Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu
The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the…
Abstract
Purpose
The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.
Design/methodology/approach
In this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.
Findings
The experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.
Originality/value
Experiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.
Details
Keywords
Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu
Automated skin lesion analysis plays a vital role in early detection. Having relatively small-sized imbalanced skin lesion datasets impedes learning and dominates research in…
Abstract
Purpose
Automated skin lesion analysis plays a vital role in early detection. Having relatively small-sized imbalanced skin lesion datasets impedes learning and dominates research in automated skin lesion analysis. The unavailability of adequate data poses difficulty in developing classification methods due to the skewed class distribution.
Design/methodology/approach
Boosting-based transfer learning (TL) paradigms like Transfer AdaBoost algorithm can compensate for such a lack of samples by taking advantage of auxiliary data. However, in such methods, beneficial source instances representing the target have a fast and stochastic weight convergence, which results in “weight-drift” that negates transfer. In this paper, a framework is designed utilizing the “Rare-Transfer” (RT), a boosting-based TL algorithm, that prevents “weight-drift” and simultaneously addresses absolute-rarity in skin lesion datasets. RT prevents the weights of source samples from quick convergence. It addresses absolute-rarity using an instance transfer approach incorporating the best-fit set of auxiliary examples, which improves balanced error minimization. It compensates for class unbalance and scarcity of training samples in absolute-rarity simultaneously for inducing balanced error optimization.
Findings
Promising results are obtained utilizing the RT compared with state-of-the-art techniques on absolute-rare skin lesion datasets with an accuracy of 92.5%. Wilcoxon signed-rank test examines significant differences amid the proposed RT algorithm and conventional algorithms used in the experiment.
Originality/value
Experimentation is performed on absolute-rare four skin lesion datasets, and the effectiveness of RT is assessed based on accuracy, sensitivity, specificity and area under curve. The performance is compared with an existing ensemble and boosting-based TL methods.
Details
Keywords
Satya Prakash Singh, Gautam Biswas and Perumal Nithiarasu
The purpose of this paper is to investigate the influence of forced, in-line oscillation of a circular cylinder on an incoming incompressible flow field at different Reynolds…
Abstract
Purpose
The purpose of this paper is to investigate the influence of forced, in-line oscillation of a circular cylinder on an incoming incompressible flow field at different Reynolds numbers.
Design/methodology/approach
A space-time finite element approach is employed to model the flow around an oscillating cylinder.
Findings
The results show that two (2S), four (2P, two pair) and three vortices (P+S, one pair and one single) are shed in each cycle. In addition, a 2P o mode is also observed, which is similar to the 2P mode but the vortices of the 2P o mode differ in strength. The 2P mode of vortex shedding is observed along the entire wake of the flow field and 2P o mode in the far wake. In some cases, the vortex street is transformed as it travels towards the exit to produce new patterns. One such pattern is observed for the first time in the present work, which is referred to as 2P o* mode. The drag and lift coefficients observed are perfectly periodic at a Reynolds number of 200 and they reach a chaotic pattern as the Reynolds number is increased to a value of 350.
Originality/value
Originality of the paper lies in the observation of 2P vortex shedding mode or its variants in the downstream of the cylinder.
Details
Keywords
Bharati Mohapatra, Sanjana Mohapatra and Sanjay Mohapatra