Deep learning methods in protein structure prediction

Torrisi, Mirko; Pollastri, Gianluca; Le, Quan

doi:10.1016/j.csbj.2019.12.011

Cited by 231 publications

(141 citation statements)

References 142 publications

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“…It resembles the structure of animal visual cortex [34,35]. Convolutional layers, pooling layers, fully connected layers, and normalization layers constitute CNN's hidden layers [23,36]. Its learning approach is supervised, as it learns image features from labeled dataset.…”

Section: Forecasting Models Using Neural Network (Gfm_nn)mentioning

confidence: 99%

“…RNN is also another class of artificial neural network. It is called recurrent because of its repetition of same process over all members of a sequence, where each predictive output is influenced by multiple previous observations [23]. RNN has its own memory, where it stores information concerning all of the calculations.…”

Section: Forecasting Models Using Neural Network (Gfm_nn)mentioning

confidence: 99%

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An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Kabir

Islam

Hossain

et al. 2020

Sensors

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Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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Section: Forecasting Models Using Neural Network (Gfm_nn)mentioning

confidence: 99%

Section: Forecasting Models Using Neural Network (Gfm_nn)mentioning

confidence: 99%

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Kabir

Islam

Hossain

et al. 2020

Sensors

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“…The recent development of machine learning techniques, especially deep learning, have provided new opportunities to improve air quality research. Deep learning consists of an artificial intelligence (AI) system that can obtain data unsupervised, in unstructured or unlabeled learning approaches such as deep neural network or deep neural learning methods [4]. Deep learning requires three essential elements: the graphics processing unit (GPU), which controls operation processing speed; vast quantities of data for experiments; and signal information processing.…”

Section: Introductionmentioning

confidence: 99%

Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

2020

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Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

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“…A graph of these hierarchies is many artificial neurons, which are connected layers as illustrated in Figure 1. In this connection, an output of one artificial neuron automatically becomes a piece of input information to another [4][5][6][7][8]. Combining fuzzy logic and DNN allows for the development of an AI model that is not only accurate in prediction but inherently interpretable and understandable to humans.…”

Section: Introductionmentioning

confidence: 99%

Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning

Kesse

Buah

Handroos

et al. 2020

Metals

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Recent developments in artificial intelligence (AI) modeling tools allows for envisaging that AI will remove elements of human mechanical effort from welding operations. This paper contributes to this development by proposing an AI tungsten inert gas (TIG) welding algorithm that can assist human welders to select desirable end factors to achieve good weld quality in the welding process. To demonstrate its feasibility, the proposed model has been tested with data from 27 experiments using current, arc length and welding speed as control parameters to predict weld bead width. A fuzzy deep neural network, which is a combination of fuzzy logic and deep neural network approaches, is applied in the algorithm. Simulations were carried out on an experimental test dataset with the AI TIG welding algorithm. The results showed 92.59% predictive accuracy (25 out of 27 correct answers) as compared to the results from the experiment. The performance of the algorithm at this nascent stage demonstrates the feasibility of the proposed method. This performance shows that in future work, if its predictive accuracy is improved with human input and more data, it could achieve the level of accuracy that could support the human welder in the field to enhance efficiency in the welding process. The findings are useful for industries that are in the welding trade and serve as an educational tool.

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Deep learning methods in protein structure prediction

Cited by 231 publications

References 142 publications

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning

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