Search

Background Purpose Methods Results Conclusions Accurate fiber orientation distribution (FOD) is crucial for resolving complex neural fiber structures. However, existing reconstruction methods often fail to integrate both global and local FOD information, as well as the directional information of fixels, which limits reconstruction accuracy. Additionally, these methods overlook the spatial positional relationships between voxels, resulting in extracted features that lack continuity. In regions with signal distortion, many methods also exhibit issues with reconstruction artifacts.This study addresses these challenges by introducing a new neural network called Fusion‐Net.Fusion‐Net comprises both the FOD reconstruction network and the peak direction estimation network. The FOD reconstruction network efficiently fuses the global and local features of the FOD, providing these features with spatial positional information through a competitive coordinate attention mechanism and a progressive updating mechanism, thus ensuring feature continuity. The peak direction estimation network redefines the task of estimating fixel peak directions as a multi‐class classification problem. It uses a direction‐aware loss function to supply directional information to the FOD reconstruction network. Additionally, we introduce a larger input scale for Fusion‐Net to compensate for local signal distortion by incorporating more global information.Experimental results demonstrate that the rich FOD features contribute to promising performance in Fusion‐Net. The network effectively utilizes these features to enhance reconstruction accuracy while incorporating more global information, effectively mitigating the issue of local signal distortion.This study demonstrates the feasibility of Fusion‐Net for reconstructing FOD, providing reliable references for clinical applications. [ABSTRACT FROM AUTHOR]

In indoor environments, air quality significantly impacts human health and well-being, with carbon monoxide (CO) posing a particular hazard due to its colorless and odorless nature and potential to cause severe health issues. Integrating the Internet of Things and remote sensing technologies has revolutionized data monitoring, collection, and evaluation, especially within the context of 'smart' homes. This study leverages these technologies to enhance indoor air quality monitoring. By collecting data on key indoor atmospheric quality indicators—carbon dioxide (CO2), methane (CH4), alcohol, liquefied petroleum gas (LPG), particulate matter (PM1 and PM2.5), humidity, and temperature—the study aims to predict indoor carbon monoxide levels. A custom dataset was compiled from August to October, consisting of 61,710 observations recorded at one-minute intervals. The methodology employs a stacking ensemble approach, integrating multiple machine learning models to boost prediction accuracy and reliability. In the stacking ensemble, six distinct models are employed: Random Forest, Multi-Layer Perceptron, Lasso, Elastic Net, XGBoost, and Support Vector Regression. Each model is individually trained and fine-tuned using the Grid Search method to optimize parameter combinations. These optimized models are then combined in the stacking ensemble, which achieves a Mean Squared Error (MSE) of 0.0140, a Root Mean Squared Error (RMSE) of 0.1185, and a Mean Absolute Error (MAE) of 0.0291. The results demonstrate that the proposed system significantly enhances the precision of CO prediction, underscoring its critical role in air quality surveillance within smart environments. [ABSTRACT FROM AUTHOR]

This paper introduces AlphaVision, an innovative decision support model designed for stock price prediction by seamlessly integrating real-time news updates and Return on Investment (ROI) values, utilizing various machine learning and deep learning approaches. The research investigates the application of these techniques to enhance the effectiveness of stock trading and investment decisions by accurately anticipating stock prices and providing valuable insights to investors and businesses. The study begins by analyzing the complexities and challenges of stock market analysis, considering factors like political, macroeconomic, and legal issues that contribute to market volatility. To address these challenges, we proposed the methodology called AlphaVision, which incorporates various machine learning algorithms, including Decision Trees, Random Forest, Naïve Bayes, Boosting, K-Nearest Neighbors, and Support Vector Machine, alongside deep learning models such as Multi-layer Perceptron (MLP), Artificial Neural Networks, and Recurrent Neural Networks. The effectiveness of each model is evaluated based on their accuracy in predicting stock prices. Experimental results revealed that the MLP model achieved the highest accuracy of approximately 92%, outperforming other deep learning models. The Random Forest algorithm also demonstrated promising results with an accuracy of around 84.6%. These findings indicate the potential of machine learning and deep learning techniques in improving stock market analysis and prediction. The AlphaVision methodology presented in this research empowers investors and businesses with valuable tools to make informed investment decisions and navigate the complexities of the stock market. By accurately forecasting stock prices based on news updates and ROI values, the model contributes to better financial management and business sustainability. The integration of machine learning and deep learning approaches offers a promising solution for enhancing stock market analysis and prediction. Future research will focus on extracting more relevant financial features to further improve the model's accuracy. By advancing decision support models for stock price prediction, researchers and practitioners can foster better investment strategies and foster economic growth. The proposed model holds potential to revolutionize stock trading and investment practices, enabling more informed and profitable decision-making in the financial sector. [ABSTRACT FROM AUTHOR]

The article focuses on the triangular multi-layer perceptron (TriMLP)-like artificial intelligence (AI) architecture model for sequential recommendation, balancing computational efficiency with improved performance. Topics include TriMLP's addressing of limitations of existing MLP models in sequential contexts, empirical studies highlighting the incompatibility of standard MLP structures with sequential data and TriMLP's competitive performance across various datasets.

The article emphasizes the critical significance of accurately anticipating cooling demand, spanning diverse sectors like commercial structures, housing developments, and industrial facilities. This precision is paramount for achieving sustainable and energy-efficient building control. Powerful tools for enhancing the accuracy and adaptability of cooling load forecasts have been introduced through the emergence of Machine Learning (ML) approaches. Methods for forecasting cooling demand are extensively examined in this scholarly document, with a particular emphasis placed on implementing Multi-Layer Perceptron (MLP) models within the field of ML. The advantages of MLP models in addressing non-linear relationships, managing multidimensional datasets, and accommodating fluctuating environmental conditions are exemplified. A predictive analysis of building energy consumption was undertaken to enhance operational efficiency, employing two distinct optimization algorithms, specifically, the Improved Grey Wolf Optimizer and the Honey Badger Algorithm. In contrast to the MLIG model, characterized by high error values during training, an average reduction of prediction errors by more than double was achieved by MLHB. Furthermore, a peak value of R2 = 0.995, which is 3.4% higher than that of MLIG, was attained for cooling load estimation by MLHB in the second layer. [ABSTRACT FROM AUTHOR]

Diabetes Mellitus is one of the most chronic diseases in all over the world. Every year, many people die due to this disease in all countries. Therefore, identifying early detection methods for this disease can reduce its mortality. Today, many diseases can be diagnosed and prevented from progressing by using data mining techniques and machine learning algorithms. In this paper, diabetes prediction has been aimed by comparing the efficiency of several classical machine-learning techniques. For this reason, for the sake of diabetes prediction algorithms such as Naïve Bayes, Logistic Regression (LR), Multi-Layer Perceptron (MLP), Sequential Minimal Optimization (SMO), J48, Random Forest (RF), Regression Tree (RT) algorithms and a new hybrid algorithm based on Multi-Verse Optimizer (MVO) and Multi-Layer Perceptron (MLP) algorithms are employed for this evaluation based on Accuracy (ACC) Indicator and Area under Curve (AUC) criteria. Numerous and diverse methods and algorithms have been used to predict diabetes. Each of these algorithms has been effective in predicting diabetes with a different level of accuracy. Our goal in this research is to introduce a new combined algorithm that has the highest level of accuracy in predicting diabetes compared to the old frequent algorithms so that it can help people in the timely treatment of this disease. In the structure of the MLP algorithm, the backpropagation algorithm is used for training. This article uses the MVO algorithm to train the MLP instead of the backpropagation algorithm, which built the hybrid algorithm called MVO-MLP. The accuracy results and the area under the ROC diagram Indicated that the proposed hybrid algorithm increases the accuracy by 107% compared to the MLP algorithm with the default structure. The outcomes of the accuracy of the new model are also higher than other algorithms used in this article [ABSTRACT FROM AUTHOR]

The remarkable mechanical properties of high-performance concrete (HPC) make it indispensable in a wide range of engineering applications. Predicting HPC’s compressive strength with precision is essential to guaranteeing its structural stability and longevity. The present work introduces a novel method for predicting HPC compressive strength (CS) by utilizing the Multi-layer Perceptron (MLP) model in conjunction with three distinct optimizers, namely Northern Goshawk Optimization (NGO), Manta Ray Foraging Optimization (MRFO), and Atom Search Optimization (ASO). An effective method for capturing complex relationships between input and output variables is the multi-layer perceptron (MLP) in artificial neural networks. Developing a dependable, robust predictive model is the goal of training the MLP model on a variety of datasets of HPC mixtures and CS. As evidenced by the MLNG model’s R2 value of 0.994 and RMSE of 1.3572, the joint efforts of MRFO, NGO, and ASO during the optimization phase improve the MLP model and produce promising results. These results clarify how different optimizers play a crucial role in improving the accuracy of HPC compressive strength predictions made with the MLP model. This information helps make better decisions regarding design and construction methods for HPC. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that results in death within a short time span (3-5 years). One of the major challenges in treating ALS is its highly heterogeneous disease progression and the lack of effective prognostic tools to forecast it. The main aim of this study was, then, to test the feasibility of predicting relevant clinical outcomes that characterize the progression of ALS with a two-year prediction horizon via artificial intelligence techniques using routine visits data. Methods: Three classification problems were considered: predicting death (binary problem), predicting death or percutaneous endoscopic gastrostomy (PEG) (multiclass problem), and predicting death or non-invasive ventilation (NIV) (multiclass problem). Two supervised learning models, a logistic regression (LR) and a deep learning multilayer perceptron (MLP), were trained ensuring technical robustness and reproducibility. Moreover, to provide insights into model explainability and result interpretability, model coefficients for LR and Shapley values for both LR and MLP were considered to characterize the relationship between each variable and the outcome. Results: On the one hand, predicting death was successful as both models yielded F1 scores and accuracy well above 0.7. The model explainability analysis performed for this outcome allowed for the understanding of how different methodological approaches consider the input variables when performing the prediction. On the other hand, predicting death alongside PEG or NIV proved to be much more challenging (F1 scores and accuracy in the 0.4-0.6 interval). Conclusions: In conclusion, predicting death due to ALS proved to be feasible. However, predicting PEG or NIV in a multiclass fashion proved to be unfeasible with these data, regardless of the complexity of the methodological approach. The observed results suggest a potential ceiling on the amount of information extractable from the database, e.g., due to the intrinsic difficulty of the prediction tasks at hand, or to the absence of crucial predictors that are, however, not currently collected during routine practice. [ABSTRACT FROM AUTHOR]

According to a recent proposal, natural concepts are represented in an optimally designed similarity space, adhering to principles a skilled engineer would use for creatures with our perceptual and cognitive capacities. One key principle is that natural concepts should be easily learnable. While evidence exists for parts of this optimal design proposal, there has been no direct evidence linking naturalness to learning until now. This article presents results from a computational study on perceptual color space, demonstrating that naturalness indeed facilitates learning. [ABSTRACT FROM AUTHOR]

NeRF can render photorealistic 3D scenes. It is widely used in virtual reality, autonomous driving, game development and other fields, and quickly becomes one of the most popular technologies in the field of 3D reconstruction. NeRF generates a realistic 3D scene by emitting light from the camera's spatial coordinates and viewpoint, passing through the scene and calculating the view seen from the viewpoint. However, when the brightness of the original input image is low, it is difficult to recover the scene. Inspired by the ambient illumination in the Phong model of computer graphics, it is assumed that the final rendered image is the product of scene color and ambient illumination. In this paper, we employ Multi-Layer Perceptron (MLP) network to train the ambient illumination tensor I , which is multiplied by the color predicted by NeRF to render images with normal illumination. Furthermore, we use tiny-cuda-nn as a backbone network to simplify the proposed network structure and greatly improve the training speed. Additionally, a new loss function is introduced to achieve a better image quality under low illumination conditions. The experimental results demonstrate the efficiency of the proposed method in enhancing low-light scene images compared with other state-of-the-art methods, with an overall average of PSNR: 20.53 , SSIM: 0.785, and LPIPS: 0.258 on the LOM dataset. [ABSTRACT FROM AUTHOR]

This study aims to assess the efficacy of machine learning models in predicting solute concentration (C) distribution in a membrane separation process, using the input parameters which are spatial coordinates. Computational fluid dynamics (CFD) was employed with machine learning for simulation of process. The models evaluated include Kernel Ridge Regression (KRR), Radius nearest neighbor regression (RNN), K-nearest neighbors (KNN), LASSO, and Multi-Layer Perceptron (MLP). Additionally, Harris Hawks Optimization (HHO) was utilized to fine-tune the hyperparameters of these models. Leading the way is the MLP model, which achieves a remarkable test R2 value of 0.98637 together with very low RMSE and MAE values. Strongness and generalization capacity are shown by its consistent performance on test and training datasets. To conclude, the effectiveness of using machine learning regression methods more especially, KRR, KNN, RNN, LASSO, and MLP in estimating concentration from spatial coordinates was demonstrated in this work. For separation science via membranes where predictive modeling of spatial data is essential, the results offer important new perspectives by developing hybrid model. [ABSTRACT FROM AUTHOR]

Speech audiometry is a vital tool in assessing an individual's ability to perceive and comprehend speech, traditionally requiring specialized testing that can be time-consuming and resource -intensive. This paper approaches a novel use of deep learning to predict speech audiometry using pure-tone audiometry (PTA) data. By utilizing PTA data, which measure hearing sensitivity at specific frequencies, we aim to develop a model that can bypass the need for direct speech testing. This study investigates two neural network architectures: a multi-layer perceptron (MLP) and a one-dimensional convolutional neural network (1D-CNN). These models are trained to predict key speech audiometry outcomes, including speech recognition thresholds and speech discrimination scores. To evaluate the effectiveness of these models, we employed two key performance metrics: the coefficient of determination (R2) and mean absolute error (MAE). The MLP model demonstrated predictive solid power with an R2 score of 88.79% and an average MAE of 7.26, while the 1D-CNN model achieved a slightly higher level of accuracy with an MAE score of 88.35% and an MAE of 6.90. The superior performance of the 1D-CNN model suggests that it captures relevant features from PTA data more effectively than the MLP. These results show that both models hold promise for predicting speech audiometry, potentially simplifying the audiological evaluation process. This approach is applied in clinical settings for hearing loss assessment, the selection of hearing aids, and the development of personalized auditory rehabilitation programs. [ABSTRACT FROM AUTHOR]

The application of carbon dioxide (CO2) in enhanced oil recovery (EOR) has increased significantly, in which CO2 solubility in oil is a key parameter in predicting CO2 flooding performance. Hydrocarbons are the major constituents of oil, thus the focus of this work lies in investigating the solubility of CO2 in hydrocarbons. However, current experimental measurements are time-consuming, and equations of state can be computationally complex. To address these challenges, we developed an artificial intelligence-based model to predict the solubility of CO2 in hydrocarbons under varying conditions of temperature, pressure, molecular weight, and density. Using experimental data from previous studies, we trained and predicted the solubility using four machine learning models: support vector regression (SVR), extreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP). Among four models, the XGBoost model has the best predictive performance, with an R2 of 0.9838. Additionally, sensitivity analysis and evaluation of the relative impacts of each input parameter indicate that the prediction of CO2 solubility in hydrocarbons is most sensitive to pressure. Furthermore, our trained model was compared with existing models, demonstrating higher accuracy and applicability of our model. The developed machine learning-based model provides a more efficient and accurate approach for predicting CO2 solubility in hydrocarbons, which may contribute to the advancement of CO2-related applications in the petroleum industry. [ABSTRACT FROM AUTHOR]

More than 800 million people worldwide suffer from chronic kidney disease (CKD). It stands as one of the primary causes of global mortality, uniquely noted for an increase in death rates over the past twenty years among non-communicable diseases. Machine learning (ML) has promise for forecasting such illnesses, but its opaque nature, difficulty in explaining predictions, and difficulty in recognizing predicted mistakes limit its use in healthcare. Addressing these challenges, our research introduces an explainable ML model designed for the early detection of CKD. Utilizing a multilayer perceptron (MLP) framework, we enhance the model's transparency by integrating Local Interpretable Model-agnostic Explanations (LIME), providing clear insights into the predictive processes. This not only demystifies the model's decision-making but also empowers healthcare professionals to identify and rectify errors, understand the model's limitations, and ascertain its reliability. By improving the model's interpretability, we aim to foster trust and expand the utilization of ML in predicting CKD, ultimately contributing to better healthcare outcomes. [ABSTRACT FROM AUTHOR]

This study investigates simulation of pharmaceutical separation via membrane distillation process by computational simulation and machine learning modeling strategy. The efficacy of three regression models, i.e., Multi-layer Perceptron (MLP), Gamma Regression, and Support Vector Regression (SVR) in predicting the solute concentration, C(mol/m³), was evaluated. The hyper-parameters were optimized by fine-tuning the models using the Red Deer Algorithm (RDA). Computational analyses were carried out for removal of pharmaceuticals from solution by membrane distillation in continuous mode. Mass transfer and machine learning models were implemented focusing on concentration of solute in the feed section of membrane. Results indicate that the Multi-layer Perceptron model achieved great accuracy with an R2 of 0.9955, an MAE of 0.0084, and an RMSE of 0.0148, effectively capturing complex nonlinear relationships in the data. Gamma Regression also performed acceptably, with fitting R2 of 0.9214, showing its suitability for positively skewed data. The Support Vector Regression model, while capturing the general trend, showed the lowest performance with an R2 of 0.8710. These findings suggest that the Multi-layer Perceptron is the most accurate model for this dataset, followed by Gamma Regression and Support Vector Regression. This underscores the importance of careful model selection and optimization in regression analysis in combination with computational simulation of membrane processes. [ABSTRACT FROM AUTHOR]

Large pre‐trained models (PTMs) have shown their powerful ability in multiple natural language processing tasks. However, using them in practical application remains a challenge due to the significant computational cost and memory requirements. In order to achieve the balance of computational cost and accuracy, MLP architecture can be used as an alternative to the self‐attention module, such as pNLP‐Mixer and Hyper‐Mixer. Experiments indicate that, MLP‐based models can attain competitive performance with low cost. They maintain the balance of computation cost and accuracy successfully, yet, this is at the expense of not being able to capture short‐range dependencies. In this paper, a novel MLP‐based model, termed TS‐Mixer, is proposed which can capture local dependencies by shifting operation. Compared with other MLP‐based models, the parameters of TS‐Mixer are decoupled from the sequence length, hence it has a smaller model size in long sequence tasks. In addition, TS‐Mixer has linear computational complexity, therefore it can be used as a lightweight alternative to the self‐attention model. Experiments show that the TS‐Mixer outperforms other MLP‐based models, which achieves higher accuracy with fewer parameters in multiple downstream tasks. Notably, compared with pre‐trained models, TS‐Mixer can reach more than 90% of their accuracy with 1% or even one thousandth of the parameters (0.174 ~ 1.2 M). These results demonstrate that TS‐Mixer can achieve a better balance between the computing resources and accuracy. Code is available at: https://github.com/wyl-privacy-project/TS-Mixer. [ABSTRACT FROM AUTHOR]

To ensure effective resource allocation for urban bike demand, it is crucial to accurately predict shared bike rental counts. This prediction process was carried out using the Gradient Boosted Machine (GBM) method optimized with the Bat Algorithm (BA). To demonstrate the effectiveness of the proposed model, its performance was compared with different methods such as Decision Tree (DT), k-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP). For this comparison, metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) were employed. The best results were achieved by BA-GBM with values of 1.8665 MAE, 2.9588 MSE, 8.7545 RMSE, and 0.9264 R². Additionally, the features with the most and least impact on bike rental prediction were identified. The most influential features were found to be temperature and time of day, while the least influential features were snowfall and year. [ABSTRACT FROM AUTHOR]

The purpose of the current study is to design and optimize Rosuvastatin calcium orally fast disintegrating tablet (OFDT) with the assistance of an Artificial Neural Network (ANN) based Multi-layer Perceptron (MLP) model. Rosuvastatin calcium is commonly employed as a cholesterollowering agent. In our previous work established literature raw material data of OFDTs were collected from 92 research articles, which contain compositional and evaluation parameters and the data trained with Machine learning techniques (ML) to evaluate the optimal ingredients which helps further to develop and optimize Rosuvastatin calcium OFDTs using ANN based MLP. Rosuvastatin calcium OFDTs were formulated according to a 32-factorial design (randomized Box-Behnken method), and formulations were compressed using the direct compression method with varying compositions of superdisintegrant (Crospovidone) 2-4% binder microcrystalline cellulose (MCC) 5-20%, Mannitol as a diluent, magnesium stearate (Mg st) as a lubricant, and talc (1-3%) as a glidant. The developed formulations were assessed to determine their thickness, hardness, friability, disintegration time, and drug content. ANN was used for optimization, and the MLP model was trained using experimental data until a satisfactory R² of 0.99 and normalized root mean square error (NRMSE) of 0.024 was reached. The compressed tablets (F19) exceeded the desired criteria in terms of thickness (2.6mm), hardness (2.8 kg), friability (0.6%), drug content (99%), and disintegration time (36 sec). The potential use of ANN in pharmaceutical formulation optimization to achieve desired performance characteristics is demonstrated by this work. This study shows the efficacy of ANN with MLP in the development of Rosuvastatin calcium OFDTs. [ABSTRACT FROM AUTHOR]

This study contributes to the growing body of literature on traffic congestion prediction using machine learning (ML) techniques. By evaluating multiple classifiers and selecting the most appropriate one for predicting traffic congestion, this research provides valuable insights for urban planners and policymakers seeking to optimize traffic flow and reduce jamming and. Traffic jamming is a global issue that wastes time, pollutes the environment, and increases fuel usage. The purpose of this project is to forecast traffic congestion at One of the most congested areas in Amman city using multiple ML classifiers. The Naïve Bayes (NB), stochastic gradient descent (SGD) fuzzy unordered rule induction algorithm (FURIA), logistic regression (LR), decision tree (DT), random forest (RF), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with our study area. These will be assessed by accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments show that FURIA is the classifier that presents the highest predictions of traffic congestion where By 100% achieved Accuracy, Precision, Sensitivity and F-measure. In the future further studies can be used more datasets and variables such as weather conditions; and drivers behavior that could integrated to predict traffic congestion accurately. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a chronic metabolic disorder that affects the way the body processes blood glucose levels. Within the medical field, machine learning (ML) has significant potential for accurately forecasting and diagnosing a range of chronic conditions. If an accurate prognosis is achieved early, the risk to health and intensity of DM can be significantly mitigated. In this study, a robust methodology for DM prognosis was proposed, which included anomaly replacement, data normalization, feature extraction, and K-fold cross-validation. Three machine learning methods, support vector machine (SVM), multi-layer perceptron (MLP), and bagging, were employed to predict diabetes mellitus using the National Health and Nutritional Examination Survey (NHANES) 2011-2012 dataset. Accuracy, AUC, and recall were chosen as the evaluation metrics and subsequently optimized during hyperparameter tweaking. From all the comprehensive tests, bagging outperformed the other two models with an accuracy of 0.966, an AUC score of 0.992, and a recall of 0.97. The proposed methodology surpasses other approaches for forecasting DM. [ABSTRACT FROM AUTHOR]

This study addresses a major cybersecurity challenge by focusing on the detection of Distributed Denial of Service (DDoS) attacks. These attacks pose a major threat to online services by overwhelming targets with traffic from multiple sources. Traditional detection approaches often fail to adapt to changing attack patterns, necessitating advanced machine-learning techniques. This study proposes a fine-tuned Multi- Layer Perceptron (MLP) model to improve DDoS detection accuracy while reducing false positives. This study uses fine-tuning techniques, such as hyperparameter optimization and transfer learning, to build a robust and adaptive detection framework. After extensive experiments with multiple data splits and cross- validation, the fine-tuned MLP model exhibited strong performance metrics with an average accuracy of 98.5%, precision of 98.1%, recall of 97.8%, and F1 score of 97.9%. These findings demonstrate the model's ability to successfully distinguish between benign and malicious traffic, enhancing network security and resilience. By overcoming the limitations of existing detection methods, this study adds new insights to the field of cybersecurity, providing a more precise and efficient approach to DDoS detection. [ABSTRACT FROM AUTHOR]

Spirometry is one of the important non-invasive, sensitive, easy-to-perform, reproducible, and objective biomedical screening and diagnostic procedures in healthcare for the assessment of lung function. To date, there is no unified system, equation, or framework for the prediction of spirometry parameters for the Indian population. In this research article, a machine-learning-based system has been proposed and evaluated, and a web application developed for the prediction of Spirometry Parameters of the Adult Indian Population. The four most commonly used supervised machine-learning algorithms (Linear Regression, Gradient Boosting Regression, Deep Neural Multi-Layer Perceptron (MLP) Regression, and Support Vector Regression) for regression tasks have been evaluated for this purpose. Based on Mean absolute error, root mean squared error and adjusted R2 value, it has emerged that Gradient Boosting and Deep Neural MLP are the best-fit models to predict Forced Vital Capacity (FVC) and Forced Expiratory Volume in one second (FEV1) respectively for the Indian population. A web application has been designed using the Flask web framework to predict the FVC, FEV1, and corresponding Lower Limit Normality. This research work paves the foundation for ML-assisted spirometry for lung function assessment of the Indian population to extend the benefits of state-of-the-art technology in healthcare. [ABSTRACT FROM AUTHOR]

Ecological and environmental problems resulting from fossil fuels are due to the harmful emissions released into the atmosphere. The rising interest in searching for alternative fuels like biodiesel is growing to solve these problems. Waste cooking oil (WCO) is transformed into methyl ester and combined with biodiesel in percentages of 25, 50, 75, and 100%. Research is done on the impacts of methyl ester blends on engine performance and emissions. Compared to diesel, the methyl ester combination showed 25% lower brake power and 24% loss in thermal efficiency at maximum load and 1500 rpm. However, diesel fuel showed 23% lower specific fuel consumption increase than biodiesel. Compared to diesel, methyl ester exhibits 15% lower air-fuel ratio and 4% volumetric efficiency. Biodiesel lowers CO, HC, and smoke concentrations by 12, 44, and 48%, respectively, compared to diesel. Biodiesel emits 23% higher NOx at 1500 rpm and 100% engine load. To predict the emissions and performance of different percentages of biodiesel at engine speed variation, an artificial neural network (ANN) model is presented. ANN modeling minimizes labor, time, and finances and uses nonlinear data. Predictions were produced about the brake output power, specific fuel consumption, thermal efficiency, air-fuel ratio, volumetric efficiency, and emissions of smoke, CO, HC, and NOx as a function of engine speed and blend ratio. All correlation coefficients (r) over 0.99 and R 2 values were beyond 0.98 for all variables. There were low values of MSE, MAPE, and MSLE with significant predictive ability. WCO’s biodiesel is a viable diesel engine replacement fuel. [ABSTRACT FROM AUTHOR]

Polymer Electrolyte Membrane Fuel Cells (PEMFCs) have emerged as a pivotal technology in the automotive industry, significantly contributing to the reduction of greenhouse gas emissions. However, the high material costs of the gas diffusion layer (GDL) and bipolar plate (BP) create a barrier for large scale commercial application. This study aims to address this challenge by optimizing the material and design of the cathode, GDL and BP. While deterministic design optimization (DDO) methods have been extensively studied, they often fall short when manufacturing uncertainties are introduced. This issue is addressed by introducing reliability-based design optimization (RBDO) to optimize four key PEMFC design variables, i.e., gas diffusion layer thickness, channel depth, channel width and land width. The objective is to maximize cell voltage considering the material cost of the cathode gas diffusion layer and cathode bipolar plate as reliability constraints. The results of the DDO show an increment in cell voltage of 31 mV, with a reliability of around 50% in material cost for both the cathode GDL and cathode BP. In contrast, the RBDO method provides a reliability of 95% for both components. Additionally, under a high level of uncertainty, the RBDO approach reduces the material cost of the cathode GDL by up to 12.25 $/stack, while the material cost for the cathode BP increases by up to 11.18 $/stack Under lower levels of manufacturing uncertainties, the RBDO method predicts a reduction in the material cost of the cathode GDL by up to 4.09 $/stack, with an increase in the material cost for the cathode BP by up to 6.71 $/stack, while maintaining a reliability of 95% for both components. These results demonstrate the effectiveness of the RBDO approach in achieving a reliable design under varying levels of manufacturing uncertainties. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a key variable in the study of the global climate system and one of the important parameters in the process of air–sea interaction. Therefore, the demand for SST is developing towards high quality and high precision. In this paper, the ability of the multi-layer perceptron (MLP) optimized by the Aquila optimizer (AO) to solve nonlinear problems and the advantages of the deep belief network (DBN) to effectively process complex data are used to construct the IMLP-DBN inversion algorithm. The algorithm takes into account the influences of atmospheric conditions and satellite zenith angles. The data set is the infrared remote sensing data of the moderate resolution imaging spectroradiometer (MODIS) and the actual measurement data of buoys on sunny days and few clouds. Analysis of the inversion results shows that the root mean square error (RMSE) of the inversion value and the measured value is 0.14, and the sum of square errors (SSE) is 0.78. Compared with the MOD28 product data, the RMSE and SSE of the inversion are reduced by 66% and 24%, respectively. [ABSTRACT FROM AUTHOR]

Land-use and land-cover change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the land-use and land-cover (LULC) spatiotemporal dynamics across a three-decade period (1998–2023) in a district area. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the random forest method with multi-layer perceptron (MLP) and Markov chain analysis. To predict the dynamics of LULC changes for the year 2035, a hybrid technique based on multi-layer perceptron and Markov chain model analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. This study also discovered that between 1998 and 2023, the built-up area increased by 468 km2 as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will increase by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. The model provides valuable insights for policymakers, land managers, and researchers to support sustainable land-use planning, conservation efforts, and climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

Drought is, among natural hazards, one of the most harmful to humanity. The forecasting of droughts is essential to reduce their impact on the economy, agriculture, tourism and water resource systems. In this study, drought forecast in the central region of the state of Zacatecas, a semi-arid region of Mexico, is explored by means of artificial neural networks (ANNs), forecasting numerical values of three drought indices—the standardized precipitation index (SPI), the standardized precipitation and evapotranspiration index (SPEI) and the reconnaissance drought index (RDI)—in an effort to establish the most suitable index for drought forecasting with ANNs in semi-arid regions. Records of 52 years of monthly precipitation and temperature were used. The indices were calculated in three different time scales: 3, 6 and 12 months. The analyzed models showed great capacity to forecast the values of the three drought indices, and it was found that for the trial set, the RDI was the drought index that was best fitted by the models, with the evaluation metrics R2 (determination coefficient), RMSE (root mean square error), MAE (mean absolute error) and MBE (Mean Bias Error) showing ranges of 0.834–0.988, 0.099–0.402, 0.072–0.343 and 0.017–0.095, respectively. For the validation set, the evaluation metrics were slightly better. [ABSTRACT FROM AUTHOR]

Despite the considerable advancements made in the field of 3D human pose estimation from single-view images, previous studies have often overlooked the exploration of global and local correlations. Recognizing this limitation, we present MGAPoseNet, a novel network architecture meticulously designed to elevate the accuracy of 3D pose estimation. Our approach is distinguished by its simultaneous extraction of both local and global features, achieved through the parallel integration of Local Graph-based Joint Connection (LGC) and Global Attention-based Body Constraint (GAC) modules. Moreover, the performance of MGAPoseNet is further elevated by the sequential Spatial-Channel Graph MLP-Like Architecture (SC-GraphMLP) module. This module adeptly leverages spatial and channel information to model intricate interactions and dependencies among joint features, thereby refining the accuracy of pose estimation. Experimental evaluation conducted on benchmark datasets, including Human3.6M and MPI-INF-3DHP, unequivocally verifies the state-of-the-art performance of MGAPoseNet. This rigorous validation underscores its superiority in 3D human pose estimation tasks, while enhancing its coherence and clarity. [ABSTRACT FROM AUTHOR]

Metabolism is a network of chemical reactions that sustain cellular life. Parts of this metabolic network are defined as metabolic pathways containing specific biochemical reactions. Products and reactants of these reactions are called metabolites, which are associated with certain human-defined metabolic pathways. Metabolic knowledgebases, such as the Kyoto Encyclopedia of Gene and Genomes (KEGG) contain metabolites, reactions, and pathway annotations; however, such resources are incomplete due to current limits of metabolic knowledge. To fill in missing metabolite pathway annotations, past machine learning models showed some success at predicting the KEGG Level 2 pathway category involvement of metabolites based on their chemical structure. Here, we present the first machine learning model to predict metabolite association to more granular KEGG Level 3 metabolic pathways. We used a feature and dataset engineering approach to generate over one million metabolite-pathway entries in the dataset used to train a single binary classifier. This approach produced a mean Matthews correlation coefficient (MCC) of 0.806 ± 0.017 SD across 100 cross-validation iterations. The 172 Level 3 pathways were predicted with an overall MCC of 0.726. Moreover, metabolite association with the 12 Level 2 pathway categories was predicted with an overall MCC of 0.891, representing significant transfer learning from the Level 3 pathway entries. These are the best metabolite pathway prediction results published so far in the field. [ABSTRACT FROM AUTHOR]

The emergence of long-range (LoRa) technology, together with the expansion of uncrewed aerial vehicles (UAVs) use in civil applications have brought significant advances to the Internet of Things (IoT) field. In this way, these technologies are used together in different scenarios, especially when it is necessary to have connectivity in remote and difficult-to-access locations, providing coverage and monitoring of greater areas. In this sense, this article seeks to determine the best positioning for the LoRa gateway coupled to the drone and the optimal spreading factor (SF) for signal transmission in a LoRa network, aiming to improve the connected devices signal-to-noise ratio (SNR), considering a suburban and densely wooded environment. Then, multi-layer perceptron (MLP) networks and generalized regression neural networks (GRNN) were trained to predict the signal behavior and determine the best network to represent this behavior. The MLP network presented the lowest RMSE, 2.41 dB, and was selected for use jointly with the bioinspired Grey-Wolf optimizer (GWO). The optimizer proved its effectiviness being able to adjust the number of UAVs used to obtain 100% coverage and determine the best SF used by the endnodes, guaranteeing a higher transmission rate and lower energy consumption. [ABSTRACT FROM AUTHOR]

Abstract Early diagnosis of breast cancer is exceptionally important in signifying the treatment results, of women’s health. The present study outlines a novel approach for analyzing breast cancer data by using the CatBoost classification model with a multi-layer perceptron neural network (CatBoost+MLP). Explainable artificial intelligence techniques are used to cohere with the proposed CatBoost with the MLP model. The proposed model aims to enhance the interpretability of predictions in breast cancer diagnosis by leveraging the benefits of CatBoost classification technique in feature identification and also contributing towards the interpretability of the decision model. The proposed CatBoost+MLP has been evaluated using the Shapley additive explanations values to analyze the feature significance in decision-making. Initially, the feature engineering is done using the analysis of variance technique to identify the significant features. The MLP model alone and the CatBoost+MLP model are being analyzed using divergent performance metrics, and the results obtained are compared with contemporary breast cancer identification techniques.

Abstract Organic photovoltaic (OPV) cells are at the forefront of sustainable energy generation due to their lightness, flexibility, and low production costs. These characteristics make OPVs a promising solution for achieving sustainable development goals. However, predicting their lifetime remains challenging task due to complex interactions between internal factors such as material degradation, interface stability, and morphological changes, and external factors like environmental conditions, mechanical stress, and encapsulation quality. In this study, we propose a machine learning-based technique to predict the degradation over time of OPVs. Specifically, we employ multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to predict the power conversion efficiency (PCE) of inverted organic solar cells (iOSCs) made from the blend PTB7-Th:PC70BM, with PFN as the electron transport layer (ETL), fabricated under an N2 environment. We evaluate the performance of the proposed technique using several statistical metrics, including mean squared error (MSE), root mean squared error (rMSE), relative squared error (RSE), relative absolute error (RAE), and the correlation coefficient (R). The results demonstrate the high accuracy of our proposed technique, evidenced by the minimal error between predicted and experimentally measured PCE values: 0.0325 for RSE, 0.0729 for RAE, 0.2223 for rMSE, and 0.0541 for MSE using the LSTM model. These findings highlight the potential of proposed models in accurately predicting the performance of OPVs, thus contributing to the advancement of sustainable energy technologies.

Abstract Background Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that results in death within a short time span (3-5 years). One of the major challenges in treating ALS is its highly heterogeneous disease progression and the lack of effective prognostic tools to forecast it. The main aim of this study was, then, to test the feasibility of predicting relevant clinical outcomes that characterize the progression of ALS with a two-year prediction horizon via artificial intelligence techniques using routine visits data. Methods Three classification problems were considered: predicting death (binary problem), predicting death or percutaneous endoscopic gastrostomy (PEG) (multiclass problem), and predicting death or non-invasive ventilation (NIV) (multiclass problem). Two supervised learning models, a logistic regression (LR) and a deep learning multilayer perceptron (MLP), were trained ensuring technical robustness and reproducibility. Moreover, to provide insights into model explainability and result interpretability, model coefficients for LR and Shapley values for both LR and MLP were considered to characterize the relationship between each variable and the outcome. Results On the one hand, predicting death was successful as both models yielded F1 scores and accuracy well above 0.7. The model explainability analysis performed for this outcome allowed for the understanding of how different methodological approaches consider the input variables when performing the prediction. On the other hand, predicting death alongside PEG or NIV proved to be much more challenging (F1 scores and accuracy in the 0.4-0.6 interval). Conclusions In conclusion, predicting death due to ALS proved to be feasible. However, predicting PEG or NIV in a multiclass fashion proved to be unfeasible with these data, regardless of the complexity of the methodological approach. The observed results suggest a potential ceiling on the amount of information extractable from the database, e.g., due to the intrinsic difficulty of the prediction tasks at hand, or to the absence of crucial predictors that are, however, not currently collected during routine practice.

Abstract This study aims to assess the efficacy of machine learning models in predicting solute concentration (C) distribution in a membrane separation process, using the input parameters which are spatial coordinates. Computational fluid dynamics (CFD) was employed with machine learning for simulation of process. The models evaluated include Kernel Ridge Regression (KRR), Radius nearest neighbor regression (RNN), K-nearest neighbors (KNN), LASSO, and Multi-Layer Perceptron (MLP). Additionally, Harris Hawks Optimization (HHO) was utilized to fine-tune the hyperparameters of these models. Leading the way is the MLP model, which achieves a remarkable test R2 value of 0.98637 together with very low RMSE and MAE values. Strongness and generalization capacity are shown by its consistent performance on test and training datasets. To conclude, the effectiveness of using machine learning regression methods more especially, KRR, KNN, RNN, LASSO, and MLP in estimating concentration from spatial coordinates was demonstrated in this work. For separation science via membranes where predictive modeling of spatial data is essential, the results offer important new perspectives by developing hybrid model.

The accuracy of landslide susceptibility prediction (LSP) mainly depends on the precision of the landslide spatial position. However, the spatial position error of landslide survey is inevitable, resulting in considerable uncertainties in LSP modeling. To overcome this drawback, this study explores the influence of positional errors of landslide spatial position on LSP uncertainties, and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error. This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example. The 30–110 m error-based multilayer perceptron (MLP) and random forest (RF) models for LSP are established by randomly offsetting the original landslide by 30, 50, 70, 90 and 110 m. The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics. Finally, a semi-supervised model is proposed to relieve the LSP uncertainties. Results show that: (1) The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors, and are lower than those of original data-based models; (2) 70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices, thus original landslides with certain position errors are acceptable for LSP; (3) Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

Abstract This study investigates simulation of pharmaceutical separation via membrane distillation process by computational simulation and machine learning modeling strategy. The efficacy of three regression models, i.e., Multi-layer Perceptron (MLP), Gamma Regression, and Support Vector Regression (SVR) in predicting the solute concentration, C(mol/m³), was evaluated. The hyper-parameters were optimized by fine-tuning the models using the Red Deer Algorithm (RDA). Computational analyses were carried out for removal of pharmaceuticals from solution by membrane distillation in continuous mode. Mass transfer and machine learning models were implemented focusing on concentration of solute in the feed section of membrane. Results indicate that the Multi-layer Perceptron model achieved great accuracy with an R2 of 0.9955, an MAE of 0.0084, and an RMSE of 0.0148, effectively capturing complex nonlinear relationships in the data. Gamma Regression also performed acceptably, with fitting R2 of 0.9214, showing its suitability for positively skewed data. The Support Vector Regression model, while capturing the general trend, showed the lowest performance with an R2 of 0.8710. These findings suggest that the Multi-layer Perceptron is the most accurate model for this dataset, followed by Gamma Regression and Support Vector Regression. This underscores the importance of careful model selection and optimization in regression analysis in combination with computational simulation of membrane processes.

This research proposes an IoT-based system for classifying plant suitability using pH data and soil humidity parameters. The system utilizes Run-Length Encoding (RLE) to compress sensor data, which is transmitted to a database server via the Esp8266 module. A Multilayer Perceptron (MLP) algorithm is employed to classify the data, achieving an accuracy of 0.82 with only two parameters. The classification results are displayed on a website, providing real-time recommendations for farmers. The system's performance is evaluated using a dataset from Kaggle. The Kaggle dataset contains 2200 instances for 22 different plants and the results show that the proposed system can effectively classify plant suitability based on environmental factors. This research contributes to the development of IoT-based recommendation systems for precision agriculture, and future studies can build upon this work to improve accuracy and quality.

Abstract Ecological and environmental problems resulting from fossil fuels are due to the harmful emissions released into the atmosphere. The rising interest in searching for alternative fuels like biodiesel is growing to solve these problems. Waste cooking oil (WCO) is transformed into methyl ester and combined with biodiesel in percentages of 25, 50, 75, and 100%. Research is done on the impacts of methyl ester blends on engine performance and emissions. Compared to diesel, the methyl ester combination showed 25% lower brake power and 24% loss in thermal efficiency at maximum load and 1500 rpm. However, diesel fuel showed 23% lower specific fuel consumption increase than biodiesel. Compared to diesel, methyl ester exhibits 15% lower air-fuel ratio and 4% volumetric efficiency. Biodiesel lowers CO, HC, and smoke concentrations by 12, 44, and 48%, respectively, compared to diesel. Biodiesel emits 23% higher NOx at 1500 rpm and 100% engine load. To predict the emissions and performance of different percentages of biodiesel at engine speed variation, an artificial neural network (ANN) model is presented. ANN modeling minimizes labor, time, and finances and uses nonlinear data. Predictions were produced about the brake output power, specific fuel consumption, thermal efficiency, air-fuel ratio, volumetric efficiency, and emissions of smoke, CO, HC, and NOx as a function of engine speed and blend ratio. All correlation coefficients (r) over 0.99 and $$R^{2}$$ R 2 values were beyond 0.98 for all variables. There were low values of MSE, MAPE, and MSLE with significant predictive ability. WCO’s biodiesel is a viable diesel engine replacement fuel.

Many researchers proved that hybrid models have better results in comparison with independent models. A combination of different methods could enhance the accuracy of time series prediction. Hence, this research used the hybrid of three methods of chaos theory, multi-layer perceptron and metaheuristic algorithm to increase the power of the model forecasting. Artificial neural networks have properly considered complex nonlinear relations and are good comprehensive approximators. Multi-objective evolutionary algorithms such as multi-objective particle swarm optimization are good at solving multi-objective optimization issues. This algorithm organized the combination of parent and children populations by elitist strategy, decreased the messy comparing factors to improve the solution variety and avoided to use of niche factors. Chaos theory controls the complexities of stochastic systems. So, this research offers Tehran Stock Exchange Index (TSEI) prediction by a hybrid model of chaos theory, multi-layer perceptron and metaheuristic algorithm. The results show that in perceptron-based mode, RMSE measures are gradually increased in all intervals. The continuous decrease of RMSE shows that the perceptron-based model could show consistency with the whole data flow. This matter could offer a better learning and consistency process by perceptron-based models to predict stock prices, as this type of learning could apply more experiences for forecasting future behaviour in order to change the system content. [ABSTRACT FROM AUTHOR]

The Bald Eagle Search (BES) algorithm is an innovative population-based method inspired by the intelligent hunting behavior of bald eagles. While BES shows promise, it faces challenges such as susceptibility to local optima and imbalances between exploration and exploitation phases. To address these limitations, this paper introduces the Multi-Strategy Boosted Bald Eagle Search (MBBES) algorithm. MBBES enhances the original BES by incorporating an adaptive parameter, two distinct mutation strategies, and replacing the swoop stage with a fall stage. We rigorously evaluate MBBES against classic and improved algorithms using the CEC2014 and CEC2017 test sets. The experimental results demonstrate that MBBES significantly improves the ability to escape local optima and achieves superior convergence accuracy. Moreover, MBBES ranks first according to the Friedman test, outperforming its counterparts in solving five practical engineering problems and three MLP classification problems, underscoring its effectiveness in real-world optimization scenarios. These findings indicate that MBBES not only surpasses BES but also sets a new benchmark in optimization performance. [ABSTRACT FROM AUTHOR]

In recent years, vision Transformers and MLPs have demonstrated remarkable performance in image understanding tasks. However, their inherently dense computational operators, such as self-attention and token-mixing layers, pose significant challenges when applied to spatio-temporal video data. To address this gap, we propose PosMLP-Video, a lightweight yet powerful MLP-like backbone for video recognition. Instead of dense operators, we use efficient relative positional encoding to build pairwise token relations, leveraging small-sized parameterized relative position biases to obtain each relation score. Specifically, to enable spatio-temporal modeling, we extend the image PosMLP's positional gating unit to temporal, spatial, and spatio-temporal variants, namely PoTGU, PoSGU, and PoSTGU, respectively. These gating units can be feasibly combined into three types of spatio-temporal factorized positional MLP blocks, which not only decrease model complexity but also maintain good performance. Additionally, we enrich relative positional relationships by using channel grouping. Experimental results on three video-related tasks demonstrate that PosMLP-Video achieves competitive speed-accuracy trade-offs compared to the previous state-of-the-art models. In particular, PosMLP-Video pre-trained on ImageNet1K achieves 59.0%/70.3% top-1 accuracy on Something-Something V1/V2 and 82.1% top-1 accuracy on Kinetics-400 while requiring much fewer parameters and FLOPs than other models. The code is released at https://github.com/zhouds1918/PosMLP_Video. [ABSTRACT FROM AUTHOR]

The field of educational data mining has gained significant traction for its pivotal role in assessing students' academic achievements. However, to ensure the compatibility of algorithms with the selected dataset, it is imperative for a comprehensive analysis of the algorithms to be done. This study delved into the development of machine learning algorithms utilizing students' online learning activities to effectively classify their academic performance. In the data cleaning stage, we employed VarianceThreshold for discarding features that have all zeros. Feature selection and oversampling techniques were integrated into the data preprocessing, using information gain to facilitate efficient feature selection and synthetic minority oversampling technique (SMOTE) to address class imbalance. In the classification phase, three supervised machine learning algorithms: k-nearest neighbors (KNN), multi-layer perceptron (MLP), and logistic regression (LR) were implemented, with 3-fold cross-validation to enhance robustness. Classifiers' performance underwent refinement through hyperparameter tuning via GridSearchCV. Evaluation metrics, encompassing accuracy, precision, recall, and F1-score, were meticulously measured for each classifier. Notably, the study revealed that both MLP and LR achieved impeccable scores of 100% across all metrics, while KNN exhibited a noticeable performance boost after using hyperparameter tuning. [ABSTRACT FROM AUTHOR]

Urban green spaces, despite their recognized importance for sustainable urban development, face significant challenges to their resilience and viability due to rapid urbanization and various associated pressures. The scientific problem addressed in this paper is the vulnerability of urban green spaces to various stressors, including proximity to settlement, land use and land cover changes, and proximity to roads, among others. Despite their importance, there is a lack of comprehensive assessments and frameworks to systematically evaluate and address this vulnerability. Therefore, this study aims to fill this gap by employing a multi-criteria decision-making framework, namely the Analytic Hierarchy Process (AHP), and the Pressure-State-Response (PSR) framework. By analyzing 10 indicators related to urban green space vulnerability through a pairwise comparison matrix, the study identifies critical factors influencing vulnerability and categorizes urban green spaces into different susceptibility levels. Additionally, the research extends its analysis to future scenario development for the years 2030 and 2040, using a Multi-Layer Perceptron (MLP) based Markov Chain model, incorporating various parameters such as distances from built-up areas, roads, rails, streams, slopes, and elevation. Through these methodologies, the study aims to provide valuable insights to urban planners, policymakers, and environmental practitioners, enabling them to make informed decisions to enhance the resilience and sustainability of urban green spaces and urban areas as a whole.

Abstract Background Computed tomography (CT) body compositions reflect age‐related metabolic derangements. We aimed to develop a multi‐outcome deep learning model using CT multi‐level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long‐term mortality. Methods The derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age‐ and sex‐stratified random sampling from two community‐based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi‐automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi‐layer perceptron (MLP)‐based multi‐label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary‐level institution (n = 10 141). Results The mean ages of the derivation and external sets were 62.8 and 59.7 years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9 kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi‐level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT‐parameter‐based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P

In the field of tourism recommendation systems, accurately recommending scenic spots and routes for users is one of the hot research directions. In order to better consider the complex interaction between user preferences and attraction features, as well as the potential connections between different information sources, this study constructed a graph attention network model using knowledge graphs for tourist attraction and route recommendations, and extracted features from visual images using visual geometry group-16. The results indicate that, in Xian, when the learning rate is 0.01, the area under the curve value is 0.916. The area under the curve of New York is 0.909, and the learning rate is 0.001. The area under the curve value of the Tokyo dataset is 0.895. When the learning rate is moderate, the model quickly stabilizes in the first 16 rounds and reaches its optimal state in 26–30 rounds. When the propagation depth is 2, the accuracy is 0.920, 0.905, and 0.895, respectively. After introducing visual features, the accuracy, recall, and F1 score improved by 10 to 15.7%. The multi-layer perceptron further increased the effect by 4–6%. These experimental data fully demonstrate the effectiveness and accuracy of the recommendation algorithm. This study provides a powerful tool for tourism recommendation systems, which helps to further improve user experience.

The correlations between the mechanical properties of HPCs and their mixture compositions are complex, non-linear, and complex to characterize employing standard statistical methods. This paper aimed to estimate HPC’s compressive strength using a machine learning algorithm including Multi-layer Perceptron (MLP) with an HPC mixed collection of 168 samples via eight input variables. In addition, three meta-heuristic optimizers have been used for improving the efficiency and accuracy of MLP, which are included Dandelion Optimization (DO), Aquila Optimizer (AO), and Sooty Tern Optimization Algorithm (STOA). After fitting the presented models, the developed models’ predictive generalization and efficiency ability is evaluated against a set of performance parameters. All models used were found to perform as suitable in predicting outcomes, which can be employed for saving time and energy. As a result, Aquila’s optimization had the most accurate by MLP compared to other hybrid models. MLAO3 obtained R 2 = 0.994 and RMSE = 1.27(MPa), which are the most suitable result compared to other models.

Background: Alzheimer's disease (AD) is a neurodegenerative disorder caused by a complex interplay of various factors. However, a satisfactory cure for AD remains elusive. Pharmacological interventions based on drug targets are considered the most cost-effective therapeutic strategy. Therefore, it is paramount to search potential drug targets and drugs for AD. Objective: We aimed to provide novel targets and drugs for the treatment of AD employing transcriptomic data of AD and normal control brain tissues from a new perspective. Methods: Our study combined the use of a multi-layer perceptron (MLP) with differential expression analysis, variance assessment and molecular docking to screen targets and drugs for AD. Results: We identified the seven differentially expressed genes (DEGs) with the most significant variation (ANKRD39, CPLX1, FABP3, GABBR2, GNG3, PPM1E, and WDR49) in transcriptomic data from AD brain. A newly built MLP was used to confirm the association between the seven DEGs and AD, establishing these DEGs as potential drug targets. Drug databases and molecular docking results indicated that arbaclofen, baclofen, clozapine, arbaclofen placarbil, BML-259, BRD-K72883421, and YC-1 had high affinity for GABBR2, and FABP3 bound with oleic, palmitic, and stearic acids. Arbaclofen and YC-1 activated GABAB receptor through PI3K/AKT and PKA/CREB pathways, respectively, thereby promoting neuronal anti-apoptotic effect and inhibiting p-tau and Aβ formation. Conclusions: This study provided a new strategy for the identification of targets and drugs for the treatment of AD using deep learning. Seven therapeutic targets and ten drugs were selected by using this method, providing new insight for AD treatment. [ABSTRACT FROM AUTHOR]