![2 R. Majid et al.
Keywords Cyclone track forecasting · Cyclone trajectory forecasting · Deep
learning · LSTM · Convolutional LSTM · GRU
1 Introduction
Tropical cyclones are catastrophic, an extreme weather condition that causes huge
economic loss. Tropical Cyclone development is a complex dynamic mechanism,
and various factors depend on it, such as sea conditions, atmosphere, place, and
other atmospheric variables [1]. Tropical Cyclone not only causes economic losses
but also causes human casualties [2]. In particular, underdeveloped or developing
countries suffer the most due to losses. As Bangladesh is a developing country, it
suffers a lot due to tropical cyclones. On top of that, Bangladesh is located at the
head of the Bay of Bengal, and every year, small and large cyclones hit Bangladesh
for its position. For example, two major cyclones that hit Bangladesh, Sidr and Aila,
created 2.31 billion and 269 million total losses, and thousands of people died. To
mitigate the loss, preparedness is the key. As a part of the preparedness, cyclone track
forecasting plays a significant role.
Researchers use different meteorological mechanisms for cyclone path prediction.
Many statistical models have been used for cyclone trajectory prediction. However
accurate cyclone trajectory prediction is needed, and it is always a region of active
research. In recent years, the prediction of cyclone trajectory has been divided into
three categories: traditional cyclone path prediction models, machine learning base
cyclone path prediction models, and deep learning base cyclone path prediction
models [2]. Traditional forecasting models are mainly statistical models, and they
also have subdomains such as empirical methods and objective methods. The empir-
ical method uses information from satellite and historical data, whereas the objective
method is fully dependent on statistics, hydrodynamics, and other science. Objec-
tive based prediction also has some classifications like dynamic based track predic-
tion, statistics-based track prediction, and statistical dynamic-based track prediction
[2]. The dynamic-based prediction method, also known as the numerical prediction
method, uses large amounts of historical data to construct the dynamic equation and
transforms the problem into a mathematical problem [3]. It is undoubtedly intensive
to solve as the constructed equation from such a method is complex.
Statistical methods are also based on mathematical statistics, and they also need a
large amount of historical data to find the relationship of track data [4]. In recent days,
the use of machine learning algorithms for prediction-based problems has prolifer-
ated. Regression, k-means algorithms, and other Gradient Boosting algorithms are
mainly those algorithms that can handle multi-variable inputs for predicting tracks
with minimal data or information [2]. With the further advancement of technology,
researchers started using deep learning models like Artificial Neural Networks
(ANN) to predict cyclone trajectories with better accuracy [5]. Satellite images and
other historical information started showing higher accuracy with minimum data.](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-16-320.jpg)
![A Deep Learning-Based Study for Cyclone Track Forecasting … 3
In this paper, we demonstrate the effectiveness of (LSTM), Convolutional LSTM
and Gated Recurrent Unit (GRU) [6] on cyclone trajectory prediction based on histor-
ical dataset and two different approaches. We mainly focus on the Bangladesh region
and have tried to predict the cyclone path accurately. Our input features are mainly
the latitude and longitude of the cyclone track. With latitude and longitude, we
have considered other variables as input features. We have worked on two different
approaches-training the historical cyclone data to predict the next trend of cyclones
and training hourly-based data of each cyclone to predict the next position of the
cyclone.
2 Literature Review
A paper related to the work introduced a deep learning model which is ConvLSTM
that predicts the path of the typhoon [2]. The paper aimed to create a strong and
effective model that can predict typhoons accurately to reduce property loss. There
is a method named Granger causality test, which is used to select the meteorological
features. In time series problems, to use the Granger causality test, one needs to
perform Augmented Dickey–Fuller techniques (ADF). The p value is ≤0.01 then
the Granger causality test can work otherwise not. The authors used both Partial
Autocorrelation Coefficient function (PACF) and Autocorrelation Coefficient func-
tion (ACF) to measure correlation between features. If residual P values are <0.01
by testing, then the series is cointegrated.
An attempt [2] has been made using the C-LSTM network. The authors used
the C-LSTM model, which is a combination of CNN and LSTM where CNN can
extract multiple features, and in shared time series situations, LSTM can acquire
temporal properties and increase model performance. The authors selected features
by Granger causality test, which was then fed into the C-LSTM model.
The paper [7] introduced new techniques which is a multimodal technique and
a combination of ML framework and DL to predict intensity and track forecasting.
They have used two types of data. One is statistical data and another one is image data.
At first, they preprocess the statistical data using one-hot encoding and all categorical
values are transformed in numerical value. The paper aimed at a multimodal approach
that can perform better on individual models, which means that multimodal models
obtain higher precision and lower standard deviations than models that just use
statistical data [7]. For 24 time-based intensities, they have seen that multimodal
models have better MAE than operation models [7]. The authors have reached their
goal and shown that machine learning models can perform better and bring additional
insight. The future work of this paper is adding more data and predicting the model
for longer periods input increasing window size as it predicts for 24 h only. The
paper worked on storm track data and reanalysis image data [8]. Their fused network
model is based on 24 h over 3000 storms [8].
The research [9] is based on remote sensing images with ConvLSTM and SAN-
EFS Model. The authors used two models to complete the task track prediction on](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-17-320.jpg)
![4 R. Majid et al.
tropical cyclones. The SAN can spatially emphasize critical pixels in the arriving
data and unseen conditions [9]. So, important pixels in distant identifying photos aid
in furthering the analysis of how spatial characteristics affect the TC track and, as a
result, increase prediction accuracy. SAN-ConvLSTM improves the model’s forecast
by including a density map.
This paper [10] approaches the new deep learning model GRU to predict the path
of storms in the Pacific Northwest. Deep learning method is more efficient, and it
can capture the complex relation between variables. The authors tried predicting the
forecast from 6 to 72 h. They also used LSTM, RNN, GRU, and among them, GRU
performs better [10]. This model performs better on forecast at 6 h and 12 h and their
average distance error is respectively 17.22 and 43.99 but when forecast surpasses
24 h, the accuracy declines [10]. At the end the authors argued that if more data can
be included, more valuable characteristics can be extracted to improve deep-learning
model’s prediction accuracy.
The paper covers a study that examines the Arabian Sea cyclone paths [11]. The
exploratory investigation of numerous cyclone track parameters in the Arabian Sea
is presented in this paper, emphasizing the historical and important shapes of those
features. The Arabian Sea basin exhibits a comparable average cyclogenesis latitude
to other ocean basins compared to other basins with larger cyclone numbers, the
Arabian Sea basin is less well-known global cyclone study. However, because of its
three-sided pattern, tapering northward, enclosure, and comparatively little shape,
future cyclone development in this basin is rather fleeting. As a result, compared
to cyclones in other areas, Arabian Sea cyclones have lower features in terms of
maximum intensity, duration, and migratory distance.
This paper uses data from 20 years of simulated outgoing long wave energy to
classify typhoons and their predecessors using deep learning techniques to explore
the tractability of TCs [12]. Based on specific circumstances, this model displays
the best and worst scenarios of detection performance for tropical cyclones and their
precursors. The best situation is shown when there are more than eight TCs and
precursors, and when the probability of detection (POD) is higher than 80.0%. The
results of predictions in regions with less data, like the North Atlantic, may benefit
from the training data from other basins.
QuantitativeresearchonthepatternoftropicalstormpathsintheNorthwestPacific
basin was undertaken in the work [13]. The authors discovered a slight helpful corre-
lation between the length of SI and TS and the remoteness covered, providing useful
information for danger valuation. Trends and Impacts demonstrates a comprehen-
sive analysis of tropical cyclone and its trajectory behavior in the Bay of Bengal
region from 1971 to 2020 [14]. According to this study, it exposes an average of 35.2
cyclones per decade without a significant increasing or decreasing trend. It discovers
inter-annual and seasonal variations, with tropical cyclones taking northward tracks
in the pre-monsoon season and making landfalls along Bangladesh and Myanmar
coasts, whereas post-monsoon tropical cyclones make landfalls directly on Orissa
and West Bengal coasts.](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-18-320.jpg)
![A Deep Learning-Based Study for Cyclone Track Forecasting … 5
3 Methodology
3.1 System Design
This section mainly covers the entire process of this experiment. It covers data acqui-
sition, data prepossessing, different test results, feature selection, construction of
the LSTM, ConvLSTM and GRU model to predict cyclone path. To understand
the insights of the data, we did a few tests on our datasets. These are Augmented
Dicky Fuller Test (ADF), Granger Causality Test and ACF PACF test. These tests
do not directly impact our training procedure but the insight from the tests gives us
information about our data and the relationships of its features.
Long short-term memory (LSTM) [15] is a recurrent neural network (RNN) that
works well in problems like time series analysis. It can deal with multi-variable and
can train a sequence of input and provide its output. Some Basic components of
LSTM model (Fig. 1) are shown below:
Input Layer: It receives input data. Then it passes to the next layer, that is a
memory cell.
Memory cell: It helps to store information and maintain the flow of information
using different gates. It is one of the key components of the LSTM model.
Forget Gate: It determines the information that we should throw from the memory
cell.
Input Gate: Determines new information that we should add to the memory cell.
Output Gate: Determines information that should be the output from the memory
cell.
Hidden State: It is the output of the LSTM model obtained from the previous
input and the previous hidden state.
The LSTM model is a powerful tool for performing time series analysis as it takes
the data sequence as input and provides meaningful output. ConvLSTM is another
Fig. 1 LSTM architecture](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-19-320.jpg)
![A Deep Learning-Based Study for Cyclone Track Forecasting … 11
Cyclone-related data can be dependent on two things, and those are type and
quality [2]. Different countries have different levels of development, and the cyclonic
data collection procedure and their dependencies can vary for every country. As we
experimented on the North Indian Ocean, especially over the Bay of Bengal, we have
used historic data of cyclones for the Bangladesh region only.
4 Result, Analysis and Discussion
All the models are able to find the underlying data patterns with a good R2
score.
The R2
score of LSTM model is 0.955 and average distance 65.111 (km). The other
model ConvLSTM scored R2
0.919 and average distance 127.912 (km). At the end
R2
score for GRU is 0.974 and average distance is 66.050 (km). In this approach
GRU model performed the best. The visual presentation of our findings is given in
Figs. 4 and 5 and performance metrics are reported in Table 1.
In another approach the models are trained on 140 storms and predict the trajectory
of 38 cyclones. In this case, the R2
score of LSTM model is 0.741 and the average
error between points is 101.144 (km). In this approach R2
score of ConvLSTM is
Fig. 4 Predicted track of Roanu (Cyclone—178) using 140 cyclones data](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-25-320.jpg)
![14 R. Majid et al.
work. The cyclone track was visualized using longitude and latitude. There, a small
error in a prediction causes many kilometers of distance between actual and predicted
value. The wrong prediction can make disaster management more challenging.
This work can play a significant role in saving lives and minimizing vulnerabilities
by providing the trajectory of the cyclone. Tropical storm frequency and intensity
have increased in recent years, and losses from flooding induced by cyclones create a
humanitarian challenge. By using prediction management, we can make preparations
that will help to enhance our ability to safeguard vulnerable areas from the devastating
impact of cyclones.
References
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6. GeeksforGeeks, “Gated Recurrent Unit Networks,” [Online]. Available: https://www.geeksf
orgeeks.org/gated-recurrent-unit-networks/.
7. L. Boussioux, C. Zeng, T. Guenais and D. Bertsimas, “Hurricane forecasting: A novel
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ical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data,”
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9. P. Dong, J. Lian, H. Yu, J. Pan, Y. Zhang and G. Chen, “Tropical Cyclone Track Prediction
with an Encoding-to-Forecasting Deep Learning Model,” Weather and Forecasting, vol. 37,
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10. L. Wang, B. Wan, S. Zhou, H. Sun and Z. Gao, “Forecasting tropical cyclone tracks in the
northwestern pacific based on a deep-learning model,” Geoscientific Model Development, vol.
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11. J. P. Terry and G. Gienko, “Quantitative observations on tropical cyclone tracks in the arabian
sea,” Theoretical and Applied Climatology, vol. 135, pp. 1413–1421, June 2019.
12. D. Matsuoka, M. Nakano, D. Sugiyama and S. Uchida, “Deep learning approach for detecting
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13. R. S. Pandey and Y.-A. Liou, Decadal behaviors of tropical storm tracks in the North West
Pacific Ocean, Atmospheric Research, vol. 246, 2020.](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-28-320.jpg)
![A Deep Learning-Based Study for Cyclone Track Forecasting … 15
14. M. Mondal, A. Biswas, S. Haldar, S. Bhattacharya, S. Mandal and S. Paul, Spatio-temporal
behaviours of tropical cyclones over the bay of Bengal basin in last five decades, Tropical
Cyclone Research and Review, vol. 11, no. 1, pp. 1–15, 2022.
15. J. Brownlee, “A Gentle Introduction to Long Short-Term Memory Networks by the Experts—
MachineLearningMastery.com,” 7 July 2021. [Online]. Available: https://machinelearning
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October 2023].](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-29-320.jpg)
![Text-Based Data Analysis for Mental Health Using Explainable AI … 19
2 Related Works
Research using Reddit data for mental health analysis has attracted attention recently.
Several studies have explored the potential of Reddit as a valuable resource for
understanding mental health-related discussions and developing machine learning
models for classification and analysis. Here are a few examples of existing research
and the limitations associated with this work.
The study [1] focused on predicting suicide risk based on Twitter data. It devel-
oped a machine learning model that analyzed language patterns and linguistic cues to
identify users at risk of self-harm. The research highlighted the potential of Twitter
for early detection and intervention in mental health crises. The study focused specifi-
cally on suicide risk prediction, and the model’s performance may vary when applied
to other mental health conditions.
In [2], the authors compared Reddit and Twitter data characteristics for biomedical
research, including mental health topics. The study analyzed the differences in user
demographics, language patterns, and topic distributions between the two platforms.
While the research provides insights into the differences between Reddit and Twitter,
it does not specifically focus on mental health analysis. The findings might not
directly address the specific needs of mental health researchers or the limitations
associated with using Reddit data for mental health analysis.
3 Data Set Descriptions
Although there are many datasets of Twitter posts for mental health analysis, we
failed to find any that suited our research requirements. There is a problem that the
dataset was already used multiple times, or it needed to be updated to use for new
work. So, we decided to create a new dataset for our research purpose. We used
Reddit data instead of Twitter data in this case.
The decision to use Reddit data instead of available Twitter data for our research
depends on various factors. Due to the platform’s nature and user characteristics,
Reddit data is more appropriate for certain types of analysis, such as sentiment
analysis [3, 4] or topic modeling [5, 6]. For example, Reddit users tend to engage in
longer and more detailed discussions, which may be more helpful in understanding
the nuances of certain topics or communities. Additionally, Reddit data is more
readily available for certain topics or communities of interest, as many subreddits are
dedicated to specific topics or interests. On the other hand, Twitter data is considered
more appropriate for other types of analysis, such as real-time monitoring or event
tracking, due to its focus on up-to-the-minute updates and its use by a broader range
of users. Reddit data has been found more suitable due to the platform’s nature
and the availability of data on mental health-related topics. It is also considered an
inexpensive source of high-quality data. Our research required data based on different
mental health conditions rather than only depression. Reddit is known for having](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-32-320.jpg)
![Text-Based Data Analysis for Mental Health Using Explainable AI … 23
Fig. 4 System diagram
our data. Then, those features were fed into the machine learning and deep learning
models. After successful training, the models were evaluated and showed our desired
output (Fig. 4).
After preprocessing and categorizing the data, we started with the model-building
part. Model building creates a predictive model that can make predictions or clas-
sifications based on input data. Building a model typically involves choosing an
appropriate algorithm, defining model inputs and outputs, preprocessing data for
modeling, training the model using a training dataset, and training the model against
a test dataset (Fig. 5).
Initially, machine learning models were used to classify text data. Specifically,
the models are trained to predict the class labels (e.g. ‘Depression’, ‘Anxiety’,
‘Addiction’, etc.) of the input text data based on patterns and relationships found
in the training data. XGBoost, Decision Tree, SVM, Naive Bayes, Simple Gradient
Descent, Stochastic Gradient Descent, K-Nearest Centroid, K-Nearest Neighbor,
AdaBoost, Random Forest, and Logistic Regression models were used using the TF-
IDF text vectorizer. All the ML models were run on the default setup with no changes
in the hyperparameter. We also added a voting classifier with all the ML models for
final prediction. A few variations of DNN models were used to evaluate the perfor-
mance of our dataset. We used CNN (Convolutional neural network) algorithm with
Word2Vec and GloVe vectors [7–9].](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-36-320.jpg)
![Text-Based Data Analysis for Mental Health Using Explainable AI … 25
7 Explainability Methods
Explainability methods are important for understanding the model’s behavior,
ensuring fairness, and building trust in its predictions [4–11, 12]. Our research
uses LIME as an explainable AI (XAI) method. LIME (Local Interpretable Model-
Agnostic Explanations) is a model-agnostic technique used to explain the predictions
of machine learning models [7]. It can be used with any type of model, especially
those used for text classification tasks. There are many XAI methods, but one of the
main reasons for using LIME in our research is to gain insights into how a model
makes its predictions on individual text samples. LIME creates locally interpretable
explanations for the predictions made by a model by identifying the most important
features or words that contributed to the prediction. LIME uses a simpler model,
such as a linear model or decision tree, to approximate the behavior of the complex
model in the local region around a specific instance. This approach is computation-
ally efficient and can provide meaningful insights into the contribution of different
words or features to the model’s prediction.
As our research is text-based, LIME mainly focuses on the words that give impor-
tant and useful information in explainability. It highlights the important words for
the detection and thus gives the outcome. This type of explainability is like a human
prediction explanation, that is why it is widely accepted.
8 Result and Analysis
This section reviews all the models (ML and DNN models) tried on our English
textual dataset to classify mental states from the text. Model performance analysis
based on accuracy involves evaluating how well a model predicts the correct outcome
compared to the total number of predictions. It measures how many predictions
are correct from the total number of predictions. As for our ML models, we have
different accuracy rates for different models. Not all models gave satisfactory results.
For now, we chose accuracy to determine a model’s performance. However, other
matrices (precision, recall, f1) were also considered to evaluate the performance. On
our dataset, XGBoost and Naïve Bayes outperformed all the models with an accuracy
of 70% and 71%, respectively. Table 2 shows the accuracy for different models.
Explainable AI: What sets our research apart is the focus on explainability.
Trust and transparency are crucial when dealing with sensitive topics like mental
health. Therefore, we have integrated Explainable AI techniques into our model,
allowing us to provide clear and understandable explanations for its predictions.
XAI (Explainable Artificial Intelligence) plays a key role in the research by providing
transparency, interpretability, and accountability to the classification model decision-
making process. This enables us to uncover important features, patterns, and rules
influencing the model’s output. We separately applied the XAI method (LIME) [13]](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-38-320.jpg)
![34 A. M. Hasan et al.
1 Introduction
A pandemic is more dangerous than a world war. A world war can be stopped by
compromising or a treaty, but a pandemic can only be stopped once we find the
cure or normalize the way of treatment. So, vaccine invention was as important as
identifying the COVID-19-affected patients during the pandemic.
First, a tiny virus particle enters one healthy body from the air and replicates itself
exponentially. So, a healthy body does not get enough time to identify the virus and
produce antibody against it. And no cure was invented then, so death ended it.
Since it directly attacks the human respiratory system, the first symptom of this
disease is shortness of breath and chest pain. No other symptoms can be identified
until the patient has already been affected by COVID-19 badly. So, it was more
dangerous that people were dying of sudden death without any treatment. If one of
any family members were affected, then there was a high chance of affecting the full
family.
Our initiative is to build a wearable IoT device to take real-time input from the
human body and pass the data to machine learning models. Machine learning models
will predict whether the user is COVID-19 positive suspect or not and show output
to the registered website.
2 Literature Review
Combining the Internet of Things (IoT) and Machine Learning has changed people’s
way of living. It includes home automation, smart health care, smart transportation,
etc. Remote health monitoring systems are becoming very popular day by day. It
reduces costs and helps to manage the chronic conditions of patients. For patients
who have difficulty traveling, doctors can monitor them remotely. Since monitoring
people with contagious diseases is difficult, remote health monitoring systems will
greatly help.
IoT-based Machine Learning systems have proven very effective in detecting
COVID-19. The authors [1] developed a Fuzzy Mamdani system to detect the
spreading risks of COVID-19. They made an app that notifies users to maintain a
6ft distance from others. They used the fuzzy system, which calculated the distance
of the user from others and used some symptoms from the user to detect the risk of
spreading the virus at that place.
Another study used a data mining approach to detect COVID-19. The authors [2]
used the Artificial Bee Colony algorithm for data mining. They used this algorithm
to select features from the dataset. After that, they used the Support Vector Machine
(SVM) to get a very impressive result. They got over 90% results for all the evaluation
metrics.
The authors [3] used contact tracing to reduce the number of COVID-19-affected
people in this study. They created an IoT device to detect contact tracing. Through](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-46-320.jpg)
![Using the Internet of Things and Machine Learning to Monitor … 35
this, they can discover who is affected and responsible for spreading the virus. They
achieved 97.1% accuracy using the Gradient Boost Algorithm.
In this research work [4], COVID-19 is detected from cough sounds and other
symptoms. They used cough and environmental sounds to differentiate between
coughs and other sounds. They used Arduino Nano 33 BLE sense, which is a micro-
controller and a sensor because it has different sensors connected to it. One of the
features of this device is that we can upload the machine learning and deep learning
code directly to it; that is why we do not need to deploy machine learning models
anywhere. Because of that, it is very handy to classify cough recordings since it has
a microphone with it, and every time it records a sound, the sound will go through
the machine learning model and generate an output instantly.
A study predicted the risks of getting affected by COVID-19 in public places [5].
The authors used an IoT device to measure the distance from one person to another
and their temperature. Then, they made a data vector from it. From the data vector,
they predicted the risk of getting affected. Along with the classifier models, they
used Locally Weighted Learning, Hoeffding Tree, and AdaBoost M1, where Locally
Weighted Learning performed well, but the others did not.
We have tried to measure our necessary information with devices like Apple
Watch. But it was too expensive for our research work. The cheaper one could not
match the expected accuracy. Then we decided to build our own, which is much
more affordable and also good in accuracy. Arduino Nano 33 BLE sense can contain
machine learning and deep learning models. But it is not available in our country.
It is also very expensive. There is no other microcontroller that can replace it. So,
we could not record cough and classify it using our IoT device. We had to make a
recorder website for it.
3 Data Collection
The COVID-19 patient data was collected from the Mugda Medical College &
Hospital, which is located in Dhaka, Bangladesh. Since it was declared a Corona
hospital at the time of the pandemic, that is why this hospital was chosen. The
hospital had COVID-19 patient data from April 2020 to August 2021. There were
threetypesofdata;oneisCOVID-19positive,oneisCOVID-19negative,andanother
one is Suspected COVID-19 positive. We only recorded the patients’ data confirmed
as COVID-19 positive and COVID-19 negative (healthy). Since we got only three
healthy people’s data from the files we had to collect the healthy people’s data from
our families, relatives, friends, and strangers.](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-47-320.jpg)
![38 A. M. Hasan et al.
Fig. 3 An illustration of the COVID-19 detection system’s workflow
4.3 Wearable IOT Device
Body temperature, oxygen saturation in the blood (SpO2), and heart rate are consid-
ered primary symptoms of COVID-19. This wearable IoT device collects users’
temperature, heart rate, and oxygen saturation (SpO2) data. The microcontroller
reads data from the connected sensors and sends it to Firebase. The description of
devices are given below.
WeMos D1 Mini
WeMos D1 Mini (Fig. 4) is a NodeMCU ESP8266. It is a wireless module. It has
Wi-Fi capability, which can send data to a cloud server. The flash memory of this
microcontroller is 4 Mb. Its length is 34.2 mm, and its width is 25.6 mm. Its weight
is 10 g. It has eleven digital I/O pins and one analog input pin. The clock speed of
this microcontroller is 80 or 160 MHz [6].](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-50-320.jpg)
![Using the Internet of Things and Machine Learning to Monitor … 39
Fig. 4 WeMos D1 Mini [7]
DS18B20 (Temperature Sensor)
One of the key symptoms of COVID-19 is fever. Since fever is considered a primary
clinical feature of COVID-19, it is very important to monitor the temperature of the
COVID-19 suspects. The DS18B20 temperature sensor was used in this project [8].
The pin connection between the DS18B20 and WeMos D1 Mini (ESP8266) is shown
in Table 1.
The DS18B20 sensor is depicted in Fig. 5. It has a long wire which will help the
user to measure his/her temperature even if he/she is far from the connection point.
The temperature sensing part is also long, which is why it can measure temperature
even if the user is not in a steady position.
MAX30100 (Pulse Oximeter and Heart-Rate Sensor)
Heart-Rate and Oxygen saturation in the blood (SpO2) are two indicative measures to
detect a COVID-19-affected person. The virus infection increases the heart rate and
decreases the oxygen saturation of the COVID-19-affected person. So, monitoring
theoxygensaturationlevelandtheheart’sfunctioningcontinuouslyisveryimportant.
Therefore, Max30100 was used to measure Oxygen saturation (SpO2) and heart rate.
The pin connection between the Pulse Oximeter and Heart-Rate Sensor and WeMos
D1 Mini (ESP8266) is shown in Table 2.
Table 1 Pin connection of the DS18B20 to WeMos D1 Mini
Dallas temperature sensor (DS18B20) WeMos D1 Mini (ESP8266)
VDD 5 V
GND G
Data Pin 14](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-51-320.jpg)
![40 A. M. Hasan et al.
Fig. 5 DS18B20 sensor [9]
Table 2 Pin connection of MAX30100 to WeMos D1 Mini
Pulse oximeter and heart-rate sensor (MAX30100) WeMos D1 Mini (ESP8266)
VIN 3.3 V
SCL Pin 5
SDA Pin 4
GND G
The MAX30100 sensor, depicted in Fig. 6, consists of two light-emitting diodes
that emit monochromatic red light at 660 nm and infrared light at 940 nm.
These frequencies were specifically chosen because deoxygenated and oxygenated
hemoglobin have distinct absorption characteristics at these wavelengths. The sensor
is made up of a photoreceiver and an emitting diode. The photodiode emits the light
and falls over the finger, so the finger needs to be positioned steadily. The released
light is partially absorbed by the oxygenated blood, partially reflected through the
finger, and falls over the detector, whose output data is processed and read by a
microcontroller. This sensor is very compact in design. Therefore, it is perfect for a
wearable device [10].
OLED Display
The OLED display in Fig. 7 is 0.96 inches in length. The display area is 21.74 ×
10.86 mm. The driving voltage is 3.3–5 V. The operating temperature is −40–70 °C.
Fig. 6 MAX30100 sensor](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-52-320.jpg)
![Using the Internet of Things and Machine Learning to Monitor … 41
Fig. 7 OLED display [11]
Table 3 Pin connection of
the OLED display to WeMos
D1 Mini
OLED display WeMos D1 Mini (ESP8266)
VCC 5 V
SCL Pin 5
SDA Pin 4
GND G
The length of the display is 27 mm, the width is 27 mm, and the height is 6 mm. The
weight of the display is 5gm. It has four pins GND, VCC, SCL, and SDA. The pin
connection between the OLED display and WeMos D1 Mini (ESP8266) is shown in
Table 3.
5 Final Design of the Wearable IoT Device
Figure 8 shows the final design of the IoT device. It is easy to use, lightweight,
comfortable, and low-cost.
5.1 Firebase
The cloud database is in charge of receiving data from the microcontroller and storing
the users’ information. These pieces of information go to the website where the
machine learning model is deployed and show the output on the website. Figure 9
shows the dashboard of our Firebase Real-time Database. In this database, four values
are being stored: Temperature, Heart Rate, Oxygen Saturation (SPO2), and Cough.
Every time new data comes, it overwrites the old data.](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-53-320.jpg)
![42 A. M. Hasan et al.
Fig. 8 Wearable IoT device
Fig. 9 Firebase real-time
database dashboard
5.2 Identification of COVID-19 Patient
The machine learning algorithms will use a user’s temperature, cough, pulse rate, and
oxygen saturation to determine whether the user is healthy or COVID-19 affected.
Figure 10 shows the complete process of COVID-19 detection. The dataset was
split to 60:40; the training set had 60% of the data, and the test set had 40%.
Five machine learning models were used (Decision Tree Classifier, Support Vector
Machine, Logistic Regression, Random Forest Classifier, Naïve Bayes Classifier).
Later, Grid-Search was used for better results.
5.3 Identification of Cough Types
The COUGHVID crowdsourcing dataset [12] was used for cough classification.
The dataset had cough recordings, which is in .wav format and .json files, which
contained the description of the cough recordings. It contained some recordings that
had no cough, and some had coughs. The recordings that had coughs were used in this
study. It contained four types of coughs, COVID, healthy, symptomatic, and no status.](https://image.slidesharecdn.com/28492840-250512180121-f1ca2fc6/85/Networking-And-Paralleldistributed-Computing-Systems-Volume-18-Roger-Lee-54-320.jpg)
Networking And Paralleldistributed Computing Systems Volume 18 Roger Lee
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- 5. Studies in Computational Intelligence 1125 Roger Lee Editor Networking and Parallel/ Distributed Computing Systems Volume 18
- 6. Studies in Computational Intelligence Volume 1125 Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
- 7. The series “Studies in Computational Intelligence” (SCI) publishes new develop- ments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self- organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publica- tion timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.
- 8. Roger Lee Editor Networking and Parallel/ Distributed Computing Systems Volume 18
- 9. Editor Roger Lee Software Engineering and Information Technology Institute Central Michigan University Mount Pleasant, MI, USA ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-031-53273-3 ISBN 978-3-031-53274-0 (eBook) https://doi.org/10.1007/978-3-031-53274-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.
- 10. Editorial Review Board Kiumi Akingbehin, University of Michigan, United States Yasmine Arafa, University of Greenwich, United Kingdom Jongmoon Baik, Korea Advanced Institute of Science and Technology, South Korea Ala Barzinji, University of Greenwich, United Kingdom Radhakrishna Bhat, Manipal Institute of Technology, India Victor Chan, Macao Polytechnic Institute, Macao Morshed Chowdhury, Deakin University, Australia Alfredo Cuzzocrea, University of Calabria, Italy Hongbin Dong, Harbin Engineering University, China Yucong Duan, Hainan University, China Zongming Fei, University of Kentucky, United States Honghao Gao, Shanghai University, China Cigdem Gencel Ambrosini, Ankara Medipol University, Italy Gwangyong Gim, Soongsil University, South Korea Takaaki Goto, Toyo University, Japan Gongzhu Hu, Central Michigan University, United States Wen-Chen Hu, University of North Dakota, United States Naohiro Ishii, Advanced Institute of Industrial Technology, Japan Motoi Iwashita, Chiba Institute of Technology, Japan Kazunori Iwata, Aichi University, Japan Keiichi Kaneko, Tokyo University of Agriculture and Technology, Japan Jong-Bae Kim, Soongsil University, South Korea Jongyeop Kim, Georgia Southern University, United States Hidetsugu Kohzaki, Kyoto University, Japan Cyril S. Ku, William Paterson University, United States Joonhee Kwon, Kyonggi University, South Korea Sungtaek Lee, Yong In University, South Korea Weimin Li, Shanghai University, China Jay Ligatti, University of South Florida, United States Chuan-Ming Liu, National Taipei University of Technology, Taiwan Man Fung Lo, The University of Hong Kong, Hong Kong v
- 11. vi Editorial Review Board Chaoying Ma, Greenwich University, United Kingdom Prabhat Mahanti, University of New Brunswick, Canada Tokuro Matsuo, Advanced Institute of Industrial Technology, Japan Mohamed Arezki Mellal, M’Hamed Bougara University, Algeria Jose M. Molina, Universidad Carlos III de Madrid, Spain Kazuya Odagiri Sugiyama, Jogakuen University, Japan Takanobu Otsuka, Nagoya Institute of Technology, Japan Anupam Panwar, Apple Inc., United States Kyungeun Park, Towson University, United States Chang-Shyh Peng, California Lutheran University, United States Taoxin Peng, Edinburgh Napier University, United Kingdom Isidoros Perikos, University of Patras, Greece Laxmisha Rai, Shandong University of Science and Technology, China Fenghui Ren, University of Wollongong, Australia Kyung-Hyune Rhee, Pukyong National University, South Korea Abdel-Badeeh Salem, Ain Shams University, Egypt Toramatsu Shintani, Nagoya Institute of Technology, Japan Junping Sun, Nova Southeastern University, United States Haruaki Tamada, Kyoto Sangyo University, Japan Takao Terano, Tokyo Institute of Technology, Japan Kar-Ann Toh, Yonsei University, South Korea Masateru Tsunoda, Kindai University, Japan Trong Van Hung, Vietnam Korea University of Information and Communications Tech, Viet Nam Shang Wenqian, Communication University of China, China John Z. Zhang, University of Lethbridge, Canada Rei Zhg, Tongji University, China
- 12. Foreword The main purpose of this book is to seek peer-reviewed original research papers on the foundations and new developments in Networking and Parallel/Distributed Computing Systems. The focus will also be on publishing in a timely manner, the results of applying new and emerging technologies originating from research in Networking and Parallel/Distributed Computing Systems. The findings of this book can be applied to a variety of areas, and applications can range across many fields. Thepapersinthisbookwerechosenbasedonreviewscoressubmittedbymembers of the editorial review board and underwent rigorous rounds of review. We would like to thank all contributors including all reviewers, and all editorial board members of this book for their cooperation in helping to publish this book. It is our sincere hope that this book provides stimulation and inspiration, and that it will be used as a foundation for works to come. December 2023 Her-Terng Yau National Cheng Kung University Taiwan Hsiung-Cheng Lin National Chin-Yi University of Technology Taiwan vii
- 13. Contents A Deep Learning-Based Study for Cyclone Track Forecasting: Comparative Analysis Using Historical Data from the Bay of Bengal . . . 1 Rafi Majid, Akmam Hasan, Shayrey Mostarin, Kazi Rabiul Alam, and Rashedur M. Rahman Text-Based Data Analysis for Mental Health Using Explainable AI and Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Tazrin Rahman, Rehnuma Shahrin, Faharia Akter Pospu, Nafia Sultana, and Rashedur M. Rahman Using the Internet of Things and Machine Learning to Monitor and Detect COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Al Mehedi Hasan, Istiak Ahmed Sheam, Md. Maruf Chowdhury, and Rashedur M. Rahman Comparison Between the Route Using Improved Detours and Shortest Route on Distributed Key-Value Store Based on Order Preserving Linear Hashing and Skip Graph . . . . . . . . . . . . . . . . 53 Ken Higuchi, Yurika Tsubouchi, and Tatsuo Tsuji Extraction Study of Leaf Area and Plant Height of Radish Seedlings Based on SAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Yaoyao Chen, Zijun Yang, Wenjing Bian, Seiichi Serikawa, and Lifeng Zhang A Scalable Middleware for IoT Vulnerability Detection . . . . . . . . . . . . . . . 85 Minami Yoda, Shigeo Nakamura, Yuichi Sei, Yasuyuki Tahara, and Akihiko Ohsuga Predicting Oil Spills in Real-Time: A Machine Learning and AIS Data-Driven Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Tanmay Bisen, Aastha Shayla, and Susham Biswas ix
- 14. x Contents Data-Driven OCL Invariant Patterns-Based Process Model Exploration for Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Duc-Hieu Nguyen, Yuichi Sei, Yasuyuki Tahara, and Akihiko Ohsuga An Analysis of Program Comprehension Process by Eye Movement Mapping to Syntax Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Haruhiko Yoshioka and Hidetake Uwano Self-supervised Monocular Depth Estimation and Ego-Motion Made Better: A Masking Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Tian Wen, Gaofei Sun, and Lifeng Zhang Using Additional Sets of Random Data to Assess Possible Dissaving Risk Against Life Expectancy for Elderly People . . . . . . . . . . . . . . . . . . . . . 167 Yuya Yokoyama A Study on the NFT and Blockchain Technology for Copyright Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Jung Jae Lee and Hee Young Jung The Effect of Enterprise Risk Management System on Risk Management Capability and Management Performance . . . . . . . . . . . . . . 201 Se Yong Lee, Dong Hyuk Jo, and Hee Jun Cho A Study on the Effect of Omnichannel Customers Acceptance Attitudes and Loyalty: Focusing on UTAUT2 . . . . . . . . . . . . . . . . . . . . . . . . . 213 So Ra Min and Dong Hyuk Jo
- 15. A Deep Learning-Based Study for Cyclone Track Forecasting: Comparative Analysis Using Historical Data from the Bay of Bengal Rafi Majid, Akmam Hasan, Shayrey Mostarin, Kazi Rabiul Alam, and Rashedur M. Rahman Abstract Accurate cyclone track forecasting is important for disaster management and mitigation. In our research, we have done a comparative study on cyclone track forecasting using deep learning models focusing on the Bay of Bengal. We employ LSTM, Convolutional LSTM, GRU architectures on datasets to train and evaluate the models. We achieve significant accuracy in predicting cyclone trajectories through extensive hyper-parameter tuning and experimentation. This research shows the effi- ciency and limitations of deep learning models to predict complex dynamics of cyclone tracks. The outcomes of our research focus on the effectiveness of deep learning models predicting complex problems like different dynamics of cyclone trajectory prediction. Multiple training methodologies and orientations are used in this research to demonstrate different insights. Though we use conventional eval- uation matrix of machine learning like MSE, MAE, RMSE, R2 , we use another evaluation matrix that is average distance error. Average distance error calculates the distance error of prediction plots and actual plots in kilometers. This matrix helps to accurately demonstrate the models’ efficiency in a different dimension. This experi- ment contributes to the ongoing research of cyclone trajectory forecasting using deep learning models and its practical implications. The findings of the work show the potential of deep learning architectures for accurate cyclone track forecasting that contributes to disaster management, preparedness and loss mitigation. R. Majid · A. Hasan · S. Mostarin · K. R. Alam · R. M. Rahman (B) Department of Electrical and Computer Engineering, North South University, Plot-15, Block-B, Bashundhara, Dhaka 1229, Bangladesh e-mail: rashedur.rahman@northsouth.edu R. Majid e-mail: rafi.majid@northsouth.edu A. Hasan e-mail: akmam.hasan@northsouth.edu S. Mostarin e-mail: shayrey.mostarin@northsouth.edu K. R. Alam e-mail: rabiul.alam@northsouth.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Networking and Parallel/Distributed Computing Systems, Studies in Computational Intelligence 1125, https://doi.org/10.1007/978-3-031-53274-0_1 1
- 16. 2 R. Majid et al. Keywords Cyclone track forecasting · Cyclone trajectory forecasting · Deep learning · LSTM · Convolutional LSTM · GRU 1 Introduction Tropical cyclones are catastrophic, an extreme weather condition that causes huge economic loss. Tropical Cyclone development is a complex dynamic mechanism, and various factors depend on it, such as sea conditions, atmosphere, place, and other atmospheric variables [1]. Tropical Cyclone not only causes economic losses but also causes human casualties [2]. In particular, underdeveloped or developing countries suffer the most due to losses. As Bangladesh is a developing country, it suffers a lot due to tropical cyclones. On top of that, Bangladesh is located at the head of the Bay of Bengal, and every year, small and large cyclones hit Bangladesh for its position. For example, two major cyclones that hit Bangladesh, Sidr and Aila, created 2.31 billion and 269 million total losses, and thousands of people died. To mitigate the loss, preparedness is the key. As a part of the preparedness, cyclone track forecasting plays a significant role. Researchers use different meteorological mechanisms for cyclone path prediction. Many statistical models have been used for cyclone trajectory prediction. However accurate cyclone trajectory prediction is needed, and it is always a region of active research. In recent years, the prediction of cyclone trajectory has been divided into three categories: traditional cyclone path prediction models, machine learning base cyclone path prediction models, and deep learning base cyclone path prediction models [2]. Traditional forecasting models are mainly statistical models, and they also have subdomains such as empirical methods and objective methods. The empir- ical method uses information from satellite and historical data, whereas the objective method is fully dependent on statistics, hydrodynamics, and other science. Objec- tive based prediction also has some classifications like dynamic based track predic- tion, statistics-based track prediction, and statistical dynamic-based track prediction [2]. The dynamic-based prediction method, also known as the numerical prediction method, uses large amounts of historical data to construct the dynamic equation and transforms the problem into a mathematical problem [3]. It is undoubtedly intensive to solve as the constructed equation from such a method is complex. Statistical methods are also based on mathematical statistics, and they also need a large amount of historical data to find the relationship of track data [4]. In recent days, the use of machine learning algorithms for prediction-based problems has prolifer- ated. Regression, k-means algorithms, and other Gradient Boosting algorithms are mainly those algorithms that can handle multi-variable inputs for predicting tracks with minimal data or information [2]. With the further advancement of technology, researchers started using deep learning models like Artificial Neural Networks (ANN) to predict cyclone trajectories with better accuracy [5]. Satellite images and other historical information started showing higher accuracy with minimum data.
- 17. A Deep Learning-Based Study for Cyclone Track Forecasting … 3 In this paper, we demonstrate the effectiveness of (LSTM), Convolutional LSTM and Gated Recurrent Unit (GRU) [6] on cyclone trajectory prediction based on histor- ical dataset and two different approaches. We mainly focus on the Bangladesh region and have tried to predict the cyclone path accurately. Our input features are mainly the latitude and longitude of the cyclone track. With latitude and longitude, we have considered other variables as input features. We have worked on two different approaches-training the historical cyclone data to predict the next trend of cyclones and training hourly-based data of each cyclone to predict the next position of the cyclone. 2 Literature Review A paper related to the work introduced a deep learning model which is ConvLSTM that predicts the path of the typhoon [2]. The paper aimed to create a strong and effective model that can predict typhoons accurately to reduce property loss. There is a method named Granger causality test, which is used to select the meteorological features. In time series problems, to use the Granger causality test, one needs to perform Augmented Dickey–Fuller techniques (ADF). The p value is ≤0.01 then the Granger causality test can work otherwise not. The authors used both Partial Autocorrelation Coefficient function (PACF) and Autocorrelation Coefficient func- tion (ACF) to measure correlation between features. If residual P values are <0.01 by testing, then the series is cointegrated. An attempt [2] has been made using the C-LSTM network. The authors used the C-LSTM model, which is a combination of CNN and LSTM where CNN can extract multiple features, and in shared time series situations, LSTM can acquire temporal properties and increase model performance. The authors selected features by Granger causality test, which was then fed into the C-LSTM model. The paper [7] introduced new techniques which is a multimodal technique and a combination of ML framework and DL to predict intensity and track forecasting. They have used two types of data. One is statistical data and another one is image data. At first, they preprocess the statistical data using one-hot encoding and all categorical values are transformed in numerical value. The paper aimed at a multimodal approach that can perform better on individual models, which means that multimodal models obtain higher precision and lower standard deviations than models that just use statistical data [7]. For 24 time-based intensities, they have seen that multimodal models have better MAE than operation models [7]. The authors have reached their goal and shown that machine learning models can perform better and bring additional insight. The future work of this paper is adding more data and predicting the model for longer periods input increasing window size as it predicts for 24 h only. The paper worked on storm track data and reanalysis image data [8]. Their fused network model is based on 24 h over 3000 storms [8]. The research [9] is based on remote sensing images with ConvLSTM and SAN- EFS Model. The authors used two models to complete the task track prediction on
- 18. 4 R. Majid et al. tropical cyclones. The SAN can spatially emphasize critical pixels in the arriving data and unseen conditions [9]. So, important pixels in distant identifying photos aid in furthering the analysis of how spatial characteristics affect the TC track and, as a result, increase prediction accuracy. SAN-ConvLSTM improves the model’s forecast by including a density map. This paper [10] approaches the new deep learning model GRU to predict the path of storms in the Pacific Northwest. Deep learning method is more efficient, and it can capture the complex relation between variables. The authors tried predicting the forecast from 6 to 72 h. They also used LSTM, RNN, GRU, and among them, GRU performs better [10]. This model performs better on forecast at 6 h and 12 h and their average distance error is respectively 17.22 and 43.99 but when forecast surpasses 24 h, the accuracy declines [10]. At the end the authors argued that if more data can be included, more valuable characteristics can be extracted to improve deep-learning model’s prediction accuracy. The paper covers a study that examines the Arabian Sea cyclone paths [11]. The exploratory investigation of numerous cyclone track parameters in the Arabian Sea is presented in this paper, emphasizing the historical and important shapes of those features. The Arabian Sea basin exhibits a comparable average cyclogenesis latitude to other ocean basins compared to other basins with larger cyclone numbers, the Arabian Sea basin is less well-known global cyclone study. However, because of its three-sided pattern, tapering northward, enclosure, and comparatively little shape, future cyclone development in this basin is rather fleeting. As a result, compared to cyclones in other areas, Arabian Sea cyclones have lower features in terms of maximum intensity, duration, and migratory distance. This paper uses data from 20 years of simulated outgoing long wave energy to classify typhoons and their predecessors using deep learning techniques to explore the tractability of TCs [12]. Based on specific circumstances, this model displays the best and worst scenarios of detection performance for tropical cyclones and their precursors. The best situation is shown when there are more than eight TCs and precursors, and when the probability of detection (POD) is higher than 80.0%. The results of predictions in regions with less data, like the North Atlantic, may benefit from the training data from other basins. QuantitativeresearchonthepatternoftropicalstormpathsintheNorthwestPacific basin was undertaken in the work [13]. The authors discovered a slight helpful corre- lation between the length of SI and TS and the remoteness covered, providing useful information for danger valuation. Trends and Impacts demonstrates a comprehen- sive analysis of tropical cyclone and its trajectory behavior in the Bay of Bengal region from 1971 to 2020 [14]. According to this study, it exposes an average of 35.2 cyclones per decade without a significant increasing or decreasing trend. It discovers inter-annual and seasonal variations, with tropical cyclones taking northward tracks in the pre-monsoon season and making landfalls along Bangladesh and Myanmar coasts, whereas post-monsoon tropical cyclones make landfalls directly on Orissa and West Bengal coasts.
- 19. A Deep Learning-Based Study for Cyclone Track Forecasting … 5 3 Methodology 3.1 System Design This section mainly covers the entire process of this experiment. It covers data acqui- sition, data prepossessing, different test results, feature selection, construction of the LSTM, ConvLSTM and GRU model to predict cyclone path. To understand the insights of the data, we did a few tests on our datasets. These are Augmented Dicky Fuller Test (ADF), Granger Causality Test and ACF PACF test. These tests do not directly impact our training procedure but the insight from the tests gives us information about our data and the relationships of its features. Long short-term memory (LSTM) [15] is a recurrent neural network (RNN) that works well in problems like time series analysis. It can deal with multi-variable and can train a sequence of input and provide its output. Some Basic components of LSTM model (Fig. 1) are shown below: Input Layer: It receives input data. Then it passes to the next layer, that is a memory cell. Memory cell: It helps to store information and maintain the flow of information using different gates. It is one of the key components of the LSTM model. Forget Gate: It determines the information that we should throw from the memory cell. Input Gate: Determines new information that we should add to the memory cell. Output Gate: Determines information that should be the output from the memory cell. Hidden State: It is the output of the LSTM model obtained from the previous input and the previous hidden state. The LSTM model is a powerful tool for performing time series analysis as it takes the data sequence as input and provides meaningful output. ConvLSTM is another Fig. 1 LSTM architecture
- 20. 6 R. Majid et al. type of neural network that combines Convolutional and LSTM layers. It contains the following components: LSTM, Merge and Output layer. It learns the temporal dependencies of input data, combines the output of the Convolutional and LSTM layer and gives final output of the model based on the previous layers. Its parameters are adjusted to mitigate the difference between actual and predicted output. GRU is another recurrent neural network model. It is the same as the LSTM model, but the structure of this model is more simplified. The basic components of GRU are Reset Gate, Update Gate, and Hidden State. Reset Gate determines which information from the previous time step should be thrown out. The reset gate determines how much should be retained or discarded from the last hidden state. Update Gate determines the new information that should be added with the hidden state. Hidden State is the output of the GRU model which predicts on the input data. GRU works step by step and processes the input data one step at a time. After every step, the model updates its hidden state based on the reset and update gate. The structure of the model is simplified than LSTM, which is widely used in deep learning, such as time series forecasting. In this research, we have gone through some procedures that are shown in Fig. 2. Data collection: Data is collected from reliable sources. For selecting data longi- tude, latitude and other relevant features were taken into consideration. Historical data of cyclones are selected for short time series analysis. Data preprocessing: The dataset underwent rigorous preprocessing techniques such as feature scaling, outlier detection, missing value handling, and categorical data encoding. These procedures, detailed in Section B, ensured suitability and data integrity for model training. The resulting precisely processed dataset forms the robust foundation upon which our analysis and conclusions are based. Data split: In this work for splitting data two methods are followed. For predicting trajectory based on hours we split data on hour basis. The first 60 h of data are used for training and the remaining data used for testing. In another approach, 140 cyclones data are used for training and the remaining data used for tests. Model selection: Based on our data patterns, three models LSTM, ConvLSTM, GRU are selected for time series analysis. For selecting the model previous research, model complexity, computational requirements are considered. Model development: We designed the model with suitable inputs. We also added layers, activation functions, parameters to ensure the optimized uses of the model. Model testing: For evaluating its performance we applied the model into unseen data. There are many evaluations metric such as R2 , MAE, MSE, MAE, Average distance between actual and predicted values are calculated. Based on the results, iteration and fine-tuning of the model are done. Resultanalysis:Aftercompletingtheevaluation,analysisofmodel’sperformance and interpreting the result is done. We have also identified the limitations of our work and the areas of improvement. Visualization of data: The actual and predicted data are visualized. By visualizing data, we tried to increase the effectiveness of our data. The visualization of data
- 21. A Deep Learning-Based Study for Cyclone Track Forecasting … 7 Fig. 2 Workflow diagram enhances understanding, gives clear insights and facilitates data- driven decision making. 3.2 Data Preprocessing We performed data cleaning for handling missing values and outliers. For removing outliers Z-score method is used which calculates the point how many standard devi- ations are away from the mean. Our dataset contains cyclone data of Bangladesh and its surrounding region. It consists of 178 cyclone data which is shown in Fig. 3. The
- 22. 8 R. Majid et al. Fig. 3 Data of 178 cyclones dataset contains historic information on cyclones used in this experiment. Some data preprocessing methods are used in this dataset to make them suitable for time series analysis. Our northeastern Indian Ocean dataset provides historic cyclone information of 3579 data rows information and features like latitude, longitude, max wind speed, hours from first observation, etc. In this dataset, data is updated every 6 h of a cyclone. For cyclone track forecasting, we need to train the information such as latitude, longitude, and max wind speed of a particular cyclone as a single matrix. For this, we created groups of each cyclone using the cyclone number and trained each group of cyclonic information into the model. Besides, we drop some corrupted data containing null values or show outliers. For identifying the outliers, we have used the z-score method. Generally, the z-score method identifies outliers, and it pulls out every data point that is 3 standard deviations away from the mean. It is widely used for handling outliers, and it worked for our dataset. We did not find null values in any of our features except storm names. The storm name contains 1988 null values. As these null values did not have any effect on our training procedure, so we did not handle it. The correlation matrix is widely used to analyze the relations between the vari- ables. It shows us the correlation coefficients between variables. To reduce over- fitting and training models properly, examining correlation matrices is essential. In the correlation matrix, the range of the correlation matrix is from −1 to 1, where 0
- 23. A Deep Learning-Based Study for Cyclone Track Forecasting … 9 indicates no correlation, −1 indicates a perfectly negative and 1 indicates perfectly positive correlation. In this work, output features are ‘x(latitudes)’ and ‘y(longitudes).‘For feature x(latitudes), it has the highest negative correlation with variable’ hours from First Observation’, which is −0.43, and it has the highest positive correlation with ‘Storm Number’ which is 0.25. There is also a correlation between the ‘x’ and ‘y’ variables which is −0.38. In ADF Testing, if the probability is less than 0.05, then it is non-stationary, where we can use models like ARIMA, LSTM, convolutional LSTM, etc. We have done ADF unit root tests for latitude and longitude. We have done this test using two approaches. Firstly, we use the whole dataset. After that, we use a particular cyclone called Ayla. In the analysis the null hypothesis is non-stationary. When the lag is 0, the series of X is stationary because the p > 0.05. But when lag is 3, it rejects the null hypothesis. It is rejected because the p < 0.05. The series X is stationary at the level and non- stationary at difference 3. For series y, it is non-stationary at the level. Therefore, the p-value of the series is less than 0.05. The presence of a unit indicates that the series has the trend to revert to its meaning. LSTM, GRU, and ConvLSTM are commonly used in non-stationary data, so those models can be used in this novel work as non-stationary time series data are found. The Granger Causality test determines dependencies of a variable with another. It can provide a significant outcome in time series analysis, using multiple variables to get a meaningful result. The Granger causality test helps us to determine whether we can use one series for another. For example, we can decide if the past values of X can be used to predict the value of Y. We can get an overview of whether the past value of X has any power over the current or future values. We can consider a variable to be dependent on another variable if the test result is greater than 0.05. One primary purpose of the Granger-Causality test is to find additional predictive information for a time series that may or may not have a causal relationship. We have taken X, Y that is latitude and longitude as our input variable as it repre- sents some dependencies. Hours from first observation also can provide meaningful information. To understand the data in a clearer way, we have done the same test using the data of a particular cyclone. Here, we can see only latitude, longitude, and hours from the first observa- tion features that show some values that represent dependencies. Other features do not show any dependencies with one another. From the analysis, there is bidi- rectional causality between HOURS_FROM_FIRST_OBSERVATION to Y and unidirectional causality running to X. From the null hypothesis, ‘X’ does not granger ‘Y’ is rejected because the p-value is <0.05, but ‘Y’ does not granger ‘X’ can be accepted because its value is larger than 0.05. The past value of ‘X’ impacts predicting the values of ‘Y.’ So, X can be used in the model to improve and influence the future values of ‘Y.‘ Causality is also found in the case of ‘Storm_Number’ but it is used for sorting and grouping each cyclone. In the above, a test has been made to find causality with ‘X’ and ‘Y’, but no
- 24. 10 R. Majid et al. other variables are found which have an impact on predicting the value of ‘X’ and ‘Y’. 3.3 Training Procedure We have used multiple models to understand the underlying patterns in the dataset. An attempt has been made to forecast the cyclone’s trajectory by using the data. Three models have been used for this work: LSTM, Convolutional LSTM, and GRU. Each model shows satisfactory performance. An attempt has been made to find the future positions of longitude and latitudes using the historical data. By training 140 storms, we have tried to capture the data pattern of past data to predict the next positions of cyclones. Another approach is made to predict the trajectory of the cyclone based on information of the past 60 h. By finding the behavior and underlying patterns of data, it will predict the trajectory of the cyclone. For calculating the average difference, the Haversine formula is used. It calculates the difference between each predicted and actual data point. The Haversine formula is given by: a = sin2 ( Δφ 2 ) + cosφ1 · cosφ2 · sin2 ( Δλ 2 ) c = 2 · atan2 (/ a, √ 1 − a ) where: Δφ actual lat – predicted lat Δλ actual lon − predicted lon R raius of the Earth (approximately 6371 km) The calculated error between points: distance = R · c The output features are the same for both approaches. But in the input feature, we have used the feature “Hours from first observation” as we tried to predict the next latitude and longitude of a particular storm hourly. We have used three models: LSTM, convolutional LSTM, and GRU. We have used different evaluation metrics for evaluating our work. RMSE, MSE, MAE R2 and distance error is based on the predicted and actual data. The procedure has gone through multiple time hyper parameters tuning. We have used all evaluation metrics to determine the efficiency and accuracy of models like LSTM, convolutional LSTM, and GRU.
- 25. A Deep Learning-Based Study for Cyclone Track Forecasting … 11 Cyclone-related data can be dependent on two things, and those are type and quality [2]. Different countries have different levels of development, and the cyclonic data collection procedure and their dependencies can vary for every country. As we experimented on the North Indian Ocean, especially over the Bay of Bengal, we have used historic data of cyclones for the Bangladesh region only. 4 Result, Analysis and Discussion All the models are able to find the underlying data patterns with a good R2 score. The R2 score of LSTM model is 0.955 and average distance 65.111 (km). The other model ConvLSTM scored R2 0.919 and average distance 127.912 (km). At the end R2 score for GRU is 0.974 and average distance is 66.050 (km). In this approach GRU model performed the best. The visual presentation of our findings is given in Figs. 4 and 5 and performance metrics are reported in Table 1. In another approach the models are trained on 140 storms and predict the trajectory of 38 cyclones. In this case, the R2 score of LSTM model is 0.741 and the average error between points is 101.144 (km). In this approach R2 score of ConvLSTM is Fig. 4 Predicted track of Roanu (Cyclone—178) using 140 cyclones data
- 26. 12 R. Majid et al. Fig. 5 Predicted track of Roanu based on data of 60 h Table 1 Result of models Approach Model R2 score Distance error (km) MSE MAE RMSE Train ≤140 storm to predict next trend (for cyclone-Roanu) LSTM 0.955 65.111 0.653 0.646 0.808 C-LSTM 0.919 127.912 1.353 0.793 1.163 GRU 0.974 66.050 0.382 0.524 0.618 Train ≤140 storm to predict next trend (for next 38 Storms) LSTM 0.741 101.144 3.733 0.936 1.932 C-LSTM 0.709 108.408 4.269 1.129 2.066 GRU 0.762 82.787 3.517 0.894 1.875 Train first 60 h prediction (for cyclone-Roanu) LSTM 0.875 154.747 1.385 0.942 1.177 C-LSTM 0.405 54.595 2.797 1.320 1.672 GRU 0.810 183.033 2.023 1.158 1.422 Train first 60 h prediction (for next 38 Storms) LSTM 0.594 191.412 6.112 1.749 2.472 C-LSTM 0.610 262.481 6.570 1.915 2.563 GRU 0.603 182.938 5.931 1.699 2.435
- 27. A Deep Learning-Based Study for Cyclone Track Forecasting … 13 0.709 and the average error between points is 108.408 (km). The R2 score of GRU is close to LSTM. The R2 score of GRU is 0.762, and the average distance between the predicted and actual points is 82.787 (km). After analyzing the performance, all models have performed moderately on this approach. GRU scored lowest in this case of average distance, which indicates GRU performed best in this approach. Predicting the trajectory of Cyclone Roanu (Cyclone—178) by analyzing the characteristics of the first 60 h is given in Fig. 5. The predicted path based on the first 60 h has been plotted. For this plotting, data of the first 60 are trained, and the next values are predicted. In this short time series analysis, LSTM worked well. The R2 score for this model is 0.874 and the average distance between the predicted and actual points is 154.747 (km). The performance of LSTM and GRU are pretty similar. The R2 score of Convolutional LSTM is 0.405 and the average distance between predicted and actual values is 54.595 (km). GRU R2 score is 0.810 and average distance between actual and predicted data points is 183.033 (km). 5 Conclusion The main objective of the research was to build a reliable model depending on features.Throughouttheresearchthreemodelsareimplemented:LSTM,ConvLSTM and GRU with some unique architecture. Historical data set of tropical cyclones is used for training and evaluation. Careful preprocessing, splitting, training, and testing have been done to complete the work. In this research, for evaluating performance, various performance metrics such as R2 , MSE, MAE, RMSE are calculated. There is also the calculation of the average distance between actual and predicted values converted into kilometers. In this paper, the main focus was on the prediction of cyclone trajectory using deep learning models. Tropical cyclones have an intensive impact on the Bay of Bengal region. Bangladesh has vast coastal areas and for this geographical location tropical cyclones make high vulnerability. For disaster management, cyclone path prediction plays a crucial role. By collaborating with government agencies and research institutes, our research can contribute to capacity building and knowledge sharing. Several engineering challenges are encountered in this research, such as predicting accurate values with very small amounts of data, overfitting issues, and adjusting model complexity. By applying suitable techniques, we overcame these issues and developed a reliable model. The paper reflects the successful use of deep learning models to predict cyclone trajectories. Among the three models, GRU performed best. GRU has a simple architecture and reduces the risk of overfitting. As data was limited, all the models were applied to find the best model. Forecasting tropical cyclone trajectory needs a good amount of high-quality data. Data availability is one of the significant limitations of this research work. The deep learning model works well on large data. The data amount and quality were the main obstacles to our research. We need more data and implement advanced methodology to improve our
- 28. 14 R. Majid et al. work. The cyclone track was visualized using longitude and latitude. There, a small error in a prediction causes many kilometers of distance between actual and predicted value. The wrong prediction can make disaster management more challenging. This work can play a significant role in saving lives and minimizing vulnerabilities by providing the trajectory of the cyclone. Tropical storm frequency and intensity have increased in recent years, and losses from flooding induced by cyclones create a humanitarian challenge. By using prediction management, we can make preparations that will help to enhance our ability to safeguard vulnerable areas from the devastating impact of cyclones. References 1. P. Wang, P. Wang, C. Wang, B. Xue and D. Wang, “Using a 3d convolutional neural network and gated recurrent unit for tropical cyclone track forecasting,” Atmospheric Research, vol. 269, May 2022. 2. R. Jia, X. Nan and C. Yani, “Typhoon Track Prediction Based on Deep Learning,” 2022. 3. N. C, Z. D, O. L, H. X, Z. B and T. Y, Dynamic Prediction of Longitudinal Settlement of Existing TunnelUsingConvRes-DLinearModelwithIntegrationofUndercrossingConstructionProcess Information. Geosciences. 4. N. “An alternate to the HURRAN (Hurricane Analog) tropical cyclone forecast system,” 1972. 5. R. Kovordanyi and C. Roy, “Cyclone track forecasting based on satellite images using artificial neural networks,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 64, no. 6, pp. 513–521, November 2009. 6. GeeksforGeeks, “Gated Recurrent Unit Networks,” [Online]. Available: https://www.geeksf orgeeks.org/gated-recurrent-unit-networks/. 7. L. Boussioux, C. Zeng, T. Guenais and D. Bertsimas, “Hurricane forecasting: A novel multimodal machine learning framework,” Weather and Forecasting, vol. 37, no. 6, March 2022. 8. S. Giffard-Roisin, M. Yang, G. Charpiat, C. K. Bonfanti, B. Kégl and C. Monteleoni, “Trop- ical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data,” Frontiers in Big Data, vol. 3, January 2020. 9. P. Dong, J. Lian, H. Yu, J. Pan, Y. Zhang and G. Chen, “Tropical Cyclone Track Prediction with an Encoding-to-Forecasting Deep Learning Model,” Weather and Forecasting, vol. 37, no. 6, p. 971–987, June 2022. 10. L. Wang, B. Wan, S. Zhou, H. Sun and Z. Gao, “Forecasting tropical cyclone tracks in the northwestern pacific based on a deep-learning model,” Geoscientific Model Development, vol. 16, no. 8, p. 2167–2179, 2023. 11. J. P. Terry and G. Gienko, “Quantitative observations on tropical cyclone tracks in the arabian sea,” Theoretical and Applied Climatology, vol. 135, pp. 1413–1421, June 2019. 12. D. Matsuoka, M. Nakano, D. Sugiyama and S. Uchida, “Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydro- static atmospheric model,” Progress in Earth and Planetary Science, vol. 5, no. 1, pp. 1–16, 19 December 2018. 13. R. S. Pandey and Y.-A. Liou, Decadal behaviors of tropical storm tracks in the North West Pacific Ocean, Atmospheric Research, vol. 246, 2020.
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- 30. Text-Based Data Analysis for Mental Health Using Explainable AI and Deep Learning Tazrin Rahman, Rehnuma Shahrin, Faharia Akter Pospu, Nafia Sultana, and Rashedur M. Rahman Abstract This paper analyzes mental health through text-based data using explain- ableAIanddeeplearningtechniques,focusingontheclassificationofaddiction,alco- holism, anxiety, depression, and suicidal thoughts. The motivation for this research stems from the lack of suitable datasets from Twitter and the need for a more compre- hensive understanding of mental health using text data. We propose a methodology that involves leveraging Reddit data, employing various text vectorization techniques (TF-IDF, Word2Vec, and GloVe), and implementing multiple classification algo- rithms (XGBoost, Decision Tree, SVM, Naive Bayes, Simple Gradient Descent, Stochastic Gradient Descent, K-Nearest Centroid, K-Nearest Neighbor, AdaBoost, Random Forest, and Logistic Regression) using the TF-IDF text vectorizer. The problem revolves around accurately classifying mental health-related topics based on text data. By employing a range of classification algorithms and text vectoriza- tion techniques, we aim to develop machine-learning models that effectively identify addiction, alcoholism, anxiety, depression, and suicidal thoughts within text-based discussions. This research contributes to mental health analysis using text-based data and advanced machine-learning techniques. The results highlight the poten- tial of Reddit as a valuable resource for understanding mental health concerns and demonstrate the effectiveness of the proposed methodology. Our findings have far- reaching implications for mental health practitioners, researchers, and policymakers, T. Rahman · R. Shahrin · F. A. Pospu · N. Sultana · R. M. Rahman (B) Department of Electrical and Computer Engineering, North South University, Plot-15, Block-B, Bashundhara Residential Area, Dhaka, Bangladesh e-mail: rashedur.rahman@northsouth.edu T. Rahman e-mail: tazrin.rahman@northsouth.edu R. Shahrin e-mail: rehnuma.shahrin@northsouth.edu F. A. Pospu e-mail: faharia.pospu@northsouth.edu N. Sultana e-mail: nafia.sultana@northsouth.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Networking and Parallel/Distributed Computing Systems, Studies in Computational Intelligence 1125, https://doi.org/10.1007/978-3-031-53274-0_3 17
- 31. 18 T. Rahman et al. facilitating the development of tailored interventions and support systems for those in need. Keywords Explainability · Textual analysis · LIME · Reddit · Deep learning · Text-based data 1 Introduction Mental health has emerged as a critical global issue, affecting individuals across all walks of life. The increasing prevalence of mental health disorders calls for compre- hensive research and innovative approaches to understanding and addressing these conditions. Recently, there has been a growing interest in utilizing machine learning and AI techniques to analyze text-based data for mental health analysis. Existing studies have predominantly focused on utilizing Twitter data for mental health anal- ysis. Twitter provides a wealth of real-time information, making it suitable for certain types of analysis, such as real-time monitoring and event tracking. However, these datasets often have limitations such as overuse, outdated information, and a lack of specificity to diverse mental health conditions. These challenges researchers seeking to develop effective treatments and interventions for a broader range of mental health concerns. The motivation for this research stems from the inadequacy of existing Twitter datasets and the need for a more comprehensive understanding of mental health using text-based data. In this study, we propose using Reddit data as an alter- nativesourceformentalhealthanalysis.Redditoffersauniqueplatformcharacterized by longer and more detailed discussions, which can provide valuable insights into the language used by individuals when discussing their mental health concerns and the topics that are most relevant to them. This research aims to conduct a comprehen- sive analysis of mental health using text-based data obtained from Reddit, leveraging explainable AI and deep learning techniques. The primary goal is to develop machine learning models that can effectively classify and analyze mental health-related text data, specifically focusing on addiction, alcoholism, anxiety, depression, and suicidal thoughts. In summary, this study is motivated by the limitations of existing Twitter datasets and the need for a more comprehensive analysis of mental health using text-based data. By utilizing Reddit data and advanced AI techniques, we aim to advance the field of mental health analysis, providing valuable insights and facilitating the development of effective interventions for a wide range of mental health conditions.
- 32. Text-Based Data Analysis for Mental Health Using Explainable AI … 19 2 Related Works Research using Reddit data for mental health analysis has attracted attention recently. Several studies have explored the potential of Reddit as a valuable resource for understanding mental health-related discussions and developing machine learning models for classification and analysis. Here are a few examples of existing research and the limitations associated with this work. The study [1] focused on predicting suicide risk based on Twitter data. It devel- oped a machine learning model that analyzed language patterns and linguistic cues to identify users at risk of self-harm. The research highlighted the potential of Twitter for early detection and intervention in mental health crises. The study focused specifi- cally on suicide risk prediction, and the model’s performance may vary when applied to other mental health conditions. In [2], the authors compared Reddit and Twitter data characteristics for biomedical research, including mental health topics. The study analyzed the differences in user demographics, language patterns, and topic distributions between the two platforms. While the research provides insights into the differences between Reddit and Twitter, it does not specifically focus on mental health analysis. The findings might not directly address the specific needs of mental health researchers or the limitations associated with using Reddit data for mental health analysis. 3 Data Set Descriptions Although there are many datasets of Twitter posts for mental health analysis, we failed to find any that suited our research requirements. There is a problem that the dataset was already used multiple times, or it needed to be updated to use for new work. So, we decided to create a new dataset for our research purpose. We used Reddit data instead of Twitter data in this case. The decision to use Reddit data instead of available Twitter data for our research depends on various factors. Due to the platform’s nature and user characteristics, Reddit data is more appropriate for certain types of analysis, such as sentiment analysis [3, 4] or topic modeling [5, 6]. For example, Reddit users tend to engage in longer and more detailed discussions, which may be more helpful in understanding the nuances of certain topics or communities. Additionally, Reddit data is more readily available for certain topics or communities of interest, as many subreddits are dedicated to specific topics or interests. On the other hand, Twitter data is considered more appropriate for other types of analysis, such as real-time monitoring or event tracking, due to its focus on up-to-the-minute updates and its use by a broader range of users. Reddit data has been found more suitable due to the platform’s nature and the availability of data on mental health-related topics. It is also considered an inexpensive source of high-quality data. Our research required data based on different mental health conditions rather than only depression. Reddit is known for having
- 33. 20 T. Rahman et al. numerous subreddits dedicated to mental health, such as r/mentalhealth, r/depression, and r/anxiety, where users often share their personal experiences, discuss treatment options, and offer support to others. The data collected from these subreddits can provide valuable insights into the language used by individuals when discussing their mental health concerns and the topics most relevant to them. Using this data, we can develop machine learning models that can classify and analyze mental health-related text-based data, which can aid in developing effective treatments and improving mental health outcomes. But using Reddit data has some drawbacks, too. Lots of people informally do their subreddits. The data can contain incomplete words, random punctuations, and symbols, making the dataset noisy and inappropriate for our research. 4 Dataset Collection and Preparation Although there were no available datasets that contained data from Reddit, we created our dataset. Our approach was using the web scraping method to collect data from Reddit. Web scraping: Web scraping automatically extracts data from websites using software programs and scripts. This process allows us to easily get data from any online platform. The software programs are designed to crawl through the website’s structure, extracting specific information and storing it in a structured format such as a spreadsheet or database. There are various tools available for doing web scraping. We must select the tool based on our research necessities and complex scraping tasks. Our main target is to collect as much data as possible because the more data, the better prediction. We used the ‘Web Scraper—Free Web Scraping’ tool, a Google Chrome extension. It is free and very easy to use. It does not require any other software installed on the device. It is an extension to the browser, so it works simply by just adding it. This scraping mainly works in the backend of the browser. We have attached the interface of the web scraper below. We created five different classes, which are called sitemaps here. Those sitemaps contain the data extracted from the website through scraping. After completing web scraping from Reddit, we exported the data as a XLSX file, which we later converted as a .CSV file (Fig. 1). We divided our dataset into five classes and then merged them into one final dataset for our research. The final dataset name is ‘mental state’. The five classes are: ‘Anxiety’, ‘Addiction’, ‘Alcoholism’, ‘Suicidal Thought’, and ‘Depression’. Here is the dataset plotting summary in the Fig. 2.
- 34. Text-Based Data Analysis for Mental Health Using Explainable AI … 21 Fig. 1 Web scraping tool and method Fig. 2 Dataset summary
- 35. 22 T. Rahman et al. Fig. 3 Dataset preparation process 5 Preprocessing Techniques Preprocessing is an important step in machine learning, where raw data is cleaned and transformed into a form that can be used for further analysis. Preprocessing aims to improve data quality and usability by removing noise and inconsistencies, making the data more consistent and easier to process. Preprocessing plays an important role in machine learning models as it can greatly impact the accuracy and effectiveness of machine learning models. Poor data preprocessing can produce inaccurate predic- tions and models that lack robustness and reliability. On the other hand, proper data preprocessing ensures that machine learning models can deliver accurate predictions and valuable, relevant, and meaningful insights. Figure 3 shows the data preparation process. After successfully handling missing values, removing noise, stemming the text, and removing stop words, we were left with the following dataset: Full dataset: (4472,4). Longest String length before Preprocessing: 13690. Longest String length after Preprocessing: 7323. Among all the data, we divided our dataset into two parts, which is 80% for the training set and the 20% for the test set. 6 Methodology We started our research by collecting data from online platforms by web scraping. Almost 4500 data were collected initially from Reddit. After collecting the data, we applied different embedding matrices as feature extractors to extract features from
- 36. Text-Based Data Analysis for Mental Health Using Explainable AI … 23 Fig. 4 System diagram our data. Then, those features were fed into the machine learning and deep learning models. After successful training, the models were evaluated and showed our desired output (Fig. 4). After preprocessing and categorizing the data, we started with the model-building part. Model building creates a predictive model that can make predictions or clas- sifications based on input data. Building a model typically involves choosing an appropriate algorithm, defining model inputs and outputs, preprocessing data for modeling, training the model using a training dataset, and training the model against a test dataset (Fig. 5). Initially, machine learning models were used to classify text data. Specifically, the models are trained to predict the class labels (e.g. ‘Depression’, ‘Anxiety’, ‘Addiction’, etc.) of the input text data based on patterns and relationships found in the training data. XGBoost, Decision Tree, SVM, Naive Bayes, Simple Gradient Descent, Stochastic Gradient Descent, K-Nearest Centroid, K-Nearest Neighbor, AdaBoost, Random Forest, and Logistic Regression models were used using the TF- IDF text vectorizer. All the ML models were run on the default setup with no changes in the hyperparameter. We also added a voting classifier with all the ML models for final prediction. A few variations of DNN models were used to evaluate the perfor- mance of our dataset. We used CNN (Convolutional neural network) algorithm with Word2Vec and GloVe vectors [7–9].
- 37. 24 T. Rahman et al. Fig. 5 Model selection diagram Hyperparameters for deep neural network methods are included in the Table 1. Table 1 Hyperparameters for DNN models Hyperparameters Word2Vec-CNN GloVe-CNN + BiLSTM Pooling type Max Max Embedding dimension 300 300 Batch size 64 64 Activation function Swish, SoftMax Swish, SoftMax Learning rate 0.001 0.001 Optimizer Adam Adam Epoch 35 35
- 38. Text-Based Data Analysis for Mental Health Using Explainable AI … 25 7 Explainability Methods Explainability methods are important for understanding the model’s behavior, ensuring fairness, and building trust in its predictions [4–11, 12]. Our research uses LIME as an explainable AI (XAI) method. LIME (Local Interpretable Model- Agnostic Explanations) is a model-agnostic technique used to explain the predictions of machine learning models [7]. It can be used with any type of model, especially those used for text classification tasks. There are many XAI methods, but one of the main reasons for using LIME in our research is to gain insights into how a model makes its predictions on individual text samples. LIME creates locally interpretable explanations for the predictions made by a model by identifying the most important features or words that contributed to the prediction. LIME uses a simpler model, such as a linear model or decision tree, to approximate the behavior of the complex model in the local region around a specific instance. This approach is computation- ally efficient and can provide meaningful insights into the contribution of different words or features to the model’s prediction. As our research is text-based, LIME mainly focuses on the words that give impor- tant and useful information in explainability. It highlights the important words for the detection and thus gives the outcome. This type of explainability is like a human prediction explanation, that is why it is widely accepted. 8 Result and Analysis This section reviews all the models (ML and DNN models) tried on our English textual dataset to classify mental states from the text. Model performance analysis based on accuracy involves evaluating how well a model predicts the correct outcome compared to the total number of predictions. It measures how many predictions are correct from the total number of predictions. As for our ML models, we have different accuracy rates for different models. Not all models gave satisfactory results. For now, we chose accuracy to determine a model’s performance. However, other matrices (precision, recall, f1) were also considered to evaluate the performance. On our dataset, XGBoost and Naïve Bayes outperformed all the models with an accuracy of 70% and 71%, respectively. Table 2 shows the accuracy for different models. Explainable AI: What sets our research apart is the focus on explainability. Trust and transparency are crucial when dealing with sensitive topics like mental health. Therefore, we have integrated Explainable AI techniques into our model, allowing us to provide clear and understandable explanations for its predictions. XAI (Explainable Artificial Intelligence) plays a key role in the research by providing transparency, interpretability, and accountability to the classification model decision- making process. This enables us to uncover important features, patterns, and rules influencing the model’s output. We separately applied the XAI method (LIME) [13]
- 39. 26 T. Rahman et al. Table 2 ML models’ accuracy percentage Model Accuracy (%) Naive Bayes (TF-IDF) 71 SVM (TF-IDF) 65 XGBoost (TF-IDF) 70 Decision tree classifier (TF-IDF) 59 Stochastic gradient descent (TF-IDF) 68 Logistic regression (TF-IDF) 67 AdaBoost (TF-IDF) 61 K-nearest centroid (TF-IDF) 37 K-nearest neighbor (TF-IDF) 40 to our machine-learning models. Figures 6 and 7 provide explainability of the XAI models. Once the interpretable model is trained, LIME assigns importance weights to the features based on their contribution to the predictions. These weights explain the model’s decision by highlighting the features that most influenced the prediction. As we can see, the results are shown on the prediction probabilities mentioning the five Fig. 6 Explanation of Naive Bayes model using LIME Fig. 7 Explanation of XGBoost’s model using LIME
- 40. Text-Based Data Analysis for Mental Health Using Explainable AI … 27 mental states. The words that are highlighted are the words predicted by the model that can refer to Alcoholism (77%) and Anxiety (23%). Like the shown model, we applied XAI on each ML model we trained. Each of them showed results according to their model performance. However, we can conclude that our model’s explanation part worked successfully. For DNN models, we have evaluated two models with 35 epochs each. The first one is Word2Vec-CNN, which has an accuracy of 64%. Its training accuracy is about 98%, and its testing accuracy is 65%. This means this model needs to be trained for more and more time. By each time this model will gain better insights into the model’s pattern. The second DNN model is Glove-CNN + BiLSTM. It has an accuracy of 68% which is better than Word2Vec. Its training accuracy is 99.9%, whereas testing accuracy is 69%. We can finally tell that among both DNN models GloVe + BiLSTM is the better model. For the Word2Vec-CNN model, after running 35 epochs initially, the model is trained on the dataset properly for about 98.6%, and on the same dataset, it was tested with 64.32% accuracy (Fig. 8). Training loss is the loss the model incurs on the training data during training. It is the error that the model makes when it tries to predict the correct output for the training data. Validation loss is the model’s loss on a separate validation set during training. We can see that with each epoch, the training loss started to decrease, and the accuracy started to increase. The best accuracy achieved by the model is 67.23% (Fig. 9). In sentiment analysis, error analysis is used for getting a closer look on the misclas- sified text samples. For example, suppose our model classifies user posts as mental Fig. 8 Accuracy graph for GloVe-CNN + BiLSTM model
- 41. 28 T. Rahman et al. Fig. 9 Loss graph for Word2Vec-CNN model health analysis and misclassifies depression data as anxiety data. In that case, we can examine the words or phrases that caused the misclassification to determine why the model failed. Among DNN models, GloVe-CNN + BiLSTM showed comparatively better performance. That is why we did a thorough error analysis with a confusion matrix of that particular model. Figure 10 depicts the confusion matrix. The matrix depicts a class-by-class proportion of estimated labels. The matrix reveals that a small number of data points were incorrectly categorized. For example, 82 samples were predicted as ‘Suicidal thoughts’ in the ‘Depression’ class. This represents a huge error. In almost all the ML models, the ‘Depression’ class gave the least accuracy. It means the class ‘depression’ is frequently misclassified during the predictions. There are many possible reasons for inaccurate predictions, e.g., class imbalance in the dataset. So, by creating a balanced dataset, prediction errors could be reduced to a minimum. 9 Conclusion and Future Work This research aimed to comprehensively analyze mental health using text-based data obtained from Reddit, employing explainable AI and deep learning techniques. By addressing the limitations of existing Twitter datasets and leveraging the unique characteristics of Reddit, we sought to develop machine learning models capable of accurately classifying and analyzing mental health-related text data.
- 42. Text-Based Data Analysis for Mental Health Using Explainable AI … 29 Fig. 10 Confusion matrix of GloVe-CNN + BiLSTM model To effectively process the text data, we utilized various text vectorization tech- niques, including TF-IDF, Word2Vec, and GloVe, transforming the textual informa- tion into numerical representations suitable for machine learning algorithms. Imple- menting a range of classification algorithms, such as XGBoost, Decision Tree, SVM, Naive Bayes, Simple Gradient Descent, Stochastic Gradient Descent, K-Nearest Centroid, K-Nearest Neighbor, AdaBoost, Random Forest, and Logistic Regression, we evaluated the performance of the developed models using precision, recall, and F1 scores. These metrics provided insights into the accuracy and effectiveness of the models in correctly identifying and classifying instances of different mental health conditions. We have taken great effort to assure the correctness and dependability of our findings throughout the whole study process. We trained our model using a large and diverse dataset covering various mental illnesses and language patterns. A rigorous testing and validation process was employed to ensure the robustness and generalizability of the results. The findings of this research have significant implications for mental health research and practice. By accurately classifying mental health-related text data, we gain a deeper understanding of the prevalence, characteristics, and nuances associated with addiction, alcoholism, anxiety, depression, and suicidal thoughts.
- 43. 30 T. Rahman et al. Furthermore, integrating explainable AI techniques in our methodology enhances the interpretability and transparency of the classification models. In conclusion, this research contributes to mental health analysis by utilizing text- based data from Reddit and employing advanced machine-learning techniques. The outcomes of this research provide valuable insights into mental health conditions and pave the way for future advancements in personalized interventions, support systems, and mental health treatments. By harnessing the power of text-based data and inno- vative AI methodologies, we can continue to improve mental health outcomes and promote overall well-being in individuals facing various mental health challenges. While our dataset was a collection of Reddit data from different regions of the world, it did not have important factors such as the age, demographic, and financial capacity of the users surveyed. Another important factor the dataset did not account for was the platform from which to collect data. Most teenagers use Instagram or Snapchat these days, while elderly people mainly use Facebook as their go-to social media platform. We could factor these important aspects into our dataset and get even more accuracy. There are also recent deep-learning models that we are currently working on. We are also planning to complete that model and apply XAI to it. Another future work is using Bengali-translated data in our model. References 1. Jashinsky J, Burton SH, Hanson CL, West J, Giraud-Carrier C, Barnes MD, Argyle T. Tracking suicide risk factors through Twitter in the US. Crisis. 2014; 35(1):51–59. https://doi.org/10. 1027/0227-5910/a000234. PMID: 24121153. 2. Correia, R. B., Wood, I. B., Bollen, J., & Rocha, L. M. (2020). Mining Social Media Data for Biomedical Signals and Health-Related Behavior. Annual Review of Biomedical Data Science. https://doi.org/10.1146/annurev-biodatasci-030320-040844 3. “How To Train a Neural Network for Sentiment Analysis | DigitalOcean,” https://www.digita locean.com/community/tutorials/how-to-train-a-neural-network-for-sentiment-analysis. 4. G. Salton, A. Wong, and C. S. Yang, “A vector space model for automatic indexing,” Commu- nications of the ACM, vol. 18, no. 11, pp. 613–620, Nov. 1975, https://doi.org/10.1145/361 219.361220. 5. M. Jamnik and D. Lane, “The Use of Reddit as an Inexpensive Source for High-Quality Data,” Practical Assessment, Research, and Evaluation, vol. 22, no. 1, Nov. 2019, https://doi.org/10. 7275/swgt-rj52. 6. I. Lage et al., “An Evaluation of the Human-Interpretability of Explanation,” Aug. 28, 2019. https://arxiv.org/abs/1902.00006 (accessed Oct. 24, 2023). 7. J. Pennington, R. Socher, and C. Manning, “Glove: Global Vectors for Word Representation,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543, 2014, https://doi.org/10.3115/v1/d14-1162. 8. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” Sep. 07, 2013. https://arxiv.org/abs/1301.3781 9. T. Mikolov, E. Grave, P. Bojanowski, C. Puhrsch, and A. Joulin, “Advances in Pre-Training Distributed Word Representations,” Dec. 2017, Available: https://arxiv.org/abs/1712.09405. 10. “Using Machine Learning for Sentiment Analysis: a Deep Dive,” DataRobot AI Cloud. https:// www.datarobot.com/blog/using-machine-learning-for-sentiment-analysis-a-deep-dive/ 11. A. H. Yazdavar et al., “Semi-Supervised Approach to Monitoring Clinical Depressive Symp- toms in Social Media,” Proceedings of the 2017 IEEE/ACM International Conference on
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- 45. Using the Internet of Things and Machine Learning to Monitor and Detect COVID-19 Al Mehedi Hasan, Istiak Ahmed Sheam, Md. Maruf Chowdhury, and Rashedur M. Rahman Abstract COVID-19 has become the name of fear for people all over the world. It has been the reason for mass death for the last four years. To fight against the disease, it is important to identify the affected people early, isolate them, and treat them. In this work, a system is created to detect COVID-19 at a very early stage. The wearable IoT device reads the temperature, heart rate, and oxygen saturation using DS18B20 (Temperature Sensor) and MAX30100 (Heart Rate and Pulse Oximeter Sensor). Firebase Real-Time database is used to store the data. The “COUGHVID crowdsourcing dataset” is used for cough recordings. MFCC (Mel-frequency cepstral coefficients) extracts features from the cough recordings. These features are used to trainthemachinelearningmodels.Variousmachinelearningalgorithms(DTC,SVM, LR, RFC, and NBC) are used on the COVID-19 data (194 COVID-19-positive, 104 Healthy). Among them, DTC performed best with an accuracy of 91.03% and F1- score of 89.39% for detecting COVID-19. A website is used to record cough, show the IoT data, and send an email if COVID-19 is detected. The work shows how digital devices can monitor and detect COVID-19. Keywords COVID-19 · IoT · MAX30100 · DS18B20 · Firebase · COUGHVID · MFCC · Machine learning · Website A. M. Hasan · I. A. Sheam · Md. M. Chowdhury · R. M. Rahman (B) Department of Electrical and Computer Engineering, North South University, Plot-15, Block-B, Bashundhara, Dhaka 1229, Bangladesh e-mail: rashedur.rahman@northsouth.edu A. M. Hasan e-mail: almehedi.hasan@northsouth.edu I. A. Sheam e-mail: istiak.sheam@northsouth.edu Md. M. Chowdhury e-mail: maruf.chowdhury1@northsouth.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Networking and Parallel/Distributed Computing Systems, Studies in Computational Intelligence 1125, https://doi.org/10.1007/978-3-031-53274-0_4 33
- 46. 34 A. M. Hasan et al. 1 Introduction A pandemic is more dangerous than a world war. A world war can be stopped by compromising or a treaty, but a pandemic can only be stopped once we find the cure or normalize the way of treatment. So, vaccine invention was as important as identifying the COVID-19-affected patients during the pandemic. First, a tiny virus particle enters one healthy body from the air and replicates itself exponentially. So, a healthy body does not get enough time to identify the virus and produce antibody against it. And no cure was invented then, so death ended it. Since it directly attacks the human respiratory system, the first symptom of this disease is shortness of breath and chest pain. No other symptoms can be identified until the patient has already been affected by COVID-19 badly. So, it was more dangerous that people were dying of sudden death without any treatment. If one of any family members were affected, then there was a high chance of affecting the full family. Our initiative is to build a wearable IoT device to take real-time input from the human body and pass the data to machine learning models. Machine learning models will predict whether the user is COVID-19 positive suspect or not and show output to the registered website. 2 Literature Review Combining the Internet of Things (IoT) and Machine Learning has changed people’s way of living. It includes home automation, smart health care, smart transportation, etc. Remote health monitoring systems are becoming very popular day by day. It reduces costs and helps to manage the chronic conditions of patients. For patients who have difficulty traveling, doctors can monitor them remotely. Since monitoring people with contagious diseases is difficult, remote health monitoring systems will greatly help. IoT-based Machine Learning systems have proven very effective in detecting COVID-19. The authors [1] developed a Fuzzy Mamdani system to detect the spreading risks of COVID-19. They made an app that notifies users to maintain a 6ft distance from others. They used the fuzzy system, which calculated the distance of the user from others and used some symptoms from the user to detect the risk of spreading the virus at that place. Another study used a data mining approach to detect COVID-19. The authors [2] used the Artificial Bee Colony algorithm for data mining. They used this algorithm to select features from the dataset. After that, they used the Support Vector Machine (SVM) to get a very impressive result. They got over 90% results for all the evaluation metrics. The authors [3] used contact tracing to reduce the number of COVID-19-affected people in this study. They created an IoT device to detect contact tracing. Through
- 47. Using the Internet of Things and Machine Learning to Monitor … 35 this, they can discover who is affected and responsible for spreading the virus. They achieved 97.1% accuracy using the Gradient Boost Algorithm. In this research work [4], COVID-19 is detected from cough sounds and other symptoms. They used cough and environmental sounds to differentiate between coughs and other sounds. They used Arduino Nano 33 BLE sense, which is a micro- controller and a sensor because it has different sensors connected to it. One of the features of this device is that we can upload the machine learning and deep learning code directly to it; that is why we do not need to deploy machine learning models anywhere. Because of that, it is very handy to classify cough recordings since it has a microphone with it, and every time it records a sound, the sound will go through the machine learning model and generate an output instantly. A study predicted the risks of getting affected by COVID-19 in public places [5]. The authors used an IoT device to measure the distance from one person to another and their temperature. Then, they made a data vector from it. From the data vector, they predicted the risk of getting affected. Along with the classifier models, they used Locally Weighted Learning, Hoeffding Tree, and AdaBoost M1, where Locally Weighted Learning performed well, but the others did not. We have tried to measure our necessary information with devices like Apple Watch. But it was too expensive for our research work. The cheaper one could not match the expected accuracy. Then we decided to build our own, which is much more affordable and also good in accuracy. Arduino Nano 33 BLE sense can contain machine learning and deep learning models. But it is not available in our country. It is also very expensive. There is no other microcontroller that can replace it. So, we could not record cough and classify it using our IoT device. We had to make a recorder website for it. 3 Data Collection The COVID-19 patient data was collected from the Mugda Medical College & Hospital, which is located in Dhaka, Bangladesh. Since it was declared a Corona hospital at the time of the pandemic, that is why this hospital was chosen. The hospital had COVID-19 patient data from April 2020 to August 2021. There were threetypesofdata;oneisCOVID-19positive,oneisCOVID-19negative,andanother one is Suspected COVID-19 positive. We only recorded the patients’ data confirmed as COVID-19 positive and COVID-19 negative (healthy). Since we got only three healthy people’s data from the files we had to collect the healthy people’s data from our families, relatives, friends, and strangers.
- 48. 36 A. M. Hasan et al. Fig. 1 Distribution of COVID-19 positive and healthy people’s data across the Dataset 3.1 Data Description From the hospital, 194 COVID-19-positive patients’ data and three healthy people’s data were collected, and from outside the hospital, 104 Healthy data were collected. The Temperature, Cough, Pulse Rate, and Oxygen Saturation values were recorded. There were two types of target variables; one was “Covid,” and another one was “Healthy.” Fig. 1. shows the distribution of COVID-19-positive and healthy people’s data across the dataset. 3.2 Data Preprocessing Before training the dataset with the machine learning models, it is very important to preprocess the data to get a more accurate output. 3.2.1 Convert Categorical Data to Numerical Data The Cough and Status features had categorical data. The Cough feature had two types of categorical data, “Healthy Cough” and “COVID-19 Cough”. The “Healthy Cough” was replaced with “0” and the “COVID-19 Cough” was replaced with “1”. The Status feature also had two types of categorical data, “Healthy” and “Covid.” The “Healthy” was replaced with “0” and the “Covid” was replaced with “1”.
- 49. Using the Internet of Things and Machine Learning to Monitor … 37 Fig. 2 Three layer architecture 3.2.2 Handling Imbalance Dataset Since there were 104 healthy data and 194 COVID-19-positive data so SMOTE (synthetic minority oversampling technique) was used to balance the data. 4 Methodology 4.1 Proposed Architecture The proposed system (Fig. 2) consists of three layers: the sensor layer, the cloud layer, and the website layer. The sensor layer collects data from the IoT device. The data from the IoT devices are stored in the cloud layer. Machine learning models are deployed in the website layer. The website layer takes the data stored in the cloud layer and shows the output on the website. 4.2 System Design Figure 3 shows the illustration of the COVID-19 detection system’s workflow. The data flow of the proposed system is described as follows: • The system collects data from the sensors of the wearable device. The wearable device collects real-time temperature, heart rate, and oxygen saturation data. Then, the collected data will be sent to the cloud database. • There is a website to collect cough recordings from users. The cough classification machine learning model detects the cough type from the recordings. The output will be sent to the same cloud database. • The data from step 1 and step 2 will be used to detect the patient’s health status. • Finally, the result will be shown on the website homepage. If the user is COVID-19 positive, he/she will be notified by email.
- 50. 38 A. M. Hasan et al. Fig. 3 An illustration of the COVID-19 detection system’s workflow 4.3 Wearable IOT Device Body temperature, oxygen saturation in the blood (SpO2), and heart rate are consid- ered primary symptoms of COVID-19. This wearable IoT device collects users’ temperature, heart rate, and oxygen saturation (SpO2) data. The microcontroller reads data from the connected sensors and sends it to Firebase. The description of devices are given below. WeMos D1 Mini WeMos D1 Mini (Fig. 4) is a NodeMCU ESP8266. It is a wireless module. It has Wi-Fi capability, which can send data to a cloud server. The flash memory of this microcontroller is 4 Mb. Its length is 34.2 mm, and its width is 25.6 mm. Its weight is 10 g. It has eleven digital I/O pins and one analog input pin. The clock speed of this microcontroller is 80 or 160 MHz [6].
- 51. Using the Internet of Things and Machine Learning to Monitor … 39 Fig. 4 WeMos D1 Mini [7] DS18B20 (Temperature Sensor) One of the key symptoms of COVID-19 is fever. Since fever is considered a primary clinical feature of COVID-19, it is very important to monitor the temperature of the COVID-19 suspects. The DS18B20 temperature sensor was used in this project [8]. The pin connection between the DS18B20 and WeMos D1 Mini (ESP8266) is shown in Table 1. The DS18B20 sensor is depicted in Fig. 5. It has a long wire which will help the user to measure his/her temperature even if he/she is far from the connection point. The temperature sensing part is also long, which is why it can measure temperature even if the user is not in a steady position. MAX30100 (Pulse Oximeter and Heart-Rate Sensor) Heart-Rate and Oxygen saturation in the blood (SpO2) are two indicative measures to detect a COVID-19-affected person. The virus infection increases the heart rate and decreases the oxygen saturation of the COVID-19-affected person. So, monitoring theoxygensaturationlevelandtheheart’sfunctioningcontinuouslyisveryimportant. Therefore, Max30100 was used to measure Oxygen saturation (SpO2) and heart rate. The pin connection between the Pulse Oximeter and Heart-Rate Sensor and WeMos D1 Mini (ESP8266) is shown in Table 2. Table 1 Pin connection of the DS18B20 to WeMos D1 Mini Dallas temperature sensor (DS18B20) WeMos D1 Mini (ESP8266) VDD 5 V GND G Data Pin 14
- 52. 40 A. M. Hasan et al. Fig. 5 DS18B20 sensor [9] Table 2 Pin connection of MAX30100 to WeMos D1 Mini Pulse oximeter and heart-rate sensor (MAX30100) WeMos D1 Mini (ESP8266) VIN 3.3 V SCL Pin 5 SDA Pin 4 GND G The MAX30100 sensor, depicted in Fig. 6, consists of two light-emitting diodes that emit monochromatic red light at 660 nm and infrared light at 940 nm. These frequencies were specifically chosen because deoxygenated and oxygenated hemoglobin have distinct absorption characteristics at these wavelengths. The sensor is made up of a photoreceiver and an emitting diode. The photodiode emits the light and falls over the finger, so the finger needs to be positioned steadily. The released light is partially absorbed by the oxygenated blood, partially reflected through the finger, and falls over the detector, whose output data is processed and read by a microcontroller. This sensor is very compact in design. Therefore, it is perfect for a wearable device [10]. OLED Display The OLED display in Fig. 7 is 0.96 inches in length. The display area is 21.74 × 10.86 mm. The driving voltage is 3.3–5 V. The operating temperature is −40–70 °C. Fig. 6 MAX30100 sensor
- 53. Using the Internet of Things and Machine Learning to Monitor … 41 Fig. 7 OLED display [11] Table 3 Pin connection of the OLED display to WeMos D1 Mini OLED display WeMos D1 Mini (ESP8266) VCC 5 V SCL Pin 5 SDA Pin 4 GND G The length of the display is 27 mm, the width is 27 mm, and the height is 6 mm. The weight of the display is 5gm. It has four pins GND, VCC, SCL, and SDA. The pin connection between the OLED display and WeMos D1 Mini (ESP8266) is shown in Table 3. 5 Final Design of the Wearable IoT Device Figure 8 shows the final design of the IoT device. It is easy to use, lightweight, comfortable, and low-cost. 5.1 Firebase The cloud database is in charge of receiving data from the microcontroller and storing the users’ information. These pieces of information go to the website where the machine learning model is deployed and show the output on the website. Figure 9 shows the dashboard of our Firebase Real-time Database. In this database, four values are being stored: Temperature, Heart Rate, Oxygen Saturation (SPO2), and Cough. Every time new data comes, it overwrites the old data.
- 54. 42 A. M. Hasan et al. Fig. 8 Wearable IoT device Fig. 9 Firebase real-time database dashboard 5.2 Identification of COVID-19 Patient The machine learning algorithms will use a user’s temperature, cough, pulse rate, and oxygen saturation to determine whether the user is healthy or COVID-19 affected. Figure 10 shows the complete process of COVID-19 detection. The dataset was split to 60:40; the training set had 60% of the data, and the test set had 40%. Five machine learning models were used (Decision Tree Classifier, Support Vector Machine, Logistic Regression, Random Forest Classifier, Naïve Bayes Classifier). Later, Grid-Search was used for better results. 5.3 Identification of Cough Types The COUGHVID crowdsourcing dataset [12] was used for cough classification. The dataset had cough recordings, which is in .wav format and .json files, which contained the description of the cough recordings. It contained some recordings that had no cough, and some had coughs. The recordings that had coughs were used in this study. It contained four types of coughs, COVID, healthy, symptomatic, and no status.
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- 56. You think so, father? DE MULLIN Certainly. He saw what my objections would be and recognized that they were reasonable. Nothing could be more proper. JANET Well, father. I don’t know what you do want. Ten minutes ago you were supposed to be wanting a grandson to adopt. Here’s Hester going the right way to provide one, and you don’t like that either. HESTER What is all this about, father? What have you all been discussing while I’ve been out? MRS. DE MULLIN It was nothing about you, Hester. HESTER I’m not sure of that, mother. Anyhow I should like to hear what it was. MRS. CLOUSTON Hester, that is not at all a proper tone to use in speaking to your mother. HESTER
- 57. (fiercely) Please don’t interfere, Aunt Harriet. I suppose I can be trusted to speak to my mother properly by this time. MRS. CLOUSTON You certainly ought to, my dear. You are quite old enough. HESTER Very well then. Perhaps you will be good enough not to dictate to me in future. What was it you were discussing, father? JANET I’ll tell you, Hester. Father wanted to adopt Johnny. He wanted me to come down here to live altogether. HESTER Indeed? Well, father, understand, please, that if Janet comes down here to live I go! MRS. DE MULLIN Hester! HESTER I will not live in the same house with Janet. Nothing shall induce me. I would rather beg my bread. JANET
- 58. That settles it then. Thanks, Hester. I’m glad you had the pluck to say that. You are right. Quite right. HESTER I can do without your approval, Janet. JANET (recklessly) Of course you can. But you can have it all the same. You never wanted me down here. You always disapproved of my being sent for. I ought never to have come. I wish I hadn’t come. My coming has only done harm to Hester, as she knew it would. DE MULLIN How harm? JANET Mr. Brown would have asked Hester to marry him if I hadn’t come. He meant to; I’m sure of it. MRS. DE MULLIN But he said... JANET I know. But that was only an excuse. Young men aren’t so considerate of their future fathers-inlaw as all that nowadays. No. Mr. Brown heard some story about me from Miss Deanes. Or perhaps the Vicar put him on his guard. Isn’t it so, Hester?
- 59. [Hester nods. MRS. DE MULLIN But as your father would never have consented, dear... HESTER (slowly) Still, I’d rather he had asked me, mother. JANET Quite right, Hester! I’m glad you’ve got some wholesome feminine vanity left in your composition. And you’d have said “yes,” like a sensible woman. HESTER Oh, you’re always sneering! JANET Yes. But I’m going, Hester, going! That’s great thing! Keep your eyes fixed steadily on that and you’ll be able to bear anything else. That reminds me. (Goes to door, l., and calls loudly into the hall.) Johnny! Johnny! MRS. CLOUSTON Really, Janet! JANET
- 60. Oh, I forgot. It’s not genteel to call into the passage, is it? I ought to have rung. I apologise, Aunt Harriet. (Calls again) Johnny! MRS. DE MULLIN Why are you calling Johnny? JANET To tell him to put on his hat and coat, mother dear. I’m going to the station. DE MULLIN You’re going to-night? JANET Yes, father, to-night. I’ve done harm enough down here. I’m going away. JOHNNY (entering l.) Do you want me, Mummie? JANET Yes. Run and put on your things and say goodbye to Cook and Ellen and tell Robert to put in the pony. Mother’s going back to London. JOHNNY
- 61. Are we going now, Mummie? JANET (nods) As fast as the train can carry us. And tell Ellen to lock my trunk for me and give you the key. Run along. [Exit Johnny, l. DE MULLIN Lock your trunk! But you’ve not packed? JANET Oh yes, I have. Everything’s packed, down to my last shoelace. I don’t know how often I haven’t packed and unpacked during the last five days. MRS. DE MULLIN (astonished and hurt) You meant to leave us then, Janet? You’ve been wanting to leave us all the time? JANET Yes, mother. I’ve been wanting to leave you. I can’t stay here any longer. Brendon stifles me. It has too many ghosts. I suppose it’s your ridiculous De Mullins. DE MULLIN
- 62. Janet! JANET I know, father. That’s blasphemy, isn’t it? But I can’t help it. I must go. I’ve been meaning to tell you every day for the last four days, but somehow I always put it off. DE MULLIN Understand me, Janet. If you leave this house to-night you leave it for ever. JANET (cheerfully) All right, father. DE MULLIN (growing angrier) Understand, too, that if you leave it you are never to hold any communication either with me or with any one in it henceforward. You are cut off from the family. I will never see you or recognize you in any way, or speak to you again as long as I live. JANET (astonished) My dear father, why are you so angry? Is there anything so dreadful in my wanting to live in London instead of in the country? DE MULLIN
- 63. (getting more and more excited) Why am I angry! Why am I...! MRS. DE MULLIN Sh! Hugo! You mustn’t excite yourself. You know the doctor said... DE MULLIN Be quiet, Jane! (turning furiously to Janet) Why am I angry! You disgrace the family. You have a child, that poor fatherless boy.... JANET (quietly) Oh come, I could have got along quite well without a father if it comes to that. And so could Hester. MRS. DE MULLIN Janet! JANET Well, mother, what has father ever done for Hester or me except try and prevent us from doing something we wanted to do? Hester wanted to marry Mr. Brown. Father wouldn’t have allowed her. He’s not genteel enough to marry a De Mullin. I want to go back to my shop. Father objects to that. That’s not genteel enough for a De Mullin either. Well, hang all the De Mullins, say I. DE MULLIN
- 64. (furious) I forbid you to speak of your family in that way-of my family! I forbid it! It is an outrage. Your ancestors were honourable men and pure women. They did their duty in the position in which they were born, and handed on their name untarnished to their children. Hitherto our honour has been unsullied. You have sullied it. You have brought shame upon your parents and shame upon your son, and that shame you can never wipe out. If you had in you a spark of human feeling, if you were not worthless and heartless you would blush to look me in the face or your child in the face. But you are utterly hardened. I ought never to have offered to receive you back into this house. I ought never to have consented to see you again. I was wrong. I regret it. You are unfit for the society of decent people. Go back to London. Take up the wretched trade you practise there. It is what you are fit for. JANET That’s exactly what I think, father. As we agree about it why make such a fuss? DE MULLIN (furious) Janet.... HESTER Father, don’t argue with her. It’s no use. (solemnly) Leave her to God. JANET
- 65. Hester, Hester, don’t deceive yourself. In your heart you envy me my baby, and you know it. HESTER (indignant) I do not. JANET You do. Time is running on with you, my dear. You’re twenty-eight. Just the age that I was when I met my lover. Yes, my lover. In a few years you will be too old for love, too old to have children. So soon it passeth away and we are gone. Your best years are slipping by and you are growing faded and cross and peevish. Already the lines are hardening about your mouth and the hollows coming under your eyes. You will be an old woman before your time unless you marry and have children. And what will you do then? Keep a lap-dog, I suppose, or sit up at night with a sick cockatoo like Miss Deanes. Miss Deanes! Even she has a heart somewhere about her. Do you imagine she wouldn’t rather give it to her babies than snivel over poultry? No, Hester, make good use of your youth, my dear. It won’t last always. And once gone it is gone for ever. (Hester bursts into tears.) There, there, Hester! I’m sorry. I oughtn’t to have spoken like that. It wasn’t kind. Forgive me. (Hester weep more and more violently.) Hester, don’t cry like that. I can’t bear to hear you. I was angry and said more than I should. I didn’t mean to vex you. Come, dear, you mustn’t give way like that or you’ll make yourself ill. Dry your eyes and let me see you smile. (Caressing her. Hester, who has begun by resisting her feebly, gradually allows herself to be soothed.) That’s better! My dear, what a sight you’ve made of yourself! But all women are hideous when they’ve been crying. It makes their noses red and that’s dreadfully unbecoming. (Hester sobs out a laugh). No. You mustn’t begin to cry again or I shall scold you. I shall, really.
- 66. HESTER (half laughing, half crying hysterically) You seem to think every woman ought to behave as shamefully as you did. JANET (grimly) No, Hester. I don’t think that. To do as I did needs pluck and brains—and five hundred pounds. Everything most women haven’t got, poor things. So they must marry or remain childless. You must marry—the next curate. I suppose the Bulsteads will buy Mr. Brown a living as he’s marrying the plainest of the daughters. It’s the least they can do. But that’s no reason why I should marry unless I choose. MRS. CLOUSTON Well, I’ve never heard of anything so disgraceful. I thought Janet at least had the grace to be ashamed of what she did! JANET (genuinely astonished) Ashamed? Ashamed of wanting to have a child? What on earth were women created for, Aunt Harriet, if not to have children? MRS. CLOUSTON To marry and have children. JANET (with relentless logic)
- 67. My dear Aunt Harriet, women had children thousands of years before marriage was invented. I dare say they will go on doing so thousands of years after it has ceased to exist. MRS. DE MULLIN Janet! JANET Well, mother, that’s how I feel. And I believe it’s how all wholesome women feel if they would only acknowledge it. I wanted to have a child. I always did from the time when I got too old to play with dolls. Not an adopted child or a child of some one else’s, but a baby of my very own. Of course I wanted to marry. That’s the ordinary way a woman wants to be a mother nowadays, I suppose. But time went on and nobody came forward, and I saw myself getting old and my chance slipping away. Then I met-never mind. And I fell in love with him. Or perhaps I only fell in love with love. I don’t know. It was so splendid to find some one at last who really cared for me as women should be cared for! Not to talk to because I was clever or to play tennis with because I was strong, but to kiss me and to make love to me! Yes! To make love to me! DE MULLIN (solemnly) Listen to me, my girl. You say that now, and I dare say you believe it. But when you are older, when Johnny is grown up, you will bitterly repent having brought into the world a child who can call no man father. JANET
- 68. (passionately) Never! Never! That I’m sure of. Whatever happens, even if Johnny should come to hate me for what I did, I shall always be glad to have been his mother. At least I shall have lived. These poor women who go through life listless and dull, who have never felt the joys and the pains a mother feels, how they would envy me if they knew! If they knew! To know that a child is your very own, is a part of you. That you have faced sickness and pain and death itself for it. That it is yours and nothing can take it from you because no one can understand its wants as you do. To feel it’s soft breath on your cheek, to soothe it when it is fretful and still it when it cries, that is motherhood and that is glorious! [Johnny runs in by the door on the left. He is obviously in the highest spirits at the thought of going home. JOHNNY The trap is round, Mummie, and the luggage is in. JANET That’s right. Good-bye, father. (He does not move) Say good-bye to your grandfather, Johnny. You won’t see him again. [De Mullin kisses Johnny. MRS. DE MULLIN Janet! JANET No, mother. It’s best not. (Kisses her) It would only be painful for father. Good-bye, Aunt Harriet. Good-bye, Hester.
- 69. [Looks at Hester doubtfully. Hester rises, goes to her slowly and kisses her. HESTER Good-bye. . [Exeunt Johnny and Janet by the door the right. DE MULLIN (his grey head bowed on his chest as Mrs De Mullin timidly lays her hand on his shoulder) The last of the De Mullins! The last of the De Mullins! (The curtain falls)
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