Soft Computing Methods Research Articles

Biometric Face Identification: Utilizing Soft Computing Methods for Feature-Based Recognition

Biometric Face Identification: Utilizing Soft Computing Methods for Feature-Based Recognition

Application‐Based Review of Soft Computational Methods to Enhance Industrial Practices Abetted by the Patent Landscape Analysis

ABSTRACTSoft computing is a collective methodology that touches all engineering and technology fields owing to its easiness in solving various problems while comparing the conventional methods. Many analytical methods are taken over by this soft computing technique and resolve it accurately and the soft computing has given a paradigm shift. The flexibility in soft computing results in swift knowledge acquisition processing and the information supply renders versatile and affordable technological system. Besides, the accuracy with which the soft computing technique predicts the parameters has transformed the industrial productivity to a whole new level. The interest of this article focuses on versatile applications of SC methods to forecast the technological changes which intend to reorient the progress of various industries, and this is ascertained by a patent landscape analysis. The patent landscape revealed the players who are in the segment consistently and this also provides how this field moves on in the future and who could be a dominant country for a specific technology. Alongside, the accuracy of the soft computing method for a particular practice has also been mentioned indicating the feasibility of the technique. The novel part of this article lies in patent landscape analysis compared with the other data while the other part is the discussion of application of computational techniques to various industrial practices. The progress of various engineering applications integrating them with the patent landscape analysis must be envisaged for a better understanding of the future of all these applications resulting in an improved productivity.

Optimal Design of Smart Antenna Arrays for Beamforming, Direction Finding, and Null Placement Using the Soft Computing Method

ABSTRACTThe urge of modern communication system is to design and development of the smart antennas with adaptive radiation characteristics. The multifold capabilities of fourth‐dimensional antenna arrays can cater that much needed adaptiveness if properly designed. Compared to the conventional arrays, the fourth‐dimensional arrays have one added advantage as the ‘Time’ of all the switched‐on antenna elements can be managed to generate the required amplitude and phase tapering without even using attenuators and phase shifters. However, one inherent limitation of fourth‐dimensional control parameter is the generation of harmonics or sidebands. This article proposes various means of radiation pattern synthesis in fourth‐dimensional linear antenna arrays with pulse shifting, pulse splitting, and a combination of both. First of all, the pulse splitting and shifting techniques are combinedly proposed by reducing the sidelobe levels and sideband levels of the beamforming antenna arrays to enhance directivities and efficiencies. Then, this mathematical proposition of the direction finding fourth‐dimensional arrays is developed. Finally, broad nulls over a specific angle of arrival region are created for jamming and interference mitigation. For all these cases, the sidelobe level and the unwanted higher‐order sideband levels are suppressed to reduce the unwanted interferences and power losses. The optimal time schemes for all the synthesized patterns are generated by proposing a chaos‐based soft computing algorithm. The radiofrequency signals at each radiating array element are processed by the optimal time schemes proposed for specific applications. The outcomes are validated and compared with other state‐of‐the‐art works of this domain to prove the competency of the proposed work. The qualitative and quantitative comparisons presented for beamforming array is aimed for a good improvement over other reported works by targeting ultralow (less than −40 dB) sidelobe and sideband levels. For direction‐finding array, the proposed idea has also targeted ultralow sidelobes for the main as well as steered beam patterns. Furthermore, the null placement over a region has been aimed to cover more area for jamming and sidelobe reduction for interference mitigation. Overall, the optimal designs proposed for these advanced applications are beneficial for cutting‐edge communication systems.

Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method

This research explores multi-objective optimization in injection molding with a focus on identifying the optimal configuration for the moldability index in aviation propeller manufacturing. The study employs the Taguchi method and fuzzy analytic hierarchy process (FAHP) combined with the Technique for the Order Performance by Similarity to the Ideal Solution (TOPSIS) to systematically evaluate diverse objectives. The investigation specifically addresses two prevalent defects—shrinkage rate and sink mark—that impact the final quality of injection-molded components. Polypropylene is chosen as the injection material, and critical process parameters encompass melt temperature, mold temperature, filling time, cooling time, and pressure holding time. The Taguchi L25 orthogonal array is selected, considering the number of levels and parameters, and Finite Element Analysis (FEA) is applied to enhance precision in results. To validate both simulation outcomes and the proposed optimization methodology, Artificial Neural Network (ANN) analysis is conducted for the chosen component. The Fuzzy-TOPSIS method, in conjunction with ANN, is employed to ascertain the optimal levels of the selected parameters. The margin of error between the chosen optimization methods is found to be less than one percent, underscoring their suitability for injection molding optimization. The efficacy of the selected optimization method has been corroborated in prior research. Ultimately, employing the fuzzy-TOPSIS optimization method yields a minimum shrinkage value of 16.34% and a sink mark value of 0.0516 mm. Similarly, utilizing the ANN optimization method results in minimum values of 16.42% for shrinkage and 0.0519 mm for the sink mark. Doi: 10.28991/ESJ-2024-08-05-025 Full Text: PDF

Prediction of saturation exponent for subsurface oil and gas reservoirs using soft computing methods

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.

Estimation of foam (surfactant) consumption in earth pressure balance tunnel boring machine using statistical and soft-computing methods

The use of foam, as the most economical soil conditioning technique, in earth pressure balance tunnel boring machine (EPB-TBM) tunneling projects has significant effects on operation efficiency, excavation cost, and operation time. This study mainly focuses on developing models to predict the foam (surfactant) consumption. For this purpose, five empirical models are developed based on a database containing 11048 datasets of real-time foam consumption from three EPB-TBM tunneling projects in Iran. This database includes the most effective machine operational parameters and soil geomechanical properties on the foam consumption. Multiple linear regression analysis, multiple non-linear regression analysis, M5Prime decision tree, artificial neural network, and least squares support vector machine techniques are used to construct the models. To evaluate the performance of developed models, three performance evaluation criteria (including normalized root mean square error, variance account for, and coefficient of determination) are used based on the training and testing datasets. The results show that the developed models have high performance and their validity is guaranteed according to the testing dataset. Furthermore, the M5Prime model, which demonstrates the best performance compared to other models, is applied to predict the foam consumption in 19 excavation rings of Kohandezh station in Isfahan metro, Iran. After conducting an excavation operation in this station and comparing the results, it was found that the M5Prime model accurately predicts foam consumption with an average error of less than 13%. Therefore, the developed models, particularly M5Prime model, can be confidently applied in EPB-TBM tunneling projects for predicting foam consumption with a low error rate.

Towards sustainable industrial development: modelling the quality, scaling potential and corrosivity of groundwater using GIS, spatial statistics, soft computing and index-based methods

Towards sustainable industrial development: modelling the quality, scaling potential and corrosivity of groundwater using GIS, spatial statistics, soft computing and index-based methods

A comparative study on ensemble soft-computing methods for geothermal power production potential forecasting

Many developed countries are increasingly interested in renewable energy sources (RESs) as a result of environmental changes and the depletion of fossil fuels in recent years. Since geothermal energy can be used as both a source of electricity and heat, it occupies an important spot among renewable energy sources. In this study, soft-computing ensemble models (SCEMs) based on supervised deep neural network (SDNN) models supported by the forward stepwise regression (FSR) method are used in estimating the power generation from geothermal resources. Outputs of the FSR process led SDNN phase. Adaptive Moment Estimation (ADAM) and Nesterov-accelerated Adaptive Moment Estimation (NADAM) methods were used to optimize SDNN models. For the daily power generation, the best performance has been shown by the model of SDNN optimized using ADAM optimizer with a coefficient of determination (R2) of 0.9807 and root mean square error (RMSE) of 0.0466, respectively.

Ground Prediction by Markov-ANN Hybrid Analytics Using Baseline Geotechnical Data and Observed Field Data

ABSTRACT Geology along the tunnel length usually differs from the anticipated data as per Geotechnical Baseline Report (GBR), sometimes quite significantly. This has always led to the disruption of the planning, resource mobilisation, cost and duration of the project. One way to minimise this uncertainty is by exhaustive investigation like probe holes and geophysical prospecting. Forecasting or prediction of Geology can also be done by soft computing and analytical methods. Using the analytical tool of the Markov Model and the learning powers of Artificial Neural Networks (ANN) has been attempted in this study. The idea is to build a hybrid model that would combine both capabilities and do a probabilistic prediction of the ground condition ahead of the tunnel face in real time to better suit the site, accommodate complexities and can capture the associated uncertainties. The Geotechnical Baseline Report (GBR) having the initial survey details of the tunnel geology is used to build the Markov Model while the deterministic borehole data in the GBR is used to build the ANN model. Bayesian joint probability theorem is used to update the model with the face observed geological parameters. This hybrid model demonstrated to be a good complement to the physical forecasting methods and the geological uncertainty of a complex and challenging Himalayan region was well captured by the present approach. The window of the prediction was for a region of approximately 250m where it showed a very good prediction of ground class. The model can help in systematic planning and resource mobilisation in a better way and subsequent key decisions for the upcoming excavation method and corresponding support measures.

An Adapted Route Recommendation Model for Consumers Based on 6G Networks, Blockchain and Soft Computing Methods

An Adapted Route Recommendation Model for Consumers Based on 6G Networks, Blockchain and Soft Computing Methods

БАЗОВЫЕ ТЕХНОЛОГИИ, РЕКОМЕНДУЕМЫЕ ДЛЯ ИСПОЛЬЗОВАНИЯ В ХОДЕ СТРАТЕГИЧЕСКОГО ПЛАНИРОВАНИЯ ПРОМЫШЛЕННЫХ ПРЕДПРИЯТИЙ

Strategic planning of industrial enterprises is carried out in conditions of significant infor-mation uncertainty regarding the external conditions of the enterprise's functioning and its positioning in its industry environment, in order to ensure effective competition. Therefore, it is necessary to develop new approaches to strategic planning of enterprises in conditions of uncertainty. The aim of the study is to present a number of new basic technologies that are advisable to use in the course of strategic planning of industrial enterprises based on the application of fuzzy set theory and soft computing. The following basic technologies are analyzed in the article: analysis of industry anisotropy; application of matrix aggregate calculator (MAС) technology to assess economic resilience; fuzzy numerical interpretation of the «scarlet» and «blue» market oceans; strategic positioning of the enterprise in the «resili-ence – efficiency» space; predictive modeling of activity levels based on probability processes; assessment of the risk of non-fulfillment of the plan according to strategic indicators. All the basic technologies described in the work are recommended for implementation in standard processes of strategic planning and forecasting of industrial enterprises. Such an application will ensure that the planning procedure is consistent with the expressed strategic goals of the business owner. The study is original, it uses methods of fuzzy set theory and soft computing. A new technology for analyzing industry anisotropy based on R-lens clustering is proposed, as well as a method for predicting performance levels based on probability processes.

АНАЛИЗ ОТРАСЛЕВОЙ АНИЗОТРОПИИ В МЕЖДУНАРОДНОМ МАШИНОСТРОЕНИИ

Industry anisotropy, including in international mechanical engineering, is a poorly studied property of the industry system. At the same time, it is anisotropy that determines the nature of intra-industry competition. The aim of the study is to propose an approach for the analysis of industry anisotropy in the coordinate space “ resilience – efficiency” where a two–petalled R-lens will be formed, which is then clustered, with the allocation of seven reference clusters. The inclusion of such enterprise image points in one of the six selected clusters allows companies from the industry to assess their location, nearest competitors, risks, and methods of repositioning (if applicable). The study is original, it uses methods of fuzzy set theory and soft computing. A new technology for analyzing industry anisotropy based on clustering of R-lenses is proposed.

Soft computing and statistical analysis methods to forecast the uniaxial compressive strength of marl rocks

Soft computing and statistical analysis methods to forecast the uniaxial compressive strength of marl rocks

Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

Fresh state and strength performance evaluation of slag-based alkali-activated concrete using soft-computing methods

In this study, machine learning prediction models for the slump (SL) and compressive strength (CS) of alkali-activated concrete (AAC) were developed. Extreme gradient boosting (XGB) as an ensemble and support vector machine (SVM) as individual methods were chosen. To evaluate the performance of the models, the Taylor diagram, k-fold validation, and statistical tests were performed. Moreover, to determine the significance of features, a SHapley Additive exPlanations (SHAP) study was carried out. XGB outperformed SVM considerably in predicting the SL and CS of AAC. XGB outperformed SVM in terms of R2 (0.94 for SL and 0.97 for CS), which was 0.86 and 0.88, respectively. Precursor content had the greatest effect on the SL of AAC, followed by blast furnace slag ratio, test time, SiO2/Na2O, and quantities of NaOH, aggregate, and water, according to the results of the SHAP study. The SHAP investigation revealed that curing time had the greatest effect on the CS of AAC, followed by SiO2/Na2O, NaOH quantity, precursor content, aggregate quantity, blast furnace slag ratio, and water quantity. It was determined that curing time, SiO2/Na2O, and blast furnace slag had beneficial effects; precursor content and aggregate quantity had adverse effects, while water had both beneficial and deleterious effects.

STRATEGIC MANAGEMENT OF THE ECONOMIC SUSTAINABILITY OF A COMPANY IN THE PARADIGM OF FUZZY LOGIC

The theory of economic resilience is actively developing in Russian and foreign scientific studies. Most of them lack a comprehensive approach to the issue of resilience, the ability to assess the behaviour of economic systems under conditions of uncertainty and to offer optimal solutions to ensure the resilience of economic systems (enterprises, industries) in difficult conditions. It is necessary to develop optimal algorithms for managing economic systems under adverse effects (AE) of a natural, technological, or military nature. The article describes the method of managing economic resilience in three-dimensional spaces: ‘stability – efficiency’, ‘risk – efficiency’, and ‘chance – efficiency’. The results of three approaches to resilience management are compared.It can be tentatively assumed that the stability of an individual company is ensured if the economic stability index RI exceeds the level of 0.6 and the return on equity is at least 20% per annum. More generally, in the ‘resilience – efficiency’ space, the domain of stable states of the enterprise is described by a fuzzy parabolic R lens. In the ‘risk – efficiency’ and ‘chance – efficiency’ domains, the space of optimal solutions is represented by a set of non-dominated Pareto alternatives, united by a fuzzy parabolic efficient bound of the solution portfolio set. The organisation can control its level of economic resilience within multiple representations and act according to a predetermined plan in the event of a temporary loss of resilience. The research is original, using the methods of fuzzy set theory and soft computing. A technology has been proposed to ensure the economic resilience of systems operating in difficult conditions (e.g. in new regions of the Russian Federation where large-scale military actions are taking place). This makes the study highly relevant and practical.

Soft Computing-Based Driver Modeling for Automatic Parking of Articulated Heavy Vehicles

<div>Parking an articulated vehicle is a challenging task that requires skill, experience, and visibility from the driver. An automatic parking system for articulated vehicles can make this task easier and more efficient. This article proposes a novel method that finds an optimal path and controls the vehicle with an innovative method while considering its kinematics and environmental constraints and attempts to mathematically explain the behavior of a driver who can perform a complex scenario, called the articulated vehicle park maneuver, without falling into the jackknifing phenomena. In other words, the proposed method models how drivers park articulated vehicles in difficult situations, using different sub-scenarios and mathematical models. It also uses soft computing methods: the ANFIS-FCM, because this method has proven to be a powerful tool for managing uncertain and incomplete data in learning and inference tasks, such as learning from simulations, handling uncertainty, and capturing expert parking expertise. The results obtained from the proposed method show that the use of a soft computation method significantly reduces the cumulative errors: errors resulting from summing up each sub-maneuver. Of course, the main source of these errors is related to starting from the random point that exists at the beginning of the predefined complex scenario. This implies that our method can effectively handle the uncertainty and variability of parking scenarios.</div>

Effects of Traffic on Particulate Matter (PM2.5) in Different Built Environments

Globally, vehicular pollution is one of the greatest concerns in urban areas. Several studies on air pollution have been conducted using deterministic, statistical, and soft computing methods. However, there has been little research on how soft-computing methods like Artificial Neural Networks (ANN) can help us comprehend vehicular pollution’s non-linear and highly complex dispersion. This study uses an ANN-based vehicular pollution model to investigate the effect of vehicular traffic on PM2.5 concentrations in built-up and open terrain-surrounding environments. Five distinct pollution models were developed for two locations in Delhi, considering PM2.5 pollutants, meteorological variables, traffic flow, and traffic composition into account. The results concluded that under open terrain conditions, the significance of the traffic variable in its association with PM2.5 is almost half the significance observed under built-up conditions. Also, in terms of PM2.5 reductions, the maximum reduction observed at Location-1 (built-up environment), and Location-2 (open terrain environment) is 1.85 and 2.44 times the percent reduction in traffic during peak hours, respectively. The study’s findings have significant ramifications for the current practices of ignoring the contribution of traffic and the built environment to pollution and adopting measures like an odd-even rule and high fuel and parking prices to combat pollution.

Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining

Blasting operations in open-pit mines generally have various management strategies relating to flyrock. There are empirical models for calculating the flyrock distance, but due to the complexity and uncertainty of rock properties and their interactions with blasting properties, there are still no models that can predict the flyrock distance that may be applicable across mining operations in general. In this regard, the Jajarm bauxite mine complex was used as a case study. The purpose of this study was to develop and evaluate different methods that can predict flyrock distance. For this purpose, soft computing models were developed using generalized regression neural network (GRNN), gene expression programming (GEP) and genetic-algorithm-based GRNN (GA-GRNN) methods. To obtain statistical models, multivariable regression was applied in the form of linear and nonlinear equations. A flyrock index was introduced using a classification system developed by incorporating fuzzy decision-making trial and evaluation methods (fuzzy DEMATEL). In order to achieve this goal, the data of 118 blasts in eight mines of the Jajarm bauxite complex were collected and used. Following this, four performance benchmarks were applied: the coefficient of determination (R2), variance accounted for (VAF), root-mean-square error (RMSE) and mean absolute percentage error (MAPE). The performance of the models was evaluated, and they were compared with each other as well as with the most common previous empirical models. The obtained results indicate that the GA-GRNN model has a higher performance in predicting the flyrock distance in actual cases compared to the other models. At first, data on factors that were the main cause of flyrock (and had a direct impact on it) were collected and classified from different blasts. Then, using the collected data, 19 different combinations were established, which can be used to provide the appropriate predictive equation. The purpose of this work is to more accurately predict flyrock and prevent heavy damage to buildings and mining machines across the mining complex.

UPAFuzzySystems: A Python Library for Control and Simulation with Fuzzy Inference Systems

The main goal of control theory is input tracking or system stabilization. Different feedback-computed controlled systems exist in this area, from deterministic to soft methods. Some examples of deterministic methods are Proportional (P), Proportional Integral (PI), Proportional Derivative (PD), Proportional Integral Derivative (PID), Linear Quadratic (LQ), Linear Quadratic Gaussian (LQG), State Feedback (SF), Adaptative Regulators, and others. Alternatively, Fuzzy Inference Systems (FISs) are soft-computing methods that allow using the human expertise in logic in IF–THEN rules. The fuzzy controllers map the experience of an expert in controlling the plant. Moreover, the literature shows that optimization algorithms allow the adaptation of FISs to control different processes as a black-box problem. Python is the most used programming language, which has seen the most significant growth in recent years. Using open-source libraries in Python offers numerous advantages in software development, including saving time and resources. In this paper, we describe our proposed UPAFuzzySystems library, developed as an FISs library for Python, which allows the design and implementation of fuzzy controllers with transfer-function and state-space simulations. Additionally, we show the use of the library for controlling the position of a DC motor with Mamdani, FLS, Takagi–Sugeno, fuzzy P, fuzzy PD, and fuzzy PD-I controllers.