Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system

Wang, Dan; He, Ting; Li, Zairan; Cao, Luying; Dey, Nilanjan; Ashour, Amira S.; Balas, Valentina E.; McCauley, Pamela; Ying, Lin; Jiang, Xu; Shi, Fuqian

doi:10.1007/s00521-016-2512-4

Cited by 28 publications

(12 citation statements)

References 43 publications

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“…The proposed system achieved the highest overall classification result of ACC = 85.7% for the differential diagnosis between normal and MRD images. Wang et al [69] developed an improved parameter and structure identification of an adaptive neuro-fuzzy inference system (ANFIS) for feature extraction in images. Colour, morphology and texture features were used as inputs and the least-square and k-mean clustering methods were employed as the learning algorithms for such a system.…”

Section: Discussionmentioning

confidence: 99%

Computational diagnosis of skin lesions from dermoscopic images using combined features

Oliveira

Pereira

Tavares

2018

Neural Comput & Applic

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There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is the find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour-and texturerelated features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimumpath forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm.

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Section: Discussionmentioning

confidence: 99%

Computational diagnosis of skin lesions from dermoscopic images using combined features

Oliveira

Pereira

Tavares

2018

Neural Comput & Applic

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“…The results showed the model feasibility and the performed effectiveness for the experimental methods. By comparing with our previous studies [44][45][46], it can be concluded that:…”

Section: Resultsmentioning

confidence: 56%

Meta-KANSEI Modeling with Valence-Arousal fMRI Dataset of Brain

et al. 2018

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“…Interactions among proteins are mapped by the neural networks input weights with associated edges. Consequently, particle swarm analysis counts all the datasets of proteins under fuzzy values [40][41]. These fuzzy values allow combining greater limits of the total proteins with specific hydrocarbons angles associated with nitrogen bonds.…”

Section: Methodsmentioning

confidence: 99%

Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification

Kamal

Sarowar

Dey³

et al. 2017

Int. J. Mach. Learn. & Cyber.

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Proteins have a significant role in animals and human health. Interactions among proteins are complex and large. Proteins separations are challenging process in molecular biology. Computational tools help to simulate the analysis in order to reduce the training data into small testing data. Large proteins have been mapped using self-organizing maps (SOMs). Neural network based SOMs has a significant role in reducing the irregular shapes of proteins interactions. Iterative checking enables the organizations of all proteins. In next stage, particle swarm intelligence is applied to classify the proteins' families. In the current work, secondary (Two dimensional) and tertiary proteins (Three dimensional) proteins have been grouped. Two dimensional proteins contain fewer hydrocarbons than three dimensional proteins. For faster analysis, the angles of the proteins are taken into account. The SOMs is compared with Bounding Box approach. In final, the experimental evolutions show that swarm intelligence achieved faster processing through enabling less memory consumptions and time. Since PSO combines proteins datasets in fuzzy values, the compactness or integration of similar proteins are strong. On the other hand, Bounding Box uses the Crisp value. Therefore, it needs more space to organize the whole data. Without SOMs, swarm intelligence also results are poor due to the excessive time consuming and required storage area. Moreover, for almost all classification and clustering tools, it is observed that the overall classification task becomes slow, time consuming, space consuming and also less sensitive because of noises, irrelevant data in input datasets. Thus, the proposed SOM based PSO approach achieved less time consuming with efficient classification into secondary and tertiary proteins.

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Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system

Cited by 28 publications

References 43 publications

Computational diagnosis of skin lesions from dermoscopic images using combined features

Computational diagnosis of skin lesions from dermoscopic images using combined features

Meta-KANSEI Modeling with Valence-Arousal fMRI Dataset of Brain

Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification

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