Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively.

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An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information

Computational Intelligence and Neuroscience ◽

10.1155/2021/9615034 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Bader Alouffi ◽

Abdullah Alharbi ◽

Radhya Sahal ◽

Hager Saleh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Short Term Memory ◽

Learning Model ◽

Dense Layer ◽

Fake News ◽

Learning Models ◽

Proposed Model ◽

Deep Learning Model ◽

Machine Learning Models

Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short-term memory (LSTM) to detect COVID-19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID-19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1-measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID-19 significantly.

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Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

Symmetry ◽

10.3390/sym13040643 ◽

2021 ◽

Vol 13 (4) ◽

pp. 643

Author(s):

Rania M. Ghoniem ◽

Abeer D. Algarni ◽

Basel Refky ◽

Ahmed A. Ewees

Keyword(s):

Ovarian Cancer ◽

Deep Learning ◽

Short Term Memory ◽

Computational Cost ◽

Learning Model ◽

Learning Models ◽

Lung Cancers ◽

Proposed Model ◽

Linear Aggregation ◽

Deep Learning Model

Ovarian cancer (OC) is a common reason for mortality among women. Deep learning has recently proven better performance in predicting OC stages and subtypes. However, most of the state-of-the-art deep learning models employ single modality data, which may afford low-level performance due to insufficient representation of important OC characteristics. Furthermore, these deep learning models still lack to the optimization of the model construction, which requires high computational cost to train and deploy them. In this work, a hybrid evolutionary deep learning model, using multi-modal data, is proposed. The established multi-modal fusion framework amalgamates gene modality alongside with histopathological image modality. Based on the different states and forms of each modality, we set up deep feature extraction network, respectively. This includes a predictive antlion-optimized long-short-term-memory model to process gene longitudinal data. Another predictive antlion-optimized convolutional neural network model is included to process histopathology images. The topology of each customized feature network is automatically set by the antlion optimization algorithm to make it realize better performance. After that the output from the two improved networks is fused based upon weighted linear aggregation. The deep fused features are finally used to predict OC stage. A number of assessment indicators was used to compare the proposed model to other nine multi-modal fusion models constructed using distinct evolutionary algorithms. This was conducted using a benchmark for OC and two benchmarks for breast and lung cancers. The results reveal that the proposed model is more precise and accurate in diagnosing OC and the other cancers.

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Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i6.pp5467-5476 ◽

2021 ◽

Vol 11 (6) ◽

pp. 5467

Author(s):

Surenthiran Krishnan ◽

Pritheega Magalingam ◽

Roslina Ibrahim

Keyword(s):

Neural Network ◽

Heart Disease ◽

Deep Learning ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Learning Model ◽

Disease Prediction ◽

The Neural Network ◽

Proposed Model ◽

Deep Learning Model

<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>

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An Interpretable Deep Learning Model for Automatic Sound Classification

Electronics ◽

10.3390/electronics10070850 ◽

2021 ◽

Vol 10 (7) ◽

pp. 850

Author(s):

Pablo Zinemanas ◽

Martín Rocamora ◽

Marius Miron ◽

Frederic Font ◽

Xavier Serra

Keyword(s):

Deep Learning ◽

Web Application ◽

Domain Knowledge ◽

Learning Model ◽

Unintended Effects ◽

Learning Models ◽

Time Frequency ◽

Sound Classification ◽

Proposed Model ◽

Deep Learning Model

Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. This may lead to unintended effects, such as being susceptible to adversarial attacks or the reinforcement of biases. There is still a lack of research in the audio domain, despite the increasing interest in developing deep learning models that provide explanations of their decisions. To reduce this gap, we propose a novel interpretable deep learning model for automatic sound classification, which explains its predictions based on the similarity of the input to a set of learned prototypes in a latent space. We leverage domain knowledge by designing a frequency-dependent similarity measure and by considering different time-frequency resolutions in the feature space. The proposed model achieves results that are comparable to that of the state-of-the-art methods in three different sound classification tasks involving speech, music, and environmental audio. In addition, we present two automatic methods to prune the proposed model that exploit its interpretability. Our system is open source and it is accompanied by a web application for the manual editing of the model, which allows for a human-in-the-loop debugging approach.

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A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs

Applied Sciences ◽

10.3390/app11177940 ◽

2021 ◽

Vol 11 (17) ◽

pp. 7940

Author(s):

Mohammed Al-Sarem ◽

Abdullah Alsaeedi ◽

Faisal Saeed ◽

Wadii Boulila ◽

Omair AmeerBakhsh

Keyword(s):

Machine Learning ◽

Social Media ◽

Deep Learning ◽

Short Term Memory ◽

Learning Model ◽

Superior Performance ◽

Detection Methods ◽

The Internet ◽

Proposed Model ◽

Deep Learning Model

Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber abuse, where rumors or lies about the victim are posted on the internet to send threatening messages or to share the victim’s personal information. During pandemics, a large amount of rumors spreads on social media very fast, which have dramatic effects on people’s health. Detecting these rumors manually by the authorities is very difficult in these open platforms. Therefore, several researchers conducted studies on utilizing intelligent methods for detecting such rumors. The detection methods can be classified mainly into machine learning-based and deep learning-based methods. The deep learning methods have comparative advantages against machine learning ones as they do not require preprocessing and feature engineering processes and their performance showed superior enhancements in many fields. Therefore, this paper aims to propose a Novel Hybrid Deep Learning Model for Detecting COVID-19-related Rumors on Social Media (LSTM–PCNN). The proposed model is based on a Long Short-Term Memory (LSTM) and Concatenated Parallel Convolutional Neural Networks (PCNN). The experiments were conducted on an ArCOV-19 dataset that included 3157 tweets; 1480 of them were rumors (46.87%) and 1677 tweets were non-rumors (53.12%). The findings of the proposed model showed a superior performance compared to other methods in terms of accuracy, recall, precision, and F-score.

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Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network

Sensors ◽

10.3390/s22010174 ◽

2021 ◽

Vol 22 (1) ◽

pp. 174

Author(s):

Junhyuk Kang ◽

Jieun Shin ◽

Jaewon Shin ◽

Daeho Lee ◽

Ahyoung Choi

Keyword(s):

Neural Network ◽

Deep Learning ◽

Input Signal ◽

Short Term Memory ◽

Learning Model ◽

Sensor Data ◽

Accelerometer Data ◽

Accelerometer Sensor ◽

Skeleton Image ◽

Deep Learning Model

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.

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Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models

Applied Sciences ◽

10.3390/app11135853 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5853

Author(s):

Hyesook Son ◽

Seokyeon Kim ◽

Hanbyul Yeon ◽

Yejin Kim ◽

Yun Jang ◽

...

Keyword(s):

Deep Learning ◽

Visual Analysis ◽

Short Term Memory ◽

Learning Model ◽

Learning Models ◽

Visualization System ◽

Temporal Prediction ◽

Long Short Term Memory ◽

Gated Recurrent Units ◽

Deep Learning Model

The output of a deep-learning model delivers different predictions depending on the input of the deep learning model. In particular, the input characteristics might affect the output of a deep learning model. When predicting data that are measured with sensors in multiple locations, it is necessary to train a deep learning model with spatiotemporal characteristics of the data. Additionally, since not all of the data measured together result in increasing the accuracy of the deep learning model, we need to utilize the correlation characteristics between the data features. However, it is difficult to interpret the deep learning output, depending on the input characteristics. Therefore, it is necessary to analyze how the input characteristics affect prediction results to interpret deep learning models. In this paper, we propose a visualization system to analyze deep learning models with air pollution data. The proposed system visualizes the predictions according to the input characteristics. The input characteristics include space-time and data features, and we apply temporal prediction networks, including gated recurrent units (GRU), long short term memory (LSTM), and spatiotemporal prediction networks (convolutional LSTM) as deep learning models. We interpret the output according to the characteristics of input to show the effectiveness of the system.

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Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

Computational Intelligence and Neuroscience ◽

10.1155/2020/9868017 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Rong Liu ◽

Yan Liu ◽

Yonggang Yan ◽

Jing-Yan Wang

Keyword(s):

Deep Learning ◽

Input Data ◽

Short Term Memory ◽

Learning Model ◽

Learning Models ◽

Trend Prediction ◽

Input Point ◽

Property Price ◽

Data Points ◽

Deep Learning Model

Deep learning models, such as deep convolutional neural network and deep long-short term memory model, have achieved great successes in many pattern classification applications over shadow machine learning models with hand-crafted features. The main reason is the ability of deep learning models to automatically extract hierarchical features from massive data by multiple layers of neurons. However, in many other situations, existing deep learning models still cannot gain satisfying results due to the limitation of the inputs of models. The existing deep learning models only take the data instances of an input point but completely ignore the other data points in the dataset, which potentially provides critical insight for the classification of the given input. To overcome this gap, in this paper, we show that the neighboring data points besides the input data point itself can boost the deep learning model’s performance significantly and design a novel deep learning model which takes both the data instances of an input point and its neighbors’ classification responses as inputs. In addition, we develop an iterative algorithm which updates the neighbors of data points according to the deep representations output by the deep learning model and the parameters of the deep learning model alternately. The proposed algorithm, named “Iterative Deep Neighborhood (IDN),” shows its advantages over the state-of-the-art deep learning models over tasks of image classification, text sentiment analysis, property price trend prediction, etc.

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Distributed Deep Features Extraction Model for Air Quality Forecasting

Sustainability ◽

10.3390/su12198014 ◽

2020 ◽

Vol 12 (19) ◽

pp. 8014

Author(s):

Axel Gedeon Mengara Mengara ◽

Younghak Kim ◽

Younghwan Yoo ◽

Jaehun Ahn

Keyword(s):

Deep Learning ◽

Air Quality ◽

Short Term Memory ◽

Meteorological Data ◽

Learning Model ◽

Training Time ◽

Metropolitan City ◽

Pm2.5 And Pm10 ◽

Deep Learning Model ◽

Air Quality Forecasting

Several studies in environmental engineering emphasize the importance of air quality forecasting for sustainable development around the world. In this paper, we studied a new approach for air quality forecasting in Busan metropolitan city. We proposed a convolutional Bi-Directional Long-Short Term Memory (Bi-LSTM) autoencoder model trained using a distributed architecture to predict the concentration of the air quality particles (PM2.5 and PM10). The proposed deep learning model can automatically learn the intrinsic correlation among the pollutants in different location. Also, the meteorological and the pollution gas information at each location are fully utilized, which is beneficial for the performance of the model. We used multiple one-dimension convolutional neural network (CNN) layers to extract the local spatial features and a stacked Bi-LSTM layer to learn the spatiotemporal correlation of air quality particles. In addition, we used a stacked deep autoencoder to encode the essential transformation patterns of the pollution gas and the meteorological data, since they are very important for providing useful information that can significantly improve the prediction of the air quality particles. Finally, in order to reduce the training time and the resource consumption, we used a distributed deep leaning approach called data parallelism, which has never been used to tackle the problem of air quality forecasting. We evaluated our approach with extensive experiments based on the data collected in Busan metropolitan city. The results reveal the superiority of our framework over ten baseline models and display how the distributed deep learning model can significantly improve the training time and even the prediction accuracy.

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Micro Activities Recognition in Uncontrolled Environments

Applied Sciences ◽

10.3390/app112110327 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10327

Author(s):

Ali Abbas ◽

Michael Haslgrübler ◽

Abdul Mannan Dogar ◽

Alois Ferscha

Keyword(s):

Deep Learning ◽

Real World ◽

Image Understanding ◽

Learning Model ◽

Assembly Process ◽

Learning Models ◽

Automated Teller Machines ◽

Proposed Model ◽

And Control ◽

Deep Learning Model

Deep learning has proven to be very useful for the image understanding in efficient manners. Assembly of complex machines is very common in industries. The assembly of automated teller machines (ATM) is one of the examples. There exist deep learning models which monitor and control the assembly process. To the best of our knowledge, there exists no deep learning models for real environments where we have no control over the working style of workers and the sequence of assembly process. In this paper, we presented a modified deep learning model to control the assembly process in a real-world environment. For this study, we have a dataset which was generated in a real-world uncontrolled environment. During the dataset generation, we did not have any control over the sequence of assembly steps. We applied four different states of the art deep learning models to control the assembly of ATM. Due to the nature of uncontrolled environment dataset, we modified the deep learning models to fit for the task. We not only control the sequence, our proposed model will give feedback in case of any missing step in the required workflow. The contributions of this research are accurate anomaly detection in the assembly process in a real environment, modifications in existing deep learning models according to the nature of the data and normalization of the uncontrolled data for the training of deep learning model. The results show that we can generalize and control the sequence of assembly steps, because even in an uncontrolled environment, there are some specific activities, which are repeated over time. If we can recognize and map the micro activities to macro activities, then we can successfully monitor and optimize the assembly process.

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