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An instrument-free, user-friendly, and cost-effective PAD colorimetric sensor that can quantitative detection of H 2 O 2 has been realized by using bimetallic nickel–cobalt selenides as highly active peroxidase mimic and smartphone integrated with dark cavity as detector. Integrated with an ANN model and a self-compiled easy-to-use smartphone APP, the intelligent and on-site detection of H 2 O 2 was constructed, which exhibited an ultra-wide dynamic range. [Display omitted] • An effective paper-based colorimetric sensor was prepared based on nanozyme. • The optimized Ni 0.75 Co 0.25 Se exhibited excellent peoxidase-mimetic activities. • The on-site detection of H 2 O 2 was constructed by a self-compiled smartphone APP. • The machine learning-assisted sensor can detect H 2 O 2 over a wide dynamic range. Paper-based analytical devices (PADs) integrated with smartphones have shown great potential in various fields, but they also face challenges such as single signal reading, complex data processing and significant environmental impacting. In this study, a colorimetric PAD platform has been proposed using bimetallic nickel–cobalt selenides as highly active peroxidase mimic, smartphone with 3D-printing dark-cavity as a portable detector and an artificial neural network (ANN) model as multi-signal processing tool. Notably, the optimized nickel–cobalt selenides (Ni 0.75 Co 0.25 Se with Ni to Co ratio of 3/1) exhibit excellent peoxidase-mimetic activities and are capable of catalyzing the oxidation of four chromogenic reagents in the presence of H 2 O 2. Using a smartphone with image capture function as a friendly signal readout tool, the Ni 0.75 Co 0.25 Se based four channel colorimetric sensing paper is used for multi-signal quantitative analysis of H 2 O 2 by determining the Grey, red (R), green (G) and blue (B) channel values of the captured pictures. An intelligent on-site detection method for H 2 O 2 has been constructed by combining an ANN model and a self-programmed easy-to-use smartphone APP with a dynamic range of 5 μM to 2 M. Noteworthy, machine learning-assisted smartphone sensing devices based on nanozyme and 3D printing technology provide new insights and universal strategies for visual ultrasensitive detection in a variety of fields, including environments monitoring, biomedical diagnosis and safety screening. [ABSTRACT FROM AUTHOR]

[Display omitted] • Large datasets of U and UP are constructed with 1526 and 743 fuel samples. • The effect of C, H, and O atomic ratios on HHV is investigated by ternary plots. • HHV of any type of waste and fuel is estimated by artificial neural networks. • Hyperparameter optimization is carried out to obtain best performing ANN models. • Ultimate analysis data is observed to be sufficient for HHV estimation of fuels. Higher heating value (HHV) is one of the most important parameters in determining the quality of the fuels. In this study, comparatively large datasets of ultimate and proximate analysis are constructed to be used in HHV estimation of several classes of fuels, including char & fossil fuels, agricultural wastes, manure (chicken, cow, horse, sheep, llama, and pig), sludge (like paper, paper-mil, sewage, and pulp), micro/macro-algae's, wastes (RDF and MSW), treated woods, untreated woods, and others (non-fossil pyrolysis oils) between the HHV range of 4.22–55.55 MJ/kg. The relationships of carbon, hydrogen, and oxygen atomic ratios for fuel classes are illustrated by using ternary plots, and the effects of elemental composition on HHV was analyzed with the extensive dataset. Then, the ultimate (U) and ultimate & proximate (UP) datasets were utilized separately to estimate the HHV by using artificial neural networks (ANN). Hyperparameter optimization was carried out and the best performing ANNs were determined for each dataset, which yielded R2 values of 0.9719 and 0.9715, respectively. The results indicated that while ANNs trained by both datasets perform remarkably well, utilization of U dataset is sufficient for HHV estimation. Finally, the best performing ANN models for both U and UP datasets are given in a directly utilizable format enabling the accurate estimation of HHV of any fuel for optimization of fuel processing and waste management operations. [ABSTRACT FROM AUTHOR]

• A model to explore the multiphysics characteristic of semiconductors is presented. • The model determines the thermal and mechanical properties of complex patterns. • Results show a strong correlation between thermal and mechanical properties. • Dielectric bands asymmetrically affect thermal and mechanical behavior. • Machine learning can predict one property from another. As semiconductor performance improves through advanced package designs, it becomes important to consider both thermal and mechanical properties. Better understanding their relationship enhances system design and optimization. This study has introduced a numerical method that takes into account both thermal and mechanical fluxes to construct thermal and mechanical property maps for practical application in semiconductor engineering. Furthermore, this paper investigated the relationship between the two properties using the constructed property maps in commercially available semiconductor packages. Owing to the complexity of packaging design patterns, a coupled isoparametric mapping and machine learning (ML) method was introduced to investigate their effects. The model then determines and investigates the anisotropic equivalent thermal and mechanical properties of the commercialized semiconductor packages with complex pattern designs, according to the package materials, volume fractions of each material, and design patterns. This approach pursues more sophistication and practicality compared to simplified composite structure property evaluation models. Additionally, all processes in the algorithm are automated based on ML methods, making them practically applicable in the semiconductor industry. The study shows that there is a strong correlation between the thermal and mechanical characteristics in the complex package patterns. This relationship was able to form a fairly clear category based on the shape of the dielectric band because the dielectric band had significantly different effects on the thermal and mechanical fluxes. The study discussed the physical implications of the similarities and differences between the two behaviors and validated the results of the equivalent properties through finite element (FE) analysis. Finally, the study cross-validates the relationship by showing that information from one flux behavior can be used to predict the other based on the ML method. Based on the results, this paper can provide a better understanding of thermal and mechanical fluxes in semiconductor package patterns for package design and optimization. [ABSTRACT FROM AUTHOR]

With the rapidly evolving geo-political landscape and unceasing advancements in technology, sustainable energy security is a very important topic. Research indicates that electrochemical energy systems are quite promising to solve many of energy conversion, storage, and conservation challenges while offering high efficiencies and low pollution. The paper provides an overview of electrochemical energy devices and the various optimization techniques used to evaluate them. The optimization techniques include linear regression, factorial design, the Taguchi method, artificial neural networks, filters, and a combination of such methods to improve these systems. To support the growing interest in research, the bulk of this study focuses on a review of the most promising and highly researched electrochemical energy devices, such as fuel cells, batteries, and supercapacitors. The paper also provides modest commentary on hydrogen production technologies, electrochemical reactors, and membrane separation technologies, amongst other technologies. Building on a previous paper by the authors of this paper on artificial intelligence in hybrid renewable energy systems with fuel cells, this work provides a comparative review of optimization techniques for supercapacitors by highlighting key findings based on model accuracy. A summary of the advantages and disadvantages of the different major optimization techniques is presented. The paper concludes that a combination of optimization techniques is used to overcome the drawbacks of individual techniques, with adaptive filters being the most widely studied. This paper presents studies on the Design of Experiments (DoE) with the goal of building a better understanding of the relationships that exist between different operating variables in various electrochemical devices. • The electrochemical systems can be optimized for better performance with high accuracy. • The optimization techniques used are either model-based or data-driven. • Linear regression and artificial neural networks are the most common techniques studied. • Hybrid models could be applied to enhance systems' accuracy and minimize errors. [ABSTRACT FROM AUTHOR]

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems. • A hybrid technique for predict fault in standalone PV to improve the performance of PV • The proposed method is the joint execution of ANN and GEO • The major contribution of this work is to improve the performance of PV systems • The use of GEO provides an efficient optimization technique for ANN training • The proposed method is more accurate and effective than existing techniques [ABSTRACT FROM AUTHOR]

This paper reports the prediction results of the ionospheric irregularities represented by the ROTI index close to the equatorial ionization anomaly's Northern peak. A feedforward backpropagation Neural Network (NN) approach is implemented using a time series Nonlinear Autoregressive approach with Exogenous inputs (NARX). We used the data from a dual frequency GPS-SCINDA station located at Helwan (geographic coordinates of 29.86°N, 31.32°E, and MLAT of 29.94°N) in Egypt. To allow the model to be independently tested during varying levels of solar activities, we collected a 5-minute resolution of ROTI data for the solar cycle 24 from 2009 to 2017. The factors that influence the development of ionospheric irregularities are involved in the established model representing the diurnal and seasonal variations as well as solar and geomagnetic activity parameters. To support learning, we included IRI-foF2 and IRI-hmF2 parameters in the input layer neurons to improve the model learning about the behavior of the ionospheric F layer. The results show that the NN-ROTI values precisely match the GPS-ROTI values with an RMSE of 0.106 TECU/min and a prediction efficiency of 95%. The predicted values are highly correlated with the originals, with a regression of 0.89. Furthermore, the irregularities were more prevalent during the equinox months than during the solstice months. It is also observed that predicted NN-ROTI values in all seasons have RMSEs less than 0.03 and 0.05 TECU/min for the low and high solar activity, respectively. The results also showed a clear correlation between solar activity and the occurrence percentage of ionospheric irregularities indicating a solar cycle dependency. The prediction values of the NN-ROTI occurrence % demonstrated a remarkable correlation with the observed GPS-ROTI occurrence % across SC24. [ABSTRACT FROM AUTHOR]

In the numerical simulations of multi-phase flow using Volume-of-Fluid (VOF) method, the calculation of the interface normal is a crucial point. In this paper, a machine learning method is used to develop an artificial neural network (ANN) model to make more accurate prediction of the local normal vector from neighboring volume fractions. Spherical surfaces with different radii are intersected with a structural background grid to generate 84328 groups of data: 3×3×3 neighboring volume fractions are used as input, and normal vector as output. Using 90% data as training dataset, the ANN model is well trained by optimizing the number of hidden layers and the number of neurons on each layer. Using the remaining 10% data, normal predictions are made using ANN-VOF and the most used YOUNG and HEIGHT-FUNCTION methods. The RMSE of the ANN-VOF/YOUNG/ HEIGHT-FUNCTION methods are 0.008/0.022/0.045 respectively. In the reconstruction of a sinusoidal surface, the MSE of the ANN-VOF/YOUNG/ HEIGHT-FUNCTION methods are 0.008/0.018/0.041. It is demonstrated that the ANN-VOF method has better performance for interface normal prediction. The proposed method has a simple computational logic and does not need to deal with complex geometric topology, which lays the foundation for application in other more complex grids. [ABSTRACT FROM AUTHOR]

• Thermomechanical problem of FGMs sphere with material uncertainties is analyzed. • The interval random uncertainty model is recommended for material parameters. • A data-driven ANN fast calculation for FGMs sphere uncertainties analysis is established. • The volume fraction distribution is optimized for the safety design of the FGMs sphere. Mechanical analysis and optimal design for functionally graded materials (FGMs) structures are significant. The present paper analyzes the parameter uncertainties problem for the thermomechanical response of the FGMs spherical shells with volume fraction power distribution. Here, the interval random uncertainty model is recommended to describe the uncertainties of each component of material parameters in FGMs, and a data-driven artificial neural network (ANN) fast calculation for FGMs spherical shells thermomechanical problems with uncertainties analysis is established and trained. The thermomechanical coupling response analysis and volume fraction optimization analysis of the FGMs spherical shell with material uncertainties are realized for zirconia-titanium FGMs, with the maximum normalized Von Mises stress as the structural safety index and the ANN as the fast calculation method. In addition, the Von Mises stress of the FGMs spherical shell and a ZrO 2 -Ti alloy double-layer spherical shell under different loads is compared. The results show that material parameter uncertainties, especially those in Young's modulus, thermal expansion coefficient, and uniaxial yield limit significantly affect structural safety. The structural safety index λ calculated based on the volume fraction optimization design of FGMs spherical shell is significantly lower than that of the ZrO 2 -Ti alloy double-layer. The ANN method greatly improves the computational efficiency for uncertain problems and maintains high accuracy, compared with the statistical analysis after many calculations by randomly selecting material parameters. This study provides a path for the safety design of FGMs structures with uncertain parameters. [ABSTRACT FROM AUTHOR]

This paper introduces a novel direct torque control approach based on the decision tree (T-DTC), employing artificial neural networks that are effectively trained to enhance accuracy and robustness. The main objective of T-DTC is the substantial reduction of flux and torque ripples inherent in the conventional DTC, ensuring effective control of the induction motor. The conventional hysteresis controllers for stator flux and electromagnetic torque are replaced by two advanced controllers named M5 Prime model trees. Additionally, the traditional switching table is substituted with a novel decision tree table utilizing the classifier algorithm 4.5. The effectiveness of the proposed T-DTC strategy is demonstrated through simulation in MATLAB/Simulink and validated in real-time using an HIL platform based on OPAL-RT OP 5600 and Virtex 6 FPGA ML605. The results obtained demonstrate a notable improvement compared to existing techniques in the literature. [Display omitted] • An enhanced DTC strategy for induction motor based on decision tree is proposed. • A neural learning process is applied for the designed controllers. • Easy real-time implementation of the Tree-DTC strategy via the HIL tests is realized. • Considerable minimization of stator flux and electromagnetic torque ripples is achieved. • Robust Tree-DTC strategy in terms of speed monitoring and disturbance rejection is achieved. [ABSTRACT FROM AUTHOR]

Researchers are putting in a lot of time and energy into developing methods to use machine learning algorithms, a subfield of AI, to ‎diagnose disease in an individual patient. Extensive studies have been conducted on the potential benefits of using machine learning ‎techniques in the treatment of cardiovascular disease. For this preliminary study, the study have zeroed down on heart illness to get ‎as specific as possible about our methodology. In this paper, the authors investigate the differences in accuracy between different ‎machine learning approaches specifically, (SVM), (KNN), and a(ANN) when applied to the categorization of cardiovascular ‎illness. Our research makes use of approximately seventy thousand patient records from the Kaggle dataset, which focuses on ‎cardiovascular disease The Kaggle dataset comprises a limited number of variables per patient record, encompassing serum ‎cholesterol, diastolic and systolic blood pressure, relative blood glucose levels, and the presence or absence of angina.‎ The present study examines the Kaggle dataset and employs the (KNN), (SVM), and (ANN) methodologies. The results indicate ‎that the K-nearest neighbors (KNN) algorithm, specifically with a value of 9 for the number of neighbors, achieved an accuracy of ‎‎0.997. The SVM model using default hyperparameters achieved an accuracy of 0.9997. The Support Vector Machine (SVM) model ‎utilizing a Radial Basis Function (RBF) kernel and a C value of 100.0 exhibited an accuracy of 0.9998. Conversely, the SVM model ‎employing a linear kernel and a C value of 1000.0 achieved a perfect accuracy of 1.000. The Feedforward neural network achieved a ‎perfect accuracy of 1.000 using the Adam optimization algorithm after 10 epochs and 50 batches..In future research, the authors ‎intend to employ a deep neural network hypermodel in order to enhance the accuracy of their findings. [ABSTRACT FROM AUTHOR]

Hydrogen purification from steam methane reforming (SMR) by pressure swing adsorption (PSA) technology is a common method to obtain high-purity hydrogen. The breakthrough time can well reflect the adsorption dynamics and help PSA cycle design. In order to avoid time-consuming and labor-intensive breakthrough curve experiments, it is very necessary to develop a fast and accurate surrogate model. In this study, a genetic algorithm (GA) and artificial neural network (ANN) are combined to predict and optimize breakthrough times of adsorbates in activated carbon/zeolite layered beds. Using the Latin hypercube sampling strategy, training data sets of GA-optimized ANN (GA-ANN) obtains from the physical model of adsorption, heat and mass transfer model. The genetic algorithm (GA) optimizes the weights and biases of ANN for better performance. The ANN topology has 4 input variables (superficial velocity, activated carbon height, adsorption pressure and feed temperature) and 3 output variables (breakthrough times of CH 4 , CO and CO 2). The number of neurons in the hidden layer for the GA-ANN model was optimized as 7 to predict and optimize the maximum breakthrough time of SMR with a four-component (H 2 /CH 4 /CO/CO 2) system. The sensitivity analysis displays that relative importance to the breakthrough time is in the order of superficial velocity (40.88%) > adsorption pressure (24.55%) > activated carbon height (21.04%) > feed temperature (13.53%). To obtain high-purity hydrogen, the GA-ANN model combined with multi-objective GA optimization is used to maximize the breakthrough times of CH 4 and CO. The GA-ANN surrogate model proposed in this paper can not only accurately and quickly achieve the purpose of predicting the system breakthrough time with a high correlation coefficient (R = 0.9937) but also obtains the optimal operating conditions of PSA hydrogen purification. • A rigorous heat and mass transfer model of multi-component adsorption is established. • Artificial neural network (ANN) is well-trained by Latin hypercube sampling method. • Genetic algorithm (GA) optimizes the weights and biases of ANN for better performance. • Superficial velocity has the highest relative importance value on breakthrough time. • GA-optimized ANN model and GA combined for maximal breakthrough time of impurities. [ABSTRACT FROM AUTHOR]

The widespread use of plastic bags has had negative impacts on society, economy, environment, and health. Understanding consumers' awareness and behavioral intention (BI) to reduce the use of plastic bags is crucial. This study aims to expand the Theory of Planned Behavior（TPB）model and comprehensively analyze the public's attitude and intentions toward reducing the use of plastic bags. The study involved an in-depth analysis of data collected from 317 respondents, employed a Structural Equation Model (SEM) to establish linear relationships among the factors in the theoretical model, and an Artificial Neural Network (ANN) to assess the predictive accuracy of the significant factors identified by the SEM. The results indicated that convenience(C), perceived behavioral control (PBC), and policy promotion (PP) are key factors influencing consumers' willingness to reduce the use of plastic bags. By introducing new variables, this study has made a contribution to the research in the field of reducing plastic bag usage. [Display omitted] [ABSTRACT FROM AUTHOR]

• Analyzing reliability of a distribution company by data envelopment analysis. • Obtaining distribution companies' reliability targets using best past performance. • Estimation of reliability target level by machine learning methods. • Incorporation of outage characteristics for reliability target estimations. Electrical distribution system (EDS) reliability has always been a critical subject due to the considerable share of distribution systems in power interruptions to end-users. Meeting regulatory service quality standards requires distribution companies to achieve defined reliability levels for performance improvement. Conventionally, specialists determine target reliability based on experience, experimental knowledge, and rules of thumb. However, these methods are error-prone and may result in suboptimal investments, economic inefficiency, and inadequate resource allocation in EDS operation and planning. Accordingly, this paper presents a machine learning-based method to determine the target level for EDS's reliability indices based on its historical performance. The approach involves comprehensive analysis of historical EDS reliability data, followed by a data envelopment analysis (DEA) to assess performance across subdivisions like substations or feeders, identifying the most effective units. Subsequently, machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) network—are trained on optimal subdivisions from the DEA stage. These models accurately estimate target reliability for all subdivisions, contributing to the overall determination of the EDS target reliability level. A three-scenario case study validates the proposed method, suggesting its potential applicability as a valuable tool for regulatory agencies in setting EDS reliability targets. While admitting the potential challenges, notably the method's reliance on the quality and completeness of the historical data collected by distribution companies, the paper emphasizes the importance of collaborative efforts to ensure data accuracy and sufficiency. The proposed method leads to a fair mechanism to determine the target level for a real EDS's reliability indices in comparison with a business-as-usual scenario. Moreover, it provides a reliability performance ranking for the EDS's subdivisions. [ABSTRACT FROM AUTHOR]

The paper describes three lossy data compression techniques based on the principal component analysis (PCA), which are compared using the image compression task. The presented approach uses both classical PCA method based on the eigen-decomposition of the image data covariance matrix and two different neural network structures. The first neural structure is a two-layer feed-forward network with supervised learning acting as the so-called autoencoder, and the second one is a single-layered network with an unsupervised learning method commonly known as the generalized Hebbian algorithm. For each compression method mentioned above, the influence of the number of image segments (frames) and the number of eigenvalues/eigenvectors on the compression ratio and the image quality are examined using three different gray-scale test images. The paper also addresses a vital implementation issue regarding selecting appropriate data types to represent the compressed data. The paper's main conclusion is that the classical PCA method outperforms its neural counterparts, both in terms of the decompressed image quality and the time required to perform the compression procedure. The positive aspect of using neural networks as a tool for PCA-based lossy data compression is that they do not require calculating the correlation matrix explicitly and thus can be used in online data acquisition schemes. [Display omitted] • Classical PCA compression outweighs neural nets in image quality and operation time. • Appropriate learning algorithm is essential for the neural PCA compression. • Neural PCA compression can have an advantage in online data acquisition schemes. • Appropriate data types are important for compression ratio and image quality. [ABSTRACT FROM AUTHOR]

Multiphase flow has many applications, such as oil and gas industries. Flow meter devices must be calibrated with field or laboratory data. One of the best methods to calibrate the devices and determine flow parameters is artificial neural networks. In addition, due to development of multiphase flow sensors and computer systems, artificial neural networks are employed to determine the flow regime and phase volume fractions along the pipeline. Up until now, different methods of measurement such as flow pressure signal, radioactive, ultrasonic, impedance, and their combination with neural networks have been presented. In this paper, a review of these works is performed. The type of neural network, measurement method, neural network inputs and development of each method are investigated. Studies show that the use of the gamma-ray method is the most prevalent among the papers presented. Also, examining the flow regime detection and flow rate measurement over time has moved to the use of flow parameters, including pressure, temperature, velocity, viscosity, etc. This simple method (using flow parameters) has the potential to gradually replace more advanced techniques like ultrasonic and electrical, along with expensive techniques like gamma ray. The results of this review indicate that employing intricate neural networks with many layers and neurons slightly increases the accuracy of the results. It can also be said that the use of a combination of several techniques has led to improved results. • An extensive investigation has been conducted on using neural networks for the measurement of multiphase flow parameters. • This paper explores the utilization of 20 distinct neural network types within the realm of multiphase flow analysis. • Research has been conducted on the measurement of flow rates, volume fractions, and the identification of flow regimes. • The measurement methods are categorized into radioactive, ultrasonic, electrical, pressure signals, and flow parameters. [ABSTRACT FROM AUTHOR]

The paper introduces a deep learning (DL) model as an alternative approach to predict the supercontinuum generation in waveguides. The traditional technique of using the numerical method for this task is computationally laborious and time-consuming. The Artificial Neural Network (ANN) model described in the paper is trained from scratch and demonstrates high precision in predicting the spectral bandwidth of supercontinuum phenomena. It achieves a mean squared error of 0.0032, which indicates its ability to accurately map the optical characteristics of the waveguide. Furthermore, the deep learning model significantly reduces the computational time required compared to simulative processes conducted in COMSOL Multiphysics and MATLAB software, approximately taking only 50% of the time. By leveraging deep learning techniques, the model offers a promising alternative to the numerical method for predicting supercontinuum generation in planar waveguides. It overcomes the limitations associated with computational resources and complexity, making it an attractive substitute for traditional numerical techniques. • Investigated chalcogenide planar waveguide, generating versatile supercontinuum spectra dataset. • Preprocessed dataset for Tanh activation function compatibility. • Developed multi-layered ANN model with decaying learning rate for predicting supercontinuum spectra. • Efficiently predicts spectral broadening at waveguide's end and different lengths. • Developed ANN model handles both linear and nonlinear waveguide dynamics effectively. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the serious diseases that threaten the life of many women worldwide. The seriousness of this disease is that it is often discovered in late stages after a period of its occurrence. This causes a wide spread of the disease and difficulty in its treatment. Another important characteristic of this disease is that it can return again after a period of its treatment. Therefore, predicting the occurrence or recurrence of such disease early is the best solution to have a high cure rate. The main objective of this paper is to improve the prediction performance of the breast cancer recurrence. Many methods have been proposed to predict breast cancer recurrence. However, these methods did not achieve the desired results on one of the most famous datasets in the field of breast cancer recurrence's prediction (i.e., Wisconsin Prognosis Breast Cancer (WPBC) dataset). The highest accuracy achieved using the previous methods is 89.89%. Therefore, this paper provides a framework for improving the prediction of breast cancer recurrence. The proposed framework has the ability to overcome many of the challenges in the existing dataset such as: (a) the problem of imbalance between classes using a data over-sampling technique; and (b) the large number of data dimensions using Principal Component Analysis (PCA), and a wrapper dimensionality reduction technique based on Genetic Algorithm (GA). It also uses the neural network algorithm to fuse the results of two individual classifiers (i.e., Random Forest (RF) and Support Vector Machine (SVM)). Our proposed framework evaluation showed a significant improvement in the predication performance. It achieved an accuracy of 98.3%, area under the curve of 99%, and precision, recall, and f1-measure of 98%. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

Corrosion presents significant challenges in the marine, offshore, and oil and gas industries, resulting in annual economic losses amounting to billions of dollars. To address these losses and ensure the structural integrity of marine infrastructure, it is essential to implement effective corrosion monitoring techniques. In recent years, machine learning algorithms have gained prominence across various fields, offering innovative solutions to corrosion-related concerns. This paper provides a comprehensive critical review, primarily focusing on the two most prevalent machine learning algorithms: artificial neural networks and random forests. The review critically analyzes their applications, methodologies, and effectiveness in the realm of marine and offshore steel structures, oil and gas pipelines, as well as construction materials like Al alloys and Mg alloys and the analysis of corrosion coating behavior. Furthermore, this study explores the key findings and inherent limitations of these machine learning techniques, emphasizing their potential in corrosion prediction, detection, and the mitigation of corrosion issues in the marine, offshore, and oil and gas industries. By identifying existing research gaps and offering recommendations for future investigations, this paper emerges as an invaluable resource for researchers, engineers, and practitioners aiming to advance corrosion prevention and management strategies within these pivotal domains. • In-depth analysis of ANN and RF in maritime, offshore, and oil and gas corrosion research (2018-2023). • Critical evaluation of ML algorithms in maritime steel structures, pipelines, and construction materials. • Valuable review for cost-effective maritime corrosion maintenance strategies. • Provides concise corrosion management expertise to empower researchers and industry experts. [ABSTRACT FROM AUTHOR]

• Trapping indices stabilization is lagged as injection period is active. • Greater CO 2 cumulative volume occurs as reservoir pressure is far from caprock fracture pressure. • Larger closures will require pressure management-based brine production to reach theoretical storage capacity. • ML-based CO 2 storage modelling preliminary de-risked saline aquifers lacking numerical models. This paper presents a machine learning (ML) model designed to speed up the appraisal of geologic CO 2 storage sites by predicting the effectiveness in trapping and accommodating CO 2 in saline aquifers. Considering the urgency of de-risking as much geologic CO 2 storage resources as possible to help with CO 2 emission reduction Paris' goal, ML-based reservoir modelling has been documented as proper tool when a faster, good approximate, and less expensive approach is needed to surrogate multiple assessments of storage sites traditionally performed by long-timeframe and multi-stage geologic CO 2 storage numerical modelling approach. In this paper, a case study is presented. It consisted of a dataset comprised of six geologic aquifer parameters (CO 2 residual saturation, horizontal permeability, vertical to horizontal permeability ratio, porosity, brine salinity, and CO 2 flow rate) and elapsed time as input data, and as output data the CO 2 trapping mechanism indices (Solubility Trapping Index, Residual Trapping Index, and Structural Trapping Index) along with the dynamic storage capacity (CO 2 injected volume). Such dataset was used to train and test the artificial neural network (ANN) model. The dataset was generated from thousands of post-processed numerical realizations at several injection periods by applying design of experiment using a synthetic aquifer model derived from the Bunter Sandstone Closure 36 aquifer numerical model, from the Southern North Sea. The ANN architecture designed in Python consisted of 3 hidden layers and 40 nodes and its performance was assessed using the coefficient of determination (R2) and root mean squared error (RMSE). The ANN performance showed accuracies (R2) for training and testing with 96% and 95% of precision respectively. Practical application of the ANN model was successfully implemented to CO 2 storage aquifer sites selected from CO2Stored® database which lacking numerical models (Bunter Closure 3, 9, 35, and 40), obtaining at the end of 100-years injection case a Structural, Residual, and Solubility Trapping Index averaging 83%, 11%, and 6% respectively, with low variation coefficient indicating that trapping indices were predicted properly because aquifers selected for ANN model application have similar structures (dome-like shape) and reservoir properties. In addition, CO 2 injected volume predictions for 100-years injection case were ranging from 397 to 456 million ton (Mt) totalling 2.1 giga ton (Gt) of potential storage capacity which represents 70% of total theoretical volumetric capacity. These results show the significant impact to accelerate geologic CO 2 storage sites assessment by implementing ML-based modelling to preliminary de-risking groups of saline aquifers and reasonably consider them technically feasible CO 2 storage sites in UK. [ABSTRACT FROM AUTHOR]

• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GA) and artificial neural networks (ANN) as intelligent algorithms have been widely applied in engineering fields such as structural design. The GFRP-concrete-steel composite column (GCS), known for its corrosion resistance, has garnered significant attention in its design. However, the application of GA and ANN methods in the context of GCS has primarily been confined to enhancing the predictive accuracy of ANN. This paper introduces a novel approach for the structural optimization of GCS using GA and ANN. A total of 1050 finite element models were generated and analyzed using ANSYS for the purpose of training ANN. The validity of these finite element models was confirmed through comparative analysis with experimental data. The trained ANN was then utilized to predict the loading capacity of GCS for each generation in the GA. By collecting pricing data for GFRP tubes, steel tubes, and concrete under various parameters, pricing fitting formulas for the three materials were derived. Subsequently, the cost of GCS under different parameter sets was calculated. The maximization of the ratio of GCS loading capacity to its cost (LP) was established as the optimization objective. Through the iterative optimization process of the GA-ANN algorithm, the structural optimization was achieved to maximize the economic efficiency of GCS. Comparing the optimization results with numerical simulation results showed that errors were mostly contained within 10 % or even as low as 5 %, thereby validating the accuracy of the GA-ANN algorithm. The applicability of the GA-ANN algorithm to GCS structural optimization was demonstrated by altering a single concrete variable. The method proposed in this paper can be effectively applied to the structural design and optimization of composite columns with a sandwich structure, such as GCS. [ABSTRACT FROM AUTHOR]

Despite advancements in technology, power system faults leading to electric power interruption remain a significant issue. Efficient restoration of the power system relies on the swift classification and clearance of faults. Among the various types of faults in transmission lines, open circuit and short circuit faults are commonly encountered. This study specifically focuses on the analysis of five types of short circuit faults: line-to-line, line-to-ground, double line-to-ground, triple line, and triple line-to-ground faults. Faults can cause both power failure and power loss in transmission lines. Once a fault occurs, it is crucial to restore electricity supply promptly to prevent further losses. Therefore, the development of a system capable of accurately and swiftly detecting and removing faults is essential. Traditionally, categorizing transmission line faults required sophisticated mathematical modeling, intricate signal processing techniques, and expert knowledge to interpret the output signals. In this paper, an alternative approach is proposed, utilizing deep learning techniques for transmission line fault classification. Specifically, the paper employs techniques such as artificial neural network (ANN), long short-term memory (LSTM), with and without window regression (WR). By implementing these deep learning techniques, automatic feature extraction and signal processing are achieved, streamlining the fault classification process. The experimental results obtained from this study demonstrate an accuracy of 42.98% for ANN, 99.98% for LSTM, and 99.99% for LSTM-WR. These outcomes underscore the effectiveness of the deep learning techniques employed in accurately classifying transmission line faults, with LSTM and LSTM-WR outperforming ANN in terms of accuracy. [ABSTRACT FROM AUTHOR]

• An upscaling regional photovoltaic power forecasting scheme based on ANN is proposed. • A novel method of reference PV plants selection is proposed. • A power correction method is proposed to guarantee power forecasting accuracy. • The rolling forecasting accuracy is improved by integrating multiple ANN models. • The forecasting error is 4.04%, 3.45% and 2.86% in resolution of 1, 5 and 10mins. Along with the increasing penetration of photovoltaic (PV) power generation, regional power forecasting becomes more and more critical for stable and economical operation of power system. The key challenge of regional PV power forecasting technology is the lack of complete and accurate historical power data since not all PV plants are equipped with the precise real-time output power monitoring system. Besides, the computation burden will be heavy when the number of PV plants in the target region is large. This paper therefore proposes an upscaling minute-level regional PV power forecasting scheme using the data of the selected reference PV plants. In this paper, a novel method of reference PV plants selection is proposed by comprehensively considering the prediction accuracy of artificial neural network (ANN) as well as Pearson correlation coefficient. The reference PV plant selection coefficient μ is introduced as the comprehensive indicator for reference PV plant selection, which incorporates Pearson correlation coefficient and MAPE. In addition, a PV output power correction method is assumed to guarantee the proper operation of regional power forecasting. Besides, this paper proposes a flexible approach to effectively decrease the accumulated error of rolling forecasting by integrating the forecasting results under different temporal resolutions. In specific, the power forecasting results in temporal resolutions of 1 min, 5 min and 15 min are simultaneously derived and the performance between the traditional rolling forecasting and the proposed method is compared. The validity of the proposed method is finally verified using the collected historical power data of PV plants installed in a city of Eastern China. For time resolution of 1 min, 5 mins and 10 mins, the corresponding RMSE are 6.56, 5.73 and 4.85 and corresponding MAPE are 4.04%, 3.45% and 2.86%. [ABSTRACT FROM AUTHOR]

• The paper proposes combining DWT and RBFNN for fault detection in microgrids. • The approach achieves precise fault detection, notably for high-resistance single-phase-to-ground faults, with minimal error. • RBFNN and GRNN excel in accuracy and performance, while SVM handles high-dimensional datasets efficiently. • RBFNN exhibits high correlation coefficients, validating its robustness in fault classification. • RBFNN is the optimal choice for microgrid fault detection, balancing accuracy, training time, and simplicity. Microgrids are essential for integrating renewable energy sources into the power grid. However, fault detection is challenging due to bidirectional energy flow. Traditional relay-based systems struggle in microgrids, primarily because of limited fault currents from grid-connected renewable energy inverters. To address these challenges, this paper proposes a new methodology for fault detection and classification in a renewable microgrid. The main contributions encompass two key aspects. Firstly, it enhances fault detection performance in microgrids characterized by nonlinear relationships, including photovoltaic, hydrokinetic, and variable electric load systems. Secondly, the combination of the discrete wavelet transform with various types of neural networks and supervised learning techniques provides a robust methodology for fault detection and classification. The proposed approach is evaluated using an IEEE-5 feeder test bed representing a realistic ring network configuration. The results show that the radial basis function neural network model exhibited promising outcomes, yielding a low prediction error of 1.31 e-31, highlighting its practical potential for enhancing system reliability and performance. Furthermore, various test cases were conducted by altering the ground resistance to train the neural networks, demonstrating the effectiveness of this neural network in accurately identifying fault conditions. Additionally, this research achieved promising outcomes with other models, including support vector machine and nonlinear autoregressive with external input, emphasizing the adaptability of these models in fault detection. [ABSTRACT FROM AUTHOR]

The problem addressed in this work is the enhancement of energy efficiency in buildings, primarily focused on heating control. The paper highlights the importance of addressing this challenge, given that HVAC systems are responsible for over 60% of total energy consumption in buildings, making them the largest energy consumers in both the residential and nonresidential sectors. This paper presents a methodology for developing a model predictive control (MPC) system based on artificial neural network (ANN) models, with the aim of improving the energy efficiency of buildings. This approach involves the creation of a training dataset using dynamic thermal simulation tools, enabling the learning of the ANN model that is responsible for predicting future states within an MPC optimization loop. Additionally, the implementation leverages advanced technologies such as Field-Programmable Gate Arrays (FPGAs), specifically the PYNQ board, Wi-Fi modules, and MQTT communication to enhance scalability, deployment, and adaptability. The study evaluates the proposed Neural Network Model Predictive Control (NNMPC) strategy implemented on the PYNQ, reporting a substantial reduction of 38.53% in heating energy consumption in buildings compared to a basic On/Off control approach for 24-hour simulation. This outcome underscores the solutions significant potential for energy savings in building heating systems. [ABSTRACT FROM AUTHOR]

• This paper is to optimize the fatigue life of riveted joints in Al-alloy 2024 plates. • Fatigue crack growth test was performed for riveted joints in Al-alloy 2024 plates. • An artificial neural network to predict riveted joints parameters has been designed. • The genetic algorithm is applied to find the maximum fatigue life based on the input values. • Fatigue life assessment based on the proposed parameter is verified for riveted joints. The objective of this paper is to provide the fatigue life of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters. At first, the fatigue life of the riveted joints in AA2024 aluminum alloy plates is obtained by experimental tests. Then, an artificial neural network is applied to estimate the fatigue life of riveted lap joints based on the number of lateral and longitudinal holes, punch pressure, gap between the edge of hole and rivet, rivet shank diameter, and rivet shank length. Also, meta heuristic optimization algorithm is applied to calculate the riveting process parameters. Finally, sensitivity analysis is used to obtain the influence of parameters affecting the riveting process on the fatigue life. [ABSTRACT FROM AUTHOR]

Sustainable energy planning for power generation with renewables requires a multidimensional approach since it directly impacts several sectors such as the national grid, the environment, and the economy. This paper suggests an intelligent decision based on a combination of the Analytical Hierarchy Process and Artificial Neural Networks to assist policymakers in evaluating future renewable energy investments for power generation. A case study of Bahrain's power system is used to illustrate the usefulness of the proposed planning approach and also to discuss its efficiency. The Analytical Hierarchy Process model outcomes revealed that wind turbines are the most appropriate technology for Bahrain with 32.5 % priority, followed by PV and CSP with 32.2 % and 16.3 %, respectively. Then, the Artificial Neural Networks model was structured based on the generated scenarios from the Analytical Hierarchy Process model, and it reached its best performance after 384 cycles. The integrated approach overcomes some of the Analytical Hierarchy Process's limitations and provides an intelligent tool for advanced assessment of a sustainable power system. • Planning addresses intermittent nature of renewable energies. • Sustainability study integrates diverse, context-sensitive perspectives. • Tool merges Analytical Hierarchy Process with Neural Networks. • Developed tool applied to Bahrain's renewable energy context. [ABSTRACT FROM AUTHOR]

[Display omitted] • UV/VIS Machine vision system to determine CQAs of fully white tablets. • Rapid evaluation of compression force impact and API content using UV/VIS imaging. • Accurate analysis of critical quality attributes with multivariate data techniques. • Rapid analysis suitable for both off-line and in-line use. The paper provides a demonstration of how UV/VIS imaging can be employed to evaluate the crushing strength, friability, disintegration time and dissolution profile of tablets comprised of solely white components. The samples were produced using different levels of compression force and API content of anhydrous caffeine. Images were acquired from both sides of the samples using UV illumination for the API content prediction, while the other parameters were assessed using VIS illumination. Based on the color histograms of the UV images, API content was predicted with 5.6 % relative error. Textural analysis of the VIS images yielded crushing strength predictions under 10 % relative error. Regarding friability, three groups were established according to the weight loss of the samples. Likewise, the evaluation of disintegration time led to the identification of three groups: <10 s, 11–35 s, and over 36 s. Successful classification of the samples was achieved with machine learning algorithms. Finally, immediate release dissolution profiles were accurately predicted under 5 % of RMSE with an artificial neural network. The 50 ms exposition time during image acquisition and the resulting outcomes underscore the practicality of machine vision for real-time quality control in solid dosage forms, regardless of the color of the API. [ABSTRACT FROM AUTHOR]

Nonlinear equation systems (NESs) are common in practical applications, and solving them is an important task in numerical computation. Evolutionary algorithms (EAs) for handling NESs have received considerable attention. EAs generate a large amount of data, which reflect the evolutionary behavior. However, there are still deficiencies in the mining and use of these data. Inspired by the ability of humans to acquire knowledge from past historical experience, this paper proposes a knowledge-learning-and-transfer-aided hybrid niching-based differential evolution (HNDE/KLT). HNDE/KLT aims to acquire knowledge from historical experiences and use them to guide the evolution. The HNDE/KLT include two main features: i) artificial neural networks are embedded in EAs, so the algorithm can learn from successful historical evolutionary information and obtain the relationship between the current individual's position and the optimal evolutionary direction; ii) a knowledge-transfer-based technique is designed to perform information exchange between different subpopulations, enhancing exploitation efficiency. Experimental results show that the HNDE/KLT can better solve NESs, indicating that learning plus transfer can make problem solving more intelligent. Additionally, we employed our algorithm to solve two real-world problems, and obtained good results. [ABSTRACT FROM AUTHOR]

• This study pioneers the use of deep learning in transforming Measurement and Verification (M&V) within industrial buildings, significantly curtailing the reliance on prolonged data collection periods, and setting a new standard for efficient energy management. • Our findings elucidate the impact of dataset size on M&V accuracy, showcasing that a compact, four-month dataset is sufficient for dependable energy savings verification, challenging the traditional norms of lengthy data collection. • Through a rigorous comparison of twelve methods, this paper underscores the superior performance of LASSO regression, Sequential Feature Selector, and Recursive Feature Elimination in refining model accuracy, marking a significant step forward in feature selection methodology. • Investigating various configurations, this research identifies five hidden layers as the ideal architecture for deep neural networks, optimizing the balance between model complexity and predictive precision in energy savings. • The study significantly contributes to the acceleration of the M&V process, enabling more rapid decision-making and implementation of energy conservation measures, thereby improving operational efficiency in industrial environments. • Highlighting the extensive implications for sustainable practices, this research illustrates how the integration of sophisticated analytics and deep learning can revolutionize energy management strategies, promoting a more sustainable approach to building operations. This study explores the potential of advancing industrial building energy Measurement and Verification (M&V) using Deep Learning techniques, with a focus on the impact of data size and feature selection methods on model performance. Traditional M&V practices in the industry require extensive datasets, typically spanning 24 months, to ensure accurate energy savings verification post energy conservation measures (ECMs). This research challenges the norm by investigating whether a condensed four-month dataset could suffice for reliable M&V, thereby accelerating the verification process which is crucial for industrial applications. Utilizing a dataset with 30-minute intervals, the study first applies multi-linear regression across twelve feature selection methods to identify the most effective techniques based on a comprehensive set of performance metrics including Training Time, RMSE, MAE, R-squared, CVRMSE, NMBE, Mean MSE (via k-fold cross-validation), and the Standard Deviation of MSE (over 10-fold cross-validation). The top three methods—LASSO regression, Sequential Feature Selector, and Recursive Feature Elimination—were further analyzed as input features for a Deep Neural Network (DNN) model. The DNN's performance was evaluated across varying data sizes (20 %, 40 %, 50 %, 60 %, 80 %, and 100 %) and configurations ranging from one to ten hidden layers. The findings reveal that LASSO regression, when integrated with DNN, consistently outperforms in terms of CVRMSE and NMBE metrics across different data sizes and model complexities. Notably, increasing the dataset size from 20 % to 80 % markedly improves the model's predictive accuracy, underscoring the significance of larger datasets in enhancing DNN generalization and performance. Additionally, the study highlights a critical trade-off in DNN architecture: while models with fewer hidden layers (1–3) show greater performance variability in smaller datasets, increasing the number of layers does not linearly translate to better performance, illustrating the nuanced balance between model complexity and efficacy. This research contributes to the energy M&V field by demonstrating that shorter duration datasets, previously considered insufficient, can indeed provide accurate energy savings verification when analyzed with advanced deep learning techniques. This advancement not only paves the way for quicker, more efficient M&V processes in industrial settings but also offers valuable insights into the optimization of DNN models for energy data analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • JO was first used as a green reductant to recover iron from CS. • The ideal conditions for CS reduction: 3 wt% JO at 1250 °C for 45 min. • CS reduction is more efficient with JO as reductant than with anthracite and JO char. • The reduction mechanism of CS follows a one-dimensional diffusion model (D1). • ANN5 is an optimal model to predict CS reduction when JO acts as a reductant. Jatropha oil (JO) is a non-edible vegetable oil and a potential renewable energy source. In this paper, JO was proposed as a reductant to recover iron from copper slag (CS), and the effects of JO dosage, reduction temperature and reduction time on the recovery of iron by deep reduction of CS were investigated. Based on Coats-Redfern method, a thermogravimetric analyzer was used to explore the reactivity, mass loss behavior, and kinetic mechanism of CS reduction by JO, and to compare the reduction characteristics of anthracite and JO char when used as a reductant. Additionally, artificial neural network (ANN) modeling was used to predict the effects of reductant type, temperature, and heating rate on mass loss during CS reduction. The results showed that the optimum condition for CS reduction when JO was used as the reductant was 3 wt% JO at 1250 °C for 45 min, which resulted in 84.97 % Fe recovery and 94.58 % Fe grade in CS; the reduction mechanism of CS followed a one-dimensional diffusion model (D1); and ANN5 (15*1) was the optimum ANN model to predict the reduction of CS. This research introduces innovative strategies for the environmentally friendly and efficient recovery of iron from iron-containing furnace slag. [ABSTRACT FROM AUTHOR]

Modeling solar plants, like Linear Fresnel Reflectors, plays an essential role in analyzing plant characteristics, assessing overall efficiency, designing appropriate controllers, and optimizing system's operation. Although physical modeling approaches are widely used, their accuracy when compared to real operational data is deficient. In this context, this paper conducts a novel and comprehensive investigation for an existing Linear Fresnel Reflector using six distinct machine learning algorithms namely, Multilayer Perceptron and LSTM Neural Networks, Random Forest, Decision Trees, Extreme Gradient Boosting, and K-Nearest Neighbours to forecast the useable output power of a linear Fresnel solar plant. Datasets between May 2018 and September 2019 with a 30-s time interval from a linear Fresnel plant located in Nicosia, Cyprus are used in this research. Seven distinct statistical metrics, in conjunction with the computation time are employed to assess the performance of the different machine learning models. The objective of this study is to further improve the prediction of Linear Fresnel Reflectors performance using machine learning model, in order to increase modeling reliability on an annual basis. Outcomes reveal that, among the various models considered, K-Nearest Neighbours demonstrated the most optimal performance with a coefficient of determination 98.81 % and a mean absolute percentage error of 1.975 %. [ABSTRACT FROM AUTHOR]

Laser-induced coloration (LIC) on metallic surfaces has attracted interest for various applications. Achieving precise color generation still presents significant challenges, caused by the complex correlation between laser processing parameters and induced colors. Limited to parameter sensitivity, complex coupling of several processing parameters and unevenness of the coloring, it is difficult to achieve accurate parameter prediction and color control with traditional experimental or neural network based methods. Problems such as poor color acquisition and insufficient consideration of processing parameters exist in current studies. In this regard, a high-quality and full-pixel LIC color dataset processed by the Octree algorithm is established. In addition, modified ANN models and regulation cocktails are adopted to predict processing parameters (waveform, frequency, power, scanning spacing, scanning speed), and HSV (color model describes colors in Hue, Saturation and Value channels) values, respectively. The models proposed in this paper achieve accurate parameter control and color prediction. For Model MOC, its training and test RMSE reaches 7.82 and 8.33. Besides, precise RGB artworks were processed applying the predicted parameters. For Model MLP + C, RMSE reaches 4.83 for test set. The difference in color between the predicted color and the actual color reaches Δ E ⁎ ≤ 5. With well-tuned ANN models and curated LIC dataset, the correlation between processing parameters and induced colors can be revealed. Thus, the processing parameters for LIC are optimized. • Uneven color in LIC is studied by full factorial experiment and microstructural analysis. • The Octree Algorithm enhances the quality of LIC dataset. • Five main laser parameters are considered in the experiment, suitable for application. • The adoption of regulation cocktails balances the efficacy and accuracy of the model. [ABSTRACT FROM AUTHOR]

• A novel ANN method is established for probabilistic life prediction of corroded reinforced concrete. • DT is used to determine the uncertainty of fatigue life. • ANN is used to predict the fatigue life of the corresponding survival rate. • The ANN method is evaluated using the fatigue test data from corroded reinforced concrete. Corroded reinforced concrete is affected by many factors. Therefore, the effects of these uncertainties should be considered when predicting fatigue life. However, artificial neural networks cannot determine the uncertainty of fatigue life. This paper establishes a new artificial neural network method to solve this problem. First, survival rates are obtained using the minimum number of specimens method. Second, the samples are divided. Then, a decision tree is used to determine the uncertainty of the test sample using corrosion and loading conditions. Finally, an artificial neural network is used to predict the fatigue life of the test sample using the survival rate, corrosion conditions, and loading conditions. If the training samples entered the artificial neural network are small sample data, the sample data are expanded using a beta-variational autoencoder with sample division. The established method is evaluated using fatigue test data from corroded reinforced concrete. Moreover, the evaluation results show that the established method can effectively predict the survival rate and fatigue life. [ABSTRACT FROM AUTHOR]

This paper presents an application of artificial neural networks (ANN) to ground motion modelling. We focused on developing a generalised ground motion model (GGMM) incorporating several seismic intensity measure (IM) types and their inter-IM correlation. These range from classical IMs, such as peak ground acceleration/velocity/displacement, spectral acceleration, and significant duration, to more advanced IMs recently shown to be better descriptors of structural performa nce, such as average spectral acceleration and filtered incremental velocity. Additionally, three different horizontal component definitions were included for the spectral acceleration-based IMs. A total of nine input ground motion causal parameters are required to use the GGMM developed, based on ground motion records from the NGA-West2 database. ANN was used to perform the regression, which differs from the approaches used in many existing ground motion models (GMMs), and gives the possibility to regress all IMs simultaneously in one model. A mixed-effects regression approach was adopted for the regression and the quantification of the inter- and intra-event variability of the GGMM estimation. The correlations between the IMs were also quantified and briefly presented here, which allows for a more refined prediction of seismic shaking and a unified treatment of prediction and IM correlations. This will allow more advanced record selection for non-linear dynamic analyses to be performed, which can consider several facets of ground shaking currently overlooked in many works. We evaluated the performance of the developed GGMM using several metrics and compared it to various existing GMMs developed with either the classical approach or machine learning methods. The results show that the proposed GGMM exhibits very good predictions, especially considering the wide range of IMs tackled. Lastly, this methodology has the flexibility of being able to add more IMs or horizontal component definitions seamlessly. This manuscript presents a novel generalised ground motion model for traditional and next-generation intensity measures (IMs) used in seismic hazard and risk. Some notable points: • Utilization of artificial neural networks to capture the complex interdependencies among multiple IMs. • The flexibility and accuracy of the model allows for more refined predictions for the next-generation IMs. • All the included IMs are simultaneously regressed using a mixed-effects regression procedure. • The proposed model can be used by the user to provide outputs of several IMs, which accommodates ease of use. • Correlations between the IMs were also quantified to offer a more consistent and unified treatment of IM correlations. • Comparisons of the developed model with existing models from the literature showed good performance and reduced dispersion. [ABSTRACT FROM AUTHOR]

Indirect evaporative cooling has garnered considerable attention and research owing to its notable advantages, especially high cooling efficiency and low energy consumption. However, the complexity and diversity of parameters affecting indirect evaporative cooler (IEC) performance necessitate the development of a fast and efficient performance prediction model, which remains a critical research objective. To address the particular challenge, this paper introduces a novel approach that utilizes the principles of neural network theory to construct a predictive model based on backpropagation artificial neural networks (BP-ANN). The developed model is employed to investigate the impact of six main operating parameters of the water spray system on the IEC performance. Wet-bulb efficiency and pressure drop of secondary air channel are utilized as the performance evaluation indexes. The model is rigorously validated by relevant experimental data, enabling a comprehensive analysis of the influence of each parameter on the operational performance of the whole system. The significance of the influential parameters is evaluated through grey relational analysis. Furthermore, a multi-objective optimization approach employing a genetic algorithm (GA) is introduced to effectively determine the optimal operating parameters of the IEC system. The results demonstrate that the whole system can efficiently operate with low energy consumption when the wet-bulb efficiency and pressure drop weights are balanced. The optimal operating parameters include a nozzle pressure of 0.15 MPa, spacing of 85 mm, aperture of 6 mm, count of 11, spray density of 0.85 kg/(s ∙ m2) and air velocity of 2 m/s, at which time the wet-bulb efficiency was 0.85, the pressure drop was 210 Pa, and the coefficient of performance (COP) was 12.4, which demonstrated an efficiency increase of 23.2% in comparison with the base system. The findings of this research significantly contribute to a comprehensive understanding of the influence of water-spray operating conditions on IEC performance, and provide valuable insights for the design and operation of high-efficient IEC systems in practical applications. • The datasets were generated by a laboratory-scale prototype of the IEC system. • The ANN model for IEC considering spray operation was trained and validated. • Effects of nozzle and operation characteristics parameters were investigated. • The significance of the influential parameters was evaluated. • The optimal parameters for IEC were determined based on genetic algorithms. [ABSTRACT FROM AUTHOR]

Compared with integrated circuits based on through silicon via (TSV), monolithic inter-tier via (MIV) has been identified as a critical technique to enable three dimensional (3D) integration due to its ultra-small size and relatively superior electrical performance, which allows ultra-high integration density. However, the interconnection of monolithic 3D (M3D) design is more prone to electromigration and stress migration. Severe crosstalk during signal transmission and thermal stress at high temperatures have serious limitations on system performance. In this paper, we focus on the COMSOL Multi-physics software, which can solve multi-field problems, to study the crosstalk problem and its thermal stress problem in MIV structures and analyze the crosstalk effects and temperature stress changes of MIV under different physical coupling conditions. An MIV array based on electrical-thermal-mechanical multi-field coupling was proposed, and the temperature and stress were analyzed by finite element analysis software. Additionally, an artificial neural network scheme is proposed that uses MATLAB to train temperature and stress data to predict the stress values of MIV. Experimental results show that the proposed prediction model using a genetic algorithm to optimize the BP Neural Network (GABP) has a 23.3 % higher prediction accuracy than that of a general BP neural network. • The electrical and thermal behavior characteristics of MIV in M3D were analyzed. • An M3D array is proposed and simulated by FEM. • The data is trained by improved BP network to evaluate the circuit performance. [ABSTRACT FROM AUTHOR]

This paper presents a comparison of three different machine learning frameworks applied in the wave buoy analogy used for estimating the sea state from measured ship responses. The three frameworks output and characterise the sea state in different ways: Model 1 outputs integral parameters, Model 2 outputs a point wave spectrum and the wave direction, Model 3 outputs the full directional wave spectrum. The assessment of the models is based on simulated motion measurements, i.e. synthetic data. In the particular investigations made, the performance of Model 2, relying on a novel framework, is generally superior. However, the central take-away from the study, is the importance of considering thorough and well-prepared training data encompassing many, not to say all, possible parameter combinations and shapes in the studied wave spectra forming the training data; any machine learning model is no better than the data upon which it is trained. • Machine learning applied in the wave buoy analogy. • Comparison of three fundamentally different ML models. • Presentation of a novel framework outputting a point wave spectrum and wave direction. • Thorough investigations of the models' generalisation capability. • Well-prepared and comprehensive training data is crucial. [ABSTRACT FROM AUTHOR]

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

Urban fires are a prevalent type of fire that requires prompt and effective responses. Determining the appropriate number of firefighters at the alarm time is crucial, but it mostly relies on the experience of decision-makers. Due to the fuzziness and limitations of human knowledge, there is often a deviation between the number of firefighters dispatched relying on personal subjective experience and the realistic demand. This paper proposed a historical data-based method for predicting the demand number of firefighters in urban fire. Initially, the National Fire Incident Reporting System (NFIRS) data and Global Historical Climatology Network Daily (GHCN-D) weather data were combined to create a fused dataset. The dataset was subjected to anomaly detection and feature selection, then the processed data was used to predict the number of firefighters using artificial neural network (ANN). In this process, the Genetic Algorithm (GA) was applied to optimize ANN structure. Finally, comparative experiments were conducted to evaluate the performance of the proposed method, which demonstrated superior accuracy in comparison to common regression models and traditional ANN. This advancement brings the number of dispatched firefighters closer to the actual demand and contributes to ensuring an adequate allocation of firefighters for urban fires. Consequently, decision-makers can make more informed decisions regarding the number of firefighters to dispatch, leading to more effective responses to urban fires. [ABSTRACT FROM AUTHOR]

Date fruit (Phoenix dactylifera L.) is known for its diversity in terms of genotypes that differ in shape, color, texture, taste, and other physical and sensory attributes, in addition to biochemical compounds. This richness generates complex, long, and tedious work at the scale of farm as well as in handling, processing, and storage facilities to prepare standardized, and homogenous final products that deal with consumer preferences and needs. To alleviate this, an increased importance and particular trend were observed recently for the use of artificial intelligence (AI) techniques and computer vision technology since they are considered as accurate, objective, non-destructive, fast, and efficient emerging technologies. In this context, this manuscript provides comprehensive insights into key applications of AI techniques and computer vision technology, using different classification models and algorithms developed on the basis (for example) of neural network classifiers, such as convolutional and artificial neural network, on harvest and postharvest assessment of date fruit. Thus, the importance of date fruit dataset used in the AI domain was discussed with link to all accessible datasets in the field of date palm. In addition, available and relevant AI tools and approaches applied to the postharvest of date fruit from maturity distinguishing until storage through other key handling operations, sorting, grading, etc., were detailed in the five sections of this paper. In terms of conclusion, this review showed that these smart technologies can play a substantial role and offer new opportunities to improve the whole value chain of date palm sector. • Harvesting and post-harvest handling impact quality and storability of dates. • These operations are tedious and subjective leading to quantitative/qualitative losses. • Artificial intelligence (AI) techniques are emergent technologies that can reduce these losses. • AI techniques are more accurate, fast, non-destructive and objective. [ABSTRACT FROM AUTHOR]

• A flat plate heat pipe with dimensions of 500 × 200 × 3 mm3 and containing 19 independent microchannels inside is developed. • CFD modeling based on the VOF model and improved Lee model was performed to reproduce the heat transfer behaviors. • The ANN model agrees well with the simulated data with MSE and R of 1.30 × 10-5 and 0.9990, respectively. • The model constructed can provide an effective tool for the adaptation and optimization of heat pipe in 5G base station. To address the significantly increased heat dissipation requirements of 5G base stations, a flat plate heat pipe (FPHP) of 500 × 200 × 3 mm3 comprising of 19 independent channels was developed. To solve the problem of simulation distortion due to the high superheat of the initial boiling of the working fluid in the ultra-long channel of 490 × 5 × 1.5 mm3, this paper adopts the Lee model considering the superheat, and simulates based on the Volume of fluid method, and the simulation results are compared with the experiments, and the average relative error is less than 5 %. The FPHP was explored at 100–500 W heat power and filling ratio of 30 %-70 %, depending on the application scenarios of the base station. By partitioning the 19 channels and calculating the thermal resistance of the FPHP based, an Artificial neural network (ANN) model with 10 hidden layer nodes is established based on 675 data sets. The results showed that the mean square error and correlation coefficient were 1.30 × 10-5 and 0.9990. The ANN prediction results are validated, the mean relative error is 5.4 %, demonstrating that the ANN model can be an effective tool to optimize the FPHP for the telecommunication equipment. [ABSTRACT FROM AUTHOR]

• Developing optimized neural network-based models for trapdoor stability prediction. • New FELA solutions for 3D rectangular trapdoors in anisotropic clays. • Developing hybrid machine learning models based on ANNs with various optimization algorithms. • Examining several performance metrics, regression plots, and rank analysis. The problem of trapdoor stability is a crucial problem in geotechnical engineering. This study is the first to introduce novel neural network-based metaheuristic models for the stability prediction of 3D rectangular trapdoors in anisotropic and nonhomogeneous clays. However, no researcher has considered such trapdoor problems in the past. In this study, the dataset is obtained by using finite element limit analysis (FELA). The proposed hybrid machine learning models based on artificial neural networks (ANNs) and various types of optimization algorithms (OAs), namely, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competition algorithm (ICA), and shuffled complex evolution (SCE), are also proposed in this paper by undergoing rigorous optimization to ensure accuracy and efficiency in capturing the intricate dynamics of the stability investigations from the models. The performance of the proposed ANN-based models is assessed using several performance metrics, regression plots, and rank analysis. The proposed ANN-SCE model outperforms the other proposed models in predicting trapdoor stability, where the ANN-SCE model achieved the highest rank, with a score of 58, followed by the ANN-ALO (47), ANN-ICA (33), and ANN-ACO (22) models. The proposed neural network-based metaheuristic models deliver precise and effective forecasts of trapdoor stability to make informed decisions concerning road design and mitigation tactics, ultimately improving the robustness of infrastructure facing geotechnical challenges. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hardware architecture for a Feed-Forward Neural Network (FFNN) with the objective of minimizing the number of execution clock cycles needed for the network's computation. The proposed architecture depends mainly on using two physical layers that are multiplexed and reused during the computation of the FFNN to achieve an efficient parallel design. Two physical layers are designed to handle the computation of different sizes of Neural Networks (NN). The proposed FFNN architecture hardware resources are independent of the NN's number of layers, instead, they depend only on the number of neurons in the largest layer. This versatile architecture serves as an accelerator in Deep Neural Network (DNN) computations as it exploits parallelism by making the two physical layers work in parallel through the computations. The proposed implementation was implemented with 18-bit fixed point representation reaching 200 MHz clock speed on a Spartan7 FPGA. Furthermore, the proposed architecture achieves a lower neuron computation factor compared to previous works in the literature. [Display omitted] [ABSTRACT FROM AUTHOR]

Benchmarking coal seam gas (CSG) production from Brownfield to Greenfield is an important step for assessing the development plan for Greenfield. However, the observed Brownfield production data is impacted by many factors besides reservoir properties, e.g., well spacing, well efficiency factor (WEFAC), surface pipeline network. This paper presents a case study in the Surat Basin to discuss how to benchmark CSG production from Brownfield to Greenfield. The selected Brownfield study area includes 143 wells and the Greenfield 450 proposed wells. Various tools for static modelling, decline curve analysis, and dynamic modelling were used in this study. Results show that the peak gas rate decreases quickly and the time to peak gas rate increases quickly when the WEFAC is less than 0.65. The peak gas rate is reached when the cumulative water production is between 39% and 46% of water-in-place for the selected box model. After introducing the wells' online time ratio or WEFAC from the daily production data and the ratio of the observed cumulative water production to the expected cumulative water production, gas production rates can be predicted based on the modified Morse potential energy equation. Artificial neural networks (ANN) can be used to estimate the peak gas rate, the time to peak gas rate and the decline rate based on the reservoir properties; while the ramp-up rate can be calculated from the time to peak gas rate. • 143 wells with seven years of production data were used in this study. • This study integrated static modelling, dynamic modelling and decline curve analysis. • Artificial neural networks can be used to estimate the peak gas rate, the time to peak gas rate and the decline rate based on the reservoir properties. • The well efficiency factor impacts the wells' peak gas rate, the time to peak gas rate, and the decline rate. [ABSTRACT FROM AUTHOR]

Cold-Formed Steel Lipped (CFSL) channels are susceptible to a localized failure mechanism known as web crippling, triggered by concentrated loads or reactions applied to the web of the section. These loads induce buckling and distortion in the web, ultimately leading to the member's collapse. It is a challenging task to accurately determine the web crippling capacity of a CFSL channel due to its complexity and various influencing factors. This paper presents hybrid soft computing techniques for accurately predicting the web crippling capacity of CFSL channels subjected to two flange load cases. The developed soft computing techniques combine Artificial Neural Networks (ANN) with either Genetic Algorithms (GA) or Particle Swarm Optimization (PSO) to improve computational efficiency and accuracy. The finite element models of CFSL channels are developed and validated by experimental results and then employed to generate a database, which is used to train machine learning models, including ANN, GA-ANN, and PSO-ANN. Analysis is undertaken on the reliability of existing design formulas for determining the web crippling capacity of CFSL channels. It is shown that the PSO-ANN model outperforms the other models in terms of prediction accuracy. The existing design codes and formulas are not reliable in estimating the web crippling capacity of CFSL channels. However, the proposed model yields good correlation with finite element analysis results. A user- friendly graphical interface tool is developed for the practical design of cold-formed steel lipped channels. • FE model is developed that simulates the web crippling behavior of CFSL channels. • Hybrid ML models are developed for predicting web crippling capacity of CFSL channels. • Reliability of existing formulas for estimating web crippling capacity is investigated. • A user-friendly graphical interface tool is developed for design purpose. [ABSTRACT FROM AUTHOR]

• Micromechanics-based ANN establishes structure-property linkage under humid ageing. • Framework to generate artificial RVE with random fibre distribution is developed. • Effective approach is developed to process physical descriptors from microstructure images. • ANN established relation between transverse modulus and geometry, property, and environment. • Contribution of microstructure morphology and humid condition on FRP degradation is investigated. This paper proposes an innovative micromechanics-based artificial neural network (ANN) method to efficiently investigate the transverse modulus of unidirectional fibre/epoxy composites under humid conditions. In this research, a novel approach is developed to establish relations between the geometrical, mechanical, and environmental properties of the microstructure and the material's performance under transverse tension. A framework is developed to artificially generate periodic representative volume elements (RVEs) while taking into account the interphase region between fibre and matrix. The RVEs are analysed by the finite element method to obtain the transverse moduli of composites. Two-point correlation functions and principal component analysis techniques are applied to extract and compress statistical information from microstructure images. A database establishment framework is developed to create three batches of data with 1000, 2000, and 3000 sizes. An ANN-based prediction framework is developed by integrating 10-fold cross-validation and Bayesian optimisation to optimise the neural network architecture and establish an efficient structure-property linkage under the influence of humid conditions. The prediction results demonstrate the efficiency of ANN in mapping microstructural data to an effective transverse modulus. A parametric study by ANN reveals the role of microstructure geometrical features and humid environmental parameters on the transverse performance of the composite. [ABSTRACT FROM AUTHOR]

Electricity market liberalization and the absence of cost-efficient energy storage technologies have led to the transformation of state-owned electricity companies into complex electricity market entities, each having a different time horizon. Deregulation has intensified competition, giving rise to increased uncertainty caused by a multitude of interrelated exogenous factors, resulting in unexpected fluctuations in electricity prices. As a consequence, market participants encounter elevated risks and seek effective mitigation strategies. In this paper, the challenges described in the literature are addressed by studying price distribution histograms in the German and Finnish electricity markets. The objective is to identify normal price intervals that can serve as a foundation for an integrated Day-Ahead price forecasting methodology. A novel approach utilizing the Extreme Learning Machine in combination with Bootstrap intervals is proposed and applied to both markets. The findings demonstrate that Bootstrap intervals effectively capture normal prices, whereas extremely high prices typically align with the upper limits of Bootstrap intervals. Conversely, negative prices tend to fall outside the lower boundaries of the intervals. In order to assess the performance of the proposed methodology, a comparative analysis of its forecasting accuracy against the well-established Generalized AutoRegressive Conditional Heteroskedasticity and AutoRegressive Fractionally Integrated Moving Average models is conducted. In addition, both the computational efficiency and forecasting accuracy of the Extreme Learning Machine in comparison to the Artificial Neural Network are assessed. The results reveal the superior efficiency of the Extreme Learning Machine. The developed forecasting model could potentially assist market participants in making well-informed decisions and executing optimal bidding strategies in response to various scenarios before the Day-Ahead market closes. Notably, the proposed methodology transcends the limitations of fixed price thresholds and effectively addresses market nuances, including the occurrence of negative prices, thus offering a more comprehensive approach for electricity price forecasting. • Analysis of German & Finnish of Day-Ahead prices reveals new insights. • Proposed a new method of classifying market prices into unique classes. • ELM & Bootstrap forecast methodology with very good performance. • ELM & Bootstrap methodology outperforms traditional ones in accuracy & efficiency. [ABSTRACT FROM AUTHOR]

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes. • The paper studies behaviour of stainless steel CHS stub columns. • ANN, SVR, GEP and DTR machine learning algorithms are developed and validated. • Machine learning-based design approach for predicting the capacity is proposed. • A comparison with EC3 is presented to assess the performance of the models. [ABSTRACT FROM AUTHOR]