Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
4,714
result(s) for
"soft computing"
Sort by:
Soft computing techniques in engineering, health, mathematical and social sciences
\"The book contains theory and applications of soft computing in engineering, health, social and applied sciences. Different soft computing techniques such as artificial neural networks, fuzzy systems, evolutionary algorithms and hybrid systems are discussed. It also contains important chapters in machine learning and clustering\"-- Provided by publisher.
Book
Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete
In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.
Journal Article
Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning
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.
Journal Article
Prediction of reinforced concrete walls shear strength based on soft computing-based techniques
The precise estimation of the shear strength of reinforced concrete walls is critical for structural engineers. This projection, nevertheless, is exceedingly complicated because of the varied structural geometries, plethora of load cases, and highly nonlinear relationships between the design requirements and the shear strength. Recent related design code regulations mostly depend on experimental formulations, which have a variety of constraints and establish low prediction accuracy. Hence, different soft computing techniques are used in this study to evaluate the shear capacity of reinforced concrete walls. In particular, developed models for estimating the shear capacity of concrete walls have been investigated, based on experimental test data accessible in the relevant literature. Adaptive neuro-fuzzy inference system, the integrated genetic algorithms, and the integrated particle swarm optimization methods were used to optimize the fuzzy model’s membership function range and the results were compared to the outcomes of random forests (RF) model. To determine the accuracy of the models, the results were assessed using several indices. Outliers in the anticipated data were identified and replaced with appropriate values to ensure prediction accuracy. The comparison of the resulting findings with the relevant experimental data demonstrates the potential of hybrid models to determine the shear capacity of reinforced concrete walls reliably and effectively. The findings revealed that the RF model with RMSE = 151.89, MAE = 111.52, and
R
2
= 0.9351 has the best prediction accuracy. Integrated GAFIS and PSOFIS performed virtually identically and had fewer errors than ANFIS. The sensitivity analysis shows that the thickness of the wall
(
b
w
)
and concrete compressive strength
(
f
c
)
have the most and the least effects on shear strength, respectively.
Journal Article
Image quality assessment of computer-generated images : based on machine learning and soft computing
Image Quality Assessment is well-known for measuring the perceived image degradation of natural scene images but is still an emerging topic for computer-generated images. This book addresses this problem and presents recent advances based on soft computing. It is aimed at students, practitioners and researchers in the field of image processing and related areas such as computer graphics and visualization. In this book, we first clarify the differences between natural scene images and computer-generated images, and address the problem of Image Quality Assessment (IQA) by focusing on the visual perception of noise. Rather than using known perceptual models, we first investigate the use of soft computing approaches, classically used in Artificial Intelligence, as full-reference and reduced-reference metrics. Thus, by creating Learning Machines, such as SVMs and RVMs, we can assess the perceptual quality of a computer-generated image. We also investigate the use of interval-valued fuzzy sets as a no-reference metric. These approaches are treated both theoretically and practically, for the complete process of IQA. The learning step is performed using a database built from experiments with human users and the resulting models can be used for any image computed with a stochastic rendering algorithm. This can be useful for detecting the visual convergence of the different parts of an image during the rendering process, and thus to optimize the computation. These models can also be extended to other applications that handle complex models, in the fields of signal processing and image processing.
Book
A review on gait generation of the biped robot on various terrains
Day by day, biped robots’ usage is increasing enormously in all industrial and non-industrial applications due to their ability to move in any unstructured environment compared to wheeled robots. Keeping this in mind, worldwide, many researchers are working on various aspects of biped robots, such as gait generation, dynamic balance margin, and the design of controllers. The main aim of this review article is to discuss the main challenges encountered in the biped gait generation and design of various controllers while moving on different terrain conditions such as flat, ascending and descending slopes or stairs, avoiding obstacles/ditches, uneven terrain, and an unknown environment. As per the authors’ knowledge, no single study has been carried out in one place related to the gait generation and design of controllers for each joint of the biped robot on various terrains. This review will help researchers working in this field better understand the concepts of gait generation, dynamic balance margin, and the design of controllers while moving on various terrains. Moreover, the current article will also cover the different soft computing techniques used to tune the gains of the controllers. In this article, the authors have reviewed a vast compilation of research work on the gait generation of the biped robot on various terrains. Further, the authors have proposed taxonomies on various design issues identified while generating the gait in different aspects. The authors reviewed approximately 296 articles and discovered that all researchers attempted to generate the dynamically balanced biped gait on various terrains.
Journal Article
An enhanced soft-computing based strategy for efficient feature selection for timely breast cancer prediction: Wisconsin Diagnostic Breast Cancer dataset case
When contemplating the improvement of overall performance in machine learning (ML) models, a critical strategy for optimizing data preparation is feature selection (FS). There has been a significant rise in the popularity of metaheuristic FS algorithms in recent times. This can be attributed to their proficiency in accurately identifying and selecting the most relevant features for ML tasks. This study presents three feature selection strategies that utilize metaheuristic algorithms. The methodologies mentioned include the Gravitational Search Optimization Algorithm (GSA), Emperor Penguin Optimization (EPO), and a hybrid approach of GSA and EPO referred to as hGSAEPO. Previous research has explored the use of baseline algorithms for feature selection in various ML tasks. However, there is a lack of investigation regarding their application specifically in breast cancer(BC) classification. A combination of these two has been utilized for the first occasion. The purpose of selecting BC as the study of investigation is due to the reason that this illness is recognized as the second most prevalent cause of mortality in the female population. If the condition is detected in its initial phases, it can be remedied and can assist individuals in evading superfluous medical processes. The procedure of selecting relevant features holds significant importance in the purpose of predicting ailments like BC. The current research presents an innovative methodology that employs three soft-computing algorithms, EPO, GSA, and their proposed hybrid hGSAEPO to efficiently identify significant features while concurrently decreasing the occurrence of irrelevant ones, simplifying overall complexity and enhancing the accuracy. The utilization of these soft computing methodologies and six ML classifiers presents a viable framework for prognostic research through the classification of data instances on Wisconsin Diagnostic Breast Cancer (WDBC). The experimental findings of eight experiments conducted suggest that the suggested approach exhibits exceptional performance in the context of binary classification for BC by computing astounding results like precision of 0.9800, sensitivity of 0.9700, specificity of 0.9887, F1-score of 0.9539, area under the curve(AUC) surpassing 0.998, with an accuracy of 98.31%. We achieved our objectives by presenting a dependable clinical prediction system for healthcare professionals for efficient diagnosis.
Journal Article