JoAIRA

Implementation of Data Mining Approach to Find the Adaptability of Students in Online Education during Covid-19 Pandemic

    By  

  1. Trailokya Raj Ojha

  1. Assistant Professor,Department of Computer Science Engineering, Nepal Engineering College,Bhaktapur,Nepal

Abstract

The global Covid-19 pandemic has severely affected very aspects of human life, including education. The virus’ stunning spread created havoc in the educational system, causing educational institutions to close. As an effect, students must quickly adopt to the change to synchronous online learning. This study identified the different aspects affecting the adaptability level of students in online class. It also identifies the student’s adaptability level in different circumstances in online classes. A sample of 1205 school, college and university students from Bangladesh has been examined using data mining technique to find their adaptability level. The result shows that college and university students have more adaptability than the school students

Keywords: Online learning, adaptability, Covid-19, K-means algorithm, Kaggle repository, educational data mining

[This article belongs to Journal of Artificial Intelligence Research Advances(joaira)]

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References

1. Butnaru GI, Niță V, Anichiti A, Brînză G. The effectiveness of online education during covid 19 pandemic—a comparative analysis between the perceptions of academic students and high school students from romania. Sustainability. 2021 May 10;13(9):5311.
2. Baker RS, Yacef K. The state of educational data mining in 2009: A review and future visions. Journal of educational data mining. 2009 Oct 1;1(1):3-17.
3. Abd Elaal SA. E-learning using data mining. Chin.-Egypt. Res. J. 2013.
4. Baepler P, Murdoch CJ. Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching & Learning. 2010 Jul 1;4(2).
5. Muthu prasad T, Aiswarya S, Aditya KS, Jha GK. Students’ perception and preference for online education in India during COVID-19 pandemic. Social Sciences & Humanities Open. 2021 Jan 1;3(1):100101.
6. Dorn E, Hancock B, Sarakatsannis J, Viruleg E. COVID-19 and student learning in the United States: The hurt could last a lifetime. McKinsey & Company. 2020 Jun 1;1.
7. UNESCO. Distance Learning Solutions, More on UNESCO’s COVID-19 Education Response [Online]. Available from https://en.unesco.org/covid19/educationresponse/solutions
8. Kebritchi M, Lipschuetz A, Santiague L. Issues and challenges for teaching successful online courses in higher education: A literature review. Journal of Educational Technology Systems. 2017 Sep;46(1):4-29.
9. Hung ML, Chou C, Chen CH, Own ZY. Learner readiness for online learning: Scale development and student perceptions. Computers & Education. 2010 Nov 1;55(3):1080-90.
10. Biner PM, Summers M, Dean RS, Bink ML, Anderson JL, Gelder BC. Student satisfaction with interactive telecourses as a function of demographic variables and prior telecourse experience1. Distance Education. 1996 Jan 1;17(1):33-43.
11. Dille B, Mezack M. Identifying predictors of high risk among community college telecourse student. American journal of distance education. 1991 Jan 1;5(1):24-35.
12. Wojciechowski A, Palmer LB. Individual student characteristics: Can any be predictors of success in online classes. Online journal of distance learning administration. 2005 Nov;8(2):13.
13. Colorado JT, Eberle J. Student demographics and success in online learning environments.
14. Yabing J. Research of an improved apriori algorithm in data mining association rules. International Journal of Computer and Communication Engineering. 2013 Jan 1;2(1):25.
15. Onik AR, Haq NF, Alam L, Mamun TI. An analytical comparison on filter feature extraction method in data mining using J48 classifier. International Journal of Computer Applications. 2015;124(13).
16. San OM, Huynh VN, Nakamori Y. An alternative extension of the k-means algorithm for clustering categorical data. International journal of applied mathematics and computer science. 2004;14(2):241-7.
17. S. L. Prabha and D. A. R. M. Shanavas, “Application of Educational Data mining techniques in eLearning-A Case Study.” [Online]. Available: www.ijcsit.com

Regular Issue Open Access Article

Journal of Artificial Intelligence Research Advances

ISSN: 2395-6720
Volume 9
Issue 2
Received July 26, 2022
Accepted August 9, 2022
Published August 16, 2022
Views: 1,375
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JoAIRA

Experimental Study on Heart Disease Prediction Using Different Machine Learning Algorithms

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Heart disease which can also be referred to as the cardiovascular disease is one of the raising concerns in today’s world. It is one of the major health problems causing death among humans irrespective of the age group and therefore has made it necessary to look into different medical factors that are required to predict the same in advance using the collected historical datasets of various patients. Thus we have used various machine learning algorithms to predict the potential of person, to suffer from a heart disease with high precision and reliability so that we can admonish the patient in advance and take the required precautionary measures. In this paper an existing heart disease dataset accessible from the UCI Machine Learning Repository is used as the primary dataset. The experimental analysis and comparative study between different algorithms, helps in deciding the best suitable algorithm for the given problem statement by using results obtained which are very competitive and can be used for identification and treatment. The proposed work predicts the probability of heart condition and ranks the patient’s risk level based on different supervised learning algorithms such as K-Neighbors, AdaBoost, Gradient Boosting. The test results portrays that K- neighbors has the highest accuracy score of 91% when compared with other algorithms.

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Volume :u00a0u00a09 | Issue :u00a0u00a02 | Received :u00a0u00a0August 10, 2022 | Accepted :u00a0u00a0August 18, 2022 | Published :u00a0u00a0August 22, 2022n[if 424 equals=”Regular Issue”][This article belongs to Journal of Artificial Intelligence Research Advances(joaira)] [/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue Experimental Study on Heart Disease Prediction Using Different Machine Learning Algorithms under section in Journal of Artificial Intelligence Research Advances(joaira)] [/if 424]
Keywords Heart disease prediction, algorithms, machine learning, CNN, KNN

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1. Apurb Rajdhan, Milan Sai, Avi Agarwal, Dundigalla Ravi. Heart Disease Prediction using Machine Learning. International Journal of Engineering Research & Technology (IJERT).2020;9 (4)
2. Rindhe BU, Ahire N, Patil R, Gagare S, Darade M. Heart Disease Prediction Using Machine Learning. Heart Disease. 2021 May;5(1).
3. Jindal H, Agrawal S, Khera R, Jain R, Nagrath P. Heart disease prediction using machine learning algorithms. InIOP conference series: materials science and engineering 2021 (Vol. 1022, No. 1, p. 012072). IOP Publishing.
4. Shah D, Patel S, Bharti SK. Heart disease prediction using machine learning techniques. SN Computer Science. 2020 Nov;1(6):1-6.
5. Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P. Prediction of heart disease using a combination of machine learning and deep learning. Computational intelligence and neuroscience. 2021 Jul 1;2021.
6. Golande A, Pavan Kumar T. Heart disease prediction using effective machine learning techniques. International Journal of Recent Technology and Engineering. 2019 Jun;8(1):944-50.
7. Beyene C, Kamat P. Survey on prediction and analysis the occurrence of heart disease using data mining techniques. International Journal of Pure and Applied Mathematics. 2018 Jan;118(8):165- 74.
8. Soni J, Ansari U, Sharma D, Soni S. Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications. 2011 Mar 8;17(8):43-8.
9. Seckeler MD, Hoke TR. The worldwide epidemiology of acute rheumatic fever and rheumatic heart disease. Clinical epidemiology. 2011;3:67.
10. Mohan, Senthilkumar, Chandrasegar Thirumalai, and Gautam Srivastava, “Effective heart disease prediction using hybrid machine learning techniques” IEEE Access 7 (2019): 81542-81554.

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[if 424 not_equal=”Regular Issue”] Regular Issue[/if 424] Open Access Article

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Journal of Artificial Intelligence Research Advances

ISSN: 2395-6720

Editors Overview

joaira maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

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  1. Student, Student,Department of Computer Sciences, Global Academy of Technology, Department of Computer Sciences, Global Academy of Technology,Bangalore, Bangalore,India, India

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Abstract

nHeart disease which can also be referred to as the cardiovascular disease is one of the raising concerns in today’s world. It is one of the major health problems causing death among humans irrespective of the age group and therefore has made it necessary to look into different medical factors that are required to predict the same in advance using the collected historical datasets of various patients. Thus we have used various machine learning algorithms to predict the potential of person, to suffer from a heart disease with high precision and reliability so that we can admonish the patient in advance and take the required precautionary measures. In this paper an existing heart disease dataset accessible from the UCI Machine Learning Repository is used as the primary dataset. The experimental analysis and comparative study between different algorithms, helps in deciding the best suitable algorithm for the given problem statement by using results obtained which are very competitive and can be used for identification and treatment. The proposed work predicts the probability of heart condition and ranks the patient’s risk level based on different supervised learning algorithms such as K-Neighbors, AdaBoost, Gradient Boosting. The test results portrays that K- neighbors has the highest accuracy score of 91% when compared with other algorithms.n

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Keywords: Heart disease prediction, algorithms, machine learning, CNN, KNN

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Artificial Intelligence Research Advances(joaira)]

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1. Apurb Rajdhan, Milan Sai, Avi Agarwal, Dundigalla Ravi. Heart Disease Prediction using Machine Learning. International Journal of Engineering Research & Technology (IJERT).2020;9 (4)
2. Rindhe BU, Ahire N, Patil R, Gagare S, Darade M. Heart Disease Prediction Using Machine Learning. Heart Disease. 2021 May;5(1).
3. Jindal H, Agrawal S, Khera R, Jain R, Nagrath P. Heart disease prediction using machine learning algorithms. InIOP conference series: materials science and engineering 2021 (Vol. 1022, No. 1, p. 012072). IOP Publishing.
4. Shah D, Patel S, Bharti SK. Heart disease prediction using machine learning techniques. SN Computer Science. 2020 Nov;1(6):1-6.
5. Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P. Prediction of heart disease using a combination of machine learning and deep learning. Computational intelligence and neuroscience. 2021 Jul 1;2021.
6. Golande A, Pavan Kumar T. Heart disease prediction using effective machine learning techniques. International Journal of Recent Technology and Engineering. 2019 Jun;8(1):944-50.
7. Beyene C, Kamat P. Survey on prediction and analysis the occurrence of heart disease using data mining techniques. International Journal of Pure and Applied Mathematics. 2018 Jan;118(8):165- 74.
8. Soni J, Ansari U, Sharma D, Soni S. Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications. 2011 Mar 8;17(8):43-8.
9. Seckeler MD, Hoke TR. The worldwide epidemiology of acute rheumatic fever and rheumatic heart disease. Clinical epidemiology. 2011;3:67.
10. Mohan, Senthilkumar, Chandrasegar Thirumalai, and Gautam Srivastava, “Effective heart disease prediction using hybrid machine learning techniques” IEEE Access 7 (2019): 81542-81554.

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Volume 9
Issue 2
Received August 10, 2022
Accepted August 18, 2022
Published August 22, 2022

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JoAIRA

An Insight for Visually Impaired using AI Techniques

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u00a0Amal K.Santhosh, Natheera Beevi M, Lekshmi S.S.,

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nJanuary 27, 2023 at 5:54 am

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We know that the life of blind people is very risky. They always need an assistance or another person for helping them.In this project we introduce AI spectacles for blinds, which will help them to find what is happening in front of them and they will be able to find their own things without any help. In this proposed system,weareusing a real time object detection using YOLOv3 model.‘You only look once’, or YOLO, is one of the fastest object detection algorithms out there. Despite the fact that it is not, at this point,the most exact item identification
alculation, it is a generally excellent decision when you need continuous location, without loss of a lot of exactness. Aroute collaborator for the outwardly debilitated individuals utilizing object identification and text to discourse. The present study “An Insight for visually impaired using AI techniques”,suggeststhe spectacles consistingof an input camera and a part of headset. The working of the AI spectaclesis such that the objects and obstacles in front of blind person are captured by the camera,detected,recognized,convertedinto voice and then passedthroughheadsets.

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Volume :u00a0u00a08 | Issue :u00a0u00a02 | Received :u00a0u00a0May 11, 2021 | Accepted :u00a0u00a0May 20, 2021 | Published :u00a0u00a0June 20, 2021n[if 424 equals=”Regular Issue”][This article belongs to Journal of Artificial Intelligence Research Advances(joaira)] [/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue An Insight for Visually Impaired using AI Techniques under section in Journal of Artificial Intelligence Research Advances(joaira)] [/if 424]
Keywords YOLOv3, Google text to speech, Raspberry Pi, Darknet NN-Pack

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ISSN: 2395-6720

Editors Overview

joaira maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

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  1. Student, Professor,Department of Master of Computer Applications, Thangal Kunju Musaliar College of Engineering Kollam, Kerala Technological University, Department of Master of Computer Applications, Thangal Kunju Musaliar College of Engineering Kollam, Kerala Technological University,Kerala, Kerala,India, India

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nWe know that the life of blind people is very risky. They always need an assistance or another person for helping them.In this project we introduce AI spectacles for blinds, which will help them to find what is happening in front of them and they will be able to find their own things without any help. In this proposed system,weareusing a real time object detection using YOLOv3 model.‘You only look once’, or YOLO, is one of the fastest object detection algorithms out there. Despite the fact that it is not, at this point,the most exact item identification
alculation, it is a generally excellent decision when you need continuous location, without loss of a lot of exactness. Aroute collaborator for the outwardly debilitated individuals utilizing object identification and text to discourse. The present study “An Insight for visually impaired using AI techniques”,suggeststhe spectacles consistingof an input camera and a part of headset. The working of the AI spectaclesis such that the objects and obstacles in front of blind person are captured by the camera,detected,recognized,convertedinto voice and then passedthroughheadsets.n

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Keywords: YOLOv3, Google text to speech, Raspberry Pi, Darknet NN-Pack

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Artificial Intelligence Research Advances(joaira)]

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Volume 8
Issue 2
Received May 11, 2021
Accepted May 20, 2021
Published June 20, 2021

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