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      WaterHyacinth: A comprehensive image dataset of various Water hyacinth species from different regions of Bangladesh

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          Abstract

          The “WaterHyacinth” dataset, a recently gathered collection of images featuring four distinct species of Water hyacinth from different regions of Bangladesh, is presented in this article. There are four different classifications: Lemna minor, Eichhornia crassipes, Monochoria korsakowii, and Pistia stratiotes. The collection consists of 1790 original images and in addition 4050 augmented photos of Water hyacinth species. Every original picture was captured with the appropriate background and in sufficient natural light. Every image was correctly placed in its corresponding subfolder, providing optimal use of the pictures by various machine learning and deep learning models. Researchers could make major progress in agriculture, environmental monitoring, aquatic science, and remote sensing domains by utilizing this enormous dataset and various machine learning and deep learning approaches. In addition to opening opportunities for significant developments in these domains, it offers an essential asset for further study.

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          Most cited references13

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          Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning

          Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma.
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            Application of common duckweed (Lemna minor) in phytoremediation of chemicals in the environment: State and future perspective

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              • Record: found
              • Abstract: not found
              • Article: not found

              Water hyacinths as a resource in agriculture and energy production: A literature review

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                Author and article information

                Contributors
                Journal
                Data Brief
                Data Brief
                Data in Brief
                Elsevier
                2352-3409
                29 November 2023
                February 2024
                29 November 2023
                : 52
                : 109872
                Affiliations
                [a ]Department of Computer Science and Engineering, Khwaja Yunus Ali University, Sirajganj, Bangladesh
                [b ]Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
                [c ]Department of Computer Science and Engineering, Uttara University, Dhaka, Bangladesh
                Author notes
                [* ]Corresponding author at: Department of Computer Science and Engineering, Khwaja Yunus Ali University, Sirajganj, Bangladesh. tareq.cse@ 123456kyau.edu.bd
                Article
                S2352-3409(23)00932-0 109872
                10.1016/j.dib.2023.109872
                10754702
                1bfb613b-6466-4556-8a5b-4d3de93ef4b7
                © 2023 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 October 2023
                : 15 November 2023
                : 24 November 2023
                Categories
                Data Article

                eichhornia species classification,computer vision,deep learning,image classification

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