3, Mg, Mn, NO3, pH, SO4, and turbidity (NTU). Second, the hidden layer consists of eleven neuro-nodes accountable for computational tasks. Lastly, the output layer features one neuron responsible for conveying network outcomes using a single-digit WQI rating extending from zero to one hundred, where zero represents substandard water quality and one hundred denotes exceptional water quality. The AI-based model was developed using water quality data obtained from six monitoring locations within four drainage basins under the management of the Umgeni Water Board in the KwaZulu-Natal Province of South Africa. The dataset comprises 416 samples randomly divided into training, testing, and validation sets using a proportional split of 70:15:15%. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) technique was utilised to conduct backpropagation training and adjust synapse weights. The dependent variables are the WQI scores from the universal water quality index (UWQI) model developed specifically for South African river basins. The ANN demonstrated enhanced efficiency through an overall correlation coefficient (R) of 0.985. Furthermore, the neural network attained R-values of 0.987, 0.992, and 0.977 for the training, testing, and validation intervals. The ANN model achieved a Nash–Sutcliffe efficiency (NSE) value of 0.974 and coefficient of determination (R2) of 0.970. Sensitivity analysis provided additional validation of the preparedness and computational competence of the ANN model. The typical target-to-output error tolerance for the ANN model is 0.242, demonstrating an adequate predictive ability to deliver results comparable with the target UWQI, having the lowest and highest index ratings of 75.995 and 94.420, respectively. Accordingly, the three-layer neural network is scientifically sound, with index values and water quality evaluations corresponding to the UWQI results. The current research project seeks to document the processes used and the outcomes obtained. [ABSTRACT FROM AUTHOR]

Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction efficiency, hyperparameter autotuning via Bayesian optimization was used to identify the optimal combination of the eight key hyperparameters. The autotuned model was validated by comparing its predictive performance with that of a base model (with all hyperparameters set to the default values) using the coefficient of variation of root-mean-square error (CvRMSE) and coefficient of determination (R2) based on the Measurement and Verification Guideline evaluation metrics. To confirm the model's industrial applicability, its predictions were compared with values measured at a small-to-medium-sized food factory. The optimized model performed better than the base model, achieving a CvRMSE of 12.30% and an R2 of 0.94, and achieving a predictive accuracy of 91.49%. By predicting energy consumption, these findings are expected to promote the efficient operation and management of energy in the food industry. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive investigation into the structural response of curved composite beams enhanced with carbon nanotube (CNT). Employing a multiscale framework, our analysis leverages the finite element method (FEM) to account for both bending and shear deformations across six degrees of freedom. The inquiry encompasses diverse mechanical, geometrical, and boundary configurations to assess these composite beams' natural vibration features. Moreover, we introduce a multilayer perceptron (MLP) neural network architecture designed to forecast such beams' dimensionless first natural frequency. Trained on a meticulously curated dataset derived from FEM simulations, the neural network model exhibits promising predictive capabilities concerning the free vibration frequency. To ascertain the efficacy and precision of our proposed methodology, we conduct a comparative analysis between FEM results and employ statistical metrics to evaluate the neural network's predictive performance. The findings of this study reveal an impressive predictive accuracy of over 95 % with regards to the initial natural frequency of the composite beams, thereby emphasizing the potential effectiveness of neural network methodologies in engineering analyses. This study significantly contributes to advancing our comprehension of the vibrational dynamics inherent in carbon nanotube-reinforced composite beams, while concurrently underscoring the potential efficacy of neural networks in forecasting their dynamic attributes.

Data-driven models for the prediction of lake eutrophication essentially rely on water quality datasets for a longer duration. If such data are not readily available, lake management through data-driven modeling becomes impractical. So, a novel approach is presented here for the prediction of eutrophication indicators, such as dissolved oxygen, Secchi depth, total nitrogen, and total phosphorus, in the waterbodies of Assam, India. These models were developed using water quality datasets collected through laboratory investigation in artificially simulated lake systems. Two artificial prototype lakes were eutrophied in a controlled environment with the gradual application of wastewater. A periodic assessment of water quality was done for model development. Data-driven modeling in the form of multilayer perceptron (MLP), time-delay neural network (TDNN), support vector regression (SVR), and Gaussian process regression (GPR) were utilized. The trained model's accuracy was evaluated based on statistical parameters and a reasonable correlation was observed between targeted and model predicted values. Finally, the trained models were tested against some natural waterbodies in Assam and a satisfactory prediction accuracy was obtained. TDNN and GPR models were found superior compared to other methods. Results of the study indicate feasibility of the adopted modeling approach in predicting lake eutrophication when periodic water quality data are limited for the waterbody under consideration. HIGHLIGHTS A novel approach is proposed for predicting eutrophication indicators.; Two prototype lakes were artificially eutrophied.; Data-driven modeling techniques were employed.; Developed models were used to predict natural water bodies.; Further studies will help in framing the policies.;

Existing statistical data indicates that an increasing number of people now require rehabilitation to restore compromised physical mobility. During the rehabilitation process, physical therapists evaluate and guide the movements of patients, aiding them in a more effective recovery of rehabilitation and preventing secondary injuries. However, the immutability of mobility and the expensive price of rehabilitation training hinder some patients from timely access to rehabilitation. Utilizing virtual reality for rehabilitation training might offer a potential alleviation to these issues. However, prevalent pose reconstruction algorithms in rehabilitation primarily rely on images, limiting their applicability to virtual reality. Furthermore, existing pose evaluation and correction methods in the field of rehabilitation focus on providing clinical metrics for doctors, and failed to offer patients efficient movement guidance. In this paper, a virtual reality-based rehabilitation training method is proposed. The sparse motion signals from virtual reality devices, specifically head-mounted displays hand controllers, is used to reconstruct full body poses. Subsequently, the reconstructed poses and the standard poses are fed into a natural language processing model, which contrasts the difference between the two poses and provides effective pose correction guidance in the form of natural language. Quantitative and qualitative results indicate that the proposed method can accurately reconstruct full body poses from sparse motion signals in real-time. By referencing standard poses, the model generates professional motion correction guidance text. This approach facilitates virtual reality-based rehabilitation training, reducing the cost of rehabilitation training and enhancing the efficiency of self-rehabilitation training. [ABSTRACT FROM AUTHOR]

The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field, Scotian Basin. An integrated study of instantaneous frequency, P-impedance, volume of clay and neutron-porosity attributes, and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area. Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region. P-impedance was estimated using model-based seismic inversion. Petrophysical properties such as the neutron porosity (NPHI) and volume of clay (VCL) were estimated using the multilayer perceptron neural network with high accuracy. Comparatively, a combination of low instantaneous frequency (15e30 Hz), moderate to high impedance (7000e9500 gm/cc*m/s), low neutron porosity (27%e40%) and low volume of clay (40%e60%), suggests fair-to-good sandstone development in the Dawson Canyon Formation. After calibration with the welllog data, it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons. The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition (Dawson Canyon Formation) in the Penobscot field, Scotian Basin. Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs. The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration. [ABSTRACT FROM AUTHOR]

Uninterrupted monitoring of fetal cardiac health is essential for the timely diagnosis of congenital diseases. The maternal Electrocardiogram (mECG), which has the most significant impact, always tampers with the signals collected from the pregnant woman's abdomen. So, an efficient nonlinear filtering network based on artificial neural network (ANN) is required to eliminate the maternal part from the abdominal Electrocardiogram (aECG) that is traveled from the thoracic of the mother to the abdomen following nonlinear dynamics. In this work, we have presented an adaptive noise canceler (ANC) using 3-layer perceptron architecture where the inputs are expanded by the functional link expansion using the second-order Volterra series, and the weights are updated using backpropagation. The adaptive filter approximates the nonlinear mapping between the thoracic Electrocardiogram (tECG) and the maternal component present in the aECG. Here the thoracic signal is the reference signal, and the abdominal signal is the desired signal to the adaptive filter. The proposed methodology uses the advantages of both multilayer perceptron (MLP) as well as functional link neural network (FLNN) in mapping the nonlinearity and effectively determining the fetal Electrocardiogram (fECG) from the aECG. For the detailed analysis, we have used the real Daisy database, the Non-invasive Fetal ECG database, and the fetal ECG synthetic database from Physionet. The results show that the nonlinear functional link MLP using the Volterra series gives a high-level performance compared to other classical adaptive filtering techniques, as all the evaluation metrics are above 90%. [ABSTRACT FROM AUTHOR]

In this study, a novel system was designed to enhance the efficiency of data acquisition in a portable and compact instrument dedicated to the spectral analysis of various surfaces, including plant leaves, and materials requiring characterization within the 410 to 915 nm range. The proposed system incorporates two nine-band detectors positioned on the top and bottom of the target surface, each equipped with a digitally controllable LED. The detectors are capable of measuring both reflection and transmission properties, depending on the LED configuration. Specifically, when the upper LED is activated, the lower detector operates without its LED, enabling the precise measurement of light transmitted through the sample. The process is reversed in subsequent iterations, facilitating an accurate assessment of reflection and transmission for each side of the target surface. For reliability, the error estimation utilizes a color checker, followed by a multi-layer perceptron (MLP) implementation integrated into the microcontroller unit (MCU) using TinyML technology for real-time refined data acquisition. The system is constructed with 3D-printed components and cost-effective electronics. It also supports USB or Bluetooth communication for data transmission. This innovative detector marks a significant advancement in spectral analysis, particularly for plant research, offering the potential for disease detection and nutritional deficiency assessment. [ABSTRACT FROM AUTHOR]

A multilayer perceptron artificial neural network (MLP-ANN) was developed to calculate the cracking stress, tensile strength, and strain at tensile strength of ultra-high-performance concrete (UHPC), using the mixture design parameters and strain rate during testing as inputs. This tool is envisioned to provide reference values for direct tension test results performed on UHPC specimens, or to be employed as a framework to determine the tension response characteristics of UHPC in the absence of experimental testing, with minimal computational effort to determine the tensile characteristics. A database of 470 data points was compiled from 19 different experimental programs with the direct tensile strength, cracking stress, and strain at tensile strength corresponding to different UHPC mixtures. The model was trained, and its accuracy was tested using this database. A reasonably good performance was achieved with the coefficients of determination, R2, of 0.91, 0.81, and 0.92 for the tensile strength, cracking stress, and strain at tensile strength, respectively. The results showed an increase in the cracking tensile stress and tensile strength for higher strain rates, whereas the strain at tensile strength was unaffected by the strain rate. [ABSTRACT FROM AUTHOR]

The development of low-cost structural and environmental sensors has sparked a transformation across numerous fields, offering cost-effective solutions for monitoring infrastructures and buildings. However, the affordability of these solutions often comes at the expense of accuracy. To enhance precision, the LARA (Low-cost Adaptable Reliable Anglemeter) system averaged the measurements of a set of five different accelerometers working as inclinometers. However, it is worth noting that LARA's sensitivity still falls considerably short of that achieved by other high-accuracy commercial solutions. There are no works presented in the literature to enhance the accuracy, precision, and resolution of low-cost inclinometers using artificial intelligence (AI) tools for measuring structural deformation. To fill these gaps, artificial intelligence (AI) techniques are used to elevate the precision of the LARA system working as an inclinometer. The proposed AI-driven tool uses Multilayer Perceptron (MLP) to glean insight from high-accuracy devices' responses. The efficacy and practicality of the proposed tools are substantiated through the structural and environmental monitoring of a real steel frame located in Cuenca, Spain. [ABSTRACT FROM AUTHOR]

The multilayer perceptron (MLP) has gained widespread popularity and demonstrated outstanding performance in hyperspectral image (HSI) classification in recent years. However, the native MLP architecture and its variants are insufficient in expressing fine spatial structural information and long-range dependencies. To this end, a learnable dilated spectral-spatial MLP (LDS$^{2}$MLP) is proposed for HSI classification. LDS$^{2}$MLP applies the learnable dilated receptive field and grouped MLP to extract more discriminative spectral-spatial features with fewer computational costs, which improves the classification performance. Specifically, a plug-and-play spectral-spatial mixing (S$^{2}$Mixing) block is designed to aggregate grouped spectral detail information and fine spatial structural features. The S$^{2}$Mixing block consists of two feature extraction modules operating on the spectral and spatial domains. The spectral grouping mixer module captures subtle spectral differences through grouped MLP. The spatial dilating with learnable Spacing mixer module employs a dilated receptive field with learnable spacing to enhance the refined expression of spatial structural features. Extensive experiments on four public HSI datasets illustrate that the proposed LDS$^{2}$MLP outperforms state-of-the-art deep learning models in classification performance. In addition, the proposed model is shown to be efficient and generalizable in HSI classification with limited samples.

Barrier, solid insulator, is inserted between conductors to make compact power equipment. Prediction of the dielectric strength is significant owing to nonlinear effect of barrier. In this paper, positive lightning impulse breakdown voltages are predicted under sphere-to-barrier-to-plane air gaps using machine learning algorithms including a support vector regression (SVR), Bayesian regression (BR), and a multilayer perceptron (MLP), which are rarely used to derive breakdown voltages. Previous studies have generally considered background electric fields in field arrangements that lacked barriers. In contrast, electrostatic features are suggested based on the electro-geometric equivalency of each electrode, electric field distributions between sphere and barrier or between barrier and plane, and a condition for stable penetration of discharge channels, influencing background fields and discharge propagation characteristics in air gaps. SVR yielded more precise Breakdown voltages than BR or MLP. Predictions from algorithms were in good agreement with experimental results, regardless of geometrical parameters such as spherical radius, gap distance and barrier width. In particular, the SVR-predicted voltages were even more accurate than the calculated voltages from streamer propagation method in strongly inhomogeneous field with barrier. Our proposed method derives breakdown voltages without the need to consider geometrical parameters affecting streamer propagation.

Malnutrition is one of the problems that occurs in children due to a lack of nutritional intake. Indonesia contributed 36%, making it the fifth country with the largest cases of malnutrition in the world. On this basis, a solution is needed to reduce the growth rate of malnutrition cases. This research aims to carry out classification analysis to determine nutritional status by optimizing machine learning (ML) performance. The ML classification analysis process will later utilize the performance of the artificial neural network (ANN) method with the Multilayer Perceptron (MLP) algorithm. ML performance can be optimized using the Pearson’s correlation (PC) method to produce optimal classification analysis patterns. This research data set uses child nutrition case data from 576 patients sourced from the M. Djamil Padang Province Regional General Hospital (RSUP). The data set is divided into 417 training data and 159 test data. On the basis of the tests that have been carried out, the performance of the PC method can provide precise and accurate analysis patterns. This analysis pattern has also been able to provide a fairly good level of accuracy, namely 95%. Not only that, this research is also able to present analysis patterns with the best ANN architectural model in classifying nutritional status. Based on the overall results, this research can be used as an alternative solution to the treatment of nutritional problems in children.

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

Faults and fractures play a significant role in oilfield drilling operations, hydrocarbon trapping, and reservoir development. Exploring faults quickly and accurately can help reach the target more efficiently. In this study, applicable seismic attributes were combined using a multilayer perceptron and an unsupervised vector quantizer and applied to a 3D seismic cube to identify discontinuities. First, high-probability faulted areas were picked manually on a seismic section as an input pattern for the MLP. Then, particular seismic attributes (dip-steering, similarity, coherency, curvature, instantaneous) were applied to the data. Consequently, the MLP and UVQ were used to determine the most contributed attributes. Using the MLP and UVQ, the most relevant attributes were integrated to find faults and fractures in the 3D seismic volume. In contrast to some fault-identifying methods and prior studies, this study used not just steered attributes but also compared supervised and unsupervised neural networks. Eventually, comparing the outputs of the MLP, faulted and non-faulted cubes, with the initial seismic section and the UVQ's output revealed discrepancies. For a specific set of attributes, the MLP was obviously superior to the UVQ in terms of creating detailed outputs, analyzing time, and rendering more precise and trustworthy results. [ABSTRACT FROM AUTHOR]

Functional movement screening (FMS) is a crucial testing method that evaluates fundamental movement patterns in the human body and identifies functional limitations. However, due to the inherent complexity of human movements, the automated assessment of FMS poses significant challenges. Prior methodologies have struggled to effectively capture and model critical human features in video data. To address this challenge, this paper introduces an automatic assessment approach for FMS by leveraging deep learning techniques. The proposed method harnesses an I3D network to extract spatiotemporal video features across various scales and levels. Additionally, an attention mechanism (AM) module is incorporated to enable the network to focus more on human movement characteristics, enhancing its sensitivity to diverse location features. Furthermore, the multilayer perceptron (MLP) module is employed to effectively discern intricate patterns and features within the input data, facilitating its classification into multiple categories. Experimental evaluations conducted on publicly available datasets demonstrate that the proposed approach achieves state-of-the-art performance levels. Notably, in comparison to existing state-of-the-art (SOTA) methods, this approach exhibits a marked improvement in accuracy. These results corroborate the efficacy of the I3D-AM-MLP framework, indicating its significance in extracting advanced human movement feature expressions and automating the assessment of functional movement screening. [ABSTRACT FROM AUTHOR]

The safety, integrity, and longevity of steel bridge elements are affected by various environmental factors, such as moisture, atmospheric pollutants, and temperature. Protective coatings of steel bridge elements are especially sensitive to the service environments (e.g., atmospheric environments, water environments) of bridges, as many environmental stressors may accelerate premature failures of coatings. By leveraging the data on both bridge-related features (e.g., bridge age, type of service under bridge) and environmental features (e.g., chloride, moisture, atmospheric pollutant, temperature), this study focuses on bringing a data-driven understanding of steel bridge coating deterioration patterns and how the service environments of bridges may impact such patterns. Steel bridge coating performance prediction (SBCPP) models were built based on multilayer perceptron (MLP)–based artificial neural network (ANN) algorithm to predict and assess the coating conditions of girder elements of steel bridges in Florida. The results show that the SBCPP model with the best performance can precisely predict steel bridge coating conditions with a mean absolute error (MAE) of 0.0807. As compared to the models that do not account for environmental features, the performance of the proposed SBCPP models was significantly improved. Shapley additive explanations (SHAP) analysis was further conducted to interpret and analyze the influences of input features on the performance of the SBCPP model. The study offers an effective decision-making tool that has the potential to benefit state transportation agencies by allowing for easier and more efficient analysis or prediction of steel bridge coating performance. This study proposes data-driven models that analyze and predict the coating conditions of steel bridge elements based on the data of bridge-related features (e.g., bridge age, average daily traffic, type of service under bridge) and environmental features (e.g., chloride, moisture, atmospheric pollutant, temperature), thus providing knowledge and insights on corrosion-induced coating degradation for steel bridges in Florida. The proposed models serve as the foundations for an automatic coating performance assessment and prediction tool that can help state transportation agencies and other bridge owners easily evaluate the coating conditions of their steel bridges and identify those bridges that are in critical maintenance needs. Inspection of coating conditions traditionally requires significant manual efforts, which are expensive, time-consuming, and cause safety concerns. An automatic tool based on the proposed models can quickly and easily analyze the element-based coating conditions while considering the impacts of the service environments. By using the tool, a bridge owner can quickly identify those bridges that require more attention or funding support and decide which bridges should be given priority in terms of inspection, maintenance, and/or repair. Hence, acting as the foundation of such a decision support tool, the proposed work has the potential to reduce field-level coating inspection costs, efforts, and risks and enhance the efficiency of maintenance and repair-related decision-making. [ABSTRACT FROM AUTHOR]

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Aiming to address the limitations of traditional channel estimation methods in time- varying channel environments, as well as the low estimation accuracy or high complexity of deep learning-based channel estimation methods, a channel estimation network based on long short-term memory structure is proposed, which consists of a bidirectional long short-term memory (BILSTM) network and a multilayer perceptron (MLP) network, namely BiLSTM-MLP. First, the BiLSTM network is used to learn the time-varying characteristics of the channel. Then, a MLP network is used to denoise and reconstruct the channel estimation. Simulation results show that the proposed channel estimation method has better performance than traditional methods, and has lower complexity and better performance compared with the same type of deep learning-based estimation methods. Furthermore, the proposed method is also robust to different pilot densities. [ABSTRACT FROM AUTHOR]

Students' academic performance prediction is one of the most important applications of Educational Data Mining (EDM) that helps to improve the quality of the education process. The attainment of student outcomes in an Outcome-based Education (OBE) system adds invaluable rewards to facilitate corrective measures to the learning processes. Furthermore, the explosive increase of e-learning platforms generates a large volume of data that demands the extraction of useful information using up-to-date techniques. Keeping this view in mind and to check the impact of various features on student outcomes during online classes, we have analyzed two sets of datasets; the Kalboard 360 dataset (a larger dataset) that contains academic, demographic as well as behavioral features which have been observed and recorded during the classes held and a local Institute dataset that does not acquire behavioral features. To achieve this, we have selected a few machine learning algorithms such as Decision Tree (J48), Naïve Bayes (NB), Random Forest (RF), and Multilayer Perceptron (MLP) to classify the students, along with a few filter-based feature selection methods like Info gain, gain ratio, and correlation features have been applied to select the key attributes. Finally, we have fine-tuned the learning parameters of MLP called "Opt-MLP" to get an optimized output and compared it with other classification models. Our experimental results conclude that Opt-MLP proves its superiority over other classification models by predicting an accuracy of 87.14% without the feature selection (WOFS) and 90.74% accuracy with the feature selection (WFS) method for data set 1 and an accuracy of 79.37% without feature selection and 97.08% with feature selection for dataset 2. But, when the students' behavioral feature is considered along with other features, the RF model provides 100% accuracy justifying that students' behavior during class hours has a great impact on attaining the students' outcomes. [ABSTRACT FROM AUTHOR]

The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field, Scotian Basin. An integrated study of instantaneous frequency, P-impedance, volume of clay and neutron-porosity attributes, and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area. Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region. P-impedance was estimated using model-based seismic inversion. Petrophysical properties such as the neutron porosity (NPHI) and volume of clay (VCL) were estimated using the multilayer perceptron neural network with high accuracy. Comparatively, a combination of low instantaneous frequency (15–30 Hz), moderate to high impedance (7000–9500 gm/cc∗m/s), low neutron porosity (27%–40%) and low volume of clay (40%–60%), suggests fair-to-good sandstone development in the Dawson Canyon Formation. After calibration with the well-log data, it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons. The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition (Dawson Canyon Formation) in the Penobscot field, Scotian Basin. Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs. The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.

This study worked with Chunghwa Telecom to collect data from 17 rooftop solar photovoltaic plants installed on top of office buildings, warehouses, and computer rooms in northern, central and southern Taiwan from January 2021 to June 2023. A data pre-processing method combining linear regression and K Nearest Neighbor (k-NN) was proposed to estimate missing values for weather and power generation data. Outliers were processed using historical data and parameters highly correlated with power generation volumes were used to train an artificial intelligence (AI) model. To verify the reliability of this data pre-processing method, this study developed multilayer perceptron (MLP) and long short-term memory (LSTM) models to make short-term and medium-term power generation forecasts for the 17 solar photovoltaic plants. Study results showed that the proposed data pre-processing method reduced normalized root mean square error (nRMSE) for short- and medium-term forecasts in the MLP model by 17.47% and 11.06%, respectively, and also reduced the nRMSE for short- and medium-term forecasts in the LSTM model by 20.20% and 8.03%, respectively.

In this paper, the Direction of Arrival - DoA estimation for two mobile sources was performed by using the Single Multilayer Perceptron (MLP) neural network model (SMLP-DoA) and the Cascade MLP model(CMLP). The latter model consists of two neural networks connected in a cascade where the outputs of the first MLP that rejects noise represent the inputs to the second network in a cascade. The outputs of the neural network models determine the direction of arrival of the incoming signals. Two cases were considered, in the first case the neural networks were trained on the samples that were without noise, and in the second with samples containing noise. Both considered neural network models were tested with noisy samples. The results of these two neural models are compared to the results achieved by the RootMUSIC algorithm. The presented results show that the proposed CMLP model has a higher accuracy in determining the angular positions of sources compared to the classical SMLP-DoA model and the RootMUSIC algorithm. Moreover, the CMLP model executes significantly faster compared to the model based on the RootMUSIC algorithm.

Working with pushbroom imagery in photogrammetry and remote sensing presents a fundamental challenge in object-to-image space transformation. For this transformation, accurate estimation of Exterior Orientation Parameters (EOPs) for each scanline is required. To tackle this challenge, Best Scanline Search or Determination (BSS/BSD) methods have been developed. However, the current BSS/BSD methods are not efficient for real-time applications due to their complex procedures and interpolations. This paper introduces a new non-iterative BSD method specifically designed for line-type pushbroom images. The method involves simulating a pair of sets of points, Simulated Control Points (SCOPs), and Simulated Check Points (SCPs), to train and test a Multilayer Perceptron (MLP) model. The model establishes a strong relationship between object and image spaces, enabling a direct transformation and determination of best scanlines. This proposed method does not rely on the Collinearity Equation (CE) or iterative search. After training, the MLP model is applied to the SCPs for accuracy assessment. The proposed method is tested on ten images with diverse landscapes captured by eight sensors, exploiting five million SCPs per image for statistical assessments. The Root Mean Square Error (RMSE) values range between 0.001 and 0.015 pixels across ten images, demonstrating the capability of achieving the desired sub-pixel accuracy within a few seconds. The proposed method is compared with conventional and state-of-the-art BSS/BSD methods, indicating its higher applicability regarding accuracy and computational efficiency. These results position the proposed BSD method as a practical solution for transforming object-to-image space, especially for real-time applications.

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.

Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction efficiency, hyperparameter autotuning via Bayesian optimization was used to identify the optimal combination of the eight key hyperparameters. The autotuned model was validated by comparing its predictive performance with that of a base model (with all hyperparameters set to the default values) using the coefficient of variation of root-mean-square error (CvRMSE) and coefficient of determination (R2) based on the Measurement and Verification Guideline evaluation metrics. To confirm the model’s industrial applicability, its predictions were compared with values measured at a small-to-medium-sized food factory. The optimized model performed better than the base model, achieving a CvRMSE of 12.30% and an R2 of 0.94, and achieving a predictive accuracy of 91.49%. By predicting energy consumption, these findings are expected to promote the efficient operation and management of energy in the food industry.