Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics
    (MDPI (Basel, Switzerland), 2022-12) Ramaswami G; Susnjak T; Mathrani A
    Learning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area of development. Prescriptive analytics is the next step towards increasing LA maturity, leading to proactive decision-making for improving students’ performance. This aims to provide data-driven suggestions to students who are at risk of non-completions or other sub-optimal outcomes. These suggestions are based on what-if modeling, which leverages machine learning to model what the minimal changes to the students’ behavioral and performance patterns would be required to realize a more desirable outcome. The results of the what-if modeling lead to precise suggestions that can be converted into evidence-based advice to students. All existing studies in the educational domain have, until now, predicted students’ performance and have not undertaken further steps that either explain the predictive decisions or explore the generation of prescriptive modeling. Our proposed method extends much of the work performed in this field to date. Firstly, we demonstrate the use of model explainability using anchors to provide reasons and reasoning behind predictive models to enable the transparency of predictive models. Secondly, we show how prescriptive analytics based on what-if counterfactuals can be used to automate student feedback through prescriptive analytics.
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    Device-Free Localization Using Privacy-Preserving Infrared Signatures Acquired from Thermopiles and Machine Learning
    (IEEE, 4/06/2021) Faulkner N; Alam F; Legg M; Demidenko S
    The development of an accurate passive localization system utilizing thermopile sensing and artificial intelligence is discussed in this paper. Several machine learning techniques are explored to create robust angular and radius coordinate models for a localization target with respect to thermopile sensors. These models are leveraged to develop a reconfigurable passive localization system that can use a varying number of thermopiles without the need for retraining. The proposed robust system achieves high localization accuracy (with the median error between 0.13 m and 0.2 m) while being trained using a single human subject and tested against multiple other subjects. It is shown that the proposed system does not experience any significant performance deterioration when localizing a subject at different ambient temperatures or with different configurations of the thermopile sensors placement.
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    A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks
    (IEEE, 20/01/2021) Aslam S; Alam F; Hasan SF; Rashid MA
    Clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters.
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    Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks
    (MDPI AG, 23/09/2022) Konings D; Alam F; Faulkner N; de Jong C
    In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.