Application Of Soft Computing Research Articles

Application of Soft Computing to Address Uncertainty in Construction Project Management: A Systematic Literature Review

Application of Soft Computing to Address Uncertainty in Construction Project Management: A Systematic Literature Review

A novel design of layered recurrent neural networks for fractional order Caputo–Fabrizio stiff electric circuit models

Electrical engineering models often rely on complex circuit configurations that facilitate the dynamic flow of electrically charged particles within a closed conductive network. These circuits serve as essential tools for simulating and analyzing diverse electrical systems and components. This paper introduces a study on nonlinear fractional circuits modeling through the development of a stochastic neuro-computational artificial intelligent-based solver to address mathematical models governing the Fractional order Caputo–Fabrizio stiff electric circuit model (FO-CFSECM) by manipulating the knacks of layered recurrent neural networks (LRNNs) trained with Gradient-based local search algorithm (GLA). In fractional calculus, the Caputo–Fabrizio (CF) fractional order derivative (FOD) emerges as a powerful instrument, binding its capabilities to deliver remarkably accurate solutions for fractional stiff systems. The objective of this work is to exploit the numerical treatment comprehensively for the dynamics of fractal Resistor–Capacitor (RC) and fractal Resistor–Inductor (RL) circuit models by introducing the CF fractional operator. Through the application of artificial intelligence-based soft computing and advanced back-propagative deep neural networks, a deeper understanding of the behavior and distinctive characteristics inherent in these models is sought. The Levenberg–Marquardt optimizer serves as an efficient training GLA tool for learning of LRNNs weights of fractal RL/RC circuit models. The comparative studies on variants of FO-CFSECM demonstrate that LRNNs achieve an impressive mean square error (MSE) ranging from 10[Formula: see text] to 10[Formula: see text] and absolute error (AE) within 10[Formula: see text] to 10[Formula: see text]. The accuracy, reliability, and efficiency of LRNNs for solving the FO-CFSECM were further validated through MSE, AE, controlling parameters of state transitions, error histograms, and correlation measures.

Investigating the Application of Soft Computing in Skin Cancer Diagnosis

Skin cancer is one of the foremost important causes of passing within the world. Skin cancer is one of the cancers. In expansion to being unsafe, it can moreover influence a person's self-confidence and magnificence. Luckily, the indications of this infection can be recognized early. Ponders appear that the utilize of advanced computer innovations such as picture preparing components have been effective within the forms related to the determination and classification of cancers. The reason of this consider is to examine the utilize of computing is delicate within the conclusion of skin cancer, in this manner, in this extend, we utilize dermoscopic pictures of skin injuries and preparatory preparing (1-remove additional foundation, 2-remove clamors and names in pictures, 3-remove bubbles and oil from pictures, 4-increase the quality of pictures) Their handling is based on surface and shape highlights and the reduction of dimensions of Barosh's LDA (Direct Discriminant Design Examination) which we have utilized to discover the most excellent highlights from among the highlights that we have extricated from the multi-objective hereditary calculation of non-dominant positioning (NSGA-II), which concurring to the significance of the subject in expansion It has tall speed and exactness, and by preparing a neural arrange after mistake engendering, we planned a framework that's a reasonable elective for domestic conclusion and comparative strategies due to its tall exactness and speed of conclusion. DOI: https://doi.org/10.52783/pst.305

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.

Analysis of Faculty Teaching Performance Based On Student Feedback Using Fuzzy Mamdani Inference System

The objective of this study was to develop an Inference System based on the Fuzzy Mamdani system for assessing the teaching skills of faculty members in academic or educational institutions. Nowadays, many academic institutions have adopted web-based methods to gather students' feedback on faculty members' teaching effectiveness. Evaluating faculty performance in teaching activities is crucial for establishing a more equitable academic environment. The primary goal of assessing faculty performance is to identify areas of strength and weakness in their professional development. This application of soft computing, specifically the Fuzzy Mamdani Inference System (FMIS), in evaluating faculty teaching performance can be valuable for organizational management in assessing faculty capabilities and their impact on student outcomes.

Application of soft computing and evolutionary algorithms to estimate hydropower potential in multi-purpose reservoirs

Hydropower is a clean and efficient technology for producing renewable energy. Assessment and forecasting of hydropower production are important for strategic decision-making. This study aimed to use machine learning models, including adaptive neuro-fuzzy inference system (ANFIS), gene expression programming, random forest (RF), and least square support vector regression (LSSVR), for predicting hydroelectric energy production. A total of eight input scenarios was defined with a combination of various observed variables, including evaporation, precipitation, inflow, and outflow to the reservoir, to predict the hydroelectric energy produced during the experimental period. The Mahabad reservoir near Lake Urmia in the northwest of Iran was selected as a study object. The results showed that a combination of hydroelectric energy produced in the previous month, evaporation, and outflow from the dam resulted in the highest prediction performance using the RF model. A scenario that included all input variables except the precipitation outperformed other scenarios using the LSSVR model. Among the models, LSSVR exerted the highest prediction performance for which RMSE, MAPE, and NSE were 442.7 (MWH), 328.3 (MWH), and 0.85, respectively. The results showed that Harris hawks optimization (HHO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) was better than particle swarm optimization (PSO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) in optimizing ANFIS during the prediction. The results of Taylor’s diagram indicated that the ANFIS-HHO model had the highest accuracy. The findings of this study showed that machine learning models can be used as an essential tool for decision-making in sustainable hydropower production.

A Methodology for Designing an Automated System to Improve the Thermal Performance of a Large Building in Operation

Many buildings built before energy performance regulations are actually in a situation of thermal discomfort and energy inefficiency. The creation of intelligent environments is moving towards new opportunities, based on real-time monitoring and on the development of sensors and technologies. Furthermore, building automation and electronic systems standards enable interoperability and interconnection between control devices and systems. The application of soft computing has significantly improved the energy efficiency; however, it requires prior assessment to design the automation functions. Temperature, humidity, air quality and energy consumption are the most commonly measured parameters, but their relationships with other operational variables such as occupancy or some building states remain as a research challenge. This article presents a methodology to develop the automation of a large existing public building. This methodology consists of two stages: 1. Assessment and diagnosis to set appropriate functions, using EN ISO 52120-1 and EN 50090 for open communication networks, and EN ISO 52120-1 to assign the technical building management. 2. System control deployment of low-cost and low-consumption input and output devices. It has been proven that it is possible to effectively automate an obsolete building with a low-cost, open-source system that can be easily applied to other buildings.

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.

Genetic algorithm‐based price and warranty optimization in software systems

AbstractOwing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry.

Undulate: A framework for data-driven software engineering enabling soft computing

Especially web-facing software systems enable the collection of usage data at a massive scale. At the same time, the scale and scope of software processes have grown substantively. Automated tools are needed to increase the speed and quality of controlling software processes. The usage data has great potential as a driver for software processes. However, research still lacks constructs for collecting, refining and utilising usage data in controlling software processes. The objective of this paper is to introduce a framework for data-driven software engineering. The Undulate framework covers generating, collecting and utilising usage data from software processes and business processes supported by the software produced. In addition, we define the concepts and process of extreme continuous experimentation as an exemplar of a software engineering process. We derive requirements for the framework from the research literature, with a focus on papers inspired by practical problems. In addition, we apply a multilevel modelling language to describe the concepts related to extreme continuous experimentation. We introduce the Undulate framework and give requirements and provide an overview of the processes of collecting usage data, augmenting it with additional dimensional data, aggregating the data along the dimensions and computing different metrics based on the data and other metrics. The paper represents significant steps inspired by previous research and practical insight towards standardised processes for data-driven software engineering, enabling the application of soft computing and other methods based on artificial intelligence. • We constructed Undulate, a novel framework for systematically collecting data from software and business processes, storing the data in a dimensional database, thus creating a basis for data-driven software engineering. • We defined the general principles of how soft computing can be used to exercise automated control on and provide decision support for software engineering processes, such as continuous experimentation. • We extended and applied a multilevel modelling language to define key concepts in software experimentation to provide a scientifically rigorous conceptual basis for the Undulate framework.

Application of soft computing and statistical methods to predict rock mass permeability

Application of soft computing and statistical methods to predict rock mass permeability

Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling

The pavement management system is recognized as an assertive discipline that works on pavement indices to predict the pavement performance condition. This study used soft computing methods such as genetic algorithms and artificial intelligence to propose a modern generation of pavement indices for road networks in Jordan. The datasets used in this study were collected from multiple roads in Jordan, and 128 data points were used in this study. The input variables are the pavement condition index (PCI) and the international roughness index (IRI) in the artificial neural network (ANN) and gene expression programming (GEP) models. The output variable is the pavement serviceability rate (PSR). The results show an efficient performance benefit of using these techniques. In addition, the ANN and GEP models were able to predict the output variable with a reasonable accuracy, where the ANN model has an R2 value of 0.95, 0.87, and 0.98 for the PCI, IRI, and PSR, respectively. The (R2) values of the GEP model are 0.94, 0.89, and 0.99 for PCI, IRI, and PSR, respectively.

Application of Soft Computing in Predicting Groundwater Quality Parameters

Evaluating the quality of groundwater in a specific aquifer could be a costly and time-consuming procedure. An attempt was made in this research to predict various parameters of water quality called Fe, Cl, SO4, pH and total hardness (as CaCO3) by measuring properties of total dissolved solids (TDSs) and electrical conductivity (EC). This was reached by establishing relations between groundwater quality parameters, TDS and EC, using various machine learning (ML) models, such as linear regression (LR), tree regression (TR), Gaussian process regression (GPR), support vector machine (SVM), and ensembles of regression trees (ER). Data for these variables were gathered from five unrelated groundwater quality studies. The findings showed that the TR, GPR, and ER models have satisfactory performance compared to that of LR and SVM with respect to different assessment criteria. The ER model attained higher accuracy in terms of R2 in TDS 0.92, Fe 0.89, Cl 0.86, CaCO3 0.87, SO4 0.87, and pH 0.86, while the GPR model attained an EC 0.98 compared to all developed models. Moreover, comparisons among the different developed models were performed using accuracy improvement (AI), improvement in RMSE (PRMSE), and improvement in PMAE to determine a higher accuracy model for predicting target properties. Generally, the comparison of several data-driven regression methods indicated that the boosted ensemble of the regression tree model offered better accuracy in predicting water quality parameters. Sensitivity analysis of each parameter illustrates that CaCO3 is most influential in determining TDS and EC. These results could have a significant impact on the future of groundwater quality assessments.

Application of Fuzzy Immune Algorithm and Soft Computing in the Design of 2‐DOF PID Controller

To solve the difficulty in selecting the crossover probability and mutation probability in genetic algorithms, a fuzzy immune algorithm based on adaptive estimation of crossover probability and mutation probability in a fuzzy reasoning system is proposed, and it is used in the parameter optimization design of a two‐degree‐of‐freedom PID controller. According to the experiment and simulation results, classic genetic algorithm evolution tends to halt after 37 generations, with a fitness value of 7.135, whereas fuzzy genetic algorithm evolution tends to stop after 20 generations, with a fitness value of 7.486. The 2‐DOF PID controller that was created can give the system strong target value following and interference suppression features at the same time.

A multi-discipline predictive intelligent control method for maintaining the thermal comfort on indoor environment

The emergence and application of soft computing have significantly changed the methods to solve engineering problems. For the indoor environmental control, various machine learning methods have been used, attempting to replace the traditional engineering methods. However, the recent single data-driven machine learning approach can hardly meet the requirement, since engineering problems still require prior knowledge to extract features and set up restrictions, while this knowledge should be obtained from engineering analysis. In this case, this paper has proposed an integrated multi-discipline method combining machine learning with engineering analysis, to implement predictive intelligent indoor environmental control in terms of thermal comfort and energy consumption. This method includes three parts, i.e., environmental modelling and simulation, producing an environmental prediction model, and creating an intelligent control agent and system. Firstly, a physical model is created to simulate the indoor environment and analysed through computational fluid dynamics, whose results can guide the setup of sensors in the indoor environment for collecting real-time data. Then, a machine learning method support vector regression is used to create an environmental prediction model for key parameters within the indoor environment, based on the collected data. Finally, a reinforcement learning method is used to train an intelligent agent for the intelligent control on the indoor environment, together with a system for implementation. Experiments and evaluations are carried out in a case study within an office, demonstrating the proposed method’s feasibility, which provides a more efficient and effective intelligently predictive control on the indoor environment considering the balance of thermal comfort and energy efficiency.

Application of Soft Computing and Non-parametric Approach in the Evaluation of West Africa Economic Growth

Application of Soft Computing and Non-parametric Approach in the Evaluation of West Africa Economic Growth

Analítica Prescriptiva en Sistemas con Grandes Flujos de Eventos

Los procesos de negocio exigen tomar decisiones rápidas para lograr la adaptación constante a los cambios en búsqueda de mejorar el desempeño y aprovechar las oportunidades. Una posibilidad es resolver el proceso computacionalmente. Para ello es necesario producir analíticas que transformen los datos en conocimiento para la toma de decisiones. Existen varios tipos de analíticas, en este trabajo se introduce una línea de investigación enfocada en la analítica prescriptiva, como nivel más avanzado, capaz de calcular acciones a ser ejecutadas en el momento (decisiones operativas) o en el futuro (decisiones tácticas para corto y mediano plazo, decisiones estratégicas para largo plazo) para lograr un objetivo deseado. El cálculo de las acciones involucra el procesamiento del flujo de eventos del negocio en forma de datastreams, la aplicación de técnicas y algoritmos de Soft Computing e Inteligencia Computacional y, derivado de la necesidad de bajos tiempos de respuesta, el empleo de Computación de Alto Desempeño.

Forecasting MSW generation using artificial neural network time series model: a study from metropolitan city

Forecasting the quantity of municipal solid waste generation is an essential task for sustainable solid waste management and strategy implementation. The estimation of future waste generation rates can help to motivate for analyzing gaps in existing waste management and better planning strategies. Improper management and unsafe disposal of solid waste create a threat to the environment and human health. Hence, a sound forecasting of solid waste generation is very crucial for planning and management accordingly. Artificial intelligence is an excellent and new application of soft computing which is used as a forecasting tool. The main objective of this study is to apply ANN time series model along with autoregressive technique to forecast the monthly solid waste generation in Kolkata. For the same, data related to the monthly solid waste generation was gathered from 2010 to 2017. Total data of 96 months were divided into three categories, i.e., 70%, 15%, and 15% for training, validation, and testing, respectively. The model was evaluated based on performance value of mean square error, root mean square error, and regression coefficient. The ANN structure of 1-19-1 was considered as optimized model for solid waste forecasting because it has the lowest mean square error and the highest regression coefficient. The applied time series model forecasts that Kolkata will generate about 5205 MT/day municipal solid waste in 2030 which will add more than 1000 MT/day waste with the existing rate of generation. The present study helps in estimating and allocating essential resources that need in future for sound solid waste management and preparing alternative strategies to reach the sustainable goals.

Application of Soft Computing (ANN) Techniques to study the relationship between Solar Activity Features and Total Column Ozone

Using 30 years data (1986-2015) we have made an attempt to study the dependency of total column ozone (TCO) on solar activity features: solar flares (SF), solar active prominence (SAP) and sunspot numbers (SN) for two hill stations of Uttarakhand viz. Nainital (29.40 N.79.470E) and Mussorie (30.270 N 78.060 E) by Artificial neural network (ANN) technique. Our study supports the fact that solar activity features contribute to the production of ozone

Application of Soft Computing (ANN) Techniques to study the relationship between Solar Activity Features and Total Column Ozone

Using 30 years data (1986-2015) we have made an attempt to study the dependency of total column ozone (TCO) on solar activity features: solar flares (SF), solar active prominence (SAP) and sunspot numbers (SN) for two hill stations of Uttarakhand viz. Nainital (29.40 N.79.470E) and Mussorie (30.270 N 78.060 E) by Artificial neural network (ANN) technique. Our study supports the fact that solar activity features contribute to the production of ozone.