Predicting cardiac autonomic neuropathy category for diabetic data with missing values
- Abawajy, Jemal, Kelarev, Andrei, Chowdhury, Morshed, Stranieri, Andrew, Jelinek, Herbert
- Authors: Abawajy, Jemal , Kelarev, Andrei , Chowdhury, Morshed , Stranieri, Andrew , Jelinek, Herbert
- Date: 2013
- Type: Text , Journal article
- Relation: Computers in Biology and Medicine Vol. 43, no. 10 (2013), p. 1328-1333
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- Description: Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features. © 2013 Elsevier Ltd.
- Description: C1
- Authors: Abawajy, Jemal , Kelarev, Andrei , Chowdhury, Morshed , Stranieri, Andrew , Jelinek, Herbert
- Date: 2013
- Type: Text , Journal article
- Relation: Computers in Biology and Medicine Vol. 43, no. 10 (2013), p. 1328-1333
- Full Text:
- Reviewed:
- Description: Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features. © 2013 Elsevier Ltd.
- Description: C1
Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing features
- Abawajy, Jemal, Kelarev, Andrei, Stranieri, Andrew, Jelinek, Herbert
- Authors: Abawajy, Jemal , Kelarev, Andrei , Stranieri, Andrew , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This article is devoted to an empirical investigation of performance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using sub-sets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.
- Authors: Abawajy, Jemal , Kelarev, Andrei , Stranieri, Andrew , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This article is devoted to an empirical investigation of performance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using sub-sets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.
ECG reduction for wearable sensor
- Allami, Ragheed, Stranieri, Andrew, Balasubramanian, Venki, Jelinek, Herbert
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS); Naples, Italy; 28th November-1st December 2016 p. 520-525
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- Description: The transmission, storage and analysis of electrocardiogram (ECG) data in real-time is essential for remote patient monitoring with wearable ECG devices and mobile ECG contexts. However, this remains a challenge to achieve within the processing power and the storage capacity of mobile devices. ECG reduction algorithms have an important role to play in reducing the processing requirements for mobile devices, however many existing ECG reduction and compression algorithms are computationally expensive to execute in mobile devices and have not been designed for real-time computation and incremental data arrival. In this paper, we describe a computationally naive, yet effective, algorithm that achieves high ECG reduction rates while maintaining key diagnostic features including PR, QRS, ST, QT and RR intervals. While reduction does not enable ECG waves to be reproduced, the ability to transmit key indicators (diagnostic features) using minimal computational resources, is particularly useful in mobile health contexts involving power constrained sensors and devices. Results of the proposed reduction algorithm indicate that the proposed algorithm outperforms other ECG reduction algorithms at a reduction/compression ratio (CR) of 5:1. If power or processing capacity is low, the algorithm can readily switch to a compression ratio of up to 10: 1 while still maintaining an error rate below 10%.
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS); Naples, Italy; 28th November-1st December 2016 p. 520-525
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- Reviewed:
- Description: The transmission, storage and analysis of electrocardiogram (ECG) data in real-time is essential for remote patient monitoring with wearable ECG devices and mobile ECG contexts. However, this remains a challenge to achieve within the processing power and the storage capacity of mobile devices. ECG reduction algorithms have an important role to play in reducing the processing requirements for mobile devices, however many existing ECG reduction and compression algorithms are computationally expensive to execute in mobile devices and have not been designed for real-time computation and incremental data arrival. In this paper, we describe a computationally naive, yet effective, algorithm that achieves high ECG reduction rates while maintaining key diagnostic features including PR, QRS, ST, QT and RR intervals. While reduction does not enable ECG waves to be reproduced, the ability to transmit key indicators (diagnostic features) using minimal computational resources, is particularly useful in mobile health contexts involving power constrained sensors and devices. Results of the proposed reduction algorithm indicate that the proposed algorithm outperforms other ECG reduction algorithms at a reduction/compression ratio (CR) of 5:1. If power or processing capacity is low, the algorithm can readily switch to a compression ratio of up to 10: 1 while still maintaining an error rate below 10%.
A count data model for heart rate variability forecasting and premature ventricular contraction detection
- Allami, Ragheed, Stranieri, Andrew, Balasubramanian, Venki, Jelinek, Herbert
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Journal article
- Relation: Signal Image and Video Processing Vol. 11, no. 8 (2017), p. 1427-1435
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- Description: Heart rate variability (HRV) measures including the standard deviation of inter-beat variations (SDNN) require at least 5 min of ECG recordings to accurately measure HRV. In this paper, we predict, using counts data derived from a 3-min ECG recording, the 5-min SDNN and also detect premature ventricular contraction (PVC) beats with a high degree of accuracy. The approach uses counts data combined with a Poisson-generated function that requires minimal computational resources and is well suited to remote patient monitoring with wearable sensors that have limited power, storage and processing capacity. The ease of use and accuracy of the algorithm provide opportunity for accurate assessment of HRV and reduce the time taken to review patients in real time. The PVC beat detection is implemented using the same count data model together with knowledge-based rules derived from clinical knowledge.
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Journal article
- Relation: Signal Image and Video Processing Vol. 11, no. 8 (2017), p. 1427-1435
- Full Text:
- Reviewed:
- Description: Heart rate variability (HRV) measures including the standard deviation of inter-beat variations (SDNN) require at least 5 min of ECG recordings to accurately measure HRV. In this paper, we predict, using counts data derived from a 3-min ECG recording, the 5-min SDNN and also detect premature ventricular contraction (PVC) beats with a high degree of accuracy. The approach uses counts data combined with a Poisson-generated function that requires minimal computational resources and is well suited to remote patient monitoring with wearable sensors that have limited power, storage and processing capacity. The ease of use and accuracy of the algorithm provide opportunity for accurate assessment of HRV and reduce the time taken to review patients in real time. The PVC beat detection is implemented using the same count data model together with knowledge-based rules derived from clinical knowledge.
Missing health data pattern matching technique for continuous remote patient monitoring
- Arora, Teena, Balasubramanian, Venki, Stranieri, Andrew
- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew
- Date: 2023
- Type: Text , Conference paper
- Relation: 20th International Conference on Smart Living and Public Health, ICOST 2023, Wonju, Korea, 7-8 July 2023, Digital Health Transformation, Smart Ageing, and Managing Disability, 20th International Conference, ICOST 2023, Wonju, South Korea, July 7–8, 2023, Proceedings Vol. 14237 LNCS, p. 130-143
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- Description: Remote patient monitoring (RPM) has been gaining popularity recently. However, health data acquisition is a significant challenge associated with patient monitoring. In continuous RPM, health data acquisition may miss health data during transmission. Missing data compromises the quality and reliability of patient risk assessment. Several studies suggested techniques for analyzing missing data; however, many are unsuitable for RPM. These techniques neglect the variability of missing data and provide biased results with imputation. Therefore, a holistic approach must consider the correlation and variability of the various vitals and avoid biased imputation. This paper proposes a coherent computation pattern-matching technique to identify and predict missing data patterns. The performance of the proposed approach is evaluated using data collected from a field trial. Results show that the technique can effectively identify and predict missing patterns. © 2023, The Author(s).
- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew
- Date: 2023
- Type: Text , Conference paper
- Relation: 20th International Conference on Smart Living and Public Health, ICOST 2023, Wonju, Korea, 7-8 July 2023, Digital Health Transformation, Smart Ageing, and Managing Disability, 20th International Conference, ICOST 2023, Wonju, South Korea, July 7–8, 2023, Proceedings Vol. 14237 LNCS, p. 130-143
- Full Text:
- Reviewed:
- Description: Remote patient monitoring (RPM) has been gaining popularity recently. However, health data acquisition is a significant challenge associated with patient monitoring. In continuous RPM, health data acquisition may miss health data during transmission. Missing data compromises the quality and reliability of patient risk assessment. Several studies suggested techniques for analyzing missing data; however, many are unsuitable for RPM. These techniques neglect the variability of missing data and provide biased results with imputation. Therefore, a holistic approach must consider the correlation and variability of the various vitals and avoid biased imputation. This paper proposes a coherent computation pattern-matching technique to identify and predict missing data patterns. The performance of the proposed approach is evaluated using data collected from a field trial. Results show that the technique can effectively identify and predict missing patterns. © 2023, The Author(s).
Classification of methods to reduce clinical alarm signals for remote patient monitoring : a critical review
- Arora, Teena, Balasubramanian, Venki, Stranieri, Andrew, Shenhan, Mai, Buyya, Rajkumar, Islam, Sardar
- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew , Shenhan, Mai , Buyya, Rajkumar , Islam, Sardar
- Date: 2023
- Type: Text , Book chapter
- Relation: Cloud Computing in Medical Imaging Chapter 10 p. 173-194
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- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew , Shenhan, Mai , Buyya, Rajkumar , Islam, Sardar
- Date: 2023
- Type: Text , Book chapter
- Relation: Cloud Computing in Medical Imaging Chapter 10 p. 173-194
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Feature selection using misclassification counts
- Bagirov, Adil, Yatsko, Andrew, Stranieri, Andrew
- Authors: Bagirov, Adil , Yatsko, Andrew , Stranieri, Andrew
- Date: 2011
- Type: Conference proceedings , Unpublished work
- Relation: Proceedings of the 9th Australasian Data Mining Conference (AusDM 2011), 51-62. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 121.
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- Description: Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and instance acquisition effort, considering all the data attributes accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance and redundancy, what ranking does not immediately decide. Additionally, feature ranking methods from different independent sources are called in for the direct comparison.
- Description: Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and the data acquisition effort, considering all data components being accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree, to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance, what ranking does not immediately decide. Additionally, feature ranking methods available from different independent sources are called in for direct comparison.
- Authors: Bagirov, Adil , Yatsko, Andrew , Stranieri, Andrew
- Date: 2011
- Type: Conference proceedings , Unpublished work
- Relation: Proceedings of the 9th Australasian Data Mining Conference (AusDM 2011), 51-62. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 121.
- Full Text:
- Description: Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and instance acquisition effort, considering all the data attributes accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance and redundancy, what ranking does not immediately decide. Additionally, feature ranking methods from different independent sources are called in for the direct comparison.
- Description: Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and the data acquisition effort, considering all data components being accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree, to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance, what ranking does not immediately decide. Additionally, feature ranking methods available from different independent sources are called in for direct comparison.
A global optimisation approach to classification in medical diagnosis and prognosis
- Bagirov, Adil, Rubinov, Alex, Yearwood, John, Stranieri, Andrew
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
A scalable cloud Platform for Active healthcare monitoring applications
- Balasubramanian, Venki, Stranieri, Andrew
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2015
- Type: Text , Conference paper
- Relation: 2014 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2014; Melbourne, Australia; 10th-12th December 2014 p. 93-98
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- Reviewed:
- Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a real-time-like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on monitoring requirements from the health care providers, and are aligned with scalable economic models. © 2014 IEEE.
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2015
- Type: Text , Conference paper
- Relation: 2014 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2014; Melbourne, Australia; 10th-12th December 2014 p. 93-98
- Full Text:
- Reviewed:
- Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a real-time-like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on monitoring requirements from the health care providers, and are aligned with scalable economic models. © 2014 IEEE.
AppA : Assistive patient monitoring cloud platform for active healthcare applications
- Balasubramanian, Venki, Stranieri, Andrew, Kaur, Ranjit
- Authors: Balasubramanian, Venki , Stranieri, Andrew , Kaur, Ranjit
- Date: 2015
- Type: Text , Conference paper
- Relation: 9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015; Bali, Indonesia; 8th-10th January 2015
- Full Text:
- Reviewed:
- Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a realtime- like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on the monitoring requirements from the health care providers, and is aligned with scalable economic models.
- Authors: Balasubramanian, Venki , Stranieri, Andrew , Kaur, Ranjit
- Date: 2015
- Type: Text , Conference paper
- Relation: 9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015; Bali, Indonesia; 8th-10th January 2015
- Full Text:
- Reviewed:
- Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a realtime- like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on the monitoring requirements from the health care providers, and is aligned with scalable economic models.
Performance evaluation of the dependable properties of a body area wireless sensor network
- Balasubramanian, Venki, Stranieri, Andrew
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2014
- Type: Text , Conference paper
- Relation: 2014 International Conference on Reliabilty, Optimization, & Information Technology (Icroit 2014); Faridabad, India; 6th-8th February 2014 p. 229-234
- Full Text:
- Reviewed:
- Description: Body Area Wireless Sensor Networks (BAWSNs) are self-organizing networks capable of monitoring health intrinsic data of a patient. BAWSNs extended with a health care application can be used to perform medical assessments by remotely monitoring patients. The accuracy of medical assessments fundamentally depends on the correctness of the data received from the BAWSN. However, data errors may arise at the sensor or during transmission across the wireless sensor network. Therefore, it is imperative to measure the health intrinsic data of a patient precisely. The formulated measurable properties in our work precisely measure the performance of the BAWSN in a remote Healthcare Monitoring Application (HMA). In this paper, we collated various performances using the measurable properties in our real-time test-bed and presented a comprehensive evaluation of these properties in a BAWSN.
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2014
- Type: Text , Conference paper
- Relation: 2014 International Conference on Reliabilty, Optimization, & Information Technology (Icroit 2014); Faridabad, India; 6th-8th February 2014 p. 229-234
- Full Text:
- Reviewed:
- Description: Body Area Wireless Sensor Networks (BAWSNs) are self-organizing networks capable of monitoring health intrinsic data of a patient. BAWSNs extended with a health care application can be used to perform medical assessments by remotely monitoring patients. The accuracy of medical assessments fundamentally depends on the correctness of the data received from the BAWSN. However, data errors may arise at the sensor or during transmission across the wireless sensor network. Therefore, it is imperative to measure the health intrinsic data of a patient precisely. The formulated measurable properties in our work precisely measure the performance of the BAWSN in a remote Healthcare Monitoring Application (HMA). In this paper, we collated various performances using the measurable properties in our real-time test-bed and presented a comprehensive evaluation of these properties in a BAWSN.
Online dispute resolution in mediating EHR disputes : a case study on the impact of emotional intelligence
- Bellucci, Emilia, Venkatraman, Sitalakshmi, Stranieri, Andrew
- Authors: Bellucci, Emilia , Venkatraman, Sitalakshmi , Stranieri, Andrew
- Date: 2020
- Type: Text , Journal article
- Relation: Behaviour and Information Technology Vol. 39, no. 10 (2020), p. 1124-1139
- Full Text:
- Reviewed:
- Description: An Electronic Health Record (EHR) is an individual’s record of all health events that enables critical information to be documented and shared electronically amongst health care providers and patients. The introduction of an EHR, particularly a patient-accessible EHR, can be expected to lead to an escalation of enquiries, complaints and ultimately, disputes. Prevailing opinion is that Online Dispute Resolution (ODR) systems can help with the mediation of certain types of disputes electronically, particularly systems which deploy Artificial Intelligence (AI) to reduce the need for a human mediator. However, disputes regarding health tend to invoke emotional responses from patients that may conceivably impact ODR efficacy. This raises an interesting question on the influence of emotional intelligence (EI) in the process of mediation. Using a phenomenological research methodology simulating doctor–patient disputes mediated with an AI Smart ODR system in place of a human mediator, we found an association between EI and the propensity for a participant to change their previously asserted claims. Our results indicate participants with lower EI tend to prolong resolution compared to those with higher EI. Future research include trialling larger scale ODR systems for specific cohorts of patients in the area of health related dispute resolution are advanced. © 2019 Informa UK Limited, trading as Taylor & Francis Group.
- Authors: Bellucci, Emilia , Venkatraman, Sitalakshmi , Stranieri, Andrew
- Date: 2020
- Type: Text , Journal article
- Relation: Behaviour and Information Technology Vol. 39, no. 10 (2020), p. 1124-1139
- Full Text:
- Reviewed:
- Description: An Electronic Health Record (EHR) is an individual’s record of all health events that enables critical information to be documented and shared electronically amongst health care providers and patients. The introduction of an EHR, particularly a patient-accessible EHR, can be expected to lead to an escalation of enquiries, complaints and ultimately, disputes. Prevailing opinion is that Online Dispute Resolution (ODR) systems can help with the mediation of certain types of disputes electronically, particularly systems which deploy Artificial Intelligence (AI) to reduce the need for a human mediator. However, disputes regarding health tend to invoke emotional responses from patients that may conceivably impact ODR efficacy. This raises an interesting question on the influence of emotional intelligence (EI) in the process of mediation. Using a phenomenological research methodology simulating doctor–patient disputes mediated with an AI Smart ODR system in place of a human mediator, we found an association between EI and the propensity for a participant to change their previously asserted claims. Our results indicate participants with lower EI tend to prolong resolution compared to those with higher EI. Future research include trialling larger scale ODR systems for specific cohorts of patients in the area of health related dispute resolution are advanced. © 2019 Informa UK Limited, trading as Taylor & Francis Group.
Multivariate data-driven decision guidance for clinical scientists
- Burstein, Frada, De Silva, Daswin, Jelinek, Herbert, Stranieri, Andrew
- Authors: Burstein, Frada , De Silva, Daswin , Jelinek, Herbert , Stranieri, Andrew
- Date: 2013
- Type: Text , Conference paper
- Relation: 29th International Conference on Data Engineering Workshops, ICDEW 2013; Proceedings - International Conference on Data Engineering p. 193-199
- Full Text:
- Reviewed:
- Description: Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards utilising better information management for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges created for effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. A Data-driven Decision Guidance Management System (DD-DGMS) architecture can encompass solutions into a single closed-loop integrated platform to empower clinical scientists to seamlessly explore a multivariate data space in search of novel patterns and correlations to inform their research and practice. The paper describes the components of such an architecture, which includes a robust data warehouse as an infrastructure for comprehensive clinical knowledge management. The proposed DD-DGMS architecture incorporates the dynamic dimensional data model as its elemental core. Given the heterogeneous nature of clinical contexts and corresponding data, the dimensional data model presents itself as an adaptive model that facilitates knowledge discovery, distribution and application, which is essential for clinical decision support. The paper reports on a trial of the DD-DGMS system prototype conducted on diabetes screening data which further establishes the relevance of the proposed architecture to a clinical context.
- Description: E1
- Authors: Burstein, Frada , De Silva, Daswin , Jelinek, Herbert , Stranieri, Andrew
- Date: 2013
- Type: Text , Conference paper
- Relation: 29th International Conference on Data Engineering Workshops, ICDEW 2013; Proceedings - International Conference on Data Engineering p. 193-199
- Full Text:
- Reviewed:
- Description: Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards utilising better information management for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges created for effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. A Data-driven Decision Guidance Management System (DD-DGMS) architecture can encompass solutions into a single closed-loop integrated platform to empower clinical scientists to seamlessly explore a multivariate data space in search of novel patterns and correlations to inform their research and practice. The paper describes the components of such an architecture, which includes a robust data warehouse as an infrastructure for comprehensive clinical knowledge management. The proposed DD-DGMS architecture incorporates the dynamic dimensional data model as its elemental core. Given the heterogeneous nature of clinical contexts and corresponding data, the dimensional data model presents itself as an adaptive model that facilitates knowledge discovery, distribution and application, which is essential for clinical decision support. The paper reports on a trial of the DD-DGMS system prototype conducted on diabetes screening data which further establishes the relevance of the proposed architecture to a clinical context.
- Description: E1
Addressing the complexities of big data analytics in healthcare : The diabetes screening case
- De Silva, Daswin, Burstein, Frada, Jelinek, Herbert, Stranieri, Andrew
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Full Text:
- Reviewed:
- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Full Text:
- Reviewed:
- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
Are ERP simulation games assisting students to be job-ready? An Australian universities’ perspective
- Faisal, Nadia, Chadhar, Mehmood, Goriss-Hunter, Anitra, Stranieri, Andrew
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2019
- Type: Text , Conference paper
- Relation: 30th Australiasian Conference on Information Systems (ACIS), 9-11 December 2019, Perth, Australia
- Full Text:
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- Description: Deep and rapid changes in digital enterprise technology exceed the ability of traditional teaching methods to prepare students for challenges encountered in modern enterprises. Researchers proposed different pedagogical approaches to teach ERP (Enterprise Resource Planning) concepts such as ERPsim games to enhance students’ learning and job-readiness. Although the ERPsim studies verified the role of these games in enhancing students’ learning, whether these games contribute to student’s job readiness still needs to be explored. Using the mixed-method approach, this research-in-progress is designed to fill this gap by investigating the role of ERPsim game in increasing skills, learning levels, and job-readiness among university students in Australia. The findings from this study can contribute to the improvement of ERP pedagogical techniques. In addition, this research-in-progress will provide a concrete mapping to align learning outcomes/skills with ICT industry competencies standards as defined in SFIA (Skills framework for Information Age) and AQF (Australian Qualifications Framework).
Are ERP simulation games assisting students to be job-ready? An Australian universities’ perspective
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2019
- Type: Text , Conference paper
- Relation: 30th Australiasian Conference on Information Systems (ACIS), 9-11 December 2019, Perth, Australia
- Full Text:
- Reviewed:
- Description: Deep and rapid changes in digital enterprise technology exceed the ability of traditional teaching methods to prepare students for challenges encountered in modern enterprises. Researchers proposed different pedagogical approaches to teach ERP (Enterprise Resource Planning) concepts such as ERPsim games to enhance students’ learning and job-readiness. Although the ERPsim studies verified the role of these games in enhancing students’ learning, whether these games contribute to student’s job readiness still needs to be explored. Using the mixed-method approach, this research-in-progress is designed to fill this gap by investigating the role of ERPsim game in increasing skills, learning levels, and job-readiness among university students in Australia. The findings from this study can contribute to the improvement of ERP pedagogical techniques. In addition, this research-in-progress will provide a concrete mapping to align learning outcomes/skills with ICT industry competencies standards as defined in SFIA (Skills framework for Information Age) and AQF (Australian Qualifications Framework).
Business simulation games in higher education : a systematic review of empirical research
- Faisal, Nadia, Chadhar, Mehmood, Goriss-Hunter, Anitra, Stranieri, Andrew
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2022
- Type: Text , Journal article , Review
- Relation: Human Behavior and Emerging Technologies Vol. 2022, no. (2022), p.
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- Description: Over the last few years, business simulation games (BSGs) in higher education have attracted attention. BSGs tend to actively engage students with course material, promoting higher engagement and motivation and enabling learning outcomes. Increasingly, researchers are trying to explore the full potential of these games with an upsurge of research in the BSG field in recent years. There is a need to understand the current state of research and future research opportunities; however, there is a lack of recent systematic literature reviews in BSG literature. This study addresses this gap by systematically compiling online empirical research from January 2015 to April 2022. We followed PRISMA guidelines to identify fifty-seven (57) papers reporting empirical evidence of the effectiveness of BSGs in teaching and learning. Findings showed that BSGs improve learning outcomes such as knowledge acquisition, cognitive and interactive skills, and behaviour. The review also summarises different issues concerning the integration of BSGs into the curriculum, learning theories used in the selected studies, and assessment methods used to evaluate student achievement in learning outcomes. The findings of this review summarise the current research activities and indicate existing deficiencies and potential research directions that can be used as the basis for future research into the use of BSGs in higher education. © 2022 Nadia Faisal et al.
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2022
- Type: Text , Journal article , Review
- Relation: Human Behavior and Emerging Technologies Vol. 2022, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Over the last few years, business simulation games (BSGs) in higher education have attracted attention. BSGs tend to actively engage students with course material, promoting higher engagement and motivation and enabling learning outcomes. Increasingly, researchers are trying to explore the full potential of these games with an upsurge of research in the BSG field in recent years. There is a need to understand the current state of research and future research opportunities; however, there is a lack of recent systematic literature reviews in BSG literature. This study addresses this gap by systematically compiling online empirical research from January 2015 to April 2022. We followed PRISMA guidelines to identify fifty-seven (57) papers reporting empirical evidence of the effectiveness of BSGs in teaching and learning. Findings showed that BSGs improve learning outcomes such as knowledge acquisition, cognitive and interactive skills, and behaviour. The review also summarises different issues concerning the integration of BSGs into the curriculum, learning theories used in the selected studies, and assessment methods used to evaluate student achievement in learning outcomes. The findings of this review summarise the current research activities and indicate existing deficiencies and potential research directions that can be used as the basis for future research into the use of BSGs in higher education. © 2022 Nadia Faisal et al.
A systematic literature review on the evaluation of business simulation games using PRISMA
- Faisal, Nadia, Chadhar, Mehmood, Goriss-Hunter, Anitra, Stranieri, Andrew
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2022
- Type: Text , Conference paper
- Relation: 33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022, Melbourne, 4-7 December 2022, ACIS 2022 - Australasian Conference on Information Systems, Proceedings
- Full Text:
- Reviewed:
- Description: In recent years, organisational software process education has seen a considerable uptick in interest in adopting business simulation games (BSGs) as a novel learning resource. However, the lack of reliable and valid instruments to evaluate simulation learning outcomes inhibits the adoption and progress of simulation in Information System education. To fill this need, we performed a systematic review of 33 empirical studies using the PRISMA declaration approach to identify the different evaluation methods used to analyse BSG learning outcomes. We created a concept matrix using a didactic framework that categorised these assessment methodologies into three game stages (pre-game, in-game and post-game). We established a comprehensive evaluation strategy using this concept matrix, which teachers and researchers may use to choose the best appropriate evaluation method to analyse a wide range of learning outcomes of business simulation games. Copyright © 2022 Faisal, Chadhar, Goriss-Hunter & Stranieri.
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2022
- Type: Text , Conference paper
- Relation: 33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022, Melbourne, 4-7 December 2022, ACIS 2022 - Australasian Conference on Information Systems, Proceedings
- Full Text:
- Reviewed:
- Description: In recent years, organisational software process education has seen a considerable uptick in interest in adopting business simulation games (BSGs) as a novel learning resource. However, the lack of reliable and valid instruments to evaluate simulation learning outcomes inhibits the adoption and progress of simulation in Information System education. To fill this need, we performed a systematic review of 33 empirical studies using the PRISMA declaration approach to identify the different evaluation methods used to analyse BSG learning outcomes. We created a concept matrix using a didactic framework that categorised these assessment methodologies into three game stages (pre-game, in-game and post-game). We established a comprehensive evaluation strategy using this concept matrix, which teachers and researchers may use to choose the best appropriate evaluation method to analyse a wide range of learning outcomes of business simulation games. Copyright © 2022 Faisal, Chadhar, Goriss-Hunter & Stranieri.
Instructors’ perceptions of the development of work-readiness through simulations
- Faisal, Nadia, Chadhar, Mehmood, Goriss-Hunter, Anitra, Stranieri, Andrew
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2022
- Type: Text , Conference paper
- Relation: 33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022, Melbourne, 4-7 December 2022, ACIS 2022 - Australasian Conference on Information Systems, Proceedings
- Full Text:
- Reviewed:
- Description: The global ERP software market is expected to reach $117.09 billion by 2030 (Biel, July 12, 2022). To boost graduate work-readiness, Australian institutions are adopting new pedagogical strategies by familiarising Information systems (IS) students with this highly sought-after software. One of these techniques is simulation games that provide students with a risk-free, real-world simulation of popular software to develop soft and hard skills needed by the IS industry. This exploratory study employed the Grounded Theory approach to evaluate instructors' perceptions of the influence of simulation games on the work-readiness of information systems students. We conducted semi-structured interviews with (Enterprise Resource Planning Simulation) ERPsim game laboratory instructors. The authors utilised Work Readiness Integrated Competency Model to map the three learning outcomes from the interviews’ analysis: abilities, knowledge, and attitudes. The mapping demonstrated that simulation games could support the development of specific skills and attitudes needed by the information systems sector. Copyright © 2022 Faisal, Chadhar, Goriss-Hunter & Stranieri.
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2022
- Type: Text , Conference paper
- Relation: 33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022, Melbourne, 4-7 December 2022, ACIS 2022 - Australasian Conference on Information Systems, Proceedings
- Full Text:
- Reviewed:
- Description: The global ERP software market is expected to reach $117.09 billion by 2030 (Biel, July 12, 2022). To boost graduate work-readiness, Australian institutions are adopting new pedagogical strategies by familiarising Information systems (IS) students with this highly sought-after software. One of these techniques is simulation games that provide students with a risk-free, real-world simulation of popular software to develop soft and hard skills needed by the IS industry. This exploratory study employed the Grounded Theory approach to evaluate instructors' perceptions of the influence of simulation games on the work-readiness of information systems students. We conducted semi-structured interviews with (Enterprise Resource Planning Simulation) ERPsim game laboratory instructors. The authors utilised Work Readiness Integrated Competency Model to map the three learning outcomes from the interviews’ analysis: abilities, knowledge, and attitudes. The mapping demonstrated that simulation games could support the development of specific skills and attitudes needed by the information systems sector. Copyright © 2022 Faisal, Chadhar, Goriss-Hunter & Stranieri.
Deep learning model to empower student engagement in online synchronous learning environment
- Godly, Cinthia, Balasubramanian, Venki, Stranieri, Andrew, Saikrishna, Vidya, Mohammed, Rehena, Chackappan, Godly
- Authors: Godly, Cinthia , Balasubramanian, Venki , Stranieri, Andrew , Saikrishna, Vidya , Mohammed, Rehena , Chackappan, Godly
- Date: 2022
- Type: Text , Conference paper
- Relation: 19th IEEE India Council International Conference, INDICON 2022, Kochi India, 24-26 November 2022, INDICON 2022 - 2022 IEEE 19th India Council International Conference
- Full Text:
- Reviewed:
- Description: Following the start of the pandemic, online synchronous learning has grown significantly. The higher education sector is searching for new creative ways to provide the information online because of the switch from face-to-face to online synchronous course delivery. Students are also becoming accustomed to studying online, and research has shown that synchronous online learning has a variety of effects on student engagement. For instance, according to statistics from the National Survey of Student Engagement, students are less likely to participate in collaborative learning, studentfaculty interactions, and conversations when learning online if they use quantitative reasoning during face-to-face instruction. Additionally, studies suggest that because they depend on their devices to take online classes, students feel more alienated from their lecturers. This has been linked to a drop in contacts with peers and teachers as a result. By using a cutting-edge deep learning model to predict learner engagement behaviour in a synchronous teaching environment, our research intends to improve online engagement. The model with a clever trigger will encourage the disengaged pupils to communicate with the teachers online. Smart triggers will be built around factors that have been found, focusing on disengaged students to engage them in real-time with automatic, personalized feedback. © 2022 IEEE.
- Authors: Godly, Cinthia , Balasubramanian, Venki , Stranieri, Andrew , Saikrishna, Vidya , Mohammed, Rehena , Chackappan, Godly
- Date: 2022
- Type: Text , Conference paper
- Relation: 19th IEEE India Council International Conference, INDICON 2022, Kochi India, 24-26 November 2022, INDICON 2022 - 2022 IEEE 19th India Council International Conference
- Full Text:
- Reviewed:
- Description: Following the start of the pandemic, online synchronous learning has grown significantly. The higher education sector is searching for new creative ways to provide the information online because of the switch from face-to-face to online synchronous course delivery. Students are also becoming accustomed to studying online, and research has shown that synchronous online learning has a variety of effects on student engagement. For instance, according to statistics from the National Survey of Student Engagement, students are less likely to participate in collaborative learning, studentfaculty interactions, and conversations when learning online if they use quantitative reasoning during face-to-face instruction. Additionally, studies suggest that because they depend on their devices to take online classes, students feel more alienated from their lecturers. This has been linked to a drop in contacts with peers and teachers as a result. By using a cutting-edge deep learning model to predict learner engagement behaviour in a synchronous teaching environment, our research intends to improve online engagement. The model with a clever trigger will encourage the disengaged pupils to communicate with the teachers online. Smart triggers will be built around factors that have been found, focusing on disengaged students to engage them in real-time with automatic, personalized feedback. © 2022 IEEE.
Supporting discretionary decision-making with information technology
- Hall, Mary Jean, Calabro, Domenico, Sourdin, Tania, Stranieri, Andrew, Zeleznikow, John
- Authors: Hall, Mary Jean , Calabro, Domenico , Sourdin, Tania , Stranieri, Andrew , Zeleznikow, John
- Date: 2005
- Type: Text , Journal article
- Relation: University of Ottawa Law & Technology Journal Vol. 2, no. 1 (2005), p. 1-36
- Full Text:
- Reviewed:
- Description: A NUMBER OF INCREASINGLY SOPHISTICATED technologies are now being used to support complex decision-making in a range of contexts. This paper reports on a project undertaken to provide decision support in discretionary legal domains by referring to a recently created model that involves the interplay and weighting of relevant rule-based and discretionary factors used in a decision-making process. The case study used in the modelling process is the Criminal Jurisdiction of the Victorian Magistrate’s Court (Australia), where the handing down of an appropriate custodial or non-custodial sentence requires the consideration of many factors. Tools and techniques used to capture relevant expert knowledge and to display it both as a paper model and as an online prototype application are discussed. Models of sentencing decision-making with rule-based and discretionary elements are presented and analyzed. This paper concludes by discussing the benefits and disadvantages of such technology and considers some potential appropriate uses of the model and web-based prototype application.
- Description: C1
- Description: 2003001431
- Authors: Hall, Mary Jean , Calabro, Domenico , Sourdin, Tania , Stranieri, Andrew , Zeleznikow, John
- Date: 2005
- Type: Text , Journal article
- Relation: University of Ottawa Law & Technology Journal Vol. 2, no. 1 (2005), p. 1-36
- Full Text:
- Reviewed:
- Description: A NUMBER OF INCREASINGLY SOPHISTICATED technologies are now being used to support complex decision-making in a range of contexts. This paper reports on a project undertaken to provide decision support in discretionary legal domains by referring to a recently created model that involves the interplay and weighting of relevant rule-based and discretionary factors used in a decision-making process. The case study used in the modelling process is the Criminal Jurisdiction of the Victorian Magistrate’s Court (Australia), where the handing down of an appropriate custodial or non-custodial sentence requires the consideration of many factors. Tools and techniques used to capture relevant expert knowledge and to display it both as a paper model and as an online prototype application are discussed. Models of sentencing decision-making with rule-based and discretionary elements are presented and analyzed. This paper concludes by discussing the benefits and disadvantages of such technology and considers some potential appropriate uses of the model and web-based prototype application.
- Description: C1
- Description: 2003001431