Soft Computing Methods Research Articles

Mechanisms of flexible four-bar linkages: A brief review

The usages of linkage mechanisms in diverse applications, including manufacturing, are extensively appreciated. The synthesis of mechanisms for given motion and path generation has becomemore significant. The data of the kinematics of machines, thus, is a very important aspect of modern-day manufacturing. From conventional perception, kinematics' knowledge prevents many realistic mechanisms like pin deformation, joint clearances, and manufacturing inexactitudes, which cannot be overlooked. This study aims to explain the different design optimization processes and the analysis of flexible four-bar linkages for achieving a better solution. This study could be an essential step for setting the appropriate resources and optimizing the four-bar mechanism design to reduce the deviation from the desired output. The high-speed rotation of the mechanism may cause vibration and deformation in the system. So, different methods may be applied for improved four-bar mechanisms. This study provides us withprobable to resolve such conditions as precise load values regarding other vibrating elements. Like MATLAB, ADAMS, and ANSYS, the soft-computing method has been formulated to strengthen the researcher's analysis ability. These software packages may provide the improved design data in the form of displacement, deformation, and vibration frequency of the four-bar Mechanisms.

Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

Software fault prediction (SFP) is a challenging process that any successful software should go through it to make sure that all software components are free of faults. In general, soft computing and machine learning methods are useful in tackling this problem. The size of fault data is usually huge since it is obtained from mining software historical repositories. This data consists of a large number of features (metrics). Determining the most valuable features (i.e., Feature Selection (FS) is an excellent solution to reduce data dimensionality. In this paper, we proposed an enhanced version of the Whale Optimization Algorithm (WOA) by combining it with a single point crossover method. The proposed enhancement helps the WOA to escape from local optima by enhancing the exploration process. Five different selection methods are employed: Tournament, Roulette wheel, Linear rank, Stochastic universal sampling, and random-based. To evaluate the performance of the proposed enhancement, 17 available SFP datasets are adopted from the PROMISE repository. The deep analysis shows that the proposed approach outperformed the original WOA and the other six state-of-the-art methods, as well as enhanced the overall performance of the machine learning classifier.

Skull stripping using traditional and soft-computing approaches for magnetic resonance images: a semi-systematic meta-analysis

MRI scanner captures the skull along with the brain and the skull needs to be removed for enhanced reliability and validity of medical diagnostic practices. Skull Stripping from Brain MR Images is significantly a core area in medical applications. It is a complicated task to segment an image for skull stripping manually. It is not only time consuming but expensive as well. An automated skull stripping method with good efficiency and effectiveness is required. Currently, a number of skull stripping methods are used in practice. In this review paper, many soft-computing segmentation techniques have been discussed. The purpose of this research study is to review the existing literature to compare the existing traditional and modern methods used for skull stripping from Brain MR images along with their merits and demerits. The semi-systematic review of existing literature has been carried out using the meta-synthesis approach. Broadly, analyses are bifurcated into traditional and modern, i.e. soft-computing methods proposed, experimented with, or applied in practice for effective skull stripping. Popular databases with desired data of Brain MR Images have also been identified, categorized and discussed. Moreover, CPU and GPU based computer systems and their specifications used by different researchers for skull stripping have also been discussed. In the end, the research gap has been identified along with the proposed lead for future research work.

Interaction of hydro-socio-technology-knowledge indicators in integrated water resources management using soft-computing techniques

AbstractVarious factors affect the development of social, cultural, and economic aspects of societies. One of these factors is the state of water resources. In this study, countries of the world with decreasing renewable water per capita were examined during the period 2005–2017. Specifically, 35, 5, 20, 48, 43, and 151 countries were selected from the American, Oceania, European, African, Asian continents, and the world respectively. Further, three hydro-socio-technology-knowledge indicators associated with demographic, technology, and knowledge dimensions were estimated with soft-computing methods (i.e. Group Method of Data Handling (GMDH), Radial Basis Function (RBF), and Regression Trees (R Trees)) for the world's continents). The GMDH model's performance was the best among the other soft-computing methods in estimating the hydro-socio-technology-knowledge indicators for all the world's continents based on statistical criteria (coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE)). The values of RMSE for GMDH models for the ratio of rural to urban population (PRUP), population density (PD), number of internet users (IU) and education index (EI) indicators equaled (0.291, 0.046, 0.127, 0.199), (0.094, 0.023, 0.174, 0.137), (0.237, 0.044, 0.166, 0.225), (0.173, 0.031, 0.126, 0.163), (0.218, 0.058, 0.142, 0.196) and (0.231, 0.049, 0.167, 0.195) for America, Oceania, Europe, Africa, Asia and the world, respectively. The results indicate that there is an interaction between socio-technology-knowledge indicators. Thus, for water resources in all continents and the world, the hydro-socio-technology-knowledge indicators can be used for proper planning and management of water resources.

An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer.

One of the deadliest diseases which affects the large intestine is colon cancer. Older adults are typically affected by colon cancer though it can happen at any age. It generally starts as small benign growth of cells that forms on the inside of the colon, and later, it develops into cancer. Due to the propagation of somatic alterations that affects the gene expression, colon cancer is caused. A standardized format for assessing the expression levels of thousands of genes is provided by the DNA microarray technology. The tumors of various anatomical regions can be distinguished by the patterns of gene expression in microarray technology. As the microarray data is too huge to process due to the curse of dimensionality problem, an amalgamated approach of utilizing bilevel feature selection techniques is proposed in this paper. In the first level, the genes or the features are dimensionally reduced with the help of Multivariate Minimum Redundancy–Maximum Relevance (MRMR) technique. Then, in the second level, six optimization techniques are utilized in this work for selecting the best genes or features before proceeding to classification process. The optimization techniques considered in this work are Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), League Championship Optimization (LCO), Beetle Antennae Search Optimization (BASO), Crow Search Optimization (CSO), and Fruit Fly Optimization (FFO). Finally, it is classified with five suitable classifiers, and the best results show when IWO is utilized with MRMR, and then classified with Quadratic Discriminant Analysis (QDA), a classification accuracy of 99.16% is obtained.

Recent Trends in Prediction of Concrete Elements Behavior Using Soft Computing (2010–2020)

Soft computing (SC), due to its high abilities to solve the complex problems with uncertainty and multiple parameters, has been widely investigated and used, especially in structural engineering. They have successfully estimated the capacity of structural reinforced concrete (RC) members and determined the properties of concrete. There are so many articles in literature that applied SC methods for the above goals. However, there is no work to present the capability of such approaches by providing an overview on the available and existing studies. The lack of state-of-the-art review on the subject is the main motivation to present a comprehensive review on the latest trends between 2010 and 2020 in predicting the behavior of concrete elements using soft computing methods. The considered RC structural elements are beams, columns, joints, slabs, frames, concrete filled tube sections and strengthened elements with fibre reinforced polymer. The purpose of the investigated works was predicting the concrete characteristics such as crack, bond, shrinkage, or the strength of the elements. The review showed that SC methods are powerful tools which could provide flexible computational techniques with high level of accuracy for civil engineering problems. However, most of the published works neglected to present the required details and mathematical framework.

Application of soft-computing techniques in forecasting sediment load and concentration

ABSTRACT This paper investigates conventional and soft-computing methods for the estimation of suspended sediment concentration (SSC) and load (SSL) in rivers. Frequently used methods of sediment rate curve (SRC) and multi-nonlinear regression, and soft-computing methods of multi-layer perceptron, multi-linear regression and adaptive neuro-fuzzy inference system are implemented using various hydrological and hydraulic parameters for the Little Kickapoo Creek Watershed, Illinois, USA. All methods performed equally well in the estimation of SSL, without any noticeable outperformance from any from the methods. However, the application of soft-computing methods decreased SSC estimation errors considerably as compared to the results of SRC. The results are significant in the way they reconcile traditionally used hydrological parameters into the soft-computing methods. Overall, soft-computing methods are recommended for the estimation of SSC in rivers because of their reasonably better performance and ease of implementation.

Insights and trends of optimal voltage‐frequency control DG‐based inverter for autonomous microgrid: State‐of‐the‐art review

This paper endeavours a control scheme for optimal control of voltage and frequency of power inverter which relied upon distributed generation in cognitive microgrid using soft computational methods. It gives sound direction on how to control the active power flow, voltage-frequency regulation, and sharing of power among various components of MG. The classical voltage–frequency control strategy in parallel with the modern control method is well excavated in the present paper. Moreover, modulation techniques, variants of reference frames, power quality indices are covered blatantly. Thus, the paper gives fast insight and understanding to the engineers working in this niche area.

Maximum Power Point Tracking Menggunakan Algoritma Artificial Neural Network Berbasis Arus Hubung Singkat Panel Surya

The conversion of solar energy into electrical can be utilized by using the solar panel, but the energy conversion ratio is still low. Maximum Power Point Tracking (MPPT) is a method used to increase energy production in the process of converting electrical to the solar panel. Artificial Neural Network (ANN) is one of the soft-computing methods that can be applied as MPPT with the advantage of having a learning process, very stable, fast, doesn’t require complicated mathematical modeling, and has good performance. ANN is proposed with input from the short circuit current of the solar panel and is used as a reference for the ANN to reach the maximum power. The process of detecting a short circuit current is indicated by a momentary decrease of the power by the solar panel. The results show the proposed algorithm can reach the maximum power operating point of the solar panel despite the change of radiation. When at maximum power operating point, ANN can hold the value, so the resulting value doesn’t change and doesn’t generate ripple. At radiation of 1000 W/m 2 and using 100 WP, ANN can produce a maximum power of 99.97 Watts with a time of 0.063 seconds.

On the Use of Soft Computing Methods in Educational Data Mining and Learning Analytics Research: a Review of Years 2010–2018

The aim of this paper is to survey recent research publications that use Soft Computing methods to answer education-related problems based on the analysis of educational data ‘mined’ mainly from interactive/e-learning systems. Such systems are known to generate and store large volumes of data that can be exploited to assess the learner, the system and the quality of the interaction between them. Educational Data Mining (EDM) and Learning Analytics (LA) are two distinct and yet closely related research areas that focus on this data aiming to address open education-related questions or issues. Besides ‘classic’ data analysis methods such as clustering, classification, identification or regression/analysis of variances, soft computing methods are often employed by EDM and LA researchers to achieve their various tasks. Their very nature as iterative optimization algorithms that avoid the exhaustive search of the solutions space and go for possibly suboptimal solutions yet at realistic time and effort, along with their heavy reliance on rich data sets for training, make soft computing methods ideal tools for the EDM or LA type of problems. Decision trees, random forests, artificial neural networks, fuzzy logic, support vector machines and genetic/evolutionary algorithms are a few examples of soft computing approaches that, given enough data, can successfully deal with uncertainty, qualitatively stated problems and incomplete, imprecise or even contradictory data sets – features that the field of education shares with all humanities/social sciences fields. The present review focuses, therefore, on recent EDM and LA research that employs at least one soft computing method, and aims to identify (i) the major education problems/issues addressed and, consequently, research goals/objectives set, (ii) the learning contexts/settings within which relevant research and educational interventions take place, (iii) the relation between classic and soft computing methods employed to solve specific problems/issues, and (iv) the means of dissemination (publication journals) of the relevant research results. Selection and analysis of a body of 300 journal publications reveals that top research questions in education today seeking answers through soft computing methods refer directly to the issue of quality – a critical issue given the currently dominant educational/pedagogical models that favor e-learning or computer- or technology-mediated learning contexts. Moreover, results identify the most frequently used methods and tools within EDM/LA research and, comparatively, within their soft computing subsets, along with the major journals relevant research is being published worldwide. Weaknesses and issues that need further attention in order to fully exploit the benefits of research results to improve both the learning experience and the learning outcomes are discussed in the conclusions.

Experimental evaluation and modeling of the compressive strength of concretes with various strength classes of cements

This study aimed to propose a prediction model for estimation of strength of concretes with various cements and mixture proportions. The strength of the samples produced with three different types of cement at different rates of water-to-cement ratios and cement richness were investigated experimentally and evaluated statistically. Three type of cement possessing 28-day strengths of 32.5, 42.5, and 52.5 MPa was used in the production of concretes. The concretes were produced at cement richness values of 300, 400, and 500 kg/m3 and w/c rates at changing levels within the interval of between 0.3 and 0.6. By this way, combined influences of cement strength, amount of cement and w/c ratio was experimentally investigated. Totally 36 mixes were cast then the compressive strength values were examined after specified moist curing periods (7 and 28 day). A statistical study were conducted on the experimental results and the significances of the cement strength, w/c values and amount of cement on the compressive strength of the concretes were assessed. Another crucial focus of the current paper is to generate an explicit expression to predict the compressive strength of the concretes tackled with the current study. To derive an explicit formula for estimation, a soft computing method called gene expression programming (GEP) was benefited. The GEP model was also compared with a less complicated estimation model developed by multi linear regression method. The results revealed that compressive strength of the samples were significantly influenced by cement type and aggregate-to-cement ratio. The proposed GEP model indicated a high correlation between experimental and predicted values.

Traditional and Soft Computing Techniques for Image Enhancement

Now-a-days, there is a growing demand for image processing. The target of image enhancement is to find details present in images having low luminance for better image quality. Enhancement is required to improve the picture quality. In this process, we can enhance an image, by applying the suitable technique. In enhancement, there is a conversion in image contrast, quality, color vision, brightness, clarity etc. So we need image enhancement. A comparative survey is carried out in this paper, explaining traditional and soft computing techniques. This paper clarifies a study of traditional such as edge detection of an image and fuzzy logic based soft computing for improvement of an image. In the result section output of image is shown as edges using traditional as well as fuzzy. A small description is also study for picture improvement using different soft computing and optimization techniques such as Neural network, Convolution Neural Network, Ant Bee Colony, Particle Swarm Optimization etc. in literature survey and in comparative table. It is concluded that Image enhancement can be done by traditional method, soft computing and optimization method. Image enhancement has found various vision applications that have the ability to enhance the visibility of images. To enhance an image it is very important that image should be clear, so before using the enhancement techniques we should need to learn about the enhancement. So this paper described a survey of image enhancement with different techniques. In future scope of this paper we can find out different type of parameters like PSNR, MSE and execution time, also we can use optimization technique. We are also showing a comparison table of image enhancement based on traditional, soft computing and optimization techniques with its open scope.

Prediction of CO2‐Oil Minimum Miscibility Pressure Using Soft Computing Methods

AbstractSearching for computational approaches for determination of the minimum miscibility pressure (MMP) is highly requested during the miscible gas injection process. New models, namely, the stochastic gradient boosting (SGB) algorithm and two distinct hybrid artificial neural network (ANN) models were used to predict CO2 MMP as a function of reservoir temperature, mole percent of volatile oil components, mole percent of intermediate oil components, molecular weight of pentane‐plus fraction in the oil phase, mole percentage of CO2 in injected gas, volatile components, and intermediate components in the injected gas based on 144 published data points. The SGB model was found to provide the better performance. The reservoir temperature turned out to be the most important factor.

Simulation and Design of Three-Level Cascaded Inverter Based on Soft Computing Method

Simulation and Design of Three-Level Cascaded Inverter Based on Soft Computing Method

An Accurate, Shade Detection-Based Hybrid Maximum Power Point Tracking Approach for PV Systems

Optimizing the operational performance of maximum power point trackers during multiple peak partial shading conditions (PSCs) prevails as a major challenge in photovoltaic power generation. Though many of the newly evolved soft computing and heuristic methods are compatible during PSCs, the need for such techniques in uniform irradiation levels is uncertain because most of these algorithms produce extensive oscillations before converging to the global maximum power point (GMPP). On the other hand, adopting conventional perturb and observe (P&O) algorithm is meritorious to reduce the transient power and voltage oscillations. Therefore, this article presents a proficient hybrid tracking technology that provides an adequate tradeoff between conventional P&O and advanced soft computing techniques by accurately detecting shade occurrences. For which, the uniqueness in the operating point conductance of P&O at the leftmost power peak in the P–V curve is utilized. Subsequently, the proposed convention utilizes P&O for two major purposes: to track MPP in uniform irradiance, and to detect PSCs. More importantly, even during PSCs, the detection algorithm is well designed to operate at GMPP with the conventional P&O method itself, and, only for extreme shade cases, flower pollination algorithm is introduced to track GMPP. The pre-eminence of the proposed technology to track GMPP with reduced transient oscillations by discriminating shade occurrences is demonstrated via extensive experimental studies in this article.

해상 이미지를 활용한 3D 합성곱 신경망과 합성곱 장단기 메모리 신경망 기반의 유의 파고 추정

One of the most common measures implemented in the operation of large vessels is to find the route that takes the least fuel consumption based on marine conditions, such as wave height. The model that predicts wave height can roughly be categorized into two methods, namely, a numerical method that calculates by physical formula and a soft-computing method that collects weather information and learns the machine learning algorithm. These models are difficult to apply in the real world because of their high computational complexity and the use of expensive radar equipment. In this study, we propose to estimate the wave height in real time using the images of the ocean. We used the image data consisting of four consecutive images instead of a single image and applied the combination of convolutional LSTM and 3D CNN networks that can best handle the data structure as a regression model. In this way of prediction, existing methods are not only outperformed but are also more robust to outliers. We used data from the “Weather 1st” ship provided by Daewoo Shipbuilding & Marine Engineering and confirmed that the mean absolute error is 1.59 cm, and the mean absolute percentage error is as low as 1.61% based on the test set.

A Soft Computing Method for Typical Computation in Medical Data

There are several methods of Soft Computing for analyzing the complex data for making decisions and predictions. Rough Set Theory (RST) is one of the best and relatively new intelligent techniques used in different research area for making predictions. RST is used to discover the patterns of data, handle all the redundant objects and attributes. RST is majorly used for extraction the rules from the given data. In this paper, we will use a medical data set example of cancer for retrieving the rule which is useful to make prediction for the unknown class.

Soft and hard computation methods for estimation of the effective thermal conductivity of sands

Thermal properties of sand are of importance in numerous engineering and scientific applications ranging from energy storage and transportation infrastructures to underground construction. All these applications require knowledge of the effective thermal parameters for proper operation. The traditional approaches for determination of the effective thermal property, such as the thermal conductivity are based on very costly, tedious and time-consuming experiments. The recent developments in computer science have allowed the use of soft and hard computational methods to compute the effective thermal conductivity (ETC). Here, two computation methods are presented based on soft and hard computing approaches, namely, the deep neural network (DNN) and the thermal lattice element method (TLEM), respectively, to compute the ETC of sands with varying porosity and moisture content values. The developed models are verified and validated with a small data set reported in the literature. The computation results are compared with the experiments, and the numerical results are found to be within reasonable error bounds. The deep learning method offers fast and robust implementation and computation, even with a small data set due to its superior backpropagation algorithm. However, the TLEM based on micro and meso physical laws outperforms it at accuracy.

Modelling of optimised neural network for classification and prediction of benchmark datasets

ABSTRACT Along with the reason of optimization of the Spread factor of a generalized regression neural network (Grnn) which is a variant of radial basis function network (Rbfn), the projected paper presents a novel algorithm which incorporates two of the soft-computing methods explicitly particle swarm optimization (Pso) as well as genetic algorithm (Ga). As in case of a normal Grnn for simulation of the network, instead of taking any random value for Spread factor, the Spread factor value has been optimized by using Pso then Ga as a result with the purpose of the network converges more rapidly with greater accuracy and minimum mean square error (MSE). Initial some random values of the Spread factor are calculated according to the positions in PSO algorithm and then the more unrealistic values of the spread are converted into realistic values using improved Ga. First on top of a few part of the UCI dataset Grnn is trained and this is used for testing on the remaining datasets. Projected algorithms classify and predict databases in the lesser amount of mean square error and with high accuracy also, the performance of the designed algorithm is tested by cross fold validation methods.

An Improved Artificial Neural Network Design for Face Recognition utilizing Harmony Search Algorithm

Face recognition has become an interesting field for researchers where it is used in many applications. One of the most common methods of soft computing is named the artificial neural network (ANN) has been suggested to achieve the face recognition process. Nonetheless, the performance of ANN depends on the number of neurons in the hidden layers and the value of the learning rate. These variables are usually defined based on the trial and error method which is time-consuming. Furthermore, in many cases, it is very difficult to find the optimum value for these variables. Hence, to improve the performance of the ANN for the face recognition process, the optimization algorithm is needed to get promising outcomes. Therefore, this paper introduces an improved ANN design for face recognition using a meta-heuristic optimization algorithm. The ANN represents a distributed processing system consists of neurons which are simply connected elements. One of the most popular techniques for pattern recognition called back propagation algorithm (BP) is used to train the ANN (BP-ANN) to achieve the face recognition process. To enhance the face recognition system performance, the ANN has been hybridized with the well-known meta-heuristic optimization algorithm namely harmony search algorithm (HSA). The HSA based on the principle work of musicians to find the best harmonies. This technique is Implemented based on the results of the fitness function evaluation. In this research, the mean squared error (MSE) has been used as a fitness function. The HSA optimizes the ANN such that the face recognition system provides the lowest MSE and thus enhances the performance of the face recognition system. The accuracy of the optimum hybrid system (HSA-ANN) is investigated using the MATLAB environment conducted for 10 persons. The results revealed that the proposed system (HSA-ANN) achieved lower MSE compare with the ANN. Furthermore, the HSA-ANN gives a better face recognition rate than traditional ANN.