A projection-based recurrent neural network and its application in solving convex quadratic bilevel optimization problems

Golbabai, A.; Ezazipour, S.

doi:10.1007/s00521-019-04391-7

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…Case studies from central banks such as JPMorgan Chase and Bank of America illustrate how the implementation of data analytics has streamlined operations, reduced costs, and increased overall efficiency (Constantiou & Kallinikos, 2015;Golbabai & Ezazipour, 2019).…”

Section: Applications Of Data Analytics In Financial Servicesmentioning

confidence: 99%

Untitled

2024

GMJ

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Section: Applications Of Data Analytics In Financial Servicesmentioning

confidence: 99%

Untitled

2024

GMJ

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“…Neural networks and ANFIS have been used in many fields of engineering, science, economics, and so on 13‐17 . So far, numerous algorithms have been proposed based on neural networks to diagnose different diseases and pests on tomato leaves.…”

Section: Related Workmentioning

confidence: 99%

“…Neural networks and ANFIS have been used in many fields of engineering, science, economics, and so on. [13][14][15][16][17] So far, numerous algorithms have been proposed based on neural networks to diagnose different diseases and pests on tomato leaves. For example, in 2017, Brahimi et al 18 In 2019, Picon et al 21 applied a mobile application to classify four different types of wheat diseases (including Rust, Septoria, Tan Spot, and Other/Healthy) for 8178 images.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Alternaria disease and leafminer pest on tomato leaves using image processing techniques

2022

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BACKGROUND: Diseases such as Alternaria and pests such as leafminer threaten tomato as one of the most widely used agricultural products. These pests and diseases first damage the leaves of tomatoes, then the flowers, and finally the fruit. Therefore, the damage to the tomato tree must be controlled in its early stages. It is difficult for farmers to distinguish Alternaria disease from leafminer pest at the early and middle stages of their outbreak on tomato leaves. In the present study, 272 tomato leaf images were prepared from the farm of the Vali-e-Asr University of Rafsanjan, including 100 healthy leaves and 172 infected leaves with both Alternaria and leafminer at the initial stages. The image processing technique, texture, neural networks and adaptive network-based fuzzy inference system (ANFIS) classifiers were used to diagnose Alternaria disease and leafminer pest on this dataset. RESULTS:The results showed that the ANFIS classifier achieved an accuracy of 84.71% when performing an equal error rate, 87.78% in the area under the curve, and 85.23% in 3.26 s on the central processing unit for the segmentation of Alternaria disease and leafminer pest in RGB color space. Also, the accuracy of 90% and 98% were obtained for segmentation and classification on the PlantVillage dataset in YCBCR color space.CONCLUSION: The present study suggests a high classification accuracy for an intelligent selection of pixel values to train the ANFIS classifier. This classifier has high accuracy and speed, low sensitivity to the light intensity of images, and practical application in diagnosing various diseases and pests on numerous datasets.

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