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Research Publications (Management Sciences)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/217

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Author: search.filters.author.Lourens, Melanie ElizabethSubject: search.filters.subject.Machine learning

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    Strategic management accounting practices between developed and emerging economies using machine learning
    (2022-11-11) Almahairah, Mohammad Salameh; Saroha, Vinod Kumar; Asokan, Anju; Umaeswari, P.; Khan, Javed Akhtar; Lourens, Melanie Elizabeth
    Data's function is changing dramatically, but new technologies like machine learning (ML) is also transforming how we can handle and use the data (AI). Nearly astounding are the changes, their pace and scope, and how they affect practically every facet of daily life, including management accounting of course. In this purview, the term "data" refers to business data in its broadest definition. Computers can now learn from experience much like humans and decision-makers do thanks to machine learning (ML). ML and AI for the management accountants have occasionally been considered in the previous 5 to 10 years, despite the fact that these ideas have been applied for a long time in other company disciplines like banking and logistics. Due to the critical role that management accountants play in an organization's success; this study demonstrates the need for greater research on numerous developing technologies in a timely manner. To make more accurate forecasts and enhance reporting and decisionmaking, many firms must use business intelligence and analytics technologies, machine learning algorithms, and Journal of Pharmaceutical Negative Results ¦ Volume 13 ¦ Special Issue 9 ¦ 2022 6318 the Internet of Things. This study explores the effects of new technology on management accountants' abilities to lead business units to success in international marketplaces. It does so by examining, describing, analyzing, and summarizing some of that research.
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    A review of physiological signal processing via Machine Learning (ML) for personal stress detection
    (IEEE, 2022-04-28) Lourens, Melanie Elizabeth; Beram, Shehab Mohamed; Borah, Bidyut Bikash; Dube, Anand Prakash; Deka, Aniruddha; Tripathi, Vikas
    Personal stress is maintained and measured by Machine learning. The device which is wearable has been used for the monitoring of personal self stress and data collection. In this research, it has been talked about the factors by which the physiological signal of the stress has been assessed. On the other hand, different type of technology has been used for the detection of the personal stress such as Electrocardiography (ECG) and many other devices. The observation and difficulties has been seen in this research by using this device and the technology. Stress disorder or ailment is one of the most common ailments in all individuals around the world. Stress and anxiety can greatly influence the life, emotion, behavioural pattern and thinking attributes of individuals. It is important to address this issue sooner or later. Psychological signal processing through machine learning effectively assists to detect the stress disorder at an early stage. The general system often considers some variables to detect stress. They are electrocardiogram, galvanic response, heart rate, respiration and many other elements. The ML tend to use algorithms to compare and contrast data to fetch effective e results. The paper has also carried out a statistical analysis based on three variables to fetch a proper result that provided the study group to comprehend a better understanding of the scenario. The researchers have taken the 'percentage of stress rate' cases' are considered independent variables whereas 'usage of a machine learning system' is considered a dependant variable. The study group has fetched and collected numerous data related to these three variables to get a better understanding.