Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

Zhang, Yudong; Yang, Xiao‐Jun; Cattani, Carlo; Rao, R. Venkata; Wang, Shuihua; Phillips, P. Jonathon

doi:10.3390/e18030077

Cited by 89 publications

(26 citation statements)

References 57 publications

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“…Similarly, in human relationships, we have daily interactions and these relationships also become disordered. We require input to stabilize any relationship, to iron out the wrinkles or differences, so that we don't possess and store things forever [1][2][3][4]. The study of interpersonal and romantic relations has been gaining popularity during the past ten years.…”

Section: Introductionmentioning

confidence: 99%

A Novel Numerical Approach for a Nonlinear Fractional Dynamical Model of Interpersonal and Romantic Relationships

Singh

Kumar

Qurashi

et al. 2017

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Abstract:In this paper, we propose a new numerical algorithm, namely q-homotopy analysis Sumudu transform method (q-HASTM), to obtain the approximate solution for the nonlinear fractional dynamical model of interpersonal and romantic relationships. The suggested algorithm examines the dynamics of love affairs between couples. The q-HASTM is a creative combination of Sumudu transform technique, q-homotopy analysis method and homotopy polynomials that makes the calculation very easy. To compare the results obtained by using q-HASTM, we solve the same nonlinear problem by Adomian's decomposition method (ADM). The convergence of the q-HASTM series solution for the model is adapted and controlled by auxiliary parameter and asymptotic parameter n. The numerical results are demonstrated graphically and in tabular form. The result obtained by employing the proposed scheme reveals that the approach is very accurate, effective, flexible, simple to apply and computationally very nice.

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Section: Introductionmentioning

confidence: 99%

A Novel Numerical Approach for a Nonlinear Fractional Dynamical Model of Interpersonal and Romantic Relationships

Singh

Kumar

Qurashi

et al. 2017

Entropy

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“…Besides, we will try to apply our method to remote-sensing related fields or hearing loss detection [58]. The advanced parameter estimation, case-based reasoning [59], and optimization [60] techniques will be carried out in a thorough way. Fuzzy method [61] may be applied to remove outliers in the dataset.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

et al. 2016

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(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256ˆ256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s "variance and entropy (VE)" features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our "DTCWT + VE + TSVM" obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible.

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“…Apart from the traditional color histogram features, they introduce fractional Fourier entropy for extracting additional features. Several tests demonstrate the effectiveness of the technique [12].…”

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confidence: 95%

Computational Complexity

Machado

Lopes

2017

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Complex systems (CS) involve many elements that interact at different scales in time and space. The challenges in modeling CS led to the development of novel computational tools with applications in a wide range of scientific areas. The computational problems posed by CS exhibit intrinsic difficulties that are a major concern in Computational Complexity Theory.This Special Issue (SI) focused on new theoretical and practical findings, and computational methods, applicable to difficult problems, whose solution demands extensive and powerful resources, approaching the limits of the available computer systems. It comprises 12 selected papers that are presented in the sequel in alphabetic order.In the paper "A Comparison of Classification Methods for Telediagnosis of Parkinson's Disease", Haydar Ozkan presents a novel telemedicine technology aimed to remotely detect Parkinson's Disease by means of dysphonia features. Several feature transformation and machine-learning methods were implemented and tested [1].The paper "A Complexity-Based Approach for the Detection of Weak Signals in Ocean Ambient Noise", by Shashidhar Siddagangaiah, Yaan Li, Xijing Guo, Xiao Chen, Qunfei Zhang, Kunde Yang and Yixin Yang, proposes using dynamical and statistical complexity to detect the presence of weak ship noise embedded in ambient noise. They demonstrate that complexity performs better than the traditional spectrogram-based methods [2].The paper "Empirical Laws and Foreseeing the Future of Technological Progress", by António M. Lopes, J. A. Tenreiro Machado and Alexandra M. Galhano, revisits the Moore's law (ML) as one of many empirical expressions that is used to characterize natural and artificial phenomena. The ML addresses technological progress and is expected to predict future trends. Data series of multiple sources, available for scientific and computational processing, can be described by means of mathematical expressions that, in some cases, follow simple expressions and empirical laws. However, the extrapolation toward the future is considered with skepticism by the scientific community, particularly in the case of phenomena involving complex behavior. The authors address these issues in the light of entropy and pseudo-state space. Their statistical and dynamical techniques lead to a more assertive perspective on the adoption of a given candidate law [3].In "Entropy Complexity and Stability of a Nonlinear Dynamic Game Model with Two Delays", Zhihui Han, Junhai Ma, Fengshan Si and Wenbo Ren propose a duopoly game model with double delays in hydropower market. They analyze the influence of time delay parameters on the system complexity [4].In "Normalized Minimum Error Entropy Algorithm with Recursive Power Estimation", Namyong Kim and Kihyeon Kwon present a normalized version of the minimum error entropy

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Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

Cited by 89 publications

References 57 publications

A Novel Numerical Approach for a Nonlinear Fractional Dynamical Model of Interpersonal and Romantic Relationships

A Novel Numerical Approach for a Nonlinear Fractional Dynamical Model of Interpersonal and Romantic Relationships

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

Computational Complexity

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