Auditor Choice Prediction Model Using Corporate Governance and Ownership Attributes: Machine Learning Approach

Rahman, Rahayu Abdul; Masrom, Suraya; Zakaria, Nor Balkish; Halid, Sunarti

doi:10.46338/ijetae0721_11

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…By using the method, we organized limited datasets. e data were stored with city names, which make the data prescribed with coordinating PV solar cells [29,30].…”

Section: Data Processing and Methodologymentioning

confidence: 99%

Machine Learning Approach on Time Series for PV-Solar Energy

Sivakumar

Neeraja²,

Rani³

et al. 2022

Advances in Materials Science and Engineering

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Solar energy is the kind of alternative energy that a photovoltaic (PV) system may generate. The PV system depends on the environmental conditions present at the moment, such as the humidity level, surface temperature of the PV system, and wind speed. These are the most important characteristics to consider when calculating the amount of electricity generated by the PV system. Therefore, the calculation of power production is not a simple task. In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. We get preliminary information from four distinct locations around the country. We arrived at this conclusion by using the ANN and regression modeling approaches for best to create the most significant overall performance across all cities.

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“…By using the method, we organized limited datasets. e data were stored with city names, which make the data prescribed with coordinating PV solar cells [29,30].…”

Section: Data Processing and Methodologymentioning

confidence: 99%

Machine Learning Approach on Time Series for PV-Solar Energy

Sivakumar

Neeraja²,

Rani³

et al. 2022

Advances in Materials Science and Engineering

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“…(2) All the aggregation nodes are connected to the BS directly, i.e., using single-hop (5) Sensor nodes and BS are stationary (6) The heterogenous nature of WSN is defined in terms of node energy (7) BS is supposed to be equipped with a battery source and hence it is not energy limiting like the other deployed nodes…”

Section: Assumptionsmentioning

confidence: 99%

“…During its voyage, it goes through the environment from which it originated. It accelerates and some dirt is eliminated [7,8]. The method employs several iterations in which water droplets attempt to uncover the optimum path from source to destination on a bed of environment particles.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Water Drops Algorithm-Based Aggregation in Heterogeneous Wireless Sensor Network

et al. 2022

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This paper provides a novel implementation of the intelligent water drops (IWD) method for resolving data aggregation issues in heterogeneous wireless sensor networks (WSN). When the aggregating node is utilized to transmit the data to the base station, the research attempts to show that the traffic situations of WSN may be modified appropriately by parameter tuning and algorithm modification. IWD is used to generate an optimum data aggregation tree in WSN as one of its applications. IWD assumes that all nodes in the environment are identical, resulting in identical parameter updates for all nodes. In practical scenarios, however, diverse nodes with variable beginning energy, communication range, and sensing range characteristics are deployed. In order to replicate the influence of heterogeneity in the environment, improved IID (IIWD) is offered as an enhancement to the original IID. The suggested enhancement is appropriate for scenarios in which the aggregation node is utilized to transmit data to the base station in heterogeneous configurations. In terms of residual energy, dead nodes, payload, and network lifespan, a series of simulation results demonstrates that the proposed IIWD significantly improves the accuracy and effectiveness of the IWD method in comparison.

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“…Unlike prior studies [7]- [11] that employed traditional statistical method, this study develops videobased learning usage based on task-technology attributes with machine learning prediction technique [14]. Prior research highlights the effectiveness and accuracy of machine learning approaches on prediction and classification of financial and accounting studies including prediction of firm financial distress [15], tax aversion and avoidance [16], [17] and auditor choice [18]. However, to date there is very limited studies that employ machine learning prediction and classification on accounting education.…”

Section: Introductionmentioning

confidence: 99%

Machine learning with task-technology fit theory factors for predicting students’ adoption in video-based learning

et al. 2023

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Nowadays, various innovative educational and instructional tools have been created to deliver learning material including video content. One of the important issues with video-based learning is to devise effective teaching strategies to ensure higher level of learning can be achieved by the students. Getting insight and predicting the students’ video-based learning adoption will help the educators. Thus, this study aims to examine the potential of using machine learning prediction models on video-based learning adoption in higher education institutions. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT), and support vector machine (SVM). The performance of each machine learning algorithm in predicting the students’ learning adoption with video-based learning has been observed based on the attributes of task-technology fit theory. The findings indicated that the task-technology fit is useful in helping the machine learning algorithm to achieve high accuracy in the prediction of video-based learning adoption. The GBT is the best outperforming algorithm, followed with RF and SVM. This paper presents a fundamental research framework useful for helping educators and researchers to enhance student interest and retention on video-based learning.

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Auditor Choice Prediction Model Using Corporate Governance and Ownership Attributes: Machine Learning Approach

Cited by 13 publications

References 18 publications

Machine Learning Approach on Time Series for PV-Solar Energy

Machine Learning Approach on Time Series for PV-Solar Energy

Intelligent Water Drops Algorithm-Based Aggregation in Heterogeneous Wireless Sensor Network

Machine learning with task-technology fit theory factors for predicting students’ adoption in video-based learning

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