Robust topology optimization using efficient neural network surrogates

Meidani, Hadi; Gladstone, Rini Jasmine; Keshavarzzadeh, Vahid; Nabian, Mohammad Amin

doi:10.26226/morressier.612f6737bc981037241008e5

Cited by 1 publication

(1 citation statement)

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“…It is also based on the functioning of the human brain. The following are the different types of learning as listed below: Supervised Learning: There are various types of supervised learning algorithms which are as follows:  Artificial Neural Network: Artificial neural networks (26)(27)(28)(29)(30)(31) are networks that have the collection of connected units or nodes called artificial neurons. The functioning of this is similar to the human brain consisting of neurons.…”

Section: Deep Learningmentioning

confidence: 99%

Identification of the Quality of Tea Leaves by Using Artificial Intelligence Techniques: A Review

Gaba

Ramamurthy

2022

ECS Trans.

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This paper summarizes the outcome of the survey carried out for quality identification of a tea leaf and eventually price prediction. Quality identification can allow to categorizing leaf in different grades, which helps the buyer and seller to acquire suitable quality to their need. Price prediction is an important feature, which can bring certainty at price and farmers can be benefitted more for their reasonable good quality. Additionally, if the leaf disease is identified at the initial stage that would also allow farmers to timely resolve the concerned issues and save their corps. In the field of agriculture, this has been always a research area to identify and predict the quality of tea leaves. Various artificial intelligence techniques are hot topics in the field of recognition and their effective combination cannot only solve the problem but also enhance recognition accuracy. Therefore, there is an imminent need for a detailed survey on compiling techniques used for the identification of different varieties of tea plants. In this research, we aim to propose a review of the various techniques which can be utilized for determining the quality and price prediction. The survey is hybrid in nature with a combination of different artificial techniques, which is a suitable approach to target effective tea leaf identification. Further for the classification of tea leaf images, various algorithms can be combined as well to obtain better results and different algorithms can be used for feature extraction on the basis of texture extraction, color extraction, and shape extraction.

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