SIT740 - Research and Development in Information Technology: AI Papers

This assignment presents summaries of three research papers on machine learning, fulfilling the requirements of SIT740's Syntopic Reading task. The first paper explores machine learning methods for image processing and radiomics, highlighting its importance and challenges in various fields. The second paper examines multi-label active learning, focusing on the challenges faced by experts and the impact of outdated algorithms and prediction biases. The third paper investigates the application of the What-If Tool in machine learning models, addressing issues related to incorrect assumptions and data conversion leading to adverse outcomes. Each summary includes the aim, contribution, and key findings of the respective research papers, providing a comprehensive overview of the selected AI topic.

RUNNING HEAD: ARTIFICIAL INTELLIGENCE 0
MACHINE LEARNING

ARTIFICIAL INTELLIGENCE 1
Introduction
Artificial intelligence (AI) is the imitation of human intelligence procedure which is done
through machines and mainly from the computer systems (Wexler et al., 2019). In this report,
three research papers are selected on the topic machine learning. Therefore, in this research
paper, objective of the study, methodologies and outcomes of the study is discussed.
Machine (deep) learning methods for image processing and radiomics
Machine learning is considered the scientific study as well as the statistical model which is used
by the computer system for performing and conducting particular task. Therefore, the main aim
of the paper is to recognize the role and significance of machine learning (Hatt et al., 2019). The
study will also describe the challenges being faced through using machine learning. This
problem is selected by the researchers it is because the estimation describes that there are some
problems which are difficult to overcome. The secondary method is used to collect the
information and qualitative approach as well as experimentation research design is used to get
accurate information. The findings revealed that machine learning has its importance in different
field and also support financial specialist (Hatt et al., 2019). However, Researchers revealed that
issue also involve in it which include that machine learning focus more o Algorithms as well as
theories.
Multi-label active learning: key issues and a novel query strategy
The majority of the researchers revealed that most of the challenges faced by the experts through
adopting machine learning (Cherman et al., 2019). The aim of conducting the study is to reveal
the challenges which are suffered through the experts. The research is important because these
challenges reduce the efficiency of the data and sometimes gives bad prediction. The information
gathering is done through adopting primary method that is data is gathered through conducting
survey and by using quantitative approach. In this researchers adopt testing method for
experimentation and do analysis through correlation method (Cherman et al., 2019). The
outcomes of the research revealed that algorithms of machine learning become outdated through
increase in data. Researchers also demonstrate that revealing receiving bad prediction will result
in increasing biases.

ARTIFICIAL INTELLIGENCE 2
The What-If Tool: Interactive Probing of Machine Learning Models
The study is conducted by the researchers because not much of the research is performed to
investigate the problems of applying machine learning (Wexler et al., 2019). The purpose of the
study is to focus on finding issues in machine learning. The solution of the problem is received
through doing experimentation of the data by using correlation method. The data is gathering
through getting the opinions of the respondents by taking interview. The limitation of the
research is that it does not give the information about the issue in particular area. The outcomes
of the research describes that machine learning provide wrong assumptions to the person as well
as convert data and give bad outcomes (Wexler et al., 2019). Researchers describes that Uber is
also the example which face issue of using machine learning.

ARTIFICIAL INTELLIGENCE 3
References
Cherman, E.A., Papanikolaou, Y., Tsoumakas, G. & Monard, M.C., 2019. Multi-label active
learning: key issues and a novel query strategy. Evolving Systems , 10(1), pp.63-78.
Hatt, M., Parmar, C., Qi, J. & Naqa, I.E., 2019. Machine (deep) learning methods for image
processing and radiomics. IEEE Transactions on Radiation and Plasma, 3(2), pp.104-08.
Wexler, J. et al., 2019. The What-If Tool: Interactive Probing of Machine Learning Models.
IEEE transactions on visualization and computer graphics, 26(1), pp.56-65.