Comparative Techniques Using Hierarchical Modelling and Machine Learning for Procedure Recognition in Smart Hospitals

Authors

  • Shaheena Noor Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan https://orcid.org/0000-0002-3130-5945
  • Muhammad Aamir Department of Telecommunication Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan
  • Najma Ismat Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan https://orcid.org/0000-0002-0712-6904
  • Muhammad Imran Saleem Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

DOI:

https://doi.org/10.13052/jicts2245-800X.1023

Keywords:

Procedure recognition • Inside-out Vision • Machine Learning • Artificial Neural Network, 6G-enabled applications

Abstract

6G is one of the key cornerstone elements of the futuristic smart system setup – the others being cloud computing, big data, wearable devices and Artificial Intelligence. Also, smart offices and homes have become even more popular than before, because of the advancement in computer vision and Machine Learning (ML) technologies. Recognition of human actions and situations are fundamental components of such systems, especially in complex environments like healthcare, for example at the dentist clinic, where we need cues such as eye movement to distinguish procedures being undertaken. In this work, we compare models based on hierarchical modelling and machine learning to identify the dental procedure. We used the objects seen while following the eye trajectories and focussed on elements including material used for treatment, equipment involved and the teeth conditions i.e. symptoms. Our experiments showed that using Artificial Neural Network (ANN) increased the accuracy of prediction compared to hierarchical modelling. Our experiments show an improvement in accuracy for each of the constituent parameters i.e., symptom (ANN: 95.58% vs. Hierarchical: 45.68%), material (ANN: 86.32% vs. Hierarchical: 45.18%) and equipment (ANN: 92.65% vs. Hierarchical: 59.39%).

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Author Biographies

Shaheena Noor, Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Shaheena Noor graduated from Hamdard University with a BS degree in Computer Engineering in 2003, the MS degree in computer systems from NED University of Engineering & Technology in 2007, and the PhD degree in Computer Engineering from Hamdard University in 2019, respectively. She is employed as an Assistant Professor at the Department of Computer Engineering, Sir Syed University of Engineering & Technology (SSUET), Karachi, Pakistan. Object recognition, activity recognition and prediction are among her research interest.

Muhammad Aamir, Department of Telecommunication Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Muhammad Aamir was born on 3 July, 1976 in Karachi Pakistan. In 1998, he received BS Electronic Engineering degree and in 2002 his MS degree in Electronic Engineering (with specialization in Telecommunication). He accomplished his PhD in Electronic Engineering from Mehran University of Engineering & Technology. During his PhD studies, he accomplished his research work at the University of Malaga under Erasmus Mundus Scholarship. He has authored and co-authored around 50 research papers and book chapters published in various journals, books and conferences of international repute. For the past 12 Years he is a life member of Pakistan Engineering Council and professional member of IEEE. He was awarded with a grant by the Ministry of Education Spain to teach at the University of Malaga which he successfully availed in May 2012. He is also Member of two separate National Curriculum Revision Committees constituted by Higher Education Commission (HEC) for revision of Electronic Engineering Curriculum and Telecommunication Engineering Curriculum at the National Level. He was served as guest editor for special issue of Springer’s Journal with title “Wireless Personal Communication” which had published in November 2016. He is also HEC approved supervisor for Pakistani PhD candidates. He is currently employed as a Professor and Associate Dean in the Faculty of Electrical & Computer Engineering at SSUET. Moreover, he is also Editor-in-Chief of Sir Syed University Research Journal of Engineering & Technology which is HEC-Recognized Research Journal which is published bi-annually.

Najma Ismat, Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Najma Ismat is an alumni of SSUET. She has received her post graduate degree in 2018. Dr. Ismat did her undergraduate and graduate degrees in year 1998 and 2002 respectively. Her current research interests are mobility, reliability, connectivity and coverage issues in Underater Sensor Networks, Wireless Sensor Networks, and IoT.

Muhammad Imran Saleem, Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Engr. Muhammad Imran Saleem is currently working as an Assistant Professor in the Department of Computer Engineering at SSUET. He is associated with the university since January 2001. He is doing Ph.D. in Telecommunication Engineering from University of Malaga Spain. He did Masters (M.S) in Computer Engineering with specialization in Computer Network from SSUET. His thesis topic was Differentiated and Integrated services of IP packet. He did Bachelor (B.S) in Electronic Engineering from SSUET.

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Published

2022-05-07

Issue

Section

6G Enabling Technologies – Innovation 6G