Design Solutions covered Data Representation

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COLLEGE OF ENGINEERING

DEPARTMENT OF INFORMATION SYSTEMS

 

 

 

 

Table of Contents

ABSTRACT.. 4

  1. INTRODUCTION/ BACKGROUND AND SIGNIFICANCE.. 5

1.1.         Introduction/background/overview/significance. 5

1.2.         Related research/Literature Review.. 5

1.3.         Motivation.. 5

1.4.         Referencing.. 5

  1. PROBLEM STATEMENT.. 6

2.1.         Overview.. 6

2.2.         Research Question/Hypothesis. 6

  1. AIMS AND OBJECTIVES. 7

3.1.         Aim.. 7

3.2.         Objectives. 7

  1. EXPECTED OUTCOMES/DELIVERABLES. 8
  2. STEPS TO BE TAKEN IN THE INVESTIGATION… 9

Phase 1: Starting the project. 9

Phase 2: Development of an analytical framework for efficient bank marketing.. 9

Phase 3: Development of the efficient classifier. 10

Phase 4: Verification and optimization of the model. 10

  1. RESEARCH DESIGN AND METHODS. 11

6.1.         Overview.. 11

6.2.         Population and Study Sample. 11

6.3.         Sample Size and Selection of Sample. 11

6.4.         Sources of Data. 11

6.5.         Collection of Data. 11

6.6.         Exposure Assessment. 11

6.7.         Data Management. 11

6.8.         Data Analysis Strategies. 11

6.9.         Research Design and Prototype. 11

6.10.      Clients/Stakeholders. 11

6.11.       Methods. 11

  1. RESULTS. 12
  2. DISCUSSION… 12
  3. CONCLUSION… 12
  4. ETHICS AND HUMAN SUBJECTS ISSUES. 12
  5. TIMEFRAMES/PROJECT PLAN/MILESTONES. 12

11.1.       Time Schedule (Ghantt Chart) 13

11.2.       Activity Sequencing.. 14

  1. STRENGTHS AND WEAKNESSES OF THE STUDY.. 15

12.1.      Strengths. 15

12.2.      Weaknesses. 15

  1. BUDGET.. 16

13.1.      Resource requirements. 16

13.2.      Budget/Funding.. 17

  1. REFERENCES. 18
  2. APPENDICES. 19

Appendix 1: Questionnaire. 19

Appendix 2: 20

 

 ABSTRACT

The purpose of this research is to provide insights on the applications of Artificial Intelligence, and how this technology can aid positively in the current pandemic of the coronavirus (COVID-19).

Background

Covid-19 has provoked a global and critical health situation in a short period of time throughout the entire globe (Arora & Bist, 2020). Everyone is considered at risk of getting COVID-19 as long as they are exposed to the virus. Unfortunately, children, elderlies and people with health conditions that weakens their immune system/lungs have a higher risk of severe symptoms and possible death. Artificial Intelligence [AI] is helpful for proper screening, tracking and predicting the current and future patients.

Methodology

The collection and analyzing of data are going to be through research using qualitative approach. The authors will research asymptomatic cases (cases where people catch the Covid-19 virus, but show no symptoms whatsoever) in depth to analyze solutions by the technology of Artificial Intelligence.

1.      INTRODUCTION/ BACKGROUND AND SIGNIFICANCE

1.1.             Significance of the Research

The objective of this study is to analyze the impact of AI on COVID-19 and the detection of the virus for early stages. The research will focus on identifying the match for the COVID-19 symptom using AI. The project will thus examine monitoring active cases, and reaching out to individuals who are or were infected with this virus. Information will be collected through conducting a survey by asking people specific questions. From the information gathered, the study will provide a clear understanding of how people get aware easily of the corona virus easily even if the symptoms do not show to them.

1.2.            Related research/Literature Review

The review of literature is done on the database of Google Scholar using the keywords Artificial Intelligence or AI and Coronavirus or COVID-19 (Dule et al., 2020). Multiple articles and research papers show that the virus is a higher risk to specific individuals and one thing these individuals have in common is a weaker immune system than the average healthy human. People with diabetes, obesity, lung diseases and also older people have a higher risk getting infected by this virus. An article published in the Journal of Infection conducted a research on a number of 337 cases over the age of 60 in Renmin Hospital of Wuhan University from Jan 1 to Feb 6, 2020 (Dhiman, 2020). Common medical conditions included hypertension, diabetes and cardiovascular diseases.  (54%) of the patients stayed in the hospital in a critical condition while (19.2%) were dead. Another article published on the BMJ on April 2020 stated that recent evidence has emerged from China indicating that the large majority of coronavirus infections do not result in symptoms. A total of 130 of 166 new infections (78%) identified in the 24 hours to the afternoon of Wednesday 1 April were asymptomatic, said China’s National Health Commission (Potaliya & Ghatak, 2020). And most of the 36 cases in which patients showed symptoms involved arrivals from overseas, down from 48 the previous day, the commission said.

1.3.            Motivation

  • Effects of Covid-19 on the Worlds Economy. The adverse negative effect of the pandemic is serving as a motivation to conduct the entire research.
  • The need for Human life protection. Human life has been greatly posed with a risk of being cut short by the spread of Covid-19, whereby many people have lost their lives, and the remaining population is working hard to secure their lives from the effect of Covid-19 (“Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network”, 2020)
  • Current medical researches that target to establish the cure for Covid-19 flu are also a motivation to this research (Borkowski, 2020).
  • This new approach of applying artificial intelligence has not been deeply ventured into, which could make its chances of improving the condition still valid.

 

2.     PROBLEM STATEMENT

2.1.            Overview

The detection of COVID-19 in early stage is somewhat difficult. Individuals who are infected with the virus but show no symptoms (asymptomatic) are rapidly spreading the virus to their families and friends which as a result, the number of people infected with corona will increase as well the number of deaths (“Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network”, 2020).

2.2.            Research Question/Hypothesis

The research will thus aim at raising awareness of AI on Covid-19 detection. It will also help in explaining the impact of AI on analyzing Covid-19. Early detection of the virus can help reduce the infections and control the current cases (Blumenstock, 2020).

The research questions:

  1. How to detect infected people who can spread the virus without having or showing any symptoms using AI?
  2. What is the impact of AI on COVID-19?

3.     AIMS AND OBJECTIVES

3.1.             Aim

  • Establish an effective covid-19 detection method using artificial intelligence

3.2.            Objectives

  • Develop a covid-19 detection mechanism based on artificial intelligence
  • Identify artificial intelligence approaches suitable for Covid-19 detection.

 

 

4.      EXPECTED OUTCOMES/DELIVERABLES

This study is expected to come up with a solution for covid-19 early detection, well documented to clearly explain the results. The technique is designed to utilize Artificial Intelligence technology, specifically machine learning to facilitate the detection of covid-19 disease in the early stages which will help to minimize the adverse effects witnessed so far  (Yasar & Ceylan, 2020). The study will benefit a wide range of users such as medical personnel, individuals in their own personal lives, employers, governments and every human being in their own area of specialization. The effects of Covid-19 are not selective since they can strike anyone, anywhere (“Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network”, 2020).

 

4.1.             Methods

This research is majorly conducted using descriptive data collection methods and analysis methods. The data collected is mainly from secondary sources as a way of minimizing the research costs. Different articles on the subject are going to be utilized to come up with suitable conclusive data which will be evaluated with a descriptive methodology.

5.     RESULTS

There is a correlation between being male and possessing a high level of serum lymphocytes and neutrophils.

Serum levels, immune cells, symptoms reported and patient gender are some of the factors that can be used to classify COVID-19 patients.

We used XGBoost model to boost the sensitivity and specificity for distinguishing COVID-19 patients from those suffering from influenza

6.      DISCUSSION

The results of our research demonstrate that, the male gender is more susceptible to COVID-19 than their female counterparts.

The COVID-19 virus infects different patients differently.

Some diseases such as influenza could have similar symptoms to COVID-19 hence we must be keen to differentiate these two so that rightful medication is given to the rightful patients.

7.      CONCLUSION

With the computations we used and clinical data we obtained use of more accurate diagnostic models for COVID-19 could be used to prevent the effect of not testing (SJ, 2020).

Unknown COVID-19 patients due to the either the virus being asymptomatic or the fact that there are other diseases with similar symptoms can now be better identified.

 

 

 

8.     STEPS TO BE TAKEN IN THE INVESTIGATION

This project focuses on design and development of an intelligent bank marketing system for a better bank marketing. The project will be carried out in four phases with significant milestone at the end of each phase. In the first phase, we will download and prepare the bank marketing data. Then we will search for information to have clear knowledge about the bank marketing problem and its solutions.

The second phase of the project would concentrate on an analytical framework to analyze the performance of different data mining algorithms that would be developed identifying the main components of the bank marketing.

The third phase would concentrate on the design and the implementation of efficient, reliable and robust data mining tool that achieves a better classification accuracy. Moreover, an ensemble of classifiers would be developed to increase classification performance of an efficient bank marketing.

The fourth and final phase would concentrate on preparing and submitting a conference paper. More details on the different phases are as follows:

Phase 1: Starting the project

First of all, we will download and prepare the bank marketing data for the implementation with WEKA data mining tool. Since the data is unbalanced we try to balance the by using SMOTE technique to have an efficient and accurate classification. Then we will check previous studies done on the same field to have clear knowledge about the bank marketing problem and its solutions and applied methods before.

Phase 2: Development of an analytical framework for efficient bank marketing

In this phase, efficient, intelligent and robust bank marketing system will be developed. The system will employ the bank marketing data with robust attribute selection. The extracted attributes will be employed by means of the Data mining techniques for intelligent and more efficient bank marketing environment. This platform will serve to design and develop application oriented bank marketing system. The downloaded data can be processed by employing a variety of feature extraction and attribute selection techniques. Then comparison of different feature extraction, attribute selection techniques, and data mining algorithms for bank marketing will be made. The employed feature extraction and attribute selection techniques should be robust and intelligent, so that a reliable bank marketing can be realized. The proposed platform will be used to understand and implement the intelligent system for a Bank Marketing. The finding of robust features and attributes of bank marketing data would be very meaningful for determining important insights into the effects of various parameters on the performance of bank marketing system to enlighten which feature extraction and attribute selection a is the most effective and less time consuming.

A classification problem is referred to as imbalanced when the instances in one or several classes, known as the majority classes, out number the instances of the other classes, called the minority classes. The synthetic minority over-sampling technique (SMOTE) (Chawla et al., 2002) is a well acknowledged over-sampling method. In the SMOTE, instead of mere data oriented duplicating, the positive classis over-sampled by creating synthetic instances in the feature space formed by the positive instances and their K-nearest neighbours.

 

In this step, feature extraction, attribute selection and data mining algorithms will be implemented as off-line. The feature extraction and attribute selection methods are in the set of data processing tools which extract features from the bank marketing data. Feature extraction and attribute selection are also one of the most important steps in data classification.  It is highly effective technique in selection of attributes and is frequently applied to complex, high dimensional, multivariate data. When the features are not appropriate for the given classification problem, obtained performances will be unsatisfactory. In this case, even the classification algorithm is optimally determined for the problem, because of the improper features/attributes; the algorithm cannot generate high performance. Therefore, it is mandatory to find and extract suitable features from the raw data to be able to obtain good classification results. Many feature extraction and attribute selection techniques will be applied. These are CFS Subset Evaluator, and Infogain Attribute Evaluator etc. will be applied.

Phase 3: Development of the efficient classifier

The marketing data data is of multi-dimensional nature. Therefore, it is difficult to find a robust feature extraction, attribute selection and data mining algorithm for Bank marketing. Data mining algorithms have ability to distinguish different type of data. In this phase the focus will be to make a comparison of different data mining algorithms in the field of Bank marketing. The outcome will be the analysis and choice of most appropriate sets of features extraction, attribute selection and data mining techniques, for the Bank marketing. After applying different feature extraction/attribute selection algorithms, the existing data mining algorithms (such as ANN, k-NN, SVM, decision tree algorithms, etc.) capable of dealing with network data will be applied, implemented and tested. For this purpose, various classification schemes for a particular data classification task will be developed. The aim of using data mining techniques is to make a better Bank marketing. Even though one of the algorithms would produce the best overall performance, ensemble classifier approach, where the idea is to consider more than one classification scheme can give better classification accuracy. Classifier ensembles are multiple classifier systems trained on different data or feature subsets, will be used to get better performance and accuracy.

Phase 4: Verification and optimization of the model

The fourth and final phase would concentrate on preparing and submitting a conference paper. Here, the performance metrics such as Area under ROC curve, F-measure, kappa statistic and total classification accuracy would be quantified with the help of WEKA software. Obtained results from experimentation would then be used to verify the accuracy, reliability and robustness of the developed models and would provide feedback for improvement and fine-tuning the models in phase 1, phase 2 and phase 3. The obtained results of the ensemble classifiers would be benchmarked with classical single classifiers. Moreover efficient, intelligent and robust methods will be achieved for bank marketing.

9.     RESEARCH DESIGN AND METHODS

9.1.             Overview

Use headings 2 and 3 as appropriate, and use these headings if appropriate.

 

9.2.            Population and Study Sample

 

9.3.            Sample Size and Selection of Sample

 

9.4.            Sources of Data

 

9.5.            Collection of Data

 

9.6.            Exposure Assessment

 

9.7.            Data Management

 

9.8.            Data Analysis Strategies

9.9.            Research Design and Prototype

  • Use case Diagrams
  • Database Diagrams
  • Relation Diagrams

 

9.10.     Clients/Stakeholders

9.11.         Methods

  • describes the research and project methods you will use in performing your project.
  • should not identify methods that you might be investigating as part of your project, but those methods you are actually using.
  • Include development methods that you are using as part of a systems development; survey methods for a case study evaluation and evaluation methods for comparing two or more systems.
  • Research methods include action research, case study, survey and experiment.

 

10. RESULTS

  • Give the tables and results of your research project one by one
  • Give the explanation on the results.

11.  DISCUSSION

  • Give discussion on the results of your research project
  • Give comments on the results.

12. CONCLUSION

  • Conclude and summarize the results of your research project.

13. ETHICS AND HUMAN SUBJECTS ISSUES

14. TIMEFRAMES/PROJECT PLAN/MILESTONES

14.1.         Time Schedule (Ghantt Chart)

Change the activities and relate  it to the topic which is covid-19

Proposed steps schedule is planned like in the table.

 

Activity Months
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
ANN Stock Market Prediction
Literature Survey
Literature Search
Literature Review
Completed Literature Review
Get Stock Market Data
Develop ANN
Investigate and Evaluate ANN
Design ANN
Develop and Test ANN
Evaluation
Train ANN
Use Stock Market Models
Analysis
Review Statistical Tests
Analyse and Evaluate
Complete Report
Project Completed

 

14.2.         Activity Sequencing

Activity-on-the-node diagram represents the tasks you are performing in your project as nodes connected by arrows (Dawson, 2005)

 

15. STRENGTHS AND WEAKNESSES OF THE STUDY

15.1.     Strengths

15.2.     Weaknesses

 

16.              REFERENCES

Dhiman, G. (2020). Coronavirus (COVID-19) Effects on Psychological Health of Indian Poultry Farmers. Coronaviruses01. https://doi.org/10.2174/2666796701999200617160755

Arora, K., & Bist, A. (2020). Artificial Intelligence Based Drug Discovery Techniques for COVID-19 Detection. Aptisi Transactions On Technopreneurship (ATT)2(2), 120-126. https://doi.org/10.34306/att.v2i2.88

Dawson, C. W. (2005). Projects in computing and information systems: a student’s guide. Pearson Education.

Borkowski, A. (2020). Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis. Federal Practitioner, (Vol 37 No 9). https://doi.org/10.12788/fp.0045

Yasar, H., & Ceylan, M. (2020). A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods. Multimedia Tools And Applications. https://doi.org/10.1007/s11042-020-09894-3

Blumenstock, J. (2020). Machine learning can help get COVID-19 aid to those who need it most. Nature. https://doi.org/10.1038/d41586-020-01393-7

Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network. (2020). https://doi.org/10.28919/cmbn/4765

Dule, C., K.M., R., & DH, M. (2020). Challenges of Artificial Intelligence to Combat COVID-19. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3608764

SJ, W. (2020). COVID-19: Evolution and Prevention. Trends In Telemedicine & E-Health2(3). https://doi.org/10.31031/tteh.2020.02.000539

Potaliya, P., & Ghatak, S. (2020). New Trends in Medical Education During and Post COVID-19 Pandemic. European Journal of Medical And Health Sciences2(3). https://doi.org/10.24018/ejmed.2020.2.3.275

 

 

 

 

 

 

 

 

 

 

17. APPENDICES

Appendix 1: Questionnaire

 

 

Appendix 2:

 

Check the related slides and Rubric for the project report details

 

 

EFFAT UNIVERSITY

COLLEGE OF ENGINEERING

DEPARTMENT OF INFORMATION SYSTEMS

 

 

 

 

Table of Contents

ABSTRACT.. 4

  1. INTRODUCTION/ BACKGROUND AND SIGNIFICANCE.. 5

1.1.         Introduction/background/overview/significance. 5

1.2.         Related research/Literature Review.. 5

1.3.         Motivation.. 5

1.4.         Referencing.. 5

  1. PROBLEM STATEMENT.. 6

2.1.         Overview.. 6

2.2.         Research Question/Hypothesis. 6

  1. AIMS AND OBJECTIVES. 7

3.1.         Aim.. 7

3.2.         Objectives. 7

  1. EXPECTED OUTCOMES/DELIVERABLES. 8
  2. STEPS TO BE TAKEN IN THE INVESTIGATION… 9

Phase 1: Starting the project. 9

Phase 2: Development of an analytical framework for efficient bank marketing.. 9

Phase 3: Development of the efficient classifier. 10

Phase 4: Verification and optimization of the model. 10

  1. RESEARCH DESIGN AND METHODS. 11

6.1.         Overview.. 11

6.2.         Population and Study Sample. 11

6.3.         Sample Size and Selection of Sample. 11

6.4.         Sources of Data. 11

6.5.         Collection of Data. 11

6.6.         Exposure Assessment. 11

6.7.         Data Management. 11

6.8.         Data Analysis Strategies. 11

6.9.         Research Design and Prototype. 11

6.10.      Clients/Stakeholders. 11

6.11.       Methods. 11

  1. RESULTS. 12
  2. DISCUSSION… 12
  3. CONCLUSION… 12
  4. ETHICS AND HUMAN SUBJECTS ISSUES. 12
  5. TIMEFRAMES/PROJECT PLAN/MILESTONES. 12

11.1.       Time Schedule (Ghantt Chart) 13

11.2.       Activity Sequencing.. 14

  1. STRENGTHS AND WEAKNESSES OF THE STUDY.. 15

12.1.      Strengths. 15

12.2.      Weaknesses. 15

  1. BUDGET.. 16

13.1.      Resource requirements. 16

13.2.      Budget/Funding.. 17

  1. REFERENCES. 18
  2. APPENDICES. 19

Appendix 1: Questionnaire. 19

Appendix 2: 20

 

 ABSTRACT

The purpose of this research is to provide insights on the applications of Artificial Intelligence, and how this technology can aid positively in the current pandemic of the coronavirus (COVID-19).

Background

Covid-19 has provoked a global and critical health situation in a short period of time throughout the entire globe (Arora & Bist, 2020). Everyone is considered at risk of getting COVID-19 as long as they are exposed to the virus. Unfortunately, children, elderlies and people with health conditions that weakens their immune system/lungs have a higher risk of severe symptoms and possible death. Artificial Intelligence [AI] is helpful for proper screening, tracking and predicting the current and future patients.

Methodology

The collection and analyzing of data are going to be through research using qualitative approach. The authors will research asymptomatic cases (cases where people catch the Covid-19 virus, but show no symptoms whatsoever) in depth to analyze solutions by the technology of Artificial Intelligence.

1.      INTRODUCTION/ BACKGROUND AND SIGNIFICANCE

1.1.             Significance of the Research

The objective of this study is to analyze the impact of AI on COVID-19 and the detection of the virus for early stages. The research will focus on identifying the match for the COVID-19 symptom using AI. The project will thus examine monitoring active cases, and reaching out to individuals who are or were infected with this virus. Information will be collected through conducting a survey by asking people specific questions. From the information gathered, the study will provide a clear understanding of how people get aware easily of the corona virus easily even if the symptoms do not show to them.

1.2.            Related research/Literature Review

The review of literature is done on the database of Google Scholar using the keywords Artificial Intelligence or AI and Coronavirus or COVID-19 (Dule et al., 2020). Multiple articles and research papers show that the virus is a higher risk to specific individuals and one thing these individuals have in common is a weaker immune system than the average healthy human. People with diabetes, obesity, lung diseases and also older people have a higher risk getting infected by this virus. An article published in the Journal of Infection conducted a research on a number of 337 cases over the age of 60 in Renmin Hospital of Wuhan University from Jan 1 to Feb 6, 2020 (Dhiman, 2020). Common medical conditions included hypertension, diabetes and cardiovascular diseases.  (54%) of the patients stayed in the hospital in a critical condition while (19.2%) were dead. Another article published on the BMJ on April 2020 stated that recent evidence has emerged from China indicating that the large majority of coronavirus infections do not result in symptoms. A total of 130 of 166 new infections (78%) identified in the 24 hours to the afternoon of Wednesday 1 April were asymptomatic, said China’s National Health Commission (Potaliya & Ghatak, 2020). And most of the 36 cases in which patients showed symptoms involved arrivals from overseas, down from 48 the previous day, the commission said.

1.3.            Motivation

  • Effects of Covid-19 on the Worlds Economy. The adverse negative effect of the pandemic is serving as a motivation to conduct the entire research.
  • The need for Human life protection. Human life has been greatly posed with a risk of being cut short by the spread of Covid-19, whereby many people have lost their lives, and the remaining population is working hard to secure their lives from the effect of Covid-19 (“Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network”, 2020)
  • Current medical researches that target to establish the cure for Covid-19 flu are also a motivation to this research (Borkowski, 2020).
  • This new approach of applying artificial intelligence has not been deeply ventured into, which could make its chances of improving the condition still valid.

 

2.     PROBLEM STATEMENT

2.1.            Overview

The detection of COVID-19 in early stage is somewhat difficult. Individuals who are infected with the virus but show no symptoms (asymptomatic) are rapidly spreading the virus to their families and friends which as a result, the number of people infected with corona will increase as well the number of deaths (“Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network”, 2020).

2.2.            Research Question/Hypothesis

The research will thus aim at raising awareness of AI on Covid-19 detection. It will also help in explaining the impact of AI on analyzing Covid-19. Early detection of the virus can help reduce the infections and control the current cases (Blumenstock, 2020).

The research questions:

  1. How to detect infected people who can spread the virus without having or showing any symptoms using AI?
  2. What is the impact of AI on COVID-19?

3.     AIMS AND OBJECTIVES

3.1.             Aim

  • Establish an effective covid-19 detection method using artificial intelligence

3.2.            Objectives

  • Develop a covid-19 detection mechanism based on artificial intelligence
  • Identify artificial intelligence approaches suitable for Covid-19 detection.

 

 

4.      EXPECTED OUTCOMES/DELIVERABLES

This study is expected to come up with a solution for covid-19 early detection, well documented to clearly explain the results. The technique is designed to utilize Artificial Intelligence technology, specifically machine learning to facilitate the detection of covid-19 disease in the early stages which will help to minimize the adverse effects witnessed so far  (Yasar & Ceylan, 2020). The study will benefit a wide range of users such as medical personnel, individuals in their own personal lives, employers, governments and every human being in their own area of specialization. The effects of Covid-19 are not selective since they can strike anyone, anywhere (“Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network”, 2020).

 

4.1.             Methods

This research is majorly conducted using descriptive data collection methods and analysis methods. The data collected is mainly from secondary sources as a way of minimizing the research costs. Different articles on the subject are going to be utilized to come up with suitable conclusive data which will be evaluated with a descriptive methodology.

5.     RESULTS

There is a correlation between being male and possessing a high level of serum lymphocytes and neutrophils.

Serum levels, immune cells, symptoms reported and patient gender are some of the factors that can be used to classify COVID-19 patients.

We used XGBoost model to boost the sensitivity and specificity for distinguishing COVID-19 patients from those suffering from influenza

6.      DISCUSSION

The results of our research demonstrate that, the male gender is more susceptible to COVID-19 than their female counterparts.

The COVID-19 virus infects different patients differently.

Some diseases such as influenza could have similar symptoms to COVID-19 hence we must be keen to differentiate these two so that rightful medication is given to the rightful patients.

7.      CONCLUSION

With the computations we used and clinical data we obtained use of more accurate diagnostic models for COVID-19 could be used to prevent the effect of not testing (SJ, 2020).

Unknown COVID-19 patients due to the either the virus being asymptomatic or the fact that there are other diseases with similar symptoms can now be better identified.

 

 

 

8.     STEPS TO BE TAKEN IN THE INVESTIGATION

This project focuses on design and development of an intelligent bank marketing system for a better bank marketing. The project will be carried out in four phases with significant milestone at the end of each phase. In the first phase, we will download and prepare the bank marketing data. Then we will search for information to have clear knowledge about the bank marketing problem and its solutions.

The second phase of the project would concentrate on an analytical framework to analyze the performance of different data mining algorithms that would be developed identifying the main components of the bank marketing.

The third phase would concentrate on the design and the implementation of efficient, reliable and robust data mining tool that achieves a better classification accuracy. Moreover, an ensemble of classifiers would be developed to increase classification performance of an efficient bank marketing.

The fourth and final phase would concentrate on preparing and submitting a conference paper. More details on the different phases are as follows:

Phase 1: Starting the project

First of all, we will download and prepare the bank marketing data for the implementation with WEKA data mining tool. Since the data is unbalanced we try to balance the by using SMOTE technique to have an efficient and accurate classification. Then we will check previous studies done on the same field to have clear knowledge about the bank marketing problem and its solutions and applied methods before.

Phase 2: Development of an analytical framework for efficient bank marketing

In this phase, efficient, intelligent and robust bank marketing system will be developed. The system will employ the bank marketing data with robust attribute selection. The extracted attributes will be employed by means of the Data mining techniques for intelligent and more efficient bank marketing environment. This platform will serve to design and develop application oriented bank marketing system. The downloaded data can be processed by employing a variety of feature extraction and attribute selection techniques. Then comparison of different feature extraction, attribute selection techniques, and data mining algorithms for bank marketing will be made. The employed feature extraction and attribute selection techniques should be robust and intelligent, so that a reliable bank marketing can be realized. The proposed platform will be used to understand and implement the intelligent system for a Bank Marketing. The finding of robust features and attributes of bank marketing data would be very meaningful for determining important insights into the effects of various parameters on the performance of bank marketing system to enlighten which feature extraction and attribute selection a is the most effective and less time consuming.

A classification problem is referred to as imbalanced when the instances in one or several classes, known as the majority classes, out number the instances of the other classes, called the minority classes. The synthetic minority over-sampling technique (SMOTE) (Chawla et al., 2002) is a well acknowledged over-sampling method. In the SMOTE, instead of mere data oriented duplicating, the positive classis over-sampled by creating synthetic instances in the feature space formed by the positive instances and their K-nearest neighbours.

 

In this step, feature extraction, attribute selection and data mining algorithms will be implemented as off-line. The feature extraction and attribute selection methods are in the set of data processing tools which extract features from the bank marketing data. Feature extraction and attribute selection are also one of the most important steps in data classification.  It is highly effective technique in selection of attributes and is frequently applied to complex, high dimensional, multivariate data. When the features are not appropriate for the given classification problem, obtained performances will be unsatisfactory. In this case, even the classification algorithm is optimally determined for the problem, because of the improper features/attributes; the algorithm cannot generate high performance. Therefore, it is mandatory to find and extract suitable features from the raw data to be able to obtain good classification results. Many feature extraction and attribute selection techniques will be applied. These are CFS Subset Evaluator, and Infogain Attribute Evaluator etc. will be applied.

Phase 3: Development of the efficient classifier

The marketing data data is of multi-dimensional nature. Therefore, it is difficult to find a robust feature extraction, attribute selection and data mining algorithm for Bank marketing. Data mining algorithms have ability to distinguish different type of data. In this phase the focus will be to make a comparison of different data mining algorithms in the field of Bank marketing. The outcome will be the analysis and choice of most appropriate sets of features extraction, attribute selection and data mining techniques, for the Bank marketing. After applying different feature extraction/attribute selection algorithms, the existing data mining algorithms (such as ANN, k-NN, SVM, decision tree algorithms, etc.) capable of dealing with network data will be applied, implemented and tested. For this purpose, various classification schemes for a particular data classification task will be developed. The aim of using data mining techniques is to make a better Bank marketing. Even though one of the algorithms would produce the best overall performance, ensemble classifier approach, where the idea is to consider more than one classification scheme can give better classification accuracy. Classifier ensembles are multiple classifier systems trained on different data or feature subsets, will be used to get better performance and accuracy.

Phase 4: Verification and optimization of the model

The fourth and final phase would concentrate on preparing and submitting a conference paper. Here, the performance metrics such as Area under ROC curve, F-measure, kappa statistic and total classification accuracy would be quantified with the help of WEKA software. Obtained results from experimentation would then be used to verify the accuracy, reliability and robustness of the developed models and would provide feedback for improvement and fine-tuning the models in phase 1, phase 2 and phase 3. The obtained results of the ensemble classifiers would be benchmarked with classical single classifiers. Moreover efficient, intelligent and robust methods will be achieved for bank marketing.

9.     RESEARCH DESIGN AND METHODS

9.1.             Overview

Use headings 2 and 3 as appropriate, and use these headings if appropriate.

 

9.2.            Population and Study Sample

 

9.3.            Sample Size and Selection of Sample

 

9.4.            Sources of Data

 

9.5.            Collection of Data

 

9.6.            Exposure Assessment

 

9.7.            Data Management

 

9.8.            Data Analysis Strategies

9.9.            Research Design and Prototype

  • Use case Diagrams
  • Database Diagrams
  • Relation Diagrams

 

9.10.     Clients/Stakeholders

9.11.         Methods

  • describes the research and project methods you will use in performing your project.
  • should not identify methods that you might be investigating as part of your project, but those methods you are actually using.
  • Include development methods that you are using as part of a systems development; survey methods for a case study evaluation and evaluation methods for comparing two or more systems.
  • Research methods include action research, case study, survey and experiment.

 

10. RESULTS

  • Give the tables and results of your research project one by one
  • Give the explanation on the results.

11.  DISCUSSION

  • Give discussion on the results of your research project
  • Give comments on the results.

12. CONCLUSION

  • Conclude and summarize the results of your research project.

13. ETHICS AND HUMAN SUBJECTS ISSUES

14. TIMEFRAMES/PROJECT PLAN/MILESTONES

14.1.         Time Schedule (Ghantt Chart)

Change the activities and relate  it to the topic which is covid-19

Proposed steps schedule is planned like in the table.

 

Activity Months
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
ANN Stock Market Prediction                                                                        
Literature Survey                                                                        
Literature Search                                                                        
Literature Review                                                                        
Completed Literature Review                                                                        
Get Stock Market Data                                                                        
Develop ANN                                                                        
Investigate and Evaluate ANN                                                                        
Design ANN                                                                        
Develop and Test ANN                                                                        
Evaluation                                                                        
Train ANN                                                                        
Use Stock Market Models                                                                        
Analysis                                                                        
Review Statistical Tests                                                                        
Analyse and Evaluate                                                                        
Complete Report                                                                        
Project Completed                                                                        

 

14.2.         Activity Sequencing

Activity-on-the-node diagram represents the tasks you are performing in your project as nodes connected by arrows (Dawson, 2005)

 

15. STRENGTHS AND WEAKNESSES OF THE STUDY

15.1.     Strengths

15.2.     Weaknesses

 

16.              REFERENCES

Dhiman, G. (2020). Coronavirus (COVID-19) Effects on Psychological Health of Indian Poultry Farmers. Coronaviruses01. https://doi.org/10.2174/2666796701999200617160755

Arora, K., & Bist, A. (2020). Artificial Intelligence Based Drug Discovery Techniques for COVID-19 Detection. Aptisi Transactions On Technopreneurship (ATT)2(2), 120-126. https://doi.org/10.34306/att.v2i2.88

Dawson, C. W. (2005). Projects in computing and information systems: a student’s guide. Pearson Education.

Borkowski, A. (2020). Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis. Federal Practitioner, (Vol 37 No 9). https://doi.org/10.12788/fp.0045

Yasar, H., & Ceylan, M. (2020). A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods. Multimedia Tools And Applications. https://doi.org/10.1007/s11042-020-09894-3

Blumenstock, J. (2020). Machine learning can help get COVID-19 aid to those who need it most. Nature. https://doi.org/10.1038/d41586-020-01393-7

Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network. (2020). https://doi.org/10.28919/cmbn/4765

Dule, C., K.M., R., & DH, M. (2020). Challenges of Artificial Intelligence to Combat COVID-19. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3608764

SJ, W. (2020). COVID-19: Evolution and Prevention. Trends In Telemedicine & E-Health2(3). https://doi.org/10.31031/tteh.2020.02.000539

Potaliya, P., & Ghatak, S. (2020). New Trends in Medical Education During and Post COVID-19 Pandemic. European Journal of Medical And Health Sciences2(3). https://doi.org/10.24018/ejmed.2020.2.3.275

 

 

 

 

 

 

 

 

 

 

17. APPENDICES

Appendix 1: Questionnaire

 

 

Appendix 2:

 

Check the related slides and Rubric for the project report details

 

20201112093542risk_insurance_and_superannuation_individual_assignment_t3_2020 20201112102610ss_ethics_nursing_18_jua_2012___orly_shapira 20201112075702book1__case_study 20201112092818risk_insurance_and_superannuation_individual_assignment_t3_2020

 

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