ORCID Profile
0000-0003-3476-8838
Current Organisation
University of Tasmania
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Other Artificial Intelligence | Expert Systems | Artificial Intelligence and Image Processing | Pattern Recognition
Application tools and system utilities | Application packages |
Publisher: IEEE
Date: 2008
DOI: 10.1109/MUE.2008.93
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: MDPI AG
Date: 23-05-2014
DOI: 10.3390/S140509313
Publisher: NADIA
Date: 31-01-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: MDPI AG
Date: 02-07-2015
DOI: 10.3390/S150715772
Publisher: Hindawi Limited
Date: 28-04-2019
DOI: 10.1155/2019/2164708
Publisher: Elsevier BV
Date: 04-2009
Publisher: Wiley
Date: 12-10-2015
DOI: 10.1002/CPE.3578
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: IEEE
Date: 12-2013
Publisher: Informa UK Limited
Date: 18-12-2015
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Hindawi Limited
Date: 2017
DOI: 10.1155/2017/8501976
Abstract: A lot of mobile applications which provided location information by using a location-based service are being developed recently. For instance, a smart phone would find my location and destination by running a program using a GPS chip in a device. However, the information leakage and the crime that misused the leaked information caused by the cyberattack of mobile information system occurred. So the interest and importance of information security are increasing. Also the number of users who has used mobile devices in Korea is increasing, and the security of mobile devices is becoming more important. Snort detection system has been used to detect and handle cyberattacks but the policy of Snort detection system is applied differently for each of the different kinds of equipment. It is expected that the security of mobile information system would be improved and information leakage would be blocked by selecting options through optimization of Snort detection policy to protect users who are using location-based service in mobile information system environment in this paper.
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: MDPI AG
Date: 14-08-2023
DOI: 10.3390/ANI13162622
Abstract: The analysis of AR is widely used to detect loss of acrosome in sperm, but the subjective decisions of experts affect the accuracy of the examination. Therefore, we develop an ARCS for objectivity and consistency of analysis using convolutional neural networks (CNNs) trained with various magnification images. Our models were trained on 215 microscopic images at 400× and 438 images at 1000× magnification using the ResNet 50 and Inception–ResNet v2 architectures. These models distinctly recognized micro-changes in the PM of AR sperms. Moreover, the Inception–ResNet v2-based ARCS achieved a mean average precision of over 97%. Our system’s calculation of the AR ratio on the test dataset produced results similar to the work of the three experts and could do so more quickly. Our model streamlines sperm detection and AR status determination using a CNN-based approach, replacing laborious tasks and expert assessments. The ARCS offers consistent AR sperm detection, reduced human error, and decreased working time. In conclusion, our study suggests the feasibility and benefits of using a sperm diagnosis artificial intelligence assistance system in routine practice scenarios.
Publisher: Springer Science and Business Media LLC
Date: 02-07-2016
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Inderscience Publishers
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 05-2021
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 03-2015
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 06-2012
Publisher: Hindawi Limited
Date: 02-2007
DOI: 10.1111/J.1365-2710.2007.00801.X
Abstract: The aim was to develop and evaluate a pilot version of a knowledge-based system that can identify existing and potential medication-related problems from patient information. This intelligent system could directly support pharmacists and other health professionals providing medication reviews. Rather than being based on static rules to trigger alerts, this system utilizes a multiple classification ripple-down rules approach, which allows the user to build rules incrementally and improve the accuracy of the knowledge base in identifying medication-related problems while the system is in use, with no outside assistance or training. The system contextualizes the potential drug therapy problems by taking into consideration the patient's demographics, and other medical condition and drugs. The system is capable of both being instructed in the domain of medication review through its routine use by an expert, and acting similarly to the expert when analysing genuine medication review cases. The system was handed over to an experienced clinical pharmacist (expert), with no knowledge or conclusions preloaded into the system. The expert was then able to add the case details and generate the rules required for 126 actual medication review cases. Over 250 rules were generated from the review cases, incorporating demographics, medical history, symptoms, medications and pathology results from these cases. At the completion of the cases, more than 80% of the potential medication-related problems identified by the expert were also detected by the system. The false positive rate, or number of incorrect medication-related problems identified by the system, was <10% overall and was zero for the last 15 cases analysed. The system found significantly more potential medication-related problems than the expert, with the system consistently remaining at least one finding ahead. There was a high incidence of missed potential medication-related problems by the expert, which were automatically repaired by the system. The knowledge-based system has already demonstrated that the technique employed is well suited to a domain of this nature and has furthermore demonstrated that it is capable of improving the quality of service that the medication reviewer can provide. The system will be further enhanced and tested prior to use in the field. It should help pharmacists in the provision of medication reviews, improving their clinical and time management capabilities, and enhancing their ability to contribute to the quality use of medications.
Publisher: Elsevier BV
Date: 11-2018
DOI: 10.1016/J.ARTMED.2015.09.008
Abstract: The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support. A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system. We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy. Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.
Publisher: Springer Science and Business Media LLC
Date: 04-06-2013
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: MDPI AG
Date: 28-08-2015
DOI: 10.3390/S150921294
Publisher: IEEE
Date: 06-2014
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: ACM
Date: 02-2016
Publisher: Elsevier BV
Date: 2022
Publisher: Hindawi Limited
Date: 12-11-2020
DOI: 10.1155/2020/8875910
Abstract: Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. Product recognition via images is a challenging task in the field of computer vision. It receives increasing consideration due to the great application prospect, such as automatic checkout, stock tracking, planogram compliance, and visually impaired assistance. In recent years, deep learning enjoys a flourishing evolution with tremendous achievements in image classification and object detection. This article aims to present a comprehensive literature review of recent research on deep learning-based retail product recognition. More specifically, this paper reviews the key challenges of deep learning for retail product recognition and discusses potential techniques that can be helpful for the research of the topic. Next, we provide the details of public datasets which could be used for deep learning. Finally, we conclude the current progress and point new perspectives to the research of related fields.
Publisher: Informa UK Limited
Date: 07-2012
Publisher: Elsevier BV
Date: 05-2015
Publisher: Springer US
Date: 2009
Publisher: Hindawi Limited
Date: 18-12-2012
DOI: 10.1111/J.1365-2710.2011.01327.X
Abstract: Drug-related problems (DRPs) are of serious concern worldwide, particularly for the elderly who often take many medications simultaneously. Medication reviews have been demonstrated to improve medication usage, leading to reductions in DRPs and potential savings in healthcare costs. However, medication reviews are not always of a consistently high standard, and there is often room for improvement in the quality of their findings. Our aim was to produce computerized intelligent decision support software that can improve the consistency and quality of medication review reports, by helping to ensure that DRPs relevant to a patient are overlooked less frequently. A system that largely achieved this goal was previously published, but refinements have been made. This paper examines the results of both the earlier and newer systems. Two prototype multiple-classification ripple-down rules medication review systems were built, the second being a refinement of the first. Each of the systems was trained incrementally using a human medication review expert. The resultant knowledge bases were analysed and compared, showing factors such as accuracy, time taken to train, and potential errors avoided. The two systems performed well, achieving accuracies of approximately 80% and 90%, after being trained on only a small number of cases (126 and 244 cases, respectively). Through analysis of the available data, it was estimated that without the system intervening, the expert training the first prototype would have missed approximately 36% of potentially relevant DRPs, and the second 43%. However, the system appeared to prevent the majority of these potential expert errors by correctly identifying the DRPs for them, leaving only an estimated 8% error rate for the first expert and 4% for the second. These intelligent decision support systems have shown a clear potential to substantially improve the quality and consistency of medication reviews, which should in turn translate into improved medication usage if they were implemented into routine use.
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: Springer Science and Business Media LLC
Date: 03-08-2013
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Hindawi Limited
Date: 2017
DOI: 10.1155/2017/1303919
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 09-2011
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IGI Global
Date: 2016
Publisher: IEEE
Date: 05-2015
Publisher: ACM
Date: 09-01-2014
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 24-11-2011
Publisher: ICST
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 08-03-2015
Start Date: 2011
End Date: 2012
Funder: Small and Medium Business Administration (Korea)
View Funded ActivityStart Date: 2013
End Date: 2016
Funder: WorkCover Tasmania
View Funded ActivityStart Date: 2015
End Date: 2016
Funder: University of Tasmania
View Funded ActivityStart Date: 2015
End Date: 2016
Funder: University of Tasmania
View Funded ActivityStart Date: 2016
End Date: 2016
Funder: Office of Naval Research Global
View Funded ActivityStart Date: 2016
End Date: 2017
Funder: Asian Office of Aerospace Research & Development
View Funded ActivityStart Date: 2016
End Date: 2017
Funder: Hyundai MNSOFT Inc.
View Funded ActivityStart Date: 07-2010
End Date: 06-2014
Amount: $310,000.00
Funder: Australian Research Council
View Funded Activity