Identification and Classification of Atmospheric Particles Based on SEM Images Using Convolutional Neural Network with Attention Mechanism

The airborne disease is a severe disease in the world that spreads exponentially. An Immunochromatography Test(IT) and Biological Aerosol Particles (BAP) test with a disposable instrument that will be recorded in the literature is a standard diagnostic procedure for respiratory infections, such as influenza and TB, in the investigation causes of suspected disease. This examination helps the examiner to identify infectious patients quickly, efficiently, and inexpensively. Self-diagnosis, however, is problematic because it requires a medical specialist to diagnose the diseases in infections. Chest radiography is also not only the primary screening method that is focused on surgical imaging but also diagnostic radiology. Various methods of classification of Tuberculosis are analyzed here. Computer-Assisted Diagnosis (CAD) has become popular, and many researchers are involved in these fields of study. Numerous methods have been suggested for airborne identification and Classification of lung diseases. The medical history of airborne tuberculosis disease in chest X-rays and a review of the different approaches to diagnosing and Classification of the airborne disease are discussed in this article. According to the literature on the relevant policies, items in this research field have been surveyed until 2020.

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“…The SVM algorithm composition is more complicated than other approaches. This results in low transparency of results [34].…”

Section: Support Vector Machinementioning

confidence: 99%

Analysis of Deep Learning Techniques for Tuberculosis Disease

2021

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“…High humidity environment has potential to produce corrosive activity, because of dissolving gases, like O2, CO2 and other minerals (Barker et al 2018). For example, in the oil and natural gas industry, oilfield formation always contains high concentrations of chlorides, carbonates, sulfates, and dissolved gases, such as H2S and CO2, which react with pipelines, causing corrosion, so oil leaking to the environment (Yin et al 2020). Corrosion causes the pipe lifespan decreases, causing economic losses and environmental pollution (Wasim et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Propolis extract as a green bacterial corrosion inhibitor on three types of metals

et al. 2022

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Abstract. Watiniasih NL, Budiarsa IN, Antara ING, Wiradana PA. 2022. Propolis extract as a green bacterial corrosion inhibitor on three types of metals. Biodiversitas 23: 4852-4860. Corrosion causes severe damage to various types of metals, which is a serious problem in the infrastructure sector. Several studies have been conducted to find more efficient natural inhibitors. This study was aimed to determine the effectiveness of antioxidants from propolis extract in inhibiting the growth of bacteria that cause corrosion on various metals. The ethanol extract was carried out to obtain the bioactive contained in the propolis extract, then analyzed its antioxidant activity with DPPH and characterized using GC-MS. Various concentration of propolis extract were tested on NA+1% Fe medium to determine its resistance to corrosion-causing bacteria. Propolis extract was tested on several types of metals in inhibiting the corrosion rate. The results showed that the tested propolis extract had strong antioxidant activity with an IC50 value of 82.496 ppm. The characterization of the metabolite profile showed that the propolis extract was dominated by chemical components that act as antioxidants and antibacterial agents. In the future, the results of this study can be used as a practical guide for the application of bioactive compounds through a biocontrol approach to the growth of corrosion-causing bacteria.

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“…It is necessary to apply the combination of three deep learning methods to study the prediction of traffic flow. In addition, the attention mechanism theory [59] has the function of improving the data extraction capabilities of deep learning by imitating human vision to assign weights to data features and has been widely used in image processing and speech recognition [60][61][62][63]. Applying it to CNN, LSTM, and GRU deep learnings for traffic flow prediction is also worthy of discussion.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

Ren

Chai

Yin

et al. 2021

Journal of Advanced Transportation

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Short-term traffic flow prediction can provide a basis for traffic management and support for travelers to make decisions. Accurate short-term traffic flow prediction also provides necessary conditions for the sustainable development of the traffic environment. Although the application of deep learning methods for traffic flow prediction has achieved good accuracy, the problem of combining multiple deep learning methods to improve the prediction accuracy of a single method still has a margin for in-depth research. In this article, a combined deep learning prediction (CDLP) model including two paralleled single deep learning models, CNN-LSTM-attention model and CNN-GRU-attention model, is established. In the model, a one-dimensional convolutional neural network (1DCNN) is used to extract traffic flow local trend features and RNN variants (LSTM and GRU) with attention mechanism are used to extract long temporal dependencies trend features. Moreover, a dynamic optimal weighted coefficient algorithm (DOWCA) is proposed to calculate the dynamic weights of CNN-LSTM-attention and CNN-GRU-attention with the goal of minimizing the sum of squared errors of the CDLP model. Then, the neuron number, loss function, optimization algorithm, and other parameters of the CDLP model are discussed and set through experiments. Finally, the training set and test set for the CDLP model are established through the processing of traffic flow data collected from the field. The CDLP model is trained and tested, and the prediction results of traffic flow are obtained and analyzed. It indicates that the CDLP model can fit the change trend of traffic flow very well and has better performance. Furthermore, under the same dataset, the results from the CDLP model are compared with baseline models. It is found that the CDLP model has higher prediction accuracy than baseline models.

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Identification and Classification of Atmospheric Particles Based on SEM Images Using Convolutional Neural Network with Attention Mechanism

Cited by 14 publications

References 42 publications

Analysis of Deep Learning Techniques for Tuberculosis Disease

Analysis of Deep Learning Techniques for Tuberculosis Disease

Propolis extract as a green bacterial corrosion inhibitor on three types of metals

Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

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