Deep Genetic Algorithm-Based Voice Pathology Diagnostic System

Ghoniem, Rania M.

doi:10.1007/978-3-030-23281-8_18

Cited by 13 publications

(14 citation statements)

References 25 publications

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“…Moreover, the domain-specific knowledge can be incorporated by the GA in all combinatorial or optimization phases to dictate the strategy of search, in contrary to TS and SA, which lack this feature. Therefore, based on the proven superiority of the GA and WOA in many applications [30][31][32]37,38] and to overcome the drawbacks of the ordinary WOA, this work further demonstrates the robustness of the proposed hybrid genetic-whale optimization algorithm (G-WOA) to optimize ontology learning from Arabic texts, in which the GA algorithm is used to optimize the exploitation capability of the ordinary WOA algorithm and solve its premature convergence issue by combining the genetic operations of GA into the WOA.…”

Section: Introductionmentioning

confidence: 76%

“…The GAs [30][31][32] are random-search algorithms that are inspired by natural genetic mechanism and biological natural selection, which belong to the computational intelligence algorithms. The GA emulates the reproduction, crossover, and mutation in the process of genetic mechanism and natural selection.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Recently, the hybrid approaches of different bio-inspired optimization algorithms [27][28][29] demonstrated competitive performances in different applications of computer science, where two or more algorithms of the following are used as hybrid to optimize the problem in the domain of interest: the genetic algorithm (GA) [30][31][32], social spider optimization [33,34], ant colony optimization (ACO) [35,36], and whale optimization algorithm (WOA) [37,38]. These methods have several merits, including having a small parameter set, simple frameworks, and capability to avoid the shortcoming of being trapped in the local optima.…”

Section: Introductionmentioning

confidence: 99%

“…Thirdly, investigating the comparable performance between the proposed G-WOA and five other bio-inspired optimization algorithms [32,39,[44][45][46], when learning ontology from Arabic text, where its solutions are also compared to those obtained by the other algorithms, across different Arabic corpora. To the best of our knowledge, the proposed and compared bio-inspired algorithms have not been investigated in Arabic or non-Arabic ontology learning yet.…”

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confidence: 99%

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A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

2019

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Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.

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Section: Introductionmentioning

confidence: 76%

Section: Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

2019

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“…They are one of the natural inspired algorithms [42][43][44]. GAs are based upon the Darwinian evolution concepts which include an initialization stage, fitness function for chromosome evaluation, natural selection method, crossover process, and mutation operator [45].…”

Section: Background 31 Genetic Algorithms For Path Planningmentioning

confidence: 99%

A Novel Path Planning System for Guidance of Blind People using Integration of GA and A* Techniques

Mohamed¹,

E.²,

F.³

2019

IJCA

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It is a challenging task for the blind people to perceive indoor environment information and walk independently. Path planning is a crucial requirement to provide constant assistance for blind people to navigate from one location to another. This paper presents a novel system for path planning to assist blind people for walking through the indoor environment and avoiding the obstacles. The algorithm is based on genetic algorithm and A* algorithm to select the shortest path that achieves the lowest computational time. A comparison a mong the GA, A* and the new algorithm shows that is faster and shorter than the previous algorithms.

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Significant pathological voice discrimination by computing posterior distribution of balanced accuracy

Pakravan

Jahed

2022

Biomedical Signal Processing and Control

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Deep Genetic Algorithm-Based Voice Pathology Diagnostic System

Cited by 13 publications

References 25 publications

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

A Novel Path Planning System for Guidance of Blind People using Integration of GA and A* Techniques

Significant pathological voice discrimination by computing posterior distribution of balanced accuracy

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