Real Life Applications of Soft Computing

Shukla, Anupam

doi:10.1201/ebk1439822876

Cited by 73 publications

(42 citation statements)

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“…An individual having a higher fitness has a high reproductive capability and is thus likely to be selected numerous times. On the other hand, the weaker individuals may not be selected at all for reproduction [26]. Mutation and crossover are two of the most common operators used with genetic algorithms that represent individuals as binary strings.…”

Section: A Genetic Algorithmsmentioning

confidence: 99%

“…EVOLUTIONARY ALGORITHMS According to Shukla et al [26], looking to any species in the natural world can give a clear idea of evolution. The most complex species are results of continuous evolution over time.…”

Section: Feature Selectionmentioning

confidence: 99%

“…The results are the most adapted species that we find today. Artificial systems keep getting better over time as a result of continuous adjustments and adaptation [26].…”

Section: Feature Selectionmentioning

confidence: 99%

“…The goodness of the solution varies from individual to individual within the population. To measure the goodness of a solution, a fitness function is used [26].…”

Section: A Genetic Algorithmsmentioning

confidence: 99%

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A Review on Evolutionary Feature Selection

Abd-Alsabour

2014

2014 European Modelling Symposium

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This paper presents a review of some of the most recent evolutionary algorithms used for solving feature selection based upon previously published research on feature selection. In addition, we discuss various research issues relating to each of the presented evolutionary algorithm. Evolutionary algorithms present several advantages over traditional search such as they require less domain-specific information. Such advantages have made them very popular within feature selection as explained in this paper. This paper covers the first part only of the evolutionary algorithms for the feature selection problem due to the limitation of the number of pages. The references cited in this paper cover the major theoretical issues, and provide access to the main branches of the literature dealing with such methods.

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Section: A Genetic Algorithmsmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

“…The results are the most adapted species that we find today. Artificial systems keep getting better over time as a result of continuous adjustments and adaptation [26].…”

Section: Feature Selectionmentioning

confidence: 99%

“…The goodness of the solution varies from individual to individual within the population. To measure the goodness of a solution, a fitness function is used [26].…”

Section: A Genetic Algorithmsmentioning

confidence: 99%

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A Review on Evolutionary Feature Selection

Abd-Alsabour

2014

2014 European Modelling Symposium

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“…Backpropagation is the generalization of the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions [20]. Input vectors and the corresponding target vectors are used to train a network until it can approximate a function, associate input vectors with spec-ific output vectors, or classify input vectors in an appropriate way as defined by you.…”

Section: Back Propagation Algorithm (Bpa)mentioning

confidence: 99%

Analysis of Artificial Neural Network for Financial Time Series Forecasting

Tarsauliya¹,

Kant²,

Kala³

et al. 2010

IJCA

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Financial forecasting has been challenging problem due to its high non-linearity and high volatility. An Artificial Neural Network (ANN) can model flexible linear or non-linear relations-hip among variables. ANN can be configured to produce desired set of output based on set of given input. In this paper we attempt at analyzing the usefulness of artificial neural network for forecasting financial data series with use of different algorithms such as backpropagation, radial basis function etc. With their ability of adapting non-linear and chaotic patterns, ANN is the current technique being used which offers the ability of predicting financial data more accurately. "A x-y-1 network topology is adopted because of x input variables in which variable y was determined by the number of hidden neurons during network selection with single output." Both x and y were changed.

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Improved pulmonary lung nodules risk stratification in computed tomography images by fusing shape and texture features in a machine‐learning paradigm

Sahu

Londhe

Verma

et al. 2020

Int J Imaging Syst Tech

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Lung cancer is one of the most deadly cancer in both men and women. Accurate and early diagnosis of pulmonary lung nodules is critical. This study presents an accurate computer-aided diagnosis (CADx) system for risk stratification of pulmonary nodules in computed tomography (CT) lung images by fusing shape and texture-based features in a machine-learning (ML) based paradigm. A database with 114 (28 high-risk) patients acquired from Lung Image Database Consortium (LIDC) is used in this study. After nodule segmentation using K-means clustering, features based on shape and texture attributes are extracted. Seven different filter and wrapper-based feature selection techniques are used for dominant feature selection. Lastly, the classification of nodules is performed by a support vector machine using six different kernel functions. The classification results are evaluated using 10-fold cross-validation and hold-out data division protocols. The performance of the proposed system is evaluated using accuracy, sensitivity, specificity, and the area under receiver operating characteristics (AUC). Using 30 dominant features from the pool of shape and texture-based features, the proposed system achieves the highest classification accuracy and AUC of 89% and 0.92, respectively. The proposed ML-based system showed an improvement in risk stratification accuracy by fusing shape and texture-based features.

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Real Life Applications of Soft Computing

Cited by 73 publications

References 0 publications

A Review on Evolutionary Feature Selection

A Review on Evolutionary Feature Selection

Analysis of Artificial Neural Network for Financial Time Series Forecasting

Improved pulmonary lung nodules risk stratification in computed tomography images by fusing shape and texture features in a machine‐learning paradigm

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