Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and Clonal Selection Theory Inspired Algorithms

Lasisi, Ayodele; Ghazali, Rozaida; Deris, Mustafa Mat; Herawan, Tutut; Lasisi, Fola

doi:10.1142/s0218001416600089

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…The MLP is known as the most widely used artificial neural networks (ANNs) [40]. ANNs nonlinear generically approximating algorithms with major pattern recognition and classification applications [41,42]. The MLP consists of two layers, i.e., input and output layers, formed by simple nerve cells, and also contains solitary or diverse layers between input and output layers.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

Khairy¹,

Hussein²,

Alrikabi³

2021

IJIES

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The movement of cash flow transactions by either electronic channels or physically created openings for the influx of counterfeit banknotes in financial markets. Aided by global economic integration and expanding international trade, attention must be geared at robust techniques for the recognition and detection of counterfeit banknotes. This paper presents ensemble learning algorithms for banknotes detection. The AdaBoost and voting ensemble are deployed in combination with machine learning algorithms. Improved detection accuracies are produced by the ensemble methods. Simulation results certify that the ensemble models of AdaBoost and voting provided accuracies of up to 100% for counterfeit banknotes.

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Section: Multilayer Perceptronmentioning

confidence: 99%

The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

Khairy¹,

Hussein²,

Alrikabi³

2021

IJIES

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“…26 The fuzzy-rough feature selection and vaguely quanti¯ed rough set feature selection are coupled with CLONALG and AIRS for improved detection and computational e±ciencies. 18 Interactive segmentation of images has become an integral part of image-processing applications. Several graph-based segmentation techniques have been developed, which depend upon global minimization of the energy cost function.…”

Section: Discussionmentioning

confidence: 99%

Predicting Model and Algorithm in RNA Folding Structure Including Pseudoknots

Liu

Zhu

Dai

2018

Int. J. Patt. Recogn. Artif. Intell.

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The prediction of RNA structure with pseudoknots is a nondeterministic polynomial-time hard (NP-hard) problem; according to minimum free energy models and computational methods, we investigate the RNA-pseudoknotted structure. Our paper presents an efficient algorithm for predicting RNA structure with pseudoknots, and the algorithm takes O([Formula: see text]) time and O([Formula: see text]) space, the experimental tests in Rfam10.1 and PseudoBase indicate that the algorithm is more effective and precise. The predicting accuracy, the time complexity and space complexity outperform existing algorithms, such as Maximum Weight Matching (MWM) algorithm, PKNOTS algorithm and Inner Limiting Layer (ILM) algorithm, and the algorithm can predict arbitrary pseudoknots. And there exists a [Formula: see text] ([Formula: see text]) polynomial time approximation scheme in searching maximum number of stackings, and we give the proof of the approximation scheme in RNA-pseudoknotted structure. We have improved several types of pseudoknots considered in RNA folding structure, and analyze their possible transitions between types of pseudoknots.

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