Comprehensive Review On Twin Support Vector Machines

Tanveer, M.; Rajani, T.; Rastogi, R.; Shao, Y. H.; Ganaie, M. A.

doi:10.48550/arxiv.2105.00336

Cited by 3 publications

(2 citation statements)

References 228 publications

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“…Cross-validation [23] is a well-known technique for calculating generalisation mistakes. Among various crossvalidation techniques, the k-Fold cross-validation approach is taken into consideration in this study.…”

Section: Cross-validation Techniquementioning

confidence: 99%

Estimation and Analysis of Building Costs Using Artificial Intelligence Support Vector Machine

Salahaldain¹,

Naimi²,

Alsultani³

2023

MMEP

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An essential component of the project feasibility assessment is the conceptual cost estimate. In actuality, it is carried out based on the estimator's prior expertise. However, budgeting and cost control are planned and carried out ineffectively as a result of inaccurate cost estimates. The purpose of this article is to introduce an intelligent model to improve modeling approaches accuracy throughout early phases of a project's development in the construction sector. A support vector machine model, which is computationally effective, is created to calculate the conceptual costs of building projects. To get accurate estimates, the suggested neural network model is trained using a cross-validation method. Through the research of the literature and interviews with experts, the cost estimate's influencing elements are determined. As training instances, the cost information from 40 structures is used. Two potent intelligence methods-Nonlinear Regression (NR) and Evolutionary Fuzzy Neural Interface Model (EFNIM)are offered to illustrate how well the suggested model performs. Based on the readily accessible dataset from the relevant literature in the construction business, their results are contrasted. The computational findings show that the intelligent model that is being provided outperforms the other two potent methods. During the planning and conceptual design phase, the inaccuracy is satisfied for a project's conceptual cost estimate. Case studies demonstrate how SVMs may help planners anticipate the cost of construction in an effective and precise manner.

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Section: Cross-validation Techniquementioning

confidence: 99%

Estimation and Analysis of Building Costs Using Artificial Intelligence Support Vector Machine

Salahaldain¹,

Naimi²,

Alsultani³

2023

MMEP

View full text Add to dashboard Cite

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“…Other methods such as k-nearest neighbour (KNN) [7], random forest (RaF) [1] have also been thoroughly studied. Interested readers can refer to the comprehensive review on TWSVM [32].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Schizophrenia: A comprehensive evaluation

Tanveer,

Jangir,

Ganaie

et al. 2022

Preprint

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Machine learning models have been successfully employed in the diagnosis of Schizophrenia disease. The impact of classification models and the feature selection techniques on the diagnosis of Schizophrenia have not been evaluated. Here, we sought to access the performance of classification models along with different feature selection approaches on the structural magnetic resonance imaging data. The data consist of 72 subjects with Schizophrenia and 74 healthy control subjects. We evaluated different classification algorithms based on support vector machine (SVM), random forest, kernel ridge regression and randomized neural networks. Moreover, we evaluated T-Test, Receiver Operator Characteristics (ROC), Wilcoxon, entropy, Bhattacharyya, Minimum Redundancy Maximum Relevance (MRMR) and Neighbourhood Component Analysis (NCA) as the feature selection techniques. Based on the evaluation, SVM based models with Gaussian kernel proved better compared to other classification models and Wilcoxon feature selection emerged as the best feature selection approach. Moreover, in terms of data modality the performance on integration of the grey matter and white matter proved better compared to the performance on the grey and white matter individually. Our evaluation showed that classification algorithms along with the feature selection approaches impact the diagnosis of Schizophrenia disease. This indicates that proper selection of the features and the classification models can improve the diagnosis of Schizophrenia.

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