Group decision making in health care : A case study of multidisciplinary meetings
- Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Daly, Sharon , Patterson, Louise , Yearwood, John , Wolff, Alan
- Date: 2016
- Type: Text , Journal article
- Relation: Journal of Decision Systems Vol. 25, no. (2016), p. 476-485
- Full Text:
- Reviewed:
- Description: Abstract: Recent studies have demonstrated that Multi-Disciplinary Meetings (MDM) practiced in some medical contexts can contribute to positive health care outcomes. The group reasoning and decision-making in MDMs has been found to be most effective when deliberations revolve around the patient’s needs, comprehensive information is available during the meeting, core members attend and the MDM is effectively facilitated. This article presents a case study of the MDMs in cancer care in a region of Australia. The case study draws on a group reasoning model called the Reasoning Community model to analyse MDM deliberations to illustrate that many factors are important to support group reasoning, not solely the provision of pertinent information. The case study has implications for the use of data analytics in any group reasoning context. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
Hybrids of support vector machine wrapper and filter based framework for malware detection
- Authors: Huda, Shamsul , Abawajy, Jemal , Alazab, Mamoun , Abdollahian, Mali , Islam, Rafiqul , Yearwood, John
- Date: 2016
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 55, no. (2016), p. 376-390
- Full Text: false
- Reviewed:
- Description: Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation Anti-Virus (AV) engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current AV engines in detecting malware. In this paper we propose a hybrid framework for malware detection by using the hybrids of Support Vector Machines Wrapper, Maximum-Relevance–Minimum-Redundancy Filter heuristics where Application Program Interface (API) call statistics are used as a malware features. The novelty of our hybrid framework is that it injects the filter’s ranking score in the wrapper selection process and combines the properties of both wrapper and filters and API call statistics which can detect malware based on the nature of infectious actions instead of signature. To the best of our knowledge, this kind of hybrid approach has not been explored yet in the literature in the context of feature selection and malware detection. Knowledge about the intrinsic characteristics of malicious activities is determined by the API call statistics which is injected as a filter score into the wrapper’s backward elimination process in order to find the most significant APIs. While using the most significant APIs in the wrapper classification on both obfuscated and benign types malware datasets, the results show that the proposed hybrid framework clearly surpasses the existing models including the independent filters and wrappers using only a very compact set of significant APIs. The performances of the proposed and existing models have further been compared using binary logistic regression. Various goodness of fit comparison criteria such as Chi Square, Akaike’s Information Criterion (AIC) and Receiver Operating Characteristic Curve ROC are deployed to identify the best performing models. Experimental outcomes based on the above criteria also show that the proposed hybrid framework outperforms other existing models of signature types including independent wrapper and filter approaches to identify malware.
Hybrids of support vector machine wrapper and filter based framework for malware detection
- Authors: Huda, Shamsul , Abawajy, Jemal , Alazab, Mamoun , Abdollalihiand, Mali , Islam, Rafiqul , Yearwood, John
- Date: 2016
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 55, no. (2016), p. 376-390
- Full Text: false
- Reviewed:
- Description: Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation Anti-Virus (AV) engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current AV engines in detecting malware. In this paper we propose a hybrid framework for malware detection by using the hybrids of Support Vector Machines Wrapper, Maximum-Relevance–Minimum-Redundancy Filter heuristics where Application Program Interface (API) call statistics are used as a malware features. The novelty of our hybrid framework is that it injects the filter’s ranking score in the wrapper selection process and combines the properties of both wrapper and filters and API call statistics which can detect malware based on the nature of infectious actions instead of signature. To the best of our knowledge, this kind of hybrid approach has not been explored yet in the literature in the context of feature selection and malware detection. Knowledge about the intrinsic characteristics of malicious activities is determined by the API call statistics which is injected as a filter score into the wrapper’s backward elimination process in order to find the most significant APIs. While using the most significant APIs in the wrapper classification on both obfuscated and benign types malware datasets, the results show that the proposed hybrid framework clearly surpasses the existing models including the independent filters and wrappers using only a very compact set of significant APIs. The performances of the proposed and existing models have further been compared using binary logistic regression. Various goodness of fit comparison criteria such as Chi Square, Akaike’s Information Criterion (AIC) and Receiver Operating Characteristic Curve ROC are deployed to identify the best performing models. Experimental outcomes based on the above criteria also show that the proposed hybrid framework outperforms other existing models of signature types including independent wrapper and filter approaches to identify malware.
Analytics service oriented architecture for enterprise information systems
- Authors: Sun, Zhaohao , Strang, Kenneth , Yearwood, John
- Date: 2014
- Type: Text , Conference paper
- Relation: 16th International Conference on Information Integration and Web-based Applications & Services
- Full Text: false
- Reviewed:
- Description: Big data analytics and business analytics are disruptive technology and innovative solution for enterprise development. However, what is the relationship between big data analytics and business analytics? What is the relationship between business analytics and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? These are still big issues for EIS development. This paper addresses these three issues by proposing an ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA can enhance to develop EIS. This paper also discusses the interrelationship between data analysis and business analytics, and between data analytics and big data analytics. The proposed approaches in this paper will facilitate research and development of EIS, business analytics, big data analytics, and business intelligence.
Profiling phishing activity based on hyperlinks extracted from phishing emails
- Authors: Yearwood, John , Mammadov, Musa , Webb, Dean
- Date: 2012
- Type: Text , Journal article
- Relation: Social Network Analysis and Mining Vol. 2, no. 1 (2012), p. 5-16
- Full Text: false
- Reviewed:
- Description: Phishing activity has recently been focused on social networking sites as a more effective way of exploiting not only the technology but also the trust that may exist between members in a social network. In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling problem as a multi-label classification problem using the hyperlinks in the phishing emails as features and structural properties of emails along with whois (i.e. DNS) information on hyperlinks as profile classes. Further, we generate profiles based on the classifier predictions. Thus, classes become elements of profiles. We employ a boosting algorithm (AdaBoost) as well as SVM to generate multi-label class predictions on three different datasets created from hyperlink information in phishing emails. These predictions are further utilized to generate complete profiles of these emails. Results show that profiling can be done with quite high accuracy using hyperlink information.
Using psycholinguistic features for profiling first language of authors
- Authors: Torney, Rosemary , Vamplew, Peter , Yearwood, John
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of the American Society for Information Science and Technology Vol. 63, no. 6 (2012), p. 1256-1269
- Full Text: false
- Reviewed:
- Description: This study empirically evaluates the effectiveness of different feature types for the classification of the first language of an author. In particular, it examines the utility of psycholinguistic features, extracted by the Linguistic Inquiry and Word Count (LIWC) tool, that have not previously been applied to the task of author profiling. As LIWC is a tool that has been developed in the psycholinguistic field rather than the computational linguistics field, it was hypothesized that it would be effective, both as a single type feature set because of its psycholinguistic basis, and in combination with other feature sets, because it should be sufficiently different to add insight rather than redundancy. It was found that LIWC features were competitive with previously used feature types in identifying the first language of an author, and that combined feature sets including LIWC features consistently showed better accuracy rates and average F measures than were achieved by the same feature sets without the LIWC features. As a secondary issue, this study also examined how effectively first language classification scaled up to a larger number of possible languages. It was found that the classification scheme scaled up effectively to the entire 16 language collection from the International Corpus of Learner English, when compared with results achieved on just 5 languages in previous research. 2012 ASIS&T.
Automated opinion detection : Implications of the level of agreement between human raters
- Authors: Osman, Deanna , Yearwood, John , Vamplew, Peter
- Date: 2010
- Type: Text , Journal article
- Relation: Information Processing and Management Vol. 46, no. 3 (2010), p. 331-342
- Full Text: false
- Reviewed:
- Description: The ability to agree with the TREC Blog06 opinion assessments was measured for seven human assessors and compared with the submitted results of the Blog06 participants. The assessors achieved a fair level of agreement between their assessments, although the range between the assessors was large. It is recommended that multiple assessors are used to assess opinion data, or a pre-test of assessors is completed to remove the most dissenting assessors from a pool of assessors prior to the assessment process. The possibility of inconsistent assessments in a corpus also raises concerns about training data for an automated opinion detection system (AODS), so a further recommendation is that AODS training data be assembled from a variety of sources. This paper establishes an aspirational value for an AODS by determining the level of agreement achievable by human assessors when assessing the existence of an opinion on a given topic. Knowing the level of agreement amongst humans is important because it sets an upper bound on the expected performance of AODS. While the AODSs surveyed achieved satisfactory results, none achieved a result close to the upper bound. © 2009 Elsevier Ltd. All rights reserved.
Understanding victims of identity theft: A grounded theory approach
- Authors: Turville, Kylie , Firmin, Sally , Yearwood, John , Miller, Charlynn
- Date: 2010
- Type: Text , Conference paper
- Relation: 5th International Conference on Qualitative Research in IT and IT in Qualitative Research, QualIT 2010
- Full Text:
- Reviewed:
- Description: Being a victim of identity theft can be a devastating and life-changing event. Once the victim discovers the misuse they need to begin the process of recovery. For the "lucky" victims this may take only a couple of phone calls and a small amount of time; however, some victims may experience difficulties for many year. In order to recover, victims of crime require support and assistance; however, within Australia this support is sadly lacking. In order to identify the issues currently faced by victims of identity theft as they work through the recovery process, a Grounded Theory methodology was identified as most appropriate. This paper provides a brief overview of the history of the research project; a brief introduction of grounded theory with a focus on preconceived ideas and their implications; and a description of the research project currently being undertaken. A discussion of some issues experienced when using grounded theory within an IT department with very little experience of qualitative research will be provided, along with some preliminary results.
- Description: E1
The impact of semantic class identification and semantic role labeling on natural language answer extraction
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ma, Liping
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at 30th European Conference on IR Research, ECIR 2008, Glasgow, UK : 30th March - 3rd April 2008 p. 430-437
- Full Text: false
- Description: In satisfying an information need by a Question Answering (QA) system, there are text understanding approaches which can enhance the performance of final answer extraction. Exploiting the FrameNet lexical resource in this process inspires analysis of the levels of semantic representation in the automated practice where the task of semantic class and role labeling takes place. In this paper, we analyze the impact of different levels of semantic parsing on answer extraction with respect to the individual sub-tasks of frame evocation and frame element assignment.
- Description: 2003006587
Applying anatomical therapeutic chemical (ATC) and critical term ontologies to Australian drug safety data for association rules and adverse event signalling
- Authors: Saunders, Gary , Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Conferences in Research and Practice in Information Technology, Advances in Ontologies 2005: Proceedings of the Australasian Ontology Workshop AOW 2005 Vol. 58, no. (2005), p. 93-98
- Full Text:
- Reviewed:
- Description: C1
- Description: 2003001450
A global optimisation approach to classification in medical diagnosis and prognosis
- 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