Rule-based back propagation neural networks for various precision rough set presented KANSEI knowledge prediction: a case study on shoe product form features extraction

Li, Zairan; Shi, Kai; Dey, Nilanjan; Ashour, Amira S.; Wang, Dan; Balas, Valentina E.; McCauley, Pamela; Shi, Fuqian

doi:10.1007/s00521-016-2707-8

Cited by 54 publications

(11 citation statements)

References 65 publications

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“…Consequently, the process is long term, time consuming and huge memory allocation method, on the contrary to the proposed approach that illustrates dynamically short time discovery ability along with poor memory consumption. Moreover, various recent work regarding real life applications of different contemporary computing techniques associated with fuzzy logic as well as artificial intelligence techniques have been proposed successfully [66][67][68][69][70][71][72][73][74][75][76][77].…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…According to [8,9], texture has been defined as that; it is a continual form of information or an organized structure that is committed at regular intervals. Moreover, texture illustrates characteristics of surface and a visible appearance of the object which means that it would demonstrate the physical magnitude of the elementary parts of the object by size, shape, amount, arrangement.…”

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confidence: 99%

“…The frequency of the grey level has been highlighted by the GLCM method, which is a matrix, and the rate of that grey level has a connectedness between the grey levels where the activities of that grey level could be seen within the specific area of investigation [8]. The amount of electromagnetic information transmitted to produce an image, the GLCM set out a tabular form of various kinds of combinations of grey levels in different stages of the section of the image [11].…”

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confidence: 99%

“…Two parameters could calculate the formative distance between the pixel pair and other relevant features that are closely related to this measure. There are many directions in which the measure could be done, but most commonly used guidelines are the 'four directions' [11]. Contrast and entropy are the most used features in GLCM where fourteen of them are available to measure the correlation of the features [9].…”

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confidence: 99%

“…There are many directions in which the measure could be done, but most commonly used guidelines are the 'four directions' [11]. Contrast and entropy are the most used features in GLCM where fourteen of them are available to measure the correlation of the features [9]. Nowadays, the most important statistical features are co-occurrence matrix and texture features, which were introduced by Haralick [13].…”

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confidence: 99%

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