This document presents the findings of a study that developed a machine learning model to predict whether a person has COVID-19 based on their symptoms. It describes building neural network and other models using a dataset of symptoms from COVID-19 patients. The models were tested using various validation techniques, with the neural network achieving over 99% accuracy. Future work proposed taking additional clinical data to improve accuracy and developing a real-time online system to demonstrate the prediction model.
Title of paper: Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System
Presented at - 9th Workshop on Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future
The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and ii) an in-depth analysis of several state-of-the art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).
Title of paper: Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System
Presented at - 9th Workshop on Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future
The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and ii) an in-depth analysis of several state-of-the art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).
Medical data management: COVID-19 detection using cough recordings, chest X-...GianlucaCavallaro3
In this project, we deal with two different tasks on medical data related to COVID-19. In the first part, we aim to train a neural network capable of recognizing COVID-positive individuals using recordings of coughs. In the second part, a neural network trained to recognize COVID from chest X-rays is implemented. Various techniques are applied to improve the classifiers' performance, particularly using GANs to generate synthetic images of X-rays.
Slidedeck for my presentation of my work for the First Year Conference which is held during 50% of the first year at Institute for Manufacturing, University of Cambridge.
Realistic image synthesis of COVID-19 chest X-rays using depthwise boundary ...IJECEIAES
Researchers in various related fields research preventing and controlling the spread of the coronavirus disease (COVID-19) virus. The spread of the COVID-19 is increasing exponentially and infecting humans massively. Preliminary detection can be observed by looking at abnormal conditions in the airways, thus allowing the entry of the virus into the patient's respiratory tract, which can be represented using computer tomography (CT) scan and chest X-ray (CXR) imaging. Particular deep learning approaches have been developed to classify COVID-19 CT or CXR images such as convolutional neural network (CNN), and deep convolutional neural network (DCNN). However, COVID-19 CXR dataset was measly opened and accessed. Particular deep learning method performance can be improved by augmenting the dataset amount. Therefore, the COVID-19 CXR dataset was possibly augmented by generating the synthetic image. This study discusses a fast and real-like image synthesis approach, namely depthwise boundary equilibrium generative adversarial network (DepthwiseBEGAN). DepthwiseBEGAN was reduced memory load 70.11% in training processes compared to the conventional BEGAN. DepthwiseBEGAN synthetic images were inspected by measuring the Fréchet inception distance (FID) score with the real-to-real score equal to 4.3866 and real-to-fake score equal to 4.4674. Moreover, generated DepthwiseBEGAN synthetic images improve 22.59% accuracy of conventional CNN models.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
Fuzzy-Based Multiple Path Selection Method for Improving Energy Efficiency in...aciijournal
In wireless sensor networks, adversaries can easily compromise sensors because the sensor resources are
limited. The compromised nodes can inject false data into the network injecting false data attacks. The
injecting false data attack has the goal of consuming unnecessary energy in en-route nodes and causing
false alarms in a sink. A bandwidth-efficient cooperative authentication scheme detects this attack based on
the random graph characteristics of sensor node deployment and a cooperative bit-compressed
authentication technique. Although this scheme maintains a high filtering probability and high reliability in
the sensor network, it wastes energy in en-route nodes due to a multireport solution. In this paper, our
proposed method effectively selects a number of multireports based on the fuzzy rule-based system. We
evaluated the performance in terms of the security level and energy savings in the presence of the injecting
false data attacks. The experimental results indicate that the proposed method improves the energy
efficiency up to 10% while maintaining the same security level as compared to the existing scheme.
Data Driven Choice of Threshold in Cepstrum Based Spectrum Estimatesipij
The technique of cepstrum thresholding, which is shown to be an effective, yet simple, way of obtaining a smoothed non parametric spectrum estimate of a stationary signal. The major problem of this method is the choice of the threshold value for variance reduction of spectrum estimates. This paper proposes a new threshold selection method which is based on cross validation schemes such as Leave-One-Out, LeaveTwo-Out and Leave-Half-Out. This new methods are easy to describe, simple to implement, and does not impose severe conditions on the unknown spectrum. Numerical results suggest that this new methods are shown to be in agreement with those obtained when the spectrum is fully known.
AN ANN APPROACH FOR NETWORK INTRUSION DETECTION USING ENTROPY BASED FEATURE S...IJNSA Journal
With the increase in Internet users the number of malicious users are also growing day-by-day posing a serious problem in distinguishing between normal and abnormal behavior of users in the network. This has led to the research area of intrusion detection which essentially analyzes the network traffic and tries to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard NSL-KDD intrusion dataset using some neural network based techniques for predicting possible intrusions. Four most effective classification methods, namely, Radial Basis Function Network, SelfOrganizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been applied. In order to enhance the performance of the classifiers, three entropy based feature selection methods have been applied as preprocessing of data. Performances of different combinations of classifiers and attribute reduction methods have also been compared.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3𝑈95 ≤ 𝑀𝑃𝐸𝑉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
Wireless sensor network (WSN) are powered by batteries to perform various sensing tasks in a given
environment. The measurements made by the sensors are sometimes unreliable and erroneous due to noise
in the sensor or hardware failure. For a large scale WSN to be economically feasible, it is important to
ensure that the faulty node does not affect the overall behaviour of the system. In this paper a binary faulttolerant
event detection technique has been proposed for the non-symmetric errors and its performance has
been analysed. Theoretical analysis and simulation show that almost 97 percent of faults can be corrected
even when 10 percent sensor nodes are faulty.
With the increase in Internet users the number of malicious users are also growing day-by-day posing a
serious problem in distinguishing between normal and abnormal behavior of users in the network. This
has led to the research area of intrusion detection which essentially analyzes the network traffic and tries
to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard
NSL-KDD intrusion dataset using some neural network based techniques for predicting possible
intrusions. Four most effective classification methods, namely, Radial Basis Function Network, Self-
Organizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been
applied. In order to enhance the performance of the classifiers, three entropy based feature selection
methods have been applied as preprocessing of data. Performances of different combinations of classifiers
and attribute reduction methods have also been compared.
Medical data management: COVID-19 detection using cough recordings, chest X-...GianlucaCavallaro3
In this project, we deal with two different tasks on medical data related to COVID-19. In the first part, we aim to train a neural network capable of recognizing COVID-positive individuals using recordings of coughs. In the second part, a neural network trained to recognize COVID from chest X-rays is implemented. Various techniques are applied to improve the classifiers' performance, particularly using GANs to generate synthetic images of X-rays.
Slidedeck for my presentation of my work for the First Year Conference which is held during 50% of the first year at Institute for Manufacturing, University of Cambridge.
Realistic image synthesis of COVID-19 chest X-rays using depthwise boundary ...IJECEIAES
Researchers in various related fields research preventing and controlling the spread of the coronavirus disease (COVID-19) virus. The spread of the COVID-19 is increasing exponentially and infecting humans massively. Preliminary detection can be observed by looking at abnormal conditions in the airways, thus allowing the entry of the virus into the patient's respiratory tract, which can be represented using computer tomography (CT) scan and chest X-ray (CXR) imaging. Particular deep learning approaches have been developed to classify COVID-19 CT or CXR images such as convolutional neural network (CNN), and deep convolutional neural network (DCNN). However, COVID-19 CXR dataset was measly opened and accessed. Particular deep learning method performance can be improved by augmenting the dataset amount. Therefore, the COVID-19 CXR dataset was possibly augmented by generating the synthetic image. This study discusses a fast and real-like image synthesis approach, namely depthwise boundary equilibrium generative adversarial network (DepthwiseBEGAN). DepthwiseBEGAN was reduced memory load 70.11% in training processes compared to the conventional BEGAN. DepthwiseBEGAN synthetic images were inspected by measuring the Fréchet inception distance (FID) score with the real-to-real score equal to 4.3866 and real-to-fake score equal to 4.4674. Moreover, generated DepthwiseBEGAN synthetic images improve 22.59% accuracy of conventional CNN models.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
Fuzzy-Based Multiple Path Selection Method for Improving Energy Efficiency in...aciijournal
In wireless sensor networks, adversaries can easily compromise sensors because the sensor resources are
limited. The compromised nodes can inject false data into the network injecting false data attacks. The
injecting false data attack has the goal of consuming unnecessary energy in en-route nodes and causing
false alarms in a sink. A bandwidth-efficient cooperative authentication scheme detects this attack based on
the random graph characteristics of sensor node deployment and a cooperative bit-compressed
authentication technique. Although this scheme maintains a high filtering probability and high reliability in
the sensor network, it wastes energy in en-route nodes due to a multireport solution. In this paper, our
proposed method effectively selects a number of multireports based on the fuzzy rule-based system. We
evaluated the performance in terms of the security level and energy savings in the presence of the injecting
false data attacks. The experimental results indicate that the proposed method improves the energy
efficiency up to 10% while maintaining the same security level as compared to the existing scheme.
Data Driven Choice of Threshold in Cepstrum Based Spectrum Estimatesipij
The technique of cepstrum thresholding, which is shown to be an effective, yet simple, way of obtaining a smoothed non parametric spectrum estimate of a stationary signal. The major problem of this method is the choice of the threshold value for variance reduction of spectrum estimates. This paper proposes a new threshold selection method which is based on cross validation schemes such as Leave-One-Out, LeaveTwo-Out and Leave-Half-Out. This new methods are easy to describe, simple to implement, and does not impose severe conditions on the unknown spectrum. Numerical results suggest that this new methods are shown to be in agreement with those obtained when the spectrum is fully known.
AN ANN APPROACH FOR NETWORK INTRUSION DETECTION USING ENTROPY BASED FEATURE S...IJNSA Journal
With the increase in Internet users the number of malicious users are also growing day-by-day posing a serious problem in distinguishing between normal and abnormal behavior of users in the network. This has led to the research area of intrusion detection which essentially analyzes the network traffic and tries to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard NSL-KDD intrusion dataset using some neural network based techniques for predicting possible intrusions. Four most effective classification methods, namely, Radial Basis Function Network, SelfOrganizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been applied. In order to enhance the performance of the classifiers, three entropy based feature selection methods have been applied as preprocessing of data. Performances of different combinations of classifiers and attribute reduction methods have also been compared.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3𝑈95 ≤ 𝑀𝑃𝐸𝑉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
Wireless sensor network (WSN) are powered by batteries to perform various sensing tasks in a given
environment. The measurements made by the sensors are sometimes unreliable and erroneous due to noise
in the sensor or hardware failure. For a large scale WSN to be economically feasible, it is important to
ensure that the faulty node does not affect the overall behaviour of the system. In this paper a binary faulttolerant
event detection technique has been proposed for the non-symmetric errors and its performance has
been analysed. Theoretical analysis and simulation show that almost 97 percent of faults can be corrected
even when 10 percent sensor nodes are faulty.
With the increase in Internet users the number of malicious users are also growing day-by-day posing a
serious problem in distinguishing between normal and abnormal behavior of users in the network. This
has led to the research area of intrusion detection which essentially analyzes the network traffic and tries
to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard
NSL-KDD intrusion dataset using some neural network based techniques for predicting possible
intrusions. Four most effective classification methods, namely, Radial Basis Function Network, Self-
Organizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been
applied. In order to enhance the performance of the classifiers, three entropy based feature selection
methods have been applied as preprocessing of data. Performances of different combinations of classifiers
and attribute reduction methods have also been compared.
Cloud computing is defined as a computer model that enables fast and with minimal effort the access, which is made on demand, from a network to a common storage computing resources.
Secure Electronic Transaction
Contents are:
Secure Electronic Transaction
SET Business Requirements
SET Protocols
Parties in SET
Implementation of SET
SET Transaction
Dual Signature in SET
Dual Signature Operation
SET Supported Transaction
Credit Card Protocols
Contents:
Introduction
Architecture
MAC Sublayer
Distribution Coordination Function
Point Coordination Function
MAC Layer Frame
Physical Layer
IEEE 802.11 FHSS
IEEE 802.11 DSSS
IEEE 802.11 Infrared
Contents:
Introduction to SONET
SONET/SDH Rates
SONET Layers Compared with OSI Model
SONET Architecture
SONET Frames
SONET Network Types
Advantages of SONET
Disadvantages of SONET
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
Describe the process of coding, testing, and converting an organizational information system and outline the deliverables and outcomes of the process.
Prepare a test plan for an information system.
Apply four installation strategies: direct, parallel, single-location, and phased installation.
List the deliverables for documenting the system and for training and supporting users.
Distinguish between system and user documentation and determine which types of documentation are necessary for a given information system.
Compare the many modes available for organizational information system training, including self-training and electronic performance support systems.
Discuss the issues of providing support for end-users.
Explain why system implementation sometimes fails.
Describe the threats to system security and remedies that can be applied.
Show how traditional implementation issues apply to electronic commerce applications.
This chapter shows how to use knowledge about the wlorld to make decisions even when the
outcomes of an action are uncertain and the rewards for acting might not be reaped until many
actions have passed. The main points are as follows:
e Sequential decision problems in uncertain envirsinments,also called Markov decision
processes, or MDPs, are defined by a transition model specifying the probabilistic
outcomes of actions and a reward function specifying the reward in each state.
o The utility of a state sequence is the sum of all the rewards over the sequence, possibly
discounted over time. The solution of an MDP is a policy that associates a decision
with every state that the agent might reach. An optimal policy maximizes the utility of
the state sequences encountered when it is execut~ed.
e The utility of a state is the expected utility of the state sequences encountered when
an optimal policy is executed, starting in that state. The value iteration algorithm for
solving MDPs works by iteratively solving the equations relating the utilities of each
state to that of its neighbors.
Policy iteration alternates between calculating the utilities of states under the current
policy and improving the current policy with respect to the current utilities.
* Partially observable MDPs, or POMDPs, are much more difficult to solve than are
MDPs. They can be solved by conversion to an MDP in the continuous space of belief
states. Optimal behavior in POMDPs includes information gathering to reduce uncertainty and therefore make better decisions in the fiuture.
A decision-theoretic agent can be constructed for POMDP environments. The agent
uses a dynamic decision network to represent the transition and observation models,
to update its belief state, and to project forward possible action sequences.
Game theory describes rational behavior for agents in situations where multiple agents
interact simultaneously. Solutions of games are Nash equilibria-strategy profiles in
which no agent has an incentive to deviate from the specified strategy.
Mechanism design can be used to set the rules by which agents will interact, in order
to maximize some global utility through the operation of individually rational agents.
Sometimes, mechanisms exist that achieve this goal without requiring each agent to
consider the choices made by other agents.
We shall return to the world of MDPs and POMDP in Chapter 21, when we study reinforcement learning methods that allow an agent to improve its behavior from experience in sequential, uncertain environments.
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2. Introduction
01
2
Agenda Corona Prediction from Symptoms
State of the Art
02
Methodology / System Diagram
03
Training & Testing Corpus
04
Result & Discussion
05
Future Work
06
Real Time System Demonstration
07
Conclusion
08
4. Coronavirus & Prevention
“Coronavirus Disease- 2019”, is a respiratory illness caused by the
severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).
● First reported to affect human life in Wuhan City, in the
Hubei province of China in December 2019
● Later, spread like wildfire throughout the rest of the world,
marking its presence in 235 countries & independent
territories with confirmed death of 9,67,164 [1]
● Immediate Countermeasures are highly need to curb this
catastrophic effects
Algorithms
That Can Be Used To Solve This Problem
● Similarity Based Algorithm
● Machine Learning Algorithm
● Deep Learning Algorithm
Introduction
4
Corona Prediction from Symptoms
5. Prediction Model
● We have worked on corona symptom dataset
● We predict knowledge from user's symptoms using
different ML/DM Techniques
Why Prediction is Important ?
5
Corona Prediction from Symptoms
6. State of the Art
Presenting By
Dipongker Sen (012191010)
Parvin Akter (012193013)
6
7. State of the Art
State-of-the-art (SoTA) is a step to demonstrate the novelty of your
research results.
● Less work is being done on and predicting using text data.
● The major problem in the identification of COVID-19 is
detection and diagnosis.
● Different prediction models are built using machine learning
algorithms
Recent Studies on COVID Data
Data Types Algorithm Findings
Blood test report and
symptoms
CNN, LR Accuracy:
CNN (77%), LR (71%)
Based on age group [24] Random Forest Regressor
Random Forest Classifier
Accuracy: 96.6%
CT images [22] Neural Network Accuracy: 93.9%
Textual clinical data [23] Naïve Bayesian
Classification
Accuracy: 96.2%
Precision: 94%
Recall: 96%
7
Corona Prediction from Symptoms
8. Applied Algorithms on COVID– 19 Based on Textual Data
Data Types Applied Algorithms Findings
Textual Clinical Data labeled with COVID,
ARDS, SARS and Both (COVID, ARDS)
** 212 Textual Report of Patients with
symptoms
● Logistic regression and
Multinomial Naïve Bayesian
Algorithm
● SVM
● Decision Tree
Accuracy: (LR ) and (MN) 94% precision,
96% recall, f1 score 95% and accuracy
96.2%.
Covid-19 regular symptoms
** 67,161 covid -19 patient
ML models based algorithm like
● SVM
● KNN+NCA
● Decision Tree Classifier
● Multilinear Regression
● Logistic Regression
● Random Forest Classifier
● XGBoost Classifier
Regressor and Random Forest Classifier
has outperformed other models in terms
of Coefficient of Determination (0.97 and
0.92) and accuracy 0.966%.
8
Corona Prediction from Symptoms
18. VS
Sigmoid in Feedforward
SoftMax in Backpropagation
Sigmoid in Both
Feedforward & Backpropagation
Findings: Sigmoid Performs Better
✔ Obtains the best accuracy within less epoch.
✔ Error rate is also much lower.
Multi-Layer Neural Network: Accuracy
MLNN
Architecture
Total Data : 1000
Learning Rate, η : 0.05
19
Corona Prediction from Symptoms
19. SoftMax & Sigmoid
VS
Sigmoid in Feedforward
SoftMax in Backpropagation
Sigmoid in Both Feedforward &
Backpropagation
20
MLNN: Predicted Result on Sample Inputs
'Fever', 'Tiredness', 'Dry-Cough', 'Difficulty-in-Breathing', 'Sore-Throat', 'Pains', 'Nasal-Congestion', 'Runny-Nose', 'Diarrhea'
20
MLNN
Architecture
Total Data : 1000
Learning Rate, η : 0.05
Corona Prediction from Symptoms
27. 2
Statistical analysis on
user’s testing in our
system.
3
Take clinical symptoms
(Pulse Rate, Oxygen Level,
Blood Pressure, Chest X-
Ray, CT scan) to provide
better and reliable
accuracy.
4
Develop our algorithm
(modified version) and publish a
research paper.
1
Apply our system on real
data.
28
Future Work Corona Prediction from Symptoms
31. [1] N. Chen et al., “Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study,” Lancet, vol. 395, no. 10223, pp. 507–513, 2020, doi: 10.1016/S0140-
6736(20)30211-7.
[2] S. J. Fong, N. Dey, and J. Chaki, “Artificial Intelligence for Coronavirus Outbreak,” An Introd. to COVID-19, pp. 1–22, 2021, doi: 10.1007/978-981-15-5936-5.
[3] C. Menni et al., “Real-time tracking of self-reported symptoms to predict potential COVID-19,” Nat. Med., vol. 26, no. 7, pp. 1037–1040, 2020, doi: 10.1038/s41591-020-0916-2.
[4] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.
[5] V. Chamola, V. Hassija, V. Gupta, and M. Guizani, “A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact,” IEEE Access, vol. 8, no. April, pp. 90225–
90265, 2020, doi: 10.1109/ACCESS.2020.2992341.
[6] X. Zhang, J. Kim, R. E. Patzer, S. R. Pitts, A. Patzer, and J. D. Schrager, “Prediction of emergency department hospital admission based on natural language processing and neural networks,” Methods Inf. Med., vol. 56, no. 5,
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