Showing papers on "Soft computing published in 2022"

An improved influence maximization method for social networks based on genetic algorithm

Abstract: Over the recent decade, much research has been conducted in the field of social networks. The structure of these networks has been irregular, complex, and dynamic, and certain challenges such as network topology, scalability, and high computational complexities are typically evident. Because of the changes in the structure of social networks over time and the widespread diffusion of ideas, seed sets also need to change over time. Since there have been limited studies on highly dynamical changes in real networks, this research intended to address the network dynamicity in the classical influence maximization problem, which discovers a small subset of nodes in a social network and maximizes the influence spread. To this end, we used soft computing methods (i.e., a dynamic generalized genetic algorithm) in social networks under independent cascade models to obtain a dynamic seed set. We modeled several graphs in a specified timestamp through which the edges and the nodes changed within different time intervals. Attempts were made to find influential individuals in each of these graphs and maximize individuals’ influences in social networks, which could thereby lead to changes in the members of the seed set. The proposed method was evaluated using standard datasets. The results showed that due to the reduction of the search areas and competition, the proposed method has higher scalability and accuracy to identify influential nodes in these snapshot graphs as compared with other comparable algorithms.

Group decision-making with Fermatean fuzzy soft expert knowledge

Abstract: Abstract With the rapid growth of population, the global impact of solar technology is increasing by the day due to its advantages over other power production technologies. Demand for solar panel systems is soaring, thus provoking the arrival of many new manufacturers. Sale dealers or suppliers face an uncertain problem to choose the most adequate technological solution. To effectively address such kind of issues, in this paper we propose the Fermatean fuzzy soft expert set model by combining Fermatean fuzzy sets and soft expert sets. We describe this hybrid model with numerical examples. From a theoretical standpoint, we demonstrate some essential properties and define operations for this setting. They comprise the definitions of complement, union and intersection, the OR operation and the AND operation. Concerning practice in this new environment, we provide an algorithm for multi-criteria group decision making whose productiveness and authenticity is dutifully tested. We explore a practical application of this approach (that is, the selection of a suitable brand of solar panel system). Lastly, we give a comparison of our model with certain related mathematical tools, including fuzzy and intuitionistic fuzzy soft expert set models.

Technical Study on 5G Using Soft Computing Methods

Abstract: With increasing advancements in the field of telecommunication, the attainment of a higher data transfer rate is essentially a greater need to meet high-performance communication. The exploitation of the fuzzy system in the wireless telecommunication systems, especially in Fifth Generation Mobile Networks (or) 5G networks is a vital paradigm in telecommunication markets. A comprehensive survey is dealt in the paper, where it initially reviews the basic understanding of fuzzy systems over 5G telecommunication. The literature studies are collected from various repositories that include reference materials, Internet, and other books. The collection of articles is based on empirical or evidence-based from various peer-reviewed journals, conference proceedings, dissertations, and theses. Most of the existing soft computing models are streamlined to certain applications of 5G networking. Firstly, it is hence essential to provide the readers to find research gaps and new innovative models on wide varied applications of 5G. Secondly, it deals with the scenarios in which the fuzzy systems are developed under the 5G platform. Thirdly, it discusses the applicability of fuzzy logic systems on various 5G telecommunication applications. Finally, the paper derives the conclusions associated with various studies on the fuzzy systems that have been utilized for the improvement of 5G telecommunication systems.

Novel Pythagorean Fuzzy Correlation Measures Via Pythagorean Fuzzy Deviation, Variance, and Covariance With Applications to Pattern Recognition and Career Placement

Abstract: Pythagorean fuzzy set (PFS) is a significant soft computing tool for tackling embedded fuzziness in decision-making. Many computing methods have been studied to facilitate the application of PFS in modeling practical problems, among which the concept of correlation coefficient is very important. This article proposes some novel methods of computing correlation between PFSs via the three characteristic parameters of PFS by incorporating the ideas of Pythagorean fuzzy deviation, variance, and covariance. These novel methods evaluate the magnitude of relationship, show the potency of correlation between the PFSs, and also indicate whether the PFSs are related in either negative or positive sense. The proposed techniques are substantiated together with some theoretical results and numerically validated to be superior in terms of reliability and accuracy compared to some similar existing techniques. Decision-making processes involving pattern recognition and career placement problems are determined using the proposed techniques.

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

Abstract: In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The model’s performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R2 (0.90) and low errors, was more accurate than the other algorithms, which had a lower R2. The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study’s findings.

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

Abstract: Abstract The use of three artificial neural network (ANN)-based models for the prediction of unconfined compressive strength (UCS) of granite using three non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and effective porosity, has been investigated in this study. For this purpose, a sum of 274 datasets was compiled and used to train and validate three ANN models including ANN constructed using Levenberg–Marquardt algorithm (ANN-LM), a combination of ANN and particle swarm optimization (ANN-PSO), and a combination of ANN and imperialist competitive algorithm (ANN-ICA). The constructed ANN-LM model was proven to be the most accurate based on experimental findings. In the validation phase, the ANN-LM model has achieved the best predictive performance with R = 0.9607 and RMSE = 14.8272. Experimental results show that the developed ANN-LM outperforms a number of existing models available in the literature. Furthermore, a Graphical User Interface (GUI) has been developed which can be readily used to estimate the UCS of granite through the ANN-LM model. The developed GUI is made available as a supplementary material.

Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components

Abstract: Precise estimation of groundwater level (GWL) might be of great importance for attaining sustainable development goals and integrated water resources management. Compared with alternative numerical models, soft computing methods are promising tools for GWL prediction, which need more hydrogeological and aquifer characteristics. The central aim of this research is to explore the performance of such well-accepted data-driven models to predict monthly GWL with emphasis on major meteorological components, including; precipitation (P), temperature (T), and evapotranspiration (ET). Artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), and least-square support vector machine (LSSVM) are used to predict one-, two-, and three-month ahead GWL in an unconfined aquifer. The main meteorological components (Tt, ETt, Pt, Pt-1) and GWL for one, two, and three lag-time (GWLt-1, GWLt-2, GWLt-3) are used as input to attain precise prediction. The results show that all models could have the best prediction for one month ahead in scenario 5, comprising inputs of GWLt-1, GWLt-2, GWLt-3, Tt, ETt, Pt, Tt-1, ETt-1, Pt-1. Based on different evaluation criteria, all employed models could predict the GWL with a desirable accuracy, and the results of LSSVM are the superior one.

A review on soft computing and nanofluid applications for battery thermal management

Abstract: This study is about applications of nanofluids and various soft computing algorithms on designs of battery thermal management systems and their potential performance enhancement in cooling. Brief information on Li-ion batteries, energy storage process and cooling techniques such as passive, active and hybrid cooling techniques are presented. Basic knowledge on nanofluids and soft computing methods are explained to deep understanding the following chapters. Potential of using nanofluids on thermal management of battery packs and effect on their life cycles and performance improvements are discussed. Application of the most common soft computing methods in battery thermal management systems is presented. Li-ion batteries are a promising solution to energy storage issue with appropriate thermal management designs such as presented in this review. When different active and hybrid cooling battery thermal management systems are operated with nanofluids, their performances are increased. Different machine learning methods have been successfully used in battery thermal management systems and outputs from the modeling have been considered for further performance enhancement and optimization studies. Even though, they are excellent tools assisting in high fidelity simulations or expensive experimental testing of systems, deep learning and other advanced machine learning methods may be considered for future studies. Exergetic performance analysis of nano enhanced thermal management along with the cost of using nanofluids is needed as the extension of the current studies. • Applications of nanofluids and soft computing algorithms on BTM systems are reviewed. • Brief information on Li-ion batteries, energy storage process and cooling are presented. • Li-ion batteries are a promising solution to energy storage with thermal management designs.

Machine learning and soft computing applications in textile and clothing supply chain: Bibliometric and network analyses to delineate future research agenda

Abstract: Machine learning (ML) and soft computing (SC) techniques have contributed immensely towards improvisation of manufacturing, process and quality control, automation of operations, and decision making in the textile and clothing supply chain. The current study systematically reviews 312 research articles published in Scopus indexed journals in last two decades (2000–2020). Bibliometric analysis revealed a drastic rise in number of studies since 2015. Operations related to manufacturing and quality control of yarns and fabrics are found to be benefitted most from ML techniques, as deduced from keyword and thematic content analyses. On the other hand, with respect to ML and SC techniques, artificial neural network, genetic algorithm and fuzzy logic have preponderance over the others. Nevertheless, over the years, many novel ML and SC techniques have emerged in accomplishing diverse objectives of the textile and clothing supply chain. A detailed analysis of their evolution has been documented. Unfortunately, the extent of knowledge sharing and collaboration within the scientific community working in this domain has been very low. This review also unearths lack of integrated focus on complete value chain of textiles and clothing. Fibre development, fuzzy inference and control in textile machines, predictive analytics for clothing engineering, and fashion forecasting have been identified as areas for future research.

Toward predicting SO2 solubility in ionic liquids utilizing soft computing approaches and equations of state

Abstract: The use of novel and green solvents like ionic liquids (ILs) for the capture of air pollutant gases has gained extensive attention in recent years. However, getting reliable and fast predictions of gases solubility in ILs is complex. Four soft computing methods including deep belief network (DBN), group method of data handling (GMDH), genetic programming (GP), and K-nearest neighbor (KNN) were utilized for estimating the solubility of sulfur dioxide (SO2) in ILs. A total of 374 experimental data points of SO2 solubility in 15 types of ILs were collected and used for model development. Moreover, Valderrama-Patel-Teja (VPT), Zudkevitch-Joffe (ZJ), Peng-Robinson (PR), Redlich-Kwong (RK), and Soave-Redlich-Kwong (SRK) equations of state (EOSs) were applied for the solubility predictions in the SO2 + ILs systems. The results illustrated that DBN model is the most reliable predictive tool for the SO2 solubility in ILs by having an average absolute percent relative error (AAPRE) of 3.56%. Furthermore, the proposed simple to use GMDH mathematical correlation also provides good estimations with an AAPRE of 8.05%. Despite the weaker performance of the EOSs than the intelligent models, the PR EOS presented better estimations among other EOSs for the SO2 solubility in ILs.

Solar radiation prediction using improved soft computing models for semi-arid, slightly-arid and humid climates

Abstract: In this research, monthly solar radiation is predicted in semi-dry, dry, and wet climates. Adaptive neurofuzzy interface system (ANFIS), radial basis function neural network (RBFNN), and multi-layer perceptron (MLP) models are used for predicting solar radiation (SR). Grasshopper algorithm (GOA) is utilized to improve the performance of ANFIS, RBFNN, and MLP models. Three stations in Iran, namely Rasht (humid climate), Yazd (semi-arid) and Tehran (slightly arid), are considered as the case studies. The accuracy of GOA is benchmarked against particle swarm optimization (PSO) and salp swarm algorithm (SSA). The results reveal that the best-input combination is relative humidity, wind speed, rainfall, and temperature at these three stations. A comprehensive study is performed to select the best-input combination. The main contribution of paper is to create new hybrid ANFIS models for predicting monthly solar radiation in different climates. Besides, effects of different parameters are comprehensively investigated on solar radiation. This study indicates that temperature is the most effective parameter for estimating SR in dry and semi-dry climate. It is found that rainfall plays a key role for estimating SR in a wet region. The main finding of this paper is that the determination of the most suitable input scenario for predicting SR is an important issue because different input scenarios in different climates provide different performances. Besides, the use of a robust optimization algorithm as a training method is a significant step of the modeling process of SR. Results indicate that mean absolute error (MAE) of ANFIS-GOA is 3.8% and 8.9% less in comparison with that of MLP-GOA and RBFNN-GOA, respectively during the training stage at Yazd station. Besides, MAE of ANFIS-GOA is 26% and 31% less than that of MLP-GOA and RBFNN-GOA, respectively during the training stage at Tehran station. Results indicate that NSE values of ANFIS-GOA, ANFIS-SSA, ANFIS-PSO, and ANFIS models are 0.94, 0.88, 0.84, and 0.79, respectively during the testing stage at Rasht station. It is found that ANFIS-GOA attains higher accuracy in predicting SR under different climates.

Prediction of rock strain using soft computing framework

Abstract: This study presents a comparative analysis of conventional soft computing techniques in predicting strain of a rock sample fitted with several strain gauges in horizontal and vertical directions. For this purpose, a total of 2040 experimental test data was obtained from an experimental setup. Six conventional soft computing techniques, namely relevance vector machine, genetic programming, multivariate adaptive regression spline, minimax probability machine regression, emotional neural network, and extreme learning machine were used. These models were trained and validated with 70% and 30% observations of the main dataset, respectively. Experimental results demonstrate that most of the employed models have attained the most accurate prediction of rock strain. Overall, the result of the RVM model is significantly better than those obtained from other soft computing methods employed in this study. In the testing phase, the RVM model attained 94.0% and 99.8% accuracies (in terms of R2 value) against horizontal and vertical directions, respectively. Based on the experimental results, the RVM model has the potential to be a new alternative to assist geological/geotechnical engineers to estimate the rock strain in the design phase of civil engineering projects.

Fuzzy Logic, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference Methodology for Soft Computation and Modeling of Ion Sensing Data of a Terpyridyl-Imidazole Based Bifunctional Receptor

Abstract: Graphical Abstract Anion and cation sensing aspects of a terpyridyl-imidazole based receptor have been utilized in this work for the fabrication of multiply configurable Boolean and fuzzy logic systems. The terpyridine moiety of the receptor is used for cation sensing through coordination, whereas the imidazole motif is utilized for anion sensing via hydrogen bonding interaction and/or anion-induced deprotonation, and the recognition event was monitored through absorption and emission spectroscopy. The receptor functions as a selective sensor for F− and Fe2+ among the studied anions and cations, respectively. Interestingly, the complexation of the receptor by Fe2+ and its decomplexation by F− and deprotonation of the receptor by F− and restoration to its initial form by acid are reversible and can be recycled. The receptor can mimic various logic operations such as combinatorial logic gate and keypad lock using its spectral responses through the sequential use of ionic inputs. Conducting very detailed sensing studies by varying the concentration of the analytes within a wide domain is often very time-consuming, laborious, and expensive. To decrease the time and expenses of the investigations, soft computing approaches such as artificial neural networks (ANNs), fuzzy logic, or adaptive neuro-fuzzy inference system (ANFIS) can be recommended to predict the experimental spectral data. Soft computing approaches to artificial intelligence (AI) include neural networks, fuzzy systems, evolutionary computation, and other tools based on statistical and mathematical optimizations. This study compares fuzzy, ANN, and ANFIS outputs to model the protonation-deprotonation and complexation-decomplexation behaviors of the receptor. Triangular membership functions (trimf) are used to model the ANFIS methodology. A good correlation is observed between experimental and model output data. The testing root mean square error (RMSE) for the ANFIS model is 0.0023 for protonation-deprotonation and 0.0036 for complexation-decomplexation data.

Modelling and Analysis of Virotherapy of Cancer Using an Efficient Hybrid Soft Computing Procedure

Abstract:

Soft Computing-Based Models for Predicting the Characteristic Impedance of Igneous Rock from Their Physico-mechanical Properties

Abstract: Abstract Rock properties are important for design of surface and underground mines as well as civil engineering projects. Among important rock properties is the characteristic impedance of rock. Characteristic impedance plays a crucial role in solving problems of shock waves in mining engineering. The characteristics impedance of rock has been related with other rock properties in literature. However, the regression models between characteristic impedance and other rock properties in literature do not consider the variabilities in rock properties and their characterizations. Therefore, this study proposed two soft computing models [i.e., artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)] for better predictions of characteristic impedance of igneous rocks. The performances of the proposed models were statistically evaluated, and they were found to satisfactorily predict characteristic impedance with very strong statistical indices. In addition, multiple linear regression (MLR) was developed and compared with the ANN and ANFIS models. ANN model has the best performance, followed by ANFIS model and lastly MLR model. The models have Pearson's correlation coefficients of close to 1, indicating that the proposed models can be used to predict characteristic impedance of igneous rocks.

Spherical Fuzzy Soft Rough Average Aggregation Operators and Their Applications to Multi-Criteria Decision Making

Abstract: The objective of this article is to explore and generalize the notions of soft set and rough set along with spherical fuzzy set and to introduce the novel concept called spherical fuzzy soft rough set that is free from all those complications faced by many modern concepts like intuitionistic fuzzy soft rough set, Pythagorean fuzzy soft rough set, and q-rung orthopair fuzzy soft rough set. Since aggregation operators are the fundamental tools to translate the complete information into a distinct number, so some spherical fuzzy soft rough new average aggregation operators are introduced, such as spherical fuzzy soft rough weighted average, spherical fuzzy soft rough ordered weighted average, and spherical fuzzy soft rough hybrid average aggregation operators. Also, the basic characteristics of these introduced operators have been elaborated in detail. Furthermore, a multi-criteria decision-making (MCDM) technique has been developed and a descriptive example is given to support newly presented work. At the end of this article, a comparative study of the introduced technique has been established that shows how our work is more superior and efficient compared to the picture fuzzy soft set.

Durable Pneumatic Artificial Muscles with Electric Conductivity for Reliable Physical Reservoir Computing

Abstract: A McKibben-type pneumatic artificial muscle (PAM) is a soft actuator that is widely used in soft robotics, and it generally exhibits complex material dynamics with nonlinearity and hysteresis. In this letter, we propose an extremely durable PAM containing carbon black aggregates and show that its dynamics can be used as a computational resource based on the framework of physical reservoir computing (PRC). By monitoring the information processing capacity of our PAM, we verified that its computational performance will not degrade even if it is randomly actuated more than one million times, which indicates extreme durability. Furthermore, we demonstrate that the sensing function can be outsourced to the soft material dynamics itself without external sensors based on the framework of PRC. Our study paves the way toward reliable information processing powered by soft material dynamics.

Oxygen aeration efficiency of gabion spillway by soft computing models

Abstract: The current paper deals with the performance evaluation of the application of three soft computing algorithms such as adaptive neuro-fuzzy inference system (ANFIS), backpropagation neural network (BPNN), and deep neural network (DNN) in predicting oxygen aeration efficiency (OAE20) of the gabion spillways. Besides, classical equations, namely multivariate linear and nonlinear regressions (MVLR and MVNLR), including previous studies, were also employed in predicting OAE20 of the gabion spillways. The analysis of results showed that the DNN demonstrated relatively lower error values (root mean square error, RMSE = 0.03465; mean square error, MSE = 0.00121; mean absolute error, MAE = 0.02721) and the highest value of correlation coefficient, CC = 0.9757, performed the best in predicting OAE20 of the gabion spillways; however, other applied models, such as ANFIS, BPNN, MVLR, and MVNLR, were giving comparable results evaluated to statistical appraisal metrics of the relative significance of input parameters based on sensitivity investigation, the porosity (n) of gabion materials was observed to be the most critical parameter, and gabion height (P) had the least impact over OAE20 of the spillways.

Fermatean fuzzy soft aggregation operators and their application in symptomatic treatment of COVID-19 (case study of patients identification)

Abstract: The main focus of this paper is the application of aggregation operators (AOs) in the environment of Fermatean fuzzy soft sets (FFSS). The unique feature of the work is its application in the symptomatic treatment of the COVID-19 disease. For this purpose, the idea of FFSS is introduced which is based on the Senapati and Yagar's Fermatean fuzzy set. Next we have defined Fermatean fuzzy soft aggregation operators (FFSAOs) like, Fermatean fuzzy soft weighted averaging (FFSWA) operator, Fermatean fuzzy soft ordered weighted averaging (FFSOWA) operator, Fermatean fuzzy soft weighted geometric (FFSWG) operator and Fermatean fuzzy soft ordered weighted geometric (FFSOWG). The prominent properties of these operators are given in details. We have also developed some approaches to solve multi-criteria decision making (MCDM) problems in Fermatean fuzzy soft (FFS) information. An introduction to the novel pandemic, safety measures, and then its possible symptomatic treatment is also provided. The developed operators are utilized in the symptomatic treatment of COVID-19 disease in order to show the practical applications and importance of these AOs as well as Fermatean fuzzy soft information. The stability of the proposed work is also proved by the comparative analysis.

Outcomes of Industry–University Collaboration in Open Innovation: An Exploratory Investigation of Their Antecedents’ Impact Based on a PLS-SEM and Soft Computing Approach

Abstract: The outcomes of industry–university collaboration, in an open innovation context, may be of great support to firms, in their response to the challenges of today’s highly competitive environment. However, there is no empirical evidence on how these outcomes are influenced by their antecedents. Aiming to fill this gap, a research model to investigate the impact of the major antecedents, identified in the literature as motives, barriers and knowledge transfer channels on the beneficial outcomes and drawbacks of open innovation between the two organizations was developed in this study. The research model was empirically assessed, using a dual-stage predictive approach, based on PLS-SEM and soft computing constituents (artificial neural networks and adaptive neuro-fuzzy inference systems). PLS-SEM was successfully used to test the hypotheses of the research model, while the soft computing approach was employed to predict the complex dependencies between the outcomes and their antecedents. Insights into the relative importance of the antecedents, in shaping the open innovation outcomes, were provided through the importance–performance map analysis. The findings revealed the antecedents that had a significant positive impact on both the beneficial outcomes and drawbacks of industry–university collaboration, in open innovation. The results also highlighted the predictor importance in the research model, as well as the relative importance of the antecedent constructs, based on their effects on the two analyzed outcomes.

Soft-sensing of effluent total phosphorus using adaptive recurrent fuzzy neural network with Gustafson-Kessel clustering

Abstract: • A soft-sensing method based on GK-ARFNN is proposed. • The GK clustering algorithm is used to develop the initial fuzzy rule base. • The recursive link is introduced into the FNN to improve dynamic mapping ability. • A hierarchical adaptive second-order optimization algorithm is developed. To address the issue of soft-sensing of effluent total phosphorus in wastewater treatment processes (WWTPs), a soft-sensing system based on an adaptive recursive fuzzy neural network with Gustafson-Kessel (GK) clustering and hierarchical adaptive second-order optimization algorithm (HAS) is proposed in this paper. In GK-ARFNN, first, the GK clustering algorithm was utilized to cluster the input–output dataset. Thus, the establishment of the initial fuzzy rule base and the determination of the parameter value of the fuzzy set membership function was realized. Then, the recursive layer was added into FNN to improve the dynamic mapping ability of the system. Finally, the HAS algorithm was developed based on the improved Levenberg-Marquardt (LM) optimization algorithm, and all the free parameters of the GK-ARFNN were adjusted online using HAS to improve the generalization capability and prediction accuracy of the soft-sensing system. In addition, the convergence of the proposed GK-ARFNN algorithm was also analyzed in this paper, which can ensure the effectiveness of the solutions to modelling issues for practical industrial processes. The simulation results demonstrate that the GK-ARFNN-based soft-sensing system introduced in this paper achieved satisfactory accuracy in the prediction of effluent total phosphorus in WWTPs. The source codes of GK-ARFNN and some competitors can be downloaded from https://github.com/hyitzhb/GK-ARFNN .