Search

The most widely used equation to calculate water saturation or suitable shaly water saturation in clean or shaly formation, respectively, is the modified Archie formula. The quality of Archie parameters including saturation exponent affects the preciseness of water saturation, and thus estimated oil and gas in place. Therefore, estimating the saturation exponent by the soft computation methods deems to be necessary. In this study, intelligent models such as multilayer perceptron neural network, least squares support vector machine, radial basis function neural network, and adaptive neuro-fuzzy inference system are developed to predict saturation exponent in terms of petrophysical data including porosity, absolute permeability, water saturation, true resistivity, and resistivity index by utilizing a databank for middle east oil and gas reservoirs. The introduced models are optimized using particle swarm optimization, genetic algorithm, and levenberg marquardt techniques. Graphical and statistical methods are used to demonstrate the capability of the constructed models. Based on the statistical indexes obtained for each model, it is found that radial basis function neural network, multilayer perceptron neural network, and least squares support vector machine are the most robust models as they possess the smallest mean squared error, root mean squared error and average absolute relative error as well as highest coefficient of determination. Moreover, the sensitivity analysis indicates that water saturation has the most effect and porosity has the least effect on the saturation exponent. The developed models are simple-to-use and time-consuming tools to predict saturation exponent without needing laboratory methods which are tedious and arduous. [ABSTRACT FROM AUTHOR]

In traditional topology optimization, the computing time required to iteratively update the material distribution within a design domain strongly depends on the complexity or size of the problem, limiting its application in real engineering contexts. This work proposes a multi‐stage machine learning strategy that aims to predict an optimal topology and the related stress fields of interest, either in 2D or 3D, without resorting to any iterative analysis and design process. The overall topology optimization is treated as regression task in a low‐dimensional latent space, that encodes the variability of the target designs. First, a fully‐connected model is employed to surrogate the functional link between the parametric input space characterizing the design problem and the latent space representation of the corresponding optimal topology. The decoder branch of an autoencoder is then exploited to reconstruct the desired optimal topology from its latent representation. The deep learning models are trained on a dataset generated through a standard method of topology optimization implementing the solid isotropic material with penalization, for varying boundary and loading conditions. The underlying hypothesis behind the proposed strategy is that optimal topologies share enough common patterns to be compressed into small latent space representations without significant information loss. Results relevant to a 2D Messerschmitt‐Bölkow‐Blohm beam and a 3D bridge case demonstrate the capabilities of the proposed framework to provide accurate optimal topology predictions in a fraction of a second. [ABSTRACT FROM AUTHOR]

This work aims to analyze the impacts of the magnetic field, activation of energy, thermal radiation, thermophoresis, and Brownian effects on the hybrid nanofluid (HNF) (Ag++silicon oil) flow past a porous spinning disk. The pressure loss due to porosity is constituted by the Darcy-Forchheimer relation. The modified Buongiorno model is considered for simulating the flow field into a mathematical form. The modeled problem is further simplified with the new group of dimensionless variables and further transformed into a first-order system of equations. The reduced system is further analyzed with the Levenberg-Marquardt algorithm using a trained artificial neural network (ANN) with a tolerance, step size of 0.001, and 1,000 epochs. The state variables under the impacts of the pertinent parameters are assessed with graphs and tables. It has been observed that when the magnetic parameter increases, the velocity gradient of mono and hybrid nanofluids (NFs) decreases. As the input of the Darcy-Forchheimer parameter increases, the velocity profiles decrease. The result shows that as the thermophoresis parameter increases, temperature and concentration increase as well. When the activation energy parameter increases, the concentration profile becomes higher. For a deep insight into the analysis of the problem, a statistical approach for data fitting in the form of regression lines and error histograms for NF and HNF is presented. The regression lines show that 100% of the data is used in curve fitting, while the error histograms depict the minimal zero error -7.1e6 for the increasing values of Nt. Furthermore, the mean square error and performance validation for each varying parameter are presented. For validation, the present results are compared with the available literature in the form of a table, where the current results show great agreement with the existing one. [ABSTRACT FROM AUTHOR]

A prediction model for the bearing capacity estimation of strip and ring footing embedded in layered sand is proposed using soft computing approaches, namely, artificial neural network (ANN) and random forest regression (RFR). The required data for the model preparation were generated by performing lower- and upper-bound finite-elements limit analysis by varying the properties of the top and bottom layers. Two types of layered sand conditions are considered in the study: (a) dense on loose sand; (b) loose on dense sand. The investigation for strip footing was carried out by varying the thickness of the top layer, embedment depth of the foundation and friction angles of top and bottom layers. For a ring footing, the internal-to-external diameter ratio forms an additional variable. In total, 1222 and 4204 data sets were generated for strip and ring footings, respectively. The performance measures obtained during the training and testing phase suggest that the RFR model outperforms the ANN. Also, following the literature, an analytical model was developed to predict the bearing capacity of strip footing on layered sand. The ANN and the generated analytical model predictions agreed with the published experimental data in the literature. [ABSTRACT FROM AUTHOR]

The precise estimation of the shear strength of reinforced concrete walls is critical for structural engineers. This projection, nevertheless, is exceedingly complicated because of the varied structural geometries, plethora of load cases, and highly nonlinear relationships between the design requirements and the shear strength. Recent related design code regulations mostly depend on experimental formulations, which have a variety of constraints and establish low prediction accuracy. Hence, different soft computing techniques are used in this study to evaluate the shear capacity of reinforced concrete walls. In particular, developed models for estimating the shear capacity of concrete walls have been investigated, based on experimental test data accessible in the relevant literature. Adaptive neuro-fuzzy inference system, the integrated genetic algorithms, and the integrated particle swarm optimization methods were used to optimize the fuzzy model's membership function range and the results were compared to the outcomes of random forests (RF) model. To determine the accuracy of the models, the results were assessed using several indices. Outliers in the anticipated data were identified and replaced with appropriate values to ensure prediction accuracy. The comparison of the resulting findings with the relevant experimental data demonstrates the potential of hybrid models to determine the shear capacity of reinforced concrete walls reliably and effectively. The findings revealed that the RF model with RMSE = 151.89, MAE = 111.52, and R 2 = 0.9351 has the best prediction accuracy. Integrated GAFIS and PSOFIS performed virtually identically and had fewer errors than ANFIS. The sensitivity analysis shows that the thickness of the wall (b w) and concrete compressive strength (f c) have the most and the least effects on shear strength, respectively. [ABSTRACT FROM AUTHOR]

High levels of node power in Mobile Wireless Sensor Networks (MWSN) can have an effect on the reliability of a number of different aspects of the service. Due to their inherently low energy efficiency, sensor nodes lose power with every bit of data they transmit. In order to accomplish any data transfer, however, the nodes must engage in cooperative communication. To aid optimal routing in mobile wireless sensor networks, researchers have developed a new method inspired by the work of bee colony optimizers. To enhance service in a mobile wireless sensor network, we present a cluster energy hop-based dynamic route selection (CEH-DRS) that takes into account individual production zones. In this case, sensor nodes can collect data and send it out to the network. They also help with the routing of data packets coming from various origin nodes. Finally, this study optimizes the system's route while still meeting the criterion of selecting the shortest way. This technique improves cluster selection by taking into account the state of affairs in the area and other characteristics (such as throughput, delay and packet delivery ratio). It is also discovered that the soft computing approaches accurately detect and select the best path, whereas the conventional methods cannot. The proposed CEH-DRS method improved network performance in a better way to achieve higher throughput than all approaches like optimized route cache protocol-ad hoc on-demand distance vector, fuzzy and bee colony optimization, selectively turning ON/OFF the sensors, hidden Markov model. [ABSTRACT FROM AUTHOR]

Non-linear optical (NLO) parameter of a series of donor–acceptor substituted N-salicylidene-4-benzenesulfonylaniline (SBSA) derivatives with a contrast of first hyperpolarisability has been investigated using density functional-based theory. The derivative of SBSA with both the donor and acceptor substitution (NMe2-SBSA-NO2) exhibits the highest first hyperpolarisability ($\sim 3 \times$ ∼ 3 × 103 a.u in gas phase) value. Frequency and solvent polarity responsive behaviours of first hyperpolarisability are utilised to demonstrate the variation of NLO as switchable electrical parameter. Each of the studied compounds displays very high NLO response at frequency corresponding to the absorption maxima of their electronic state. Likewise, the increase of solvent polarity induces a substantial increase of first hyperpolarisability. The computation of NLO responsive first hyperpolarisability by varying the frequency and solvent dielectric constant within a wide domain is very tedious and time-consuming. To overcome the lacuna, we implemented machine learning tools such as artificial neural networks (ANNs), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS) to predict the computation data of the NMe2-SBSA-NO2 and moderately good results were obtained using ANFIS as soft computing tool. [ABSTRACT FROM AUTHOR]

Forecasting river flow is an important stage in reservoir operation, urban water management, and water resource optimization. The goal of this research is to forecast daily river flows for two intermittent and ephemeral rivers. Based on the antecedent river flow, the forecasting approach used stochastic (AR, ARIMA, and SARIMA) and machine learning (ML) techniques. The ML methods consist of three shallow learning models (NARnet, OS-ELM, and NAR-GMDH) and three deep learning models (LSTM, CNN-GRU, and stacked-LSTM). The precision of all the models in the ephemeral river was higher the intermittent river considering both base flow (R2ave = 0.94 vs. 0.87) and peak flow. The study was also extended to forecasting peak river flow values, demonstrating in the superiority of deep learning (RMSEave = 7.6 m3/s) over the shallow (RMSEave = 8.9 m3/s) and stochastic (RMSEave = 9.1 m3/s). Applied models acted similarly in forecasting peak flow in both rivers due to substantial variations in the floods. Moreover, the results demonstrated that the deep learning group models surpass stochastic and shallow learning group models in terms of five evaluation criteria, including RMSE, MAE, mean bias error, correlation coefficient, and agreement index for both rivers. In general, the results indicate that the CNN-GRU outperforms the other models in terms of river flow forecasting and is suggested as a viable model for learning the complicated behavior of streamflow. [ABSTRACT FROM AUTHOR]

The accurate prediction of a lithium-ion battery's State of Health is of critical importance for efficient battery health management. Existing data-driven estimation methodologies grapple with issues such as high model complexity and a dearth of guidance from prior knowledge, which impose constraints on their efficacy. This work introduces a novel cross-modal distillation network for battery State of Health estimation, structured around a TransformerEncoder as the teacher network and a Convolutional Neural Network as the student network. Initially, the teacher model is pre-trained offline using State of Health degradation data to learn the degradation patterns. The directly measurable feature data (such as voltage, temperature, and current) is subsequently fed into the student network for online training and computation of a hard loss. the student network's output is then directed into the pre-trained the teacher network to compute a soft loss, thereby offering prior knowledge of degradation laws and steering the optimization process of the student network. Rigorous experiments are conducted utilizing various datasets, with the outcomes validating the superior estimation accuracy and degradation rule adherence of the model. Notably, among five different models, this model demonstrates the best performance on almost all datasets, achieving an RMSE of 0.0097 and an MAE of 0.0065 on Cell1 of the Oxford dataset. Moreover, the model also demonstrates robust performance across different usage scenarios, inclusive of multi-battery estimation. Furthermore, this paper also introduces a fine tuning method for State of Health predictions only using the first half of the data. Comparative analysis with other models underscores the competitiveness of the proposed model, showcasing its potential for broader application. [ABSTRACT FROM AUTHOR]

Conventional concrete causes significant environmental problems, including resource depletion, high CO2 emissions, and high energy consumption. Steel slag aggregate (SSA), a by-product of the steelmaking industry, offers a sustainable alternative due to its environmental benefits and improved mechanical properties. This study examined the predictive power of four modeling techniques—Gene Expression Programming (GEP), an Artificial Neural Network (ANN), Random Forest Regression (RFR), and Gradient Boosting (GB)—to predict the compressive strength (CS) of SSA concrete. Using 367 datasets from the literature, six input variables (cement, water, granulated furnace slag, superplasticizer, coarse aggregate, fine aggregate, and age) were utilized to predict compressive strength. The models' performance was evaluated using statistical measures such as the mean absolute error (MAE), root mean squared error (RMSE), mean values, and coefficient of determination (R2). Results indicated that the GB model consistently outperformed RFR, GEP, and the ANN, achieving the highest R2 values of 0.99 and 0.96 for the training and testing dataset, respectively, followed by RFR with R2 values of 0.97 (training) and 0.93 (testing), GEP with R2 values of 0.85 (training) and 0.87 (testing), and ANN with R2 values of 0.61 (training) and 0.82 (testing). Additionally, the GB model had the lowest MAE values of 0.79 MPa (training) and 2.61 MPa (testing) and RMSE values of 1.90 MPa (training) and 3.95 MPa (testing). This research aims to advance predictive modeling in sustainable construction through analysis and well-defined conclusions. [ABSTRACT FROM AUTHOR]

Project management requires the chaotic but important task of estimating software development effort. Several soft computing approaches have been proposed to increase estimation accuracy, and optimization techniques are utilized to concentrate on key aspects. However, a majority of works use data processing that has been found to be unreliable, time-consuming and typically leads to greater error rates. Therefore, the research proposes an efficient software development effort prediction model employing unique deep learning technology to alleviate the existing limitations. Data collection, pre-processing, feature selection and software development effort prediction are only a few of the varied phases of the proposed objective. After data collection, the data are pre-processed, including data cleaning, normalization, missing values and imputation. The expanded archer fish optimization method (Ext_AFO) is used to choose the best features from the pre-processed data. The Multilayer Perceptron Assisted Honey Bidirectional Gated Recurrent Feed Forward Network (Multi-HBiG) is built into this research work to provide an intelligent prediction model for software development effort estimation. The model parameters are adjusted using the Adaptive Honey Badger Optimisation Algorithm (A-Hba) to improve the overall estimation performance. The Albrecht dataset, China dataset, Desharnais dataset, Kemerer dataset, Maxwell dataset, Kitchenham dataset and Cocomos81 dataset are the datasets used in this study. The proposed approach surpassed other models when compared against Mean Relative Error (MRE), Mean Magnitude of Relative Error (MMRE), Mean Balanced Relative Error (MBRE) and Mean Inverted Balanced Relative Error (MIBRE) in the results section. The proposed model was assessed in this study using the MAE in each dataset, and it achieved 0.0753 for the China dataset, 0.0763 for the Cocomos81 dataset, 0.0737 for the Desharnais dataset, 0.0754 for the Kemerer dataset, 0.0759 for the Kitchenham dataset, 0.0734 for the Maxwell dataset and 0.0737 for the Albrecht dataset, respectively. [ABSTRACT FROM AUTHOR]

The business activity of the company expresses the creation of values for customers, owners and society. It implements strategic intentions in ongoing processes and activities. Establishes a link between the sustainability of long-term management and the flexibility of operational management. In Bulgarian literature, it is referred to as „economic activity“, which in turn is also used at the level of the sectoral economy, the national economy and the international economy. In applied research, business activity is usually associated with the turnover of assets and its impact on the efficiency and financial position of the company. In this article is treated as a broad concept integrating the intensity of the use of funds, the newly created value for a given period and the profitability for owners. At the same time, business activity summarizes the factor conditionality of entrepreneurial success from the dynamics of operations, the size of competitive advantages and the degree of capital profitability. The indicators Asset Turnover Ratio, Value Added Level and Return on Equity are used for business activity. The latter in today's uncertain and highly dynamic environment cannot be credibly planned by linear extrapolation of short-term trends into a long-term perspective. They require the development of a useful approach, regardless of non-linearity and uncertainties in economic development. As a solution, this article therefore proposes fuzzy financial indicators – model of scenarios for expected states, metered with fuzzy sets and soft computing. Implement tools from the field of artificial intelligence in strategic business management and analysis. In doing so, plausible alternatives for future estimates of the Asset Turnover Ratio, Value Added Level and Return on Equity are reproduced. Controlled approximation is introduced as an alternative about dealing with uncertainty, rather than classically ignoring it or reducing it to a simple prognosis. Based on the presented opportunity, a fuzzy model is proposed for planning the Temp of sustainable growth of the company's revenues is offered. It is essentially an intelligent platform that belongs to the group of Takagi-Sugeno fuzzy models and offers powerful computing potential with the ability to establish an accurate value result. Among its leading advantages can be mentioned speed, high flexibility and efficiency. It is also applicable for planning the business activity of companies from a large number of industries. [ABSTRACT FROM AUTHOR]

Plant diseases are one of the factors that lead to yield and economic losses, which have a direct effect on national and international food production systems. One of the most essential ways to avoid agricultural product loss or reduction in amount is to diagnose plant diseases promptly and accurately. Hence, the diagnosis process for plants is crucial and should be conducted accurately. Moreover, this study focuses on this process by constructing an Artificiality Diagnostics Framework (ADF) to serve the study's objectives which entailed conducting diagnosis for plants in a professional and precise manner over uncertain environments. Thus, neutrosophic theory is considered the principal ingredient in our ADF. Due to the ability of neutrosophic to divide images into Truth (T), Falsity(F), and Indeterminacy (I). Also, deep learning (DL) is considered another principal ingredient in treating vast samples of datasets. Our comparative analysis of the leaves of potatoes is conducted whether leveraging neutrosophic and without utilizing Neutrosophic. ResNet50, ResNet152, and Mobile Net are the principal ingredients for the training dataset. The findings of implementing these networks indicated that ResNet50 achieved the highest accuracy of 0.915 in the T domain, ResNet152 achieved the highest accuracy of 0.905 in the True(T) domain, and Mobile Net achieved the highest accuracy of 0.915 in Truth(T) domain. Accuracy of 0.863 in Indeterminate(I). [ABSTRACT FROM AUTHOR]

The Internet of Things, or IoT, is a rapidly expanding technology, and one of its most important application areas is sustainable transportation. Choosing the right IoT service provider is a complex process that is considered a multi-criteria decision issue. The multi-criteria decision-making (MCDM) methodology deals with massive criteria. This study proposed an MCDM methodology for selecting the best IoT service provider under different sustainable criteria. The Multi-Objective Optimization Ratio Analysis (MOORA) method is an MCDM methodology used to rank the alternatives. The MOORA method is integrated with a type 2 neutrosophic set to deal with uncertain information. The proposed MCDM methodology applied to six criteria and 13 alternatives. The results show that alternative 3 is the best and alternative 5 is the worst. The sensitivity analysis was conducted to show the stability of the rank. The sensitivity analysis was performed under seven cases; the results show that the rank of alternatives was stable under different cases. The comparative study was conducted to show the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

The novelty of this work is that it is the first to introduce complex-valued suprametric spaces and apply it to Fractal Generation and mixed Volterra–Fredholm Integral Equations. In the realm of fuzzy logic, complex-valued suprametric spaces provide a robust framework for quantifying the similarity between fuzzy sets; for instance, utilizing a complex-valued suprametric approach, we compared the similarity between fuzzy sets represented by complex-valued feature vectors, yielding quantitative measures of their relationships. Thereafter, we establish related fixed point results and their applications in algorithmic and numerical contexts. The study then delves into the generation of fractals, exemplified by the Barnsley Fern fractal, utilizing sequences of affine transformations within complex-valued suprametric spaces. Moreover, this article presents two algorithms for soft computing and fractal generation. The first algorithm uses complex-valued suprametric similarity for fuzzy clustering, iteratively assigning fuzzy sets to clusters based on similarity and updating cluster centers until convergence. The distinctive pattern of the Barnsley Fern fractal is produced by the second algorithm's repetitive affine transformations, which are chosen at random. These techniques demonstrate how well complex numbers cluster and how simple procedures can create complicated fractals. Moving beyond fractal generation, the paper addresses the solution of mixed Volterra–Fredholm integral equations in the complex plane using our results, demonstrating numerical illustrations of complex-valued integral equations. [ABSTRACT FROM AUTHOR]

This study aims to achieve the densest possible state of soil for constructing dams and roads. This requires assessing the compaction characteristics, Optimum Moisture Content (OMC), and Maximum Dry Density (MDD) to determine the soil's suitability for earthworks. However, this process is resource-intensive and time-consuming. To streamline the assessment, the study incorporates six parameters: gravel (G), sand (S), fine (F) contents, plastic limit (PL), liquid limit (LL), and plasticity index (PI). Four different models are used to predict compaction characteristics: artificial neural network (ANN), nonlinear regression (NLR), linear regression (LR), and multilinear regression (MLR). The study utilized a substantial dataset of 2162 entries, considering various soil gradation and plasticity properties as input variables. To evaluate the models' effectiveness, several statistical measures, including coefficient of determination (R2), scatter index (SI), root mean squared error (RMSE), mean absolute error (MAE), a20-index, and Objective (OBJ) value, were employed. The ANN model outperformed other models in predicting OMC, with RMSE, MAE, OBJ, SI, a20-index, and R2 values of 3.51, 2.31, 4.26, 0.202, 0.7, and 0.92%, respectively. However, for predicting MDD, the ANN model had the highest R2 value (R2 = 0.87), but the minimum RMSE (1.01), MAE (0.8), a20-index (0.998), and OBJ (1.07) were obtained from the MLR and LR models. Furthermore, sensitivity analyses revealed that the plastic limit significantly influences the OMC, while the gravel content plays a dominant role in predicting MDD. [ABSTRACT FROM AUTHOR]

A groundwater reservoir is either a solitary aquifer or a network of interconnected aquifers. A particular aquifer's groundwater purity evaluation could be time-consuming and costly. This study quantified the properties of Na%, SO42−, Cl, Na+, Mg2+, Ca2+, HCO3−, K+, and pH to predict the water quality parameter known as total hardness (as CaCO3). Groundwater quality data for the Shiraz Plain from 2002 to 2018 was utilized to accomplish this objective. The paper contrasts a hybrid methodology that combines Teaching Learning-Based Optimization (TLBO), Multiverse Optimizer (MVO), the Cuckoo Optimization Algorithm (COA), and the Evaporation Rate-based Water Cycle Algorithm (ER-WCA) with Artificial Neural Networks (ANN) this was done to design an optimal network for groundwater quality with conventional ANN. In comparison to all other TLBO-ANN, MVO-ANN, and COA-ANN developed models, the ER-WCA-ANN technique (with a population size of 500 and eight neurons in each hidden layer) provided the most accurate prediction for the TH with R2 values of 0.9983 and 0.98261, and RMSE values of 0.03698 and 0.00611, respectively, in the training and testing datasets. A comparison of the findings for the forecasting of groundwater quality showed that the ER-WCA-ANN hybrid model might increase prediction accuracy. These findings may have significant implications for future groundwater quality assessments. [ABSTRACT FROM AUTHOR]

To avoid or mitigate the unwanted water and gas content, inflow control devices (ICDs) are designed and installed in the well to disturb the water and gas breakthrough which are trying to overtake the oil inflow, water and gas coning and sand production. Smart wells with permanent downhole valves such as ICDs are used to balance production and injection in wells. A paramount issue regarding using downhole control devices is determining the required cross-sectional area of them for control of the imposed pressure drop across the device to stabilize the fluid flow. Current methods for calculating the opening size of the ICDs are mainly based on sensitivity analysis of the ICD flow area or optimization algorithms coupled with simulation models. Although these approaches are quite effective in oil field cases, they tend to be time-consuming and require demanding system models. This paper presents a fast analytical method to determine the ICD flow area validated by a genetic algorithm (GA). Analytically, a closed-form expression is introduced by manipulating Darcy's law applicable to multi-layer injection wells with different layer properties to balance the injection profile in the reservoir pay zone, based on equalizing injected front velocity in layers with different permeability. Considering various scenarios of analytical technique, GA optimization, and sensitivity analysis scenarios for ICD cross-sectional area determination, results for oil recovery, water production, water breakthrough time, and net present value (NPV) are discussed and compared. NPV values obtained by both analytical and GA approaches are virtually identical and greater than those of other scenarios. Compared to the base field case, the analytical method improved the oil recovery by almost 1%, reduced water production by almost 91%, and synchronized the water breakthrough time of high- and low-permeability layers (from a ratio of 1.76–1.06). The proposed analytical solution proved to be capable of providing desirable results with only one reservoir simulation run in contrast to GA and sensitivity analysis scenarios which require iterative simulation runs. The proposed analytical solution outperformed the GA as it is less computationally demanding in addition to its success in case of lowering water production for the field data. The findings of this study can help for a better understanding of the situation where water injection into the oil reservoir is problematic as the layers present different permeabilities which can induce problems such as early water breakthrough from the more permeable layer and hinder the success of the water injection process. Using ICDs and a faster and more accurate approach to calculate its cross-sectional area such as the analytical method that was used in this study can greatly increase the success rate of water injection in case of oil recovery and lower the amount of the produced water. [ABSTRACT FROM AUTHOR]

The accurate prediction of scour depth in culverts is crucial to ensure public safety and due to the uncertainties in classical empirical/deterministic equations, it remains challenging. This study presents new ensemble multi-models to predict scour depth and quantify model bias and uncertainty. In this study, culvert scour was predicted by gene expression programming (GEP) and least square support vector machine (LSSVM), firstly based on dimensionless parameters. In the multi-model ensemble strategy, four ensemble approaches, namely SM, WM, LSSVM, and BMA were applied. The LSSVM showed overall best skill for the multi-model approach, that reduced the error by 61.3% and increased the coefficient of determination by 52.1%. The results showed the proposed approach can satisfy the requirements of simplicity, applicability, and accuracy at the same time. For application purposes, we provided a simple code to obtain scour depth with great accuracy using LSSVM. BMA and LSSVM were used to quantify the scour culvert uncertainty. The results of the uncertainty analysis showed that the LSSVM predicts scour depth quite well and generates high-quality prediction intervals. The results revealed that a multi-model ensemble strategy improved accuracy, certainty and reliability for culvert scour compared to empirical equations. [ABSTRACT FROM AUTHOR]

Mass transfer-based models are among the extensively applied reference evapotranspiration (ETo) estimation approaches that have been proven to provide reliable estimates under certain climatic and geographical conditions. A major issue with such approaches is the high variations in wind speed time series, which is a governing meteorological parameter in mass transfer-based models. few studies have focused on applying wavelet transform as a robust pre-processing technique for ETo estimation. A major question with coupling wavelet transforms with soft computing methods would be the proper choice of the wavelet function, vanishing moments and the decomposition levels. The present study aims at assessing different wavelet functions coupled with boosted regression tree (BT) models, i.e. wavelet-BT (WBT), in estimating ETo values (through mass transfer parameters) in both coastal and island regions, where it is supposed that wind speed variations are affected by sea–inland interactions and so has the sharp variations (unlike ETo). The obtained results showed that the coupled WBT model could improve the single BT model results to a great extent, and meanwhile, the Daubechies wavelet function with six vanishing moments provided the most accurate results in all the locations. Further, the best simulation outcomes with each wavelet function were obtained when the maximum possible decomposition level was used with each function. [ABSTRACT FROM AUTHOR]

The deformation characteristics of rocks are of vital importance in addressing most geomechanical issues as they are one of the most critical input parameters in rock engineering analyses. For this reason, robust forecasting models are required when analysing the stability of tunnels, slopes, mine galleries, and other underground excavations. In this research, novel predictive models are proposed to estimate the tangential Young modulus (Eti) of weak rocks. To achieve this, an extensive literature review is performed to obtain a comprehensive database including critical physico-mechanical properties of various weak rocks. Thanks to the advantages of soft computing methods such as genetic algorithm (GA), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and multivariate adaptive regression splines (MARS), novel predictive models are established. The effectiveness of the developed predictive models is investigated using various statistical measures and it is concluded that empirical models utilizing ANN and ANFIS methodologies are the most effective tools for estimating the Eti of weak rocks. In addition, a practical design chart is also developed for assessing the Eti of weak rocks. [ABSTRACT FROM AUTHOR]

In the power diode laser beam machining (DLBM) process, the kerf width (KW) and surface roughness (SR) are important factors in evaluating the cutting quality of the machined specimens. Apart from determining the influence of process parameters on these factors, it is also very important to adopt multi-response optimization approaches for them, in order to achieve better processing of specimens, especially for hard-to-cut materials. In this investigation, adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm tuned ANFIS (GA-ANFIS) were used to predict the KW on a titanium alloy workpiece during DLBM. Five machining process factors, namely power diode, standoff distance, feed rate, duty cycle, and frequency, were used for the development of the model due to their correlation with KW. As in some cases, traditional soft computing methods cannot achieve high accuracy; in this investigation, an endeavor was made to introduce the GA-assisted ANFIS technique to predict kerf width while machining grooves in a titanium alloy workpiece using the DLBM process based on experimental results of a total of 50 combinations of the process parameters. It was observed that FIS was tuned well using the ANN in the ANFIS model with an R2 value of 0.99 for the training data but only 0.94 value for the testing dataset. The predicting performance of the GA-ANFIS model was better with less value for error parameters (MSE, RMSE, MAE) and a higher R2 value of 0.98 across different folds. Comparison with other state-of-the-art models further indicated the superiority of the GA-ANFIS predictive model, as its performance was superior in terms of all metrics. Finally, the optimal process parameters for minimum KW and SR, from gray relational–based (GRB) multi-response optimization (MRO) approach, were found as 20 W (level 2) for laser power, 22 mm (level 5) for standoff distance, 300 mm/min (level 5) for feed rate, 85% (level 5) for duty cycle, and 18 kHz (level 3) for frequency. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging. [ABSTRACT FROM AUTHOR]

Classification is a significant step in many applications like image classifications, text recognition, categorization of speech, facial expression classification and so on. And the features fed to the classifier affects the accuracy or performance. The components or patterns of an item in a picture that assist to identify it are termed as features of an item. In computer vision and image processing, the feature carries the information about the content of an image. In classification challenges, the process of extracting features from an object is essential. There are several techniques or features which are used. This paper gives an explanation of different types of feature extraction techniques or methodologies that are used to extract the features out of an image along with classification. Here the facial expressions images are taken. Support vector machine and K-Nearest Neighbor classifier are taken to classify the facial expressions images. The image features used here are local binary pattern (LBP), entropy and histogram of oriented gradients (HOG). The CK+ 48 image dataset is taken here and the MATLAB software is used to obtain the results. The features are extracted and fed to the classifiers. The results show a better accuracy when one feed the three features (LBP, HOG, and entropy) simultaneously. According to the results it is concluded that the accuracy or performance of a classifier depends on the selection of the features of an image or we can say that the classification results are dependent on selected features and classifiers. [ABSTRACT FROM AUTHOR]

A hazard zoning map is the most essential tool during the crisis management cycle's prevention and risk reduction phase. In this study, a geographic information system (GIS) is applied to the crisis management of ports through the preparation of a risk zoning map in Jazan Port, Saudi Arabia, using a novel integrated model of the fuzzy hierarchical analysis process and emotional artificial neural network (FAHP-EANN). The objective is to more accurately identify the highly potential risk zones in the port through hybrid techniques, which mitigates the associated life and financial damages through proper management during a probable hazard. Prior to creating the risk zoning map, every potential port accident is identified, categorized into six criteria, and assigned a weight through the utilization of a machine learning algorithm. The findings indicate that the three most effective criteria for the risks of Jazan Port are land fires, pollution and dangerous substances, and human behavior, respectively. A zoning map of all risks in Jazan Port was generated by using the weights obtained for each of the major accidents. This map may be utilized in the development of crisis prevention measures for the port and in the formation of crisis management units. [ABSTRACT FROM AUTHOR]

This study presents a new intelligent computing method based on the Levenberg–Marquardt back-propagation-based supervised neural network (LMB-SNN) for the flow of Oldroyd 4-constant fluid in the forward roll coating process (OFC-FRCP). The mathematical formulations for OFC-FRCP are converted into ordinary differential equations using lubrication approximation theory (LAT) and suitable dimensionless parameters. The analytic expressions for velocity distribution, pressure and pressure gradients have been obtained using Adomian's Decomposition Method (ADM) for several situations. The fluid's separation point and coating thickness are then calculated using integration. The reference dataset for LMB-SNN has been obtained by ADM for multiple scenarios by adjusting various physical variables. The neural network's training, testing and validation are carried out in parallel to minimize the mean-squared error function (MSEF). The proposed LMB-SNN's effectiveness was confirmed by analyzing the MSEF, error histograms and regression plots. The precision of the method is verified by the close agreement between numerical outputs and dataset values. The intelligent numerical results computed using ADM and LMB-SNN are presented in various graphs and tables. The method's accuracy is demonstrated by the numerical outputs closest to the built and dataset values at levels between 1 0 − 9 and 1 0 − 1 2 . [ABSTRACT FROM AUTHOR]

The present study focused on the machinability of Ti-3Al-2.5 V for wire-electrical discharge machining (WEDM) using "BroncoCut-X wire" (zinc-coated copper wire). The machining characteristics have been evaluated by varying wire-tension (Tw), wire-speed (Sw), flushing-pressure (Pf), discharge current (Id), and spark-on-time (Son). The response characteristics associated with cutting-speed (Cs), kerf-width (KW), and surface roughness (RA) have been collected and analyzed using main-effect plots, scanning electron microscope (SEM), and analysis of variance (ANOVA). The maximum Cs and minimum KW and RA are obtained upto 8.90 mm/min, 3.34 µm and 0.2218 mm, respectively. Additionally, the novelty lies in the smart hybrid prediction tool considering the conflicting nature of responses are converted into single responses using Grey Relation Analysis (GRA) and Fuzzy Interference System (FIS) (Namely: Gray-Fuzzy Reasoning Grade (GFRG)). Furthermore, the optimal performance is calculated using Rao-algorithms (i.e., Rao1, Rao2, and Rao3). The obtained ideal machining condition is 16N wire-tension, 3 m/min wire-speed, 8 kg/mm2 flushing-pressure, 21A discharge current, and 14 µs spark-on-time. The result has also been compared with the JAYA-algorithm and improved-grey wolf optimizer (I-GWO) to demonstrate the efficacy of the intended approach. The confirmation test has been conducted and obtained that the GFRG-based results are further improved by using a hybrid GFRG-based Rao-algorithm of 9.55%, 2.36%, and 7.99% as Cs, KW and RA, respectively. Furthermore, this study shows that the proposed multi-objective optimization method not only leads to more stable solutions but also to shorter run times and enhanced quality to support engineers in reducing the cost of item failures. [ABSTRACT FROM AUTHOR]

Reservoir operations involve determining the quantity of water to be released or stored from the reservoir at any given time, based on the reservoir's current condition. This research presents optimizing techniques for reservoir operating policies on Ravishankar Sagar reservoir during monsoon season. Metaheuristics algorithms – GA, NSGA II, NSGA III, and Eps MOEA were used to optimize the objective function for maximization of storage along with a different set of constraints in Ravishankarsagar reservoir. Sensitivity analysis on all algorithms was performed to calibrate different evaluation parameters. The results were analyzed to find the effectiveness of soft computing algorithms in real-world problems. The standard deviation of 359.52, 351.93,349.68 and 361.09 and Median of 512.5, 525.36,568.96 and 433.79 was observed in GA, NSGA II, Eps MOEA, and NSGA IIII so all the algorithms performed well and were in close approximation, with the least standard deviation and the most optimum storage in Eps MOEA. [ABSTRACT FROM AUTHOR]

Decision-making in Construction Project Management (CPM) involves numerous ambiguous information and uncertainties due to the nature of construction project. The Soft Computing (SC) approach, which offers several data processing strategies under uncertainty, has been extensively researched in CPM studies for decision problem solving. Decisions that cannot be adequately handled by conventional computer systems are facilitated by the SC approach. The SC approach encompasses a variety of SC techniques that are constantly developing and becoming more widely used to address real construction challenges. This study aims to conduct Systematic Literature Reviews (SLR) on the development of mainstream SC techniques and their current application in construction projects. Using an inventive SLR technique, 83 CPM papers covering the years 2018 to 2023 were selected for this study and then classified into four primary application themes of SC in CPM. The research trend was then described using bibliometric analysis. Afterwards, a topic-based qualitative analysis was conducted to investigate the application of SC approaches in the construction field. Several potential challenges to current research were then elaborated. It also contributed to suggesting future directions for the advancement of SC techniques that would be advantageous for construction research and practice. [ABSTRACT FROM AUTHOR]

Supply chain sustainability is now an imperative, particularly considering the circular supply chain (CSC) paradigm, which offers opportunities for performance improvement. Thus, it is possible to infer that there is a causal link between circularity and supply chain performance. However, the analysis of this relationship occurs under subjective perspectives. To computationally process human language can overcome this issue. Hence, this paper aims to propose an approach for assessing the influence of circular economy (CE) implementation principles on supply chain performance using the fuzzy cognitive map (FCM), a well‐established soft computing technique. The study develops a computational model and brings an illustrative application. Methodologically, is model‐based, quantitative, axiomatic and descriptive. It contributes by providing means to support the formulation of strategies for aligning CE implementation efforts with supply chain performance improvement efforts. The FCM visually represents how supply chain performance attributes are influenced by CE principles, supporting informed decision‐making towards circularity. [ABSTRACT FROM AUTHOR]

The multiple motor drives are complex and coupled MIMO nonlinear systems. Due to the complexity of their mathematical models the development of effective control systems is quite complicated task. This paper presents the design of optimal control based on the quadratic optimality criterion for the central section of the continuous production line using soft computer methods when fuzzy model of the controlled system has been used. In this case, the proposed method demonstrates non-analytical design of the optimal parameters of PI controller with emphasis on minimal knowledge on the controlled system. The realized experimental measurements on a continuous line laboratory model confirmed the effectiveness and the good dynamic properties of the optimal continuous line controller and also its applicability to others MIMO nonlinear dynamic systems. [ABSTRACT FROM AUTHOR]

A Mixture of factor analyzer (MFA) model is a powerful tool to reduce the number of free parameters in high-dimensional data through the factor-analyzer technique based on the covariance matrices. This model also prepares an efficient methodology to determine latent groups in data. In this paper, we use an MFA model with a rich and flexible class of distributions called hidden truncation hyperbolic (HTH) distribution and a Bayesian structure with several computational benefits. The MFA based on the HTH family allows the factor scores and the error component can be skewed and heavytailed. Therefore, using the HTH family leads to the robustness of the MFA in modeling asymmetrical datasets with/without outliers. Furthermore, the HTH family, because of several desired properties, including analytical flexibility, provides steps in the estimation of parameters that are computationally tractable. In the present study, the advantages of MFA based on the HTH family have been discussed and the suitable efficiency of the introduced MFA model has been demonstrated by using real data examples and simulation. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Parkinson's disease (PD) is a chronic neurological condition that progresses over time. People start to have trouble speaking, writing, walking, or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die. The patient's symptoms become more severe due to the worsening of their signs over time. In this study, we applied state-of-the-art machine learning algorithms to diagnose Parkinson's disease and identify related risk factors. The research worked on the publicly available dataset on PD, and the dataset consists of a set of significant characteristics of PD. We aim to apply soft computing techniques and provide an effective solution for medical professionals to diagnose PD accurately. This research methodology involves developing a model using a machine learning algorithm. In the model selection, eight different machine learning techniques were adopted: Namely, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Naïve Bayes (NB), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbours (KNN), Extreme Gradient Boosting (XGBoost), and Logistic Regression (LR). Subsequently, the concentrated models were validated through 10- fold Cross-Validation and Receiver Operating Characteristic (ROC)--Area Under the Curve (AUC). In addition, GridSearchCV was utilised to measure each algorithm's best parameter; eventually, the models were trained through the hyperparameter tuning approach. With 98% accuracy, LightGBM had the highest accuracy in this study. RF, KNN, and SVM came in second with 96% accuracy. Furthermore, the performance scores of NB and LR were recorded to be 76% and 83%, respectively. It is to be mentioned that after applying 10-fold cross-validation, the average performance score of LightGBM accounted for 93%. At the same time, the percentage of ROC-AUC appeared at 0.92, which indicates that this LightGBM model reached a satisfactory level. Finally, we extracted meaningful insights and figured out potential gaps on top of PD. By extracting meaningful insights and identifying potential gaps, our study contributes to the significance and impact of PD research. The application of advanced machine learning algorithms holds promise in accurately diagnosing PD and shedding light on crucial aspects of the disease. This research has the potential to enhance the understanding and management of PD, ultimately improving the lives of individuals affected by this condition. [ABSTRACT FROM AUTHOR]

MANETs are a type of wireless ad hoc network characterized by a wireless medium. The MANET's inherent characteristics, like dynamic change in topology and lack of central administration, reduce the possibility of provisioning security. Most of the studies have concentrated more on providing security than the computational cost of energy utilization. Neighbor security is proposed in this paper in addition to the existing de facto ANODR protocol for provisioning security in ANODR. The design and implementation of FB-NTT-S-ANODR to minimize energy consumption, thereby enhancing the network lifetime, are suggested. A soft computing technique for optimizing the performance of FB-NTT-S-ANODR is attempted. From experimental results, the proposed FB-NTT-S-ANODR minimizes the total energy consumption by 93.07%. Hence, the proposed method is more suitable for provisioning security and enhancing the network lifetime. [ABSTRACT FROM AUTHOR]

The Brazilian indirect tensile strength (BIT) of intact limestone rocks is a critical factor in the design of structures on or within these rocks. Nonetheless, acquiring and analyzing core samples in a laboratory environment to determine BIT can be a time-consuming and costly task, bearing in mind that it is not always possible to obtain intact rock samples. Thus, there is a need to develop models that predict BIT based on simple physical properties and non-destructive tests. In this study, an extensive program was developed to collect samples of limestone rocks from different locations in the northern part of Iraq. These samples were then subjected to destructive and nondestructive tests in dry and saturated conditions. The results were analyzed using simple regression and genetic algorithm-based regression analyses to develop new prediction models. Overall, it has been found that saturation of the pores of the rock reduces the BIT with a percentage reduction between 15 and 47%. Importantly, it has been noticed that the prediction models developed using genetic algorithm-based regression analysis provided slightly better accuracy compared to simple regression analysis, with an R2 of 0.80 for the dry condition and between 0.85 and 0.87 for the saturated condition, and a MAE range of between 3.11 and 3.15 for the dry condition and 2.04 and 2.20 for the saturated condition. Notably, the developed models could help professionals who do not have access to BIT equipment or face difficulty acquiring intact samples with the required length-to-diameter ratio. [ABSTRACT FROM AUTHOR]

Soft computing technology has attracted extensive attention in computer engineering and automatic control domains because it can deal with uncertainties, fuzziness, and complex practical problems. This study adopts a Genetic Algorithm (GA) in soft computing technology to realize the cooperative optimization of electric vehicle's dynamic and economic performance. The advantage of soft computing technology lies in its adaptability to uncertainty, fuzziness, and complex practical problems, making GA an effective tool for solving complex optimization problems. Firstly, the electric vehicles' power system structure and energy management strategy are investigated and analyzed. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is selected to optimize the parameters of electric vehicles because of its simple operation and high optimization accuracy. Thirdly, NSGA-II is used to construct the electric vehicles' power and energy configuration, with power and economic performance as the main optimization objectives. Meanwhile, a fuzzy logic controller is designed to adjust the parameters of GA online, so that the optimization process is closer to the actual operating conditions. Finally, the relevant variables are selected to achieve the optimization goal, the optimization objective function and constraint conditions are established, and the model is simulated and evaluated. The results show that the optimized electric vehicle's acceleration time is remarkably reduced, the dynamic performance is improved by more than 7 %, and the power loss is reduced by 5 %. In addition, compared with the current multi-objective optimization model, this model enables electric vehicles to travel longer distances under the same power. This study provides a new idea and method for the performance optimization of electric vehicles. Moreover, it offers a valuable reference for the innovation and development of electric vehicle technology in the intelligent manufacturing field. This study indicates that electric vehicles could be more efficient, energy-saving, and environmentally friendly to serve people's travel needs in the future.