Prediction of SO2 and PM10 air pollutants using a deep learning-based recurrent neural network: Case of industrial city Sakarya

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

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A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽

10.3390/electronics10010081 ◽

2021 ◽

Vol 10 (1) ◽

pp. 81

Author(s):

Jianbin Xiong ◽

Dezheng Yu ◽

Shuangyin Liu ◽

Lei Shu ◽

Xiaochan Wang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Plant Species ◽

Image Recognition ◽

Recurrent Neural Network ◽

Plant Diseases ◽

Learning Methods ◽

Smart Agriculture ◽

Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

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Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting

Symmetry ◽

10.3390/sym13060931 ◽

2021 ◽

Vol 13 (6) ◽

pp. 931

Author(s):

Kecheng Peng ◽

Xiaoqun Cao ◽

Bainian Liu ◽

Yanan Guo ◽

Wenlong Tian

Keyword(s):

Neural Network ◽

Time Series ◽

Deep Learning ◽

South Asian ◽

Recurrent Neural Network ◽

Ensemble Empirical Mode Decomposition ◽

South Asian High ◽

Mode Decomposition ◽

Adaptive Noise ◽

Deep Learning Model

The intensity variation of the South Asian high (SAH) plays an important role in the formation and extinction of many kinds of mesoscale systems, including tropical cyclones, southwest vortices in the Asian summer monsoon (ASM) region, and the precipitation in the whole Asia Europe region, and the SAH has a vortex symmetrical structure; its dynamic field also has the symmetry form. Not enough previous studies focus on the variation of SAH daily intensity. The purpose of this study is to establish a day-to-day prediction model of the SAH intensity, which can accurately predict not only the interannual variation but also the day-to-day variation of the SAH. Focusing on the summer period when the SAH is the strongest, this paper selects the geopotential height data between 1948 and 2020 from NCEP to construct the SAH intensity datasets. Compared with the classical deep learning methods of various kinds of efficient time series prediction model, we ultimately combine the Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which has the ability to deal with the nonlinear and unstable single system, with the Permutation Entropy (PE) method, which can extract the SAH intensity feature of IMF decomposed by CEEMDAN, and the Convolution-based Gated Recurrent Neural Network (ConvGRU) model is used to train, test, and predict the intensity of the SAH. The prediction results show that the combination of CEEMDAN and ConvGRU can have a higher accuracy and more stable prediction ability than the traditional deep learning model. After removing the redundant features in the time series, the prediction accuracy of the SAH intensity is higher than that of the classical model, which proves that the method has good applicability for the prediction of nonlinear systems in the atmosphere.

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Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽

10.3390/s20226460 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6460

Author(s):

Dae-Yeon Kim ◽

Dong-Sik Choi ◽

Jaeyun Kim ◽

Sung Wan Chun ◽

Hyo-Wook Gil ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Model ◽

Glucose Level ◽

Recurrent Neural Network ◽

Learning Algorithm ◽

Percentage Error ◽

Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

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SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS

Kursor ◽

10.21107/kursor.v10i4.233 ◽

2020 ◽

Vol 10 (4) ◽

Author(s):

Felisia Handayani ◽

Metty Mustikasari

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Recurrent Neural Network ◽

Confusion Matrix ◽

Data Representation ◽

Sequential Data ◽

Communication Tool ◽

Electric Cars

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20

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Deep Learning Based Adaptive Recurrent Neural Network for Detection of Myocardial Infarction

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3913 ◽

2021 ◽

Vol 11 (12) ◽

pp. 3044-3053

Author(s):

Rakesh Kumar Mahendran ◽

V. Prabhu ◽

V. Parthasarathy ◽

A. Mary Judith

Keyword(s):

Neural Network ◽

Myocardial Infarction ◽

Deep Learning ◽

Recurrent Neural Network ◽

Classification Accuracy ◽

Learning Algorithm ◽

Training Data ◽

Discrete Wavelet ◽

Ecg Signal ◽

Detection Techniques

Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.

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Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3850 ◽

2021 ◽

Vol 11 (10) ◽

pp. 2618-2625

Author(s):

R. T. Subhalakshmi ◽

S. Appavu Alias Balamurugan ◽

S. Sasikala

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Recurrent Neural Network ◽

Automatic Segmentation ◽

Ct Images ◽

Superior Performance ◽

Multi Scale ◽

Proposed Model ◽

Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

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Deep Cooperative Spectrum Sensing Utilizing Recurrent Convolutional Neural Networks

10.21203/rs.3.rs-22343/v1 ◽

2020 ◽

Author(s):

Rahil Sarikhani ◽

Farshid Keynia

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Cognitive Radio ◽

Spectrum Sensing ◽

Recurrent Neural Network ◽

Cooperative Spectrum Sensing ◽

Primary User ◽

Secondary Users ◽

Spectrum Holes

Abstract Cognitive Radio (CR) network was introduced as a promising approach in utilizing spectrum holes. Spectrum sensing is the first stage of this utilization which could be improved using cooperation, namely Cooperative Spectrum Sensing (CSS), where some Secondary Users (SUs) collaborate to detect the existence of the Primary User (PU). In this paper, to improve the accuracy of detection Deep Learning (DL) is used. In order to make it more practical, Recurrent Neural Network (RNN) is used since there are some memory in the channel and the state of the PUs in the network. Hence, the proposed RNN is compared with the Convolutional Neural Network (CNN), and it represents useful advantages to the contrast one, which is demonstrated by simulation.

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Combination of Recurrent Neural Network and Deep Learning for Robot Navigation Task in Off-Road Environment

Robotica ◽

10.1017/s0263574719001565 ◽

2019 ◽

Vol 38 (8) ◽

pp. 1450-1462

Author(s):

Farinaz Alamiyan-Harandi ◽

Vali Derhami ◽

Fatemeh Jamshidi

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Robot Navigation ◽

Recurrent Network ◽

Navigation Task ◽

Sensory Data ◽

Uneven Terrain ◽

Target Network ◽

Fully Connected

SUMMARYThis paper tackles the challenge of the necessity of using the sequence of past environment states as the controller’s inputs in a vision-based robot navigation task. In this task, a robot has to follow a given trajectory without falling in pits and missing its balance in uneven terrain, when the only sensory input is the raw image captured by a camera. The robot should distinguish big pits from small holes to decide between avoiding and passing over. In non-Markov processes such as the abovementioned task, the decision is done using past sensory data to ensure admissible performance. Applying images as sensory inputs naturally causes the curse of dimensionality difficulty. On the other hand, using sequences of past images intensifies this difficulty. In this paper, a new framework called recurrent deep learning (RDL) with combination of deep learning (DL) and recurrent neural network is proposed to cope with the above challenge. At first, the proper features are extracted from the raw image using DL. Then, these represented features plus some expert-defined features are used as the inputs of a fully connected recurrent network (as target network) to generate command control of the robot. To evaluate the proposed RDL framework, some experiments are established on WEBOTS and MATLAB co-simulation platform. The simulation results demonstrate the proposed framework outperforms the conventional controller based on DL for the navigation task in the uneven terrains.

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Deep Learning-based Framework for Smart Sustainable Cities

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2019100105 ◽

2019 ◽

Vol 15 (4) ◽

pp. 76-107

Author(s):

Nagarathna Ravi ◽

Vimala Rani P ◽

Rajesh Alias Harinarayan R ◽

Mercy Shalinie S ◽

Karthick Seshadri ◽

...

Keyword(s):

Neural Network ◽

Air Pollution ◽

Deep Learning ◽

Air Pollutants ◽

Human Life ◽

Deep Belief Network ◽

Motor Vehicles ◽

Human Beings ◽

Long Term Effects ◽

Belief Network

Pure air is vital for sustaining human life. Air pollution causes long-term effects on people. There is an urgent need for protecting people from its profound effects. In general, people are unaware of the levels to which they are exposed to air pollutants. Vehicles, burning various kinds of waste, and industrial gases are the top three onset agents of air pollution. Of these three top agents, human beings are exposed frequently to the pollutants due to motor vehicles. To aid in protecting people from vehicular air pollutants, this article proposes a framework that utilizes deep learning models. The framework utilizes a deep belief network to predict the levels of air pollutants along the paths people travel and also a comparison with the predictions made by a feed forward neural network and an extreme learning machine. When evaluating the deep belief neural network for the case study undertaken, a deep belief network was able to achieve a higher index of agreement and lower RMSE values.

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