CSCF: a chaotic sine cosine firefly algorithm for practical application problems

Hassan, Bryar A.

doi:10.1007/s00521-020-05474-6

Cited by 117 publications

(43 citation statements)

References 44 publications

(58 reference statements)

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“…Furthermore, an adaptive version of ECA* could be utilised for wound image clustering and analysis in telemedicine and patient monitoring [65,66]. Meanwhile, an improved ECA* could be also used in practical and engineering problems [67], library administration [68], e-government services [69], and multi-dimensional database systems [70], web science [71], and thesemantic Web [72]. On the other hand, the proposed frameworks can be adopted to learn concept hierarchies from other Latin alphabet-based text corpora.…”

Section: Discussionmentioning

confidence: 99%

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

Hassan

Rashid

Mirjalili

2021

Complex Intell. Syst.

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It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using formal context analysis (FCA). However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is require to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising formal concept analysis (FCA); (2) A framework to decrease the formal context’s ambiguity of the first framework using an adaptive version of evolutionary clustering algorithm (ECA*). Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms (Fuzzy K-means, JBOS approach, AddIntent algorithm, and FastAddExtent) to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.

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Section: Discussionmentioning

confidence: 99%

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

Hassan

Rashid

Mirjalili

2021

Complex Intell. Syst.

Self Cite

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“…We also register the mean results for 30 times run on each mentioned dataset. For the future work, adapting and applying ECA* for real applications is a good possibility., ECA* could be used for application problems [ 11 ], e-organisation services [ 12 ], ontology learning [ 15 ], multi-dimensional database systems [ 14 ], and technical and maintenance application [ 13 ].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets

2021

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This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.

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“…But this approach failed to apply in various machine learning algorithms. In recent years, many soft computing approaches has been acheived better performance in engineering applications 27–38.…”

Section: Review Of Related Workmentioning

confidence: 99%

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Alqahtani

Pandiaraj²,

Murali³

et al. 2021

Concurrency and Computation

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Presently, large recordings like audio and video are uploaded each day on the public networks particularly Facebook, YouTube, etc. Such postings generate unlimited data via the Internet. Over the next few decades adapting and dealing with data mining to obtain relevant details from social media is considered as a difficult task. To tackle these challenges, a novel text-audio-video consistency-driven multimodal sentiment analysis method is proposed in this paper. This proposed system examines the correlation among the text, audio and video followed by multimodal sentiment analysis.A different set of features are extracted and then the extracted features are chosen optimally by employing a new hybrid grass bee optimization algorithm (HGBEE) thus obtaining a feature set containing an optimal value for better precision and low computational time. Then an utterance level multimodular fusion regarding text, audio, and video features is developed. Finally, the proposed multikernal extreme learning classifier (MKELM) is employed for sentiment classification. Then the proposed system is evaluated by testing with three multimodal datasets in terms of precision, accuracy, recall, and F-measure, respectively. From the simulation, it is clear that the proposed system accomplishes the maximum classification accuracy value of 98.3% with minimum computation time. The implementation of our proposed approach is done under the MATLAB platform. K E Y W O R D Saccuracy and computation time, feature level fusion, grass bee, kernal extreme learning classifier, multimodal sentiment analysis Recently, the public network facilitates gathering information for posting and sharing various types of multimodal features in social media without knowing much detail regarding the network topology and architecture. 1 Individuals are now widely utilizing the internet-based life condition, for example, YouTube, Facebook, Blog, and Microblog so as to express their opinions. 2 Here, sentiment analysis acts as a key for unlocking huge data since

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CSCF: a chaotic sine cosine firefly algorithm for practical application problems

Cited by 117 publications

References 44 publications

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

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