WITHDRAWN: A novel approach for image forgery detection using improved crow search algorithm

Geetha, M.; Saidhbi, Sheik; Gampala, Veerraju; Maram, Balajee

doi:10.1016/j.matpr.2020.12.1135

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Chaudhuri et al [62] developed a binary CSA with a time-varying flight length to solve the feature selection problem, and the results showed that the proposed feature selection technique behaved better than other approaches. Geetha et al [63] designed an enhanced CSA for a forgery detection technique in the image, and the results exhibited that the proposed classification performs better than most algorithms. Guha et al [64] used the CSA with chaotic mapping for fine-turning the controller and varied the efficacy of the controller in its frequency regulation, as validated.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang¹,

Yan

et al. 2022

Applied Sciences

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College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang¹,

Yan

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

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Development of precise forgery detection algorithms in digital radiography images using convolution neural network

El_Tokhy

2023

Applied Soft Computing

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WITHDRAWN: A novel approach for image forgery detection using improved crow search algorithm

Cited by 2 publications

References 6 publications

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Development of precise forgery detection algorithms in digital radiography images using convolution neural network

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