Hybrid Algorithm of Cuckoo Search and Particle Swarm Optimization for Natural Terrain Feature Extraction

Kundra, Harish; Sadawarti, Harsh

doi:10.3923/rjit.2015.58.69

Cited by 25 publications

(11 citation statements)

References 2 publications

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“…Many scholars have put forward various MCDM methods based on the uncertainty theory with the aim of analyzing and solving decision-making problems [13][14][15][16][17][18][19][20]. The MCDM methods based on the extensions of fuzzy sets proposed by Zadeh [21] are increasingly common.…”

Section: The Mcdm Methods Based On Fuzzy Set Theoriesmentioning

confidence: 99%

Decision Support Algorithm for Selecting an Antivirus Mask over COVID-19 Pandemic under Spherical Normal Fuzzy Environment

Yang

Garg

et al. 2020

IJERPH

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With the rapid outbreak of COVID-19, most people are facing antivirus mask shortages. Therefore, it is necessary to reasonably select antivirus masks and optimize the use of them for everyone. However, the uncertainty of the effects of COVID-19 and limits of human cognition add to the difficulty for decision makers to perfectly realize the purpose. To maximize the utility of the antivirus mask, we proposed a decision support algorithm based on the novel concept of the spherical normal fuzzy (SpNoF) set. In it, firstly, we analyzed the new score and accuracy function, improved operational rules, and their properties. Then, in line with these operations, we developed the SpNoF Bonferroni mean operator and the weighted Bonferroni mean operator, some properties of which are also examined. Furthermore, we established a multi-criteria decision-making method, based on the proposed operators, with SpNoF information. Finally, a numerical example on antivirus mask selection over the COVID-19 pandemic was given to verify the practicability of the proposed method, which the sensitive and comparative analysis was based on and was conducted to demonstrate the availability and superiority of our method. Admittedly, with this terrible virus, everyone is eager to get the best mask, but different groups of people should have dissimilar specific needs. Especially under the situation of COVID-19 spreading and a mask shortage, it is everyone's basic duty and obligation to resist COVID-19 without excessive protection. Therefore, for different groups of people, the rational choice and utilization of masks has pivotal practical significance. Obviously, the best masks do not mean the most expensive ones. For most people, it is not necessary to use masks with the same standard as those used by front-line medical staff. The suitability of an antivirus mask is not only related to the protective effect of the mask itself, but also to the tightness (the leakage rate) of the combination of the mask and the human face. In addition, under the severe epidemic situation and the lack of antivirus masks, the choice of masks is also relevant to factors such as reusability and quality of raw materials. Therefore, for the vast majority of people, under the severe epidemic situation, it is essential to consider multiple factors when choosing a mask to optimize the allocation of medical resources. However, people's limited knowledge and the uncertainty of the COVID-19 expansion increases the difficulty and complexity for decision-making about selecting a reasonable antivirus mask. To cope with this challenge, this paper proposed a multi-criteria decision-making (MCDM) method for selecting an antivirus mask under spherical normal fuzzy using the Bonferroni Mean operator.The study yields a number of contributions as follows:(1) A novel concept of the Spherical normal fuzzy (SpNoFS) set is defined, between which the new score, the accuracy function, and some improved operational rules are established. (2) Some new information aggregation operators based on ...

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Section: The Mcdm Methods Based On Fuzzy Set Theoriesmentioning

confidence: 99%

Decision Support Algorithm for Selecting an Antivirus Mask over COVID-19 Pandemic under Spherical Normal Fuzzy Environment

Yang

Garg

et al. 2020

IJERPH

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“…So, hybridization of classification and optimization approach was proposed in the past studies to improve the accuracy and error rate. The results improved the accuracy and terrain features successfully detected from the images [6]. The improved results enforced the researchers to use the machine learning techniques to attain the required results.…”

Section: Related Workmentioning

confidence: 77%

The Hybridization of Neural Network and Particle Swarm Optimization for Natural Terrain Feature Extraction

Dhingra*,

Kumar

2019

IJITEE

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The optimization of various soft computing and metaheuristic techniques can be ameliorated in a global area network, Swarm intelligence. In this research, a hybrid algorithm of neural network and particle swarm optimization has been presented for remote sensing applications. The terrain features of the land in a remote sensing image have been classified using these algorithms. Remote sensing basically deals with the processing and interpretation of satellite images without any physical contact to that particular region. In addition, the geospatial characteristics of the data also recorded during image classification. The hybrid concept used in this research, the implementation of algorithm in this paper based on the neurons network to find the best solution, which is further resolved using the Particle Swarm Optimization approach, an optimization technique. The proposed algorithm easily classifies the terrain features with higher efficiency and kappa coefficient value. The results show that 94.36% accuracy attained from the proposed technique. The overall accuracy improved by 5.24 % and 14.93% and kappa coefficient enhancement of 6.97 % and 18.99 % in comparison to existing studies.

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“…Pixelbased as well as Object-based. The pixel-based techniques are further divided into unsupervised and supervised techniques [18]. A brief about various classification methods are tabulated in the Table 3.…”

Section: Classification Methodsmentioning

confidence: 99%

A review of remotely sensed satellite image classification

Dhingra

Kumar

2019

IJECE

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<p>Satellite image classification has a vital role for the extraction and analysis of the useful satellite image information. This paper comprises the study of the satellite images classification and Remote Sensing along with a brief overview of the previous studies that are proposed in this field. In this paper, the existing work has been explained utilizing the classification techniques on satellite images of Alwar region in India that covers decent land cover features like Vegetation, Water, Urban, Barren, and Rocky regions. The post- implementation of the classification algorithms, the classified image is obtained displaying different classes that are represented by different colours. Each feature is represented by a different colour and can be easily perceived from the image obtained after classification. The focus of this study is on enhancing the classification accuracy by using proper classifiers along with the novel feature extraction techniques and pre-processing steps. Work of different authors is being discussed in a tabular form defining the methods and outcomes of the respective studies.</p>

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Hybrid Algorithm of Cuckoo Search and Particle Swarm Optimization for Natural Terrain Feature Extraction

Cited by 25 publications

References 2 publications

Decision Support Algorithm for Selecting an Antivirus Mask over COVID-19 Pandemic under Spherical Normal Fuzzy Environment

Decision Support Algorithm for Selecting an Antivirus Mask over COVID-19 Pandemic under Spherical Normal Fuzzy Environment

The Hybridization of Neural Network and Particle Swarm Optimization for Natural Terrain Feature Extraction

A review of remotely sensed satellite image classification

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