An improvement of Gram-negative bacteria identification using convolutional neural network with fine tuning

Utoyo, Imam; Rulaningtyas, Riries; Khoendori, Eko Budi

doi:10.12928/telkomnika.v18i3.14890

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…It is not surprising then that identification or classification of individuals or species from image, video, and sound data is the most common use of deep learning in the field (Figure 3). These efforts already span many taxa, from bacteria (Satoto et al, 2020), through protozoans (Hsiang et al, 2019), plants (Unger et al, 2016;Carranza-Rojas et al, 2017;Schuettpelz et al, 2017;Younis et al, 2018) to insects (Marques et al, 2018;Boer and Vos, 2018;Valan et al, 2019;Hansen et al, 2020) and vertebrates (Villon et al, 2018;Norouzzadeh et al, 2018), both extant and fossil (Liu and Song, 2020;Miele et al, 2020;de Lima et al, 2020) and at scales ranging from local to global. Intensifying efforts to digitize natural history collections provide troves of image data that can be used for this purpose (Smith and Blagoderov, 2012).…”

Section: Automated Species Identificationmentioning

confidence: 99%

Deep learning as a tool for ecology and evolution

et al. 2022

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Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, ecological modelling, behavioural studies, DNA sequencing and population genetics and phylogenetics, among other applications. Deep learning relies on artificial neural networks for predictive modelling and excels at recognizing complex patterns. In this review we synthesize 818 studies using deep learning in the context of ecology and evolution to give a discipline‐wide perspective necessary to promote a rethinking of inference approaches in the field. We provide an introduction to machine learning and contrast it with mechanistic inference, followed by a gentle primer on deep learning. We review the applications of deep learning in ecology and evolution and discuss its limitations and efforts to overcome them. We also provide a practical primer for biologists interested in including deep learning in their toolkit and identify its possible future applications. We find that deep learning is being rapidly adopted in ecology and evolution, with 589 studies (64%) published since the beginning of 2019. Most use convolutional neural networks (496 studies) and supervised learning for image identification but also for tasks using molecular data, sounds, environmental data or video as input. More sophisticated uses of deep learning in biology are also beginning to appear. Operating within the machine learning paradigm, deep learning can be viewed as an alternative to mechanistic modelling. It has desirable properties of good performance and scaling with increasing complexity, while posing unique challenges such as sensitivity to bias in input data. We expect that rapid adoption of deep learning in ecology and evolution will continue, especially in automation of biodiversity monitoring and discovery and inference from genetic data. Increased use of unsupervised learning for discovery and visualization of clusters and gaps, simplification of multi‐step analysis pipelines, and integration of machine learning into graduate and postgraduate training are all likely in the near future.

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Section: Automated Species Identificationmentioning

confidence: 99%

Deep learning as a tool for ecology and evolution

et al. 2022

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“…Dropout reduces the loss value during the training process, also helps prevent overfitting [24]. The normalization layer normalized values come from the hidden layer.…”

Section: Transformer Modelmentioning

confidence: 99%

Summarization of COVID-19 news documents deep learning-based using transformer architecture

2021

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Facing the news on the internet about the spreading of Corona virus disease 2019 (COVID-19) is challenging because it is required a long time to get valuable information from the news. Deep learning has a significant impact on NLP research. However, the deep learning models used in several studies, especially in document summary, still have a deficiency. For example, the maximum output of long text provides incorrectly. The other results are redundant, or the characters repeatedly appeared so that the resulting sentences were less organized, and the recall value obtained was low. This study aims to summarize using a deep learning model implemented to COVID-19 news documents. We proposed transformer as base language models with architectural modification as the basis for designing the model to improve results significantly in document summarization. We make a transformer-based architecture model with encoder and decoder that can be done several times repeatedly and make a comparison of layer modifications based on scoring. From the resulting experiment used, ROUGE-1 and ROUGE-2 show the good performance for the proposed model with scores 0.58 and 0.42, respectively, with a training time of 11438 seconds. The model proposed was evidently effective in improving result performance in abstractive document summarization.

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“…The amount of convolution and pooling layer depends on the complexity of the case. The convolution layer consists of several groups of features and the pooling layer consists of a reduction or summary of several groups of features[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]. Here are the detailed steps of deep learning with PSO: a.…”

mentioning

confidence: 99%

Prediction of rainfall using improved deep learning with particle swarm optimization

Cholissodin¹,

Sutrisno²

2020

TELKOMNIKA

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Rainfall is a natural factor that is very important for farmers or certain institutions to predict the planting period of a plant. The problem is that rainfall is very difficult to predict. Trials to get optimal rainfall prediction have been carried out by BMKG through research with variety of methods in various fields, including meteorology, climatology and geophysics. The results of the study unfortunately obtained a less optimal success rate in predicting rainfall. Today, there are many new methods for predicting events. These methods include deep learning (DL) and Particle swarm optimization (PSO). The use of the deep learning method is very susceptible to initial weights that are less than optimal, so it requires a process of optimization using a metaheuristic technique, which is the PSO algorithm, because this algorithm has a level of complexity that is much lower than genetic algorithms. In this study, this method is utilized to predict rainfall by determining the exact regression equation model according to the number of layers in hidden nodes based on the size of the kernel and the weight between the layers. This research is approved achieved get more optimal rainfall prediction results that those of previous research that without optimization with PSO.

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An improvement of Gram-negative bacteria identification using convolutional neural network with fine tuning

Cited by 8 publications

References 25 publications

Deep learning as a tool for ecology and evolution

Deep learning as a tool for ecology and evolution

Summarization of COVID-19 news documents deep learning-based using transformer architecture

Prediction of rainfall using improved deep learning with particle swarm optimization

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