Incorporating time-delays in S-System model for reverse engineering genetic networks
- Chowdhury, Ahsan, Chetty, Madhu, Nguyen, Vinh
- Authors: Chowdhury, Ahsan , Chetty, Madhu , Nguyen, Vinh
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 14, no. (2013), p. 1-22
- Full Text:
- Reviewed:
- Description: Background In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneous or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously. As a result, all these approaches cannot detect important interactions of the other type. S-System model, a differential equation based approach which has been increasingly applied for modeling GRNs, also suffers from this limitation. In fact, all S-System based existing modeling approaches have been designed to capture only instantaneous interactions, and are unable to infer time-delayed interactions. Results In this paper, we propose a novel Time-Delayed S-System (TDSS) model which uses a set of delay differential equations to represent the system dynamics. The ability to incorporate time-delay parameters in the proposed S-System model enables simultaneous modeling of both instantaneous and time-delayed interactions. Furthermore, the delay parameters are not limited to just positive integer values (corresponding to time stamps in the data), but can also take fractional values. Moreover, we also propose a new criterion for model evaluation exploiting the sparse and scale-free nature of GRNs to effectively narrow down the search space, which not only reduces the computation time significantly but also improves model accuracy. The evaluation criterion systematically adapts the max-min in-degrees and also systematically balances the effect of network accuracy and complexity during optimization. Conclusion The four well-known performance measures applied to the experimental studies on synthetic networks with various time-delayed regulations clearly demonstrate that the proposed method can capture both instantaneous and delayed interactions correctly with high precision. The experiments carried out on two well-known real-life networks, namely IRMA and SOS DNA repair network in Escherichia coli show a significant improvement compared with other state-of-the-art approaches for GRN modeling.
- Authors: Chowdhury, Ahsan , Chetty, Madhu , Nguyen, Vinh
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 14, no. (2013), p. 1-22
- Full Text:
- Reviewed:
- Description: Background In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneous or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously. As a result, all these approaches cannot detect important interactions of the other type. S-System model, a differential equation based approach which has been increasingly applied for modeling GRNs, also suffers from this limitation. In fact, all S-System based existing modeling approaches have been designed to capture only instantaneous interactions, and are unable to infer time-delayed interactions. Results In this paper, we propose a novel Time-Delayed S-System (TDSS) model which uses a set of delay differential equations to represent the system dynamics. The ability to incorporate time-delay parameters in the proposed S-System model enables simultaneous modeling of both instantaneous and time-delayed interactions. Furthermore, the delay parameters are not limited to just positive integer values (corresponding to time stamps in the data), but can also take fractional values. Moreover, we also propose a new criterion for model evaluation exploiting the sparse and scale-free nature of GRNs to effectively narrow down the search space, which not only reduces the computation time significantly but also improves model accuracy. The evaluation criterion systematically adapts the max-min in-degrees and also systematically balances the effect of network accuracy and complexity during optimization. Conclusion The four well-known performance measures applied to the experimental studies on synthetic networks with various time-delayed regulations clearly demonstrate that the proposed method can capture both instantaneous and delayed interactions correctly with high precision. The experiments carried out on two well-known real-life networks, namely IRMA and SOS DNA repair network in Escherichia coli show a significant improvement compared with other state-of-the-art approaches for GRN modeling.
Learning the naive bayes classifier with optimization models
- Taheri, Sona, Mammadov, Musa
- Authors: Taheri, Sona , Mammadov, Musa
- Date: 2013
- Type: Text , Journal article
- Relation: International Journal of Applied Mathematics and Computer Science Vol. 23, no. 4 (2013), p. 787-795
- Full Text:
- Reviewed:
- Description: Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.
- Authors: Taheri, Sona , Mammadov, Musa
- Date: 2013
- Type: Text , Journal article
- Relation: International Journal of Applied Mathematics and Computer Science Vol. 23, no. 4 (2013), p. 787-795
- Full Text:
- Reviewed:
- Description: Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.
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
- 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
- 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
Calibration of an articulated CMM using stochastic approximations
- Sultan, Ibrahim, Puthiyaveettil, Prajeesh
- Authors: Sultan, Ibrahim , Puthiyaveettil, Prajeesh
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Advanced Manufacturing Technology Vol. 63, no. 1-4 (2012), p. 201-207
- Full Text:
- Reviewed:
- Description: A coordinate measuring machine (CMM) is meant to digitise the spatial locations of points and feed the resulting measurements to a CAD system for storing and processing. For reliable utilisation of a CMM, a calibration procedure is often undertaken to eliminate the inaccuracies which result from manufacturing, assembly and installation errors. In this paper, an Immersion digitizer coordinate measuring machine has been calibrated using an accurately manufactured master cuboid fixture. This CMM has been designed as an articulated manipulator to enhance its dexterity and versatility. As such, the calibration problem is tackled with the aid of a kinematic model similar to those employed for the analysis of serial robots. In addition, a stochastic-based optimisation technique is used to identify the parameters of the kinematic model in order for the accurate performance to be achieved. The experimental results demonstrate the effectiveness of this method, whereby the measuring accuracy has been improved considerably. © 2012 Springer-Verlag London Limited.
- Description: 2003010394
- Authors: Sultan, Ibrahim , Puthiyaveettil, Prajeesh
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Advanced Manufacturing Technology Vol. 63, no. 1-4 (2012), p. 201-207
- Full Text:
- Reviewed:
- Description: A coordinate measuring machine (CMM) is meant to digitise the spatial locations of points and feed the resulting measurements to a CAD system for storing and processing. For reliable utilisation of a CMM, a calibration procedure is often undertaken to eliminate the inaccuracies which result from manufacturing, assembly and installation errors. In this paper, an Immersion digitizer coordinate measuring machine has been calibrated using an accurately manufactured master cuboid fixture. This CMM has been designed as an articulated manipulator to enhance its dexterity and versatility. As such, the calibration problem is tackled with the aid of a kinematic model similar to those employed for the analysis of serial robots. In addition, a stochastic-based optimisation technique is used to identify the parameters of the kinematic model in order for the accurate performance to be achieved. The experimental results demonstrate the effectiveness of this method, whereby the measuring accuracy has been improved considerably. © 2012 Springer-Verlag London Limited.
- Description: 2003010394
Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
- Nguyen, Vinh, Chetty, Madhu, Coppel, Ross, Wangikar, Pramod
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Wangikar, Pramod
- Date: 2012
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 13, no. 131 (2012), p. 1-16
- Full Text:
- Reviewed:
- Description: Abstract Background Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. Results To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. Conclusions Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Wangikar, Pramod
- Date: 2012
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 13, no. 131 (2012), p. 1-16
- Full Text:
- Reviewed:
- Description: Abstract Background Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. Results To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. Conclusions Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.
Anxiety online-A virtual clinic: Preliminary outcomes following completion of five fully automated treatment programs for anxiety disorders and symptoms
- Klein, Britt, Meyer, Denny, Austin, David, Kyrios, Michael
- Authors: Klein, Britt , Meyer, Denny , Austin, David , Kyrios, Michael
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 13, no. 4 (2011), p.
- Full Text:
- Reviewed:
- Description: Background: The development of e-mental health interventions to treat or prevent mental illness and to enhance wellbeing has risen rapidly over the past decade. This development assists the public in sidestepping some of the obstacles that are often encountered when trying to access traditional face-to-face mental health care services. Objective: The objective of our study was to investigate the posttreatment effectiveness of five fully automated self-help cognitive behavior e-therapy programs for generalized anxiety disorder (GAD), panic disorder with or without agoraphobia (PD/A), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), and social anxiety disorder (SAD) offered to the international public via Anxiety Online, an open-access full-service virtual psychology clinic for anxiety disorders. Methods: We used a naturalistic participant choice, quasi-experimental design to evaluate each of the five Anxiety Online fully automated self-help e-therapy programs. Participants were required to have at least subclinical levels of one of the anxiety disorders to be offered the associated disorder-specific fully automated self-help e-therapy program. These programs are offered free of charge via Anxiety Online. Results: A total of 225 people self-selected one of the five e-therapy programs (GAD, n = 88; SAD, n = 50; PD/A, n = 40; PTSD, n = 30; OCD, n = 17) and completed their 12-week posttreatment assessment. Significant improvements were found on 21/25 measures across the five fully automated self-help programs. At postassessment we observed significant reductions on all five anxiety disorder clinical disorder severity ratings (Cohen d range 0.72-1.22), increased confidence in managing one's own mental health care (Cohen d range 0.70-1.17), and decreases in the total number of clinical diagnoses (except for the PD/A program, where a positive trend was found) (Cohen d range 0.45-1.08). In addition, we found significant improvements in quality of life for the GAD, OCD, PTSD, and SAD e-therapy programs (Cohen d range 0.11-0.96) and significant reductions relating to general psychological distress levels for the GAD, PD/A, and PTSD e-therapy programs (Cohen d range 0.23-1.16). Overall, treatment satisfaction was good across all five e-therapy programs, and posttreatment assessment completers reported using their e-therapy program an average of 395.60 (SD 272.2) minutes over the 12-week treatment period. Conclusions: Overall, all five fully automated self-help e-therapy programs appear to be delivering promising high-quality outcomes; however, the results require replication. © Britt Klein, Denny Meyer, David William Austin, Michael Kyrios.
- Authors: Klein, Britt , Meyer, Denny , Austin, David , Kyrios, Michael
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 13, no. 4 (2011), p.
- Full Text:
- Reviewed:
- Description: Background: The development of e-mental health interventions to treat or prevent mental illness and to enhance wellbeing has risen rapidly over the past decade. This development assists the public in sidestepping some of the obstacles that are often encountered when trying to access traditional face-to-face mental health care services. Objective: The objective of our study was to investigate the posttreatment effectiveness of five fully automated self-help cognitive behavior e-therapy programs for generalized anxiety disorder (GAD), panic disorder with or without agoraphobia (PD/A), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), and social anxiety disorder (SAD) offered to the international public via Anxiety Online, an open-access full-service virtual psychology clinic for anxiety disorders. Methods: We used a naturalistic participant choice, quasi-experimental design to evaluate each of the five Anxiety Online fully automated self-help e-therapy programs. Participants were required to have at least subclinical levels of one of the anxiety disorders to be offered the associated disorder-specific fully automated self-help e-therapy program. These programs are offered free of charge via Anxiety Online. Results: A total of 225 people self-selected one of the five e-therapy programs (GAD, n = 88; SAD, n = 50; PD/A, n = 40; PTSD, n = 30; OCD, n = 17) and completed their 12-week posttreatment assessment. Significant improvements were found on 21/25 measures across the five fully automated self-help programs. At postassessment we observed significant reductions on all five anxiety disorder clinical disorder severity ratings (Cohen d range 0.72-1.22), increased confidence in managing one's own mental health care (Cohen d range 0.70-1.17), and decreases in the total number of clinical diagnoses (except for the PD/A program, where a positive trend was found) (Cohen d range 0.45-1.08). In addition, we found significant improvements in quality of life for the GAD, OCD, PTSD, and SAD e-therapy programs (Cohen d range 0.11-0.96) and significant reductions relating to general psychological distress levels for the GAD, PD/A, and PTSD e-therapy programs (Cohen d range 0.23-1.16). Overall, treatment satisfaction was good across all five e-therapy programs, and posttreatment assessment completers reported using their e-therapy program an average of 395.60 (SD 272.2) minutes over the 12-week treatment period. Conclusions: Overall, all five fully automated self-help e-therapy programs appear to be delivering promising high-quality outcomes; however, the results require replication. © Britt Klein, Denny Meyer, David William Austin, Michael Kyrios.
Simplifying and improving ant-based clustering
- Tan, Swee, Ting, Kaiming, Teng, Shyh
- Authors: Tan, Swee , Ting, Kaiming , Teng, Shyh
- Date: 2011
- Type: Text , Conference paper
- Relation: 11th International Conference on Computational Science, ICCS 2011; Singapore, Singapore; 1st-3rd June 2011, published in Procedia Computer Science Vol. 4, p. 46-55
- Full Text:
- Reviewed:
- Description: Ant-based clustering (ABC) is a data clustering approach inspired from cemetery formation activities observed in real ant colonies. Building upon the premise of collective intelligence, such an approach uses multiple ant-like agents and a mixture of heuristics, in order to create systems that are capable of clustering real-world data. Many recently proposed ABC systems have shown competitive results, but these systems are geared towards adding new heuristics, resulting in increasingly complex systems that are harder to understand and improve. In contrast to this direction, we demonstrate that a state-of-the-art ABC system can be systematically evaluated and then simplified. The streamlined model, which we call SABC, differs fundamentally from traditional ABC systems as it does not use the ant-colony and several key components. Yet, our empirical study shows that SABC performs more effectively and effciently than the state-of-the-art ABC system.
- Authors: Tan, Swee , Ting, Kaiming , Teng, Shyh
- Date: 2011
- Type: Text , Conference paper
- Relation: 11th International Conference on Computational Science, ICCS 2011; Singapore, Singapore; 1st-3rd June 2011, published in Procedia Computer Science Vol. 4, p. 46-55
- Full Text:
- Reviewed:
- Description: Ant-based clustering (ABC) is a data clustering approach inspired from cemetery formation activities observed in real ant colonies. Building upon the premise of collective intelligence, such an approach uses multiple ant-like agents and a mixture of heuristics, in order to create systems that are capable of clustering real-world data. Many recently proposed ABC systems have shown competitive results, but these systems are geared towards adding new heuristics, resulting in increasingly complex systems that are harder to understand and improve. In contrast to this direction, we demonstrate that a state-of-the-art ABC system can be systematically evaluated and then simplified. The streamlined model, which we call SABC, differs fundamentally from traditional ABC systems as it does not use the ant-colony and several key components. Yet, our empirical study shows that SABC performs more effectively and effciently than the state-of-the-art ABC system.
Online alcohol interventions: A systematic review
- White, Angela, Kavanagh, David, Stallman, Helen, Klein, Britt, Kay-Lambkin, Frances, Proudfoot, Judith, Drennan, Judy, Connor, Jason, Baker, Amanda, Hines, Emily, Young, Ross
- Authors: White, Angela , Kavanagh, David , Stallman, Helen , Klein, Britt , Kay-Lambkin, Frances , Proudfoot, Judith , Drennan, Judy , Connor, Jason , Baker, Amanda , Hines, Emily , Young, Ross
- Date: 2010
- Type: Text , Journal article , Review
- Relation: Journal of Medical Internet Research Vol. 12, no. 5 (2010), p. e62p.1-e62p.12
- Full Text:
- Reviewed:
- Description: Background: There has been a significant increase in the availability of online programs for alcohol problems. A systematic review of the research evidence underpinning these programs is timely. Objectives: Our objective was to review the efficacy of online interventions for alcohol misuse. Systematic searches of Medline, PsycINFO, Web of Science, and Scopus were conducted for English abstracts (excluding dissertations) published from 1998 onward. Search terms were: (1) Internet, Web*; (2) online, computer *; (3) alcohol*; and (4) Eeffect *, trial*, random* (where * denotes a wildcard). Forward and backward searches from identified papers were also conducted. Articles were included if (1) the primary intervention was delivered and accessed via the Internet, (2) the intervention focused on moderating or stopping alcohol consumption, and (3) the study was a randomized controlled trial of an alcohol-related screen, assessment, or intervention. Results: The literature search initially yielded 31 randomized controlled trials (RCTs), 17 of which met inclusion criteria. Of these 17 studies, 12 (70.6%) were conducted with university students, and 11 (64.7%) specifically focused on at-risk, heavy, or binge drinkers. Sample sizes ranged from 40 to 3216 (median 261), with 12 (70.6%) studies predominantly involving brief personalized feedback interventions. Using published data, effect sizes could be extracted from 8 of the 17 studies. In relation to alcohol units per week or month and based on 5 RCTs where a measure of alcohol units per week or month could be extracted, differential effect sizes to posttreatment ranged from 0.02 to 0.81 (mean 0.42, median 0.54). Pre-post effect sizes for brief personalized feedback interventions ranged from 0.02 to 0.81, and in 2 multi-session modularized interventions, a pre-post effect size of 0.56 was obtained in both. Pre-post differential effect sizes for peak blood alcohol concentrations (BAC) ranged from 0.22 to 0.88, with a mean effect size of 0.66. Conclusions: The available evidence suggests that users can benefit from online alcohol interventions and that this approach could be particularly useful for groups less likely to access traditional alcohol-related services, such as women, young people, and at-risk users. However, caution should be exercised given the limited number of studies allowing extraction of effect sizes, the heterogeneity of outcome measures and follow-up periods, and the large proportion of student-based studies. More extensive RCTs in community samples are required to better understand the efficacy of specific online alcohol approaches, program dosage, the additive effect of telephone or face-to-face interventions, and effective strategies for their dissemination and marketing.
- Description: Background: There has been a significant increase in the availability of online programs for alcohol problems. A systematic review of the research evidence underpinning these programs is timely. Objectives: Our objective was to review the efficacy of online interventions for alcohol misuse. Systematic searches of Medline, PsycINFO, Web of Science, and Scopus were conducted for English abstracts (excluding dissertations) published from 1998 onward. Search terms were: (1) Internet, Web*; (2) online, computer *; (3) alcohol*; and (4) E\effect *, trial*, random* (where * denotes a wildcard). Forward and backward searches from identified papers were also conducted. Articles were included if (1) the primary intervention was delivered and accessed via the Internet, (2) the intervention focused on moderating or stopping alcohol consumption, and (3) the study was a randomized controlled trial of an alcohol-related screen, assessment, or intervention. Results: The literature search initially yielded 31 randomized controlled trials (RCTs), 17 of which met inclusion criteria. Of these 17 studies, 12 (70.6%) were conducted with university students, and 11 (64.7%) specifically focused on at-risk, heavy, or binge drinkers. Sample sizes ranged from 40 to 3216 (median 261), with 12 (70.6%) studies predominantly involving brief personalized feedback interventions. Using published data, effect sizes could be extracted from 8 of the 17 studies. In relation to alcohol units per week or month and based on 5 RCTs where a measure of alcohol units per week or month could be extracted, differential effect sizes to posttreatment ranged from 0.02 to 0.81 (mean 0.42, median 0.54). Pre-post effect sizes for brief personalized feedback interventions ranged from 0.02 to 0.81, and in 2 multi-session modularized interventions, a pre-post effect size of 0.56 was obtained in both. Pre-post differential effect sizes for peak blood alcohol concentrations (BAC) ranged from 0.22 to 0.88, with a mean effect size of 0.66. Conclusions: The available evidence suggests that users can benefit from online alcohol interventions and that this approach could be particularly useful for groups less likely to access traditional alcohol-related services, such as women, young people, and at-risk users. However, caution should be exercised given the limited number of studies allowing extraction of effect sizes, the heterogeneity of outcome measures and follow-up periods, and the large proportion of student-based studies. More extensive RCTs in community samples are required to better understand the efficacy of specific online alcohol approaches, program dosage, the additive effect of telephone or face-to-face interventions, and effective strategies for their dissemination and marketing.
- Authors: White, Angela , Kavanagh, David , Stallman, Helen , Klein, Britt , Kay-Lambkin, Frances , Proudfoot, Judith , Drennan, Judy , Connor, Jason , Baker, Amanda , Hines, Emily , Young, Ross
- Date: 2010
- Type: Text , Journal article , Review
- Relation: Journal of Medical Internet Research Vol. 12, no. 5 (2010), p. e62p.1-e62p.12
- Full Text:
- Reviewed:
- Description: Background: There has been a significant increase in the availability of online programs for alcohol problems. A systematic review of the research evidence underpinning these programs is timely. Objectives: Our objective was to review the efficacy of online interventions for alcohol misuse. Systematic searches of Medline, PsycINFO, Web of Science, and Scopus were conducted for English abstracts (excluding dissertations) published from 1998 onward. Search terms were: (1) Internet, Web*; (2) online, computer *; (3) alcohol*; and (4) Eeffect *, trial*, random* (where * denotes a wildcard). Forward and backward searches from identified papers were also conducted. Articles were included if (1) the primary intervention was delivered and accessed via the Internet, (2) the intervention focused on moderating or stopping alcohol consumption, and (3) the study was a randomized controlled trial of an alcohol-related screen, assessment, or intervention. Results: The literature search initially yielded 31 randomized controlled trials (RCTs), 17 of which met inclusion criteria. Of these 17 studies, 12 (70.6%) were conducted with university students, and 11 (64.7%) specifically focused on at-risk, heavy, or binge drinkers. Sample sizes ranged from 40 to 3216 (median 261), with 12 (70.6%) studies predominantly involving brief personalized feedback interventions. Using published data, effect sizes could be extracted from 8 of the 17 studies. In relation to alcohol units per week or month and based on 5 RCTs where a measure of alcohol units per week or month could be extracted, differential effect sizes to posttreatment ranged from 0.02 to 0.81 (mean 0.42, median 0.54). Pre-post effect sizes for brief personalized feedback interventions ranged from 0.02 to 0.81, and in 2 multi-session modularized interventions, a pre-post effect size of 0.56 was obtained in both. Pre-post differential effect sizes for peak blood alcohol concentrations (BAC) ranged from 0.22 to 0.88, with a mean effect size of 0.66. Conclusions: The available evidence suggests that users can benefit from online alcohol interventions and that this approach could be particularly useful for groups less likely to access traditional alcohol-related services, such as women, young people, and at-risk users. However, caution should be exercised given the limited number of studies allowing extraction of effect sizes, the heterogeneity of outcome measures and follow-up periods, and the large proportion of student-based studies. More extensive RCTs in community samples are required to better understand the efficacy of specific online alcohol approaches, program dosage, the additive effect of telephone or face-to-face interventions, and effective strategies for their dissemination and marketing.
- Description: Background: There has been a significant increase in the availability of online programs for alcohol problems. A systematic review of the research evidence underpinning these programs is timely. Objectives: Our objective was to review the efficacy of online interventions for alcohol misuse. Systematic searches of Medline, PsycINFO, Web of Science, and Scopus were conducted for English abstracts (excluding dissertations) published from 1998 onward. Search terms were: (1) Internet, Web*; (2) online, computer *; (3) alcohol*; and (4) E\effect *, trial*, random* (where * denotes a wildcard). Forward and backward searches from identified papers were also conducted. Articles were included if (1) the primary intervention was delivered and accessed via the Internet, (2) the intervention focused on moderating or stopping alcohol consumption, and (3) the study was a randomized controlled trial of an alcohol-related screen, assessment, or intervention. Results: The literature search initially yielded 31 randomized controlled trials (RCTs), 17 of which met inclusion criteria. Of these 17 studies, 12 (70.6%) were conducted with university students, and 11 (64.7%) specifically focused on at-risk, heavy, or binge drinkers. Sample sizes ranged from 40 to 3216 (median 261), with 12 (70.6%) studies predominantly involving brief personalized feedback interventions. Using published data, effect sizes could be extracted from 8 of the 17 studies. In relation to alcohol units per week or month and based on 5 RCTs where a measure of alcohol units per week or month could be extracted, differential effect sizes to posttreatment ranged from 0.02 to 0.81 (mean 0.42, median 0.54). Pre-post effect sizes for brief personalized feedback interventions ranged from 0.02 to 0.81, and in 2 multi-session modularized interventions, a pre-post effect size of 0.56 was obtained in both. Pre-post differential effect sizes for peak blood alcohol concentrations (BAC) ranged from 0.22 to 0.88, with a mean effect size of 0.66. Conclusions: The available evidence suggests that users can benefit from online alcohol interventions and that this approach could be particularly useful for groups less likely to access traditional alcohol-related services, such as women, young people, and at-risk users. However, caution should be exercised given the limited number of studies allowing extraction of effect sizes, the heterogeneity of outcome measures and follow-up periods, and the large proportion of student-based studies. More extensive RCTs in community samples are required to better understand the efficacy of specific online alcohol approaches, program dosage, the additive effect of telephone or face-to-face interventions, and effective strategies for their dissemination and marketing.
A new scoring system in Cystic Fibrosis : Statistical tools for database analysis - A preliminary report
- Hafen, Gaudenz, Hurst, Cameron, Yearwood, John, Smith, Julie, Dzalilov, Zari, Robinson, P. J.
- Authors: Hafen, Gaudenz , Hurst, Cameron , Yearwood, John , Smith, Julie , Dzalilov, Zari , Robinson, P. J.
- Date: 2008
- Type: Text , Journal article
- Relation: BMC Medical Informatics and Decision Making Vol. 8, no. 44 (2008), p.1-11
- Full Text:
- Reviewed:
- Description: Background. Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. Methods. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. Results. (1) Feature selection: CAP has a more effective "modelling" focus than DA. (2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Conclusion. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset. © 2008 Hafen et al; licensee BioMed Central Ltd.
- Authors: Hafen, Gaudenz , Hurst, Cameron , Yearwood, John , Smith, Julie , Dzalilov, Zari , Robinson, P. J.
- Date: 2008
- Type: Text , Journal article
- Relation: BMC Medical Informatics and Decision Making Vol. 8, no. 44 (2008), p.1-11
- Full Text:
- Reviewed:
- Description: Background. Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. Methods. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. Results. (1) Feature selection: CAP has a more effective "modelling" focus than DA. (2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Conclusion. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset. © 2008 Hafen et al; licensee BioMed Central Ltd.
iPod therefore I can : Enhancing the learning of children with intellectual disabilities through emerging technologies
- Authors: Marks, Genee , Milne, Jay
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at ICICTE 2008: International Conference on Information Communication Technologies in Education, Corfu, Greece : 10th-12th July 2008
- Full Text:
- Description: This paper explores the pedagogical and social potential of emerging technologies, in particular the iPod, in facilitating the learning of young Australians with severe intellectual and social disabilities. The study, which was carried out in a segregated educational setting in Victoria, Australia, sought to establish whether the intrinsic portable, multi-media capabilities of the iPod particularly lent themselves to a practical application for students with severe disabilities. It was concluded that such new technology has considerable power and potential as an emerging pedagogy with students with severe intellectual and physical disabilities.
- Description: 2003006449
- Authors: Marks, Genee , Milne, Jay
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at ICICTE 2008: International Conference on Information Communication Technologies in Education, Corfu, Greece : 10th-12th July 2008
- Full Text:
- Description: This paper explores the pedagogical and social potential of emerging technologies, in particular the iPod, in facilitating the learning of young Australians with severe intellectual and social disabilities. The study, which was carried out in a segregated educational setting in Victoria, Australia, sought to establish whether the intrinsic portable, multi-media capabilities of the iPod particularly lent themselves to a practical application for students with severe disabilities. It was concluded that such new technology has considerable power and potential as an emerging pedagogy with students with severe intellectual and physical disabilities.
- Description: 2003006449
Therapist-Assisted, Internet-Based Treatment for Panic Disorder: Can General Practitioners achieve comparable patient outcomes to Psychologists?
- Shandley, Kerrie, Austin, David, Klein, Britt, Pier, Ciaran, Schattner, Peter, Pierce, David, Wade, Victoria
- Authors: Shandley, Kerrie , Austin, David , Klein, Britt , Pier, Ciaran , Schattner, Peter , Pierce, David , Wade, Victoria
- Date: 2008
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 10, no. 2 (2008), p. 1-15
- Full Text:
- Reviewed:
- Description: Background: Mental illness is an escalating concern worldwide. The management of disorders such as anxiety and depression largely falls to family doctors or general practitioners (GPs). However, GPs are often too time constrained and may lack the necessary training to adequately manage the needs of such patients. Evidence-based Internet interventions represent a potentially valuable resource to reduce the burden of care and the cost of managing mental health disorders within primary care settings and, at the same time, improve patient outcomes. Objective: The present study sought to extend the efficacy of a therapist-assisted Internet treatment program for panic disorder, Panic Online, by determining whether comparable outcomes could be achieved and maintained when Panic Online was supported by either GPs or psychologists. Methods: Via a natural groups design, 96 people with a primary diagnosis of panic disorder (with or without agoraphobia) completed the Panic Online program over 12 weeks with the therapeutic assistance of their GP (n = 53), who had received specialist training in cognitive behavioral therapy, or a clinical psychologist (n = 43). Participants completed a clinical diagnostic telephone interview, conducted by a psychologist, and a set of online questionnaires to assess panic-related symptoms at three time periods (pretreatment, posttreatment, and 6 month follow-up). Results: Both treatments led to clinically significant improvements on measures of panic and panic-related symptomatology from pretreatment to posttreatment. Both groups were shown to significantly improve over time. Improvements for both groups were maintained at follow-up; however, the groups did differ significantly on two quality of life domains: physical (F1,82 = 9.13, P = .00) and environmental (F1,82 = 4.41, P = .04). The attrition rate was significantly higher among those being treated by their GP (χ 2 1 = 4.40, P = .02, N = 96). Conclusions: This study provides evidence that Internet-based interventions are an effective adjunct to existing mental health care systems. Consequently, this may facilitate and enhance the delivery of evidence-based mental health treatments to increasingly large segments of the population via primary care systems and through suitably trained health professionals.
- Description: Background: Mental illness is an escalating concern worldwide. The management of disorders such as anxiety and depression largely falls to family doctors or general practitioners (GPs). However, GPs are often too time constrained and may lack the necessary training to adequately manage the needs of such patients. Evidence-based Internet interventions represent a potentially valuable resource to reduce the burden of care and the cost of managing mental health disorders within primary care settings and, at the same time, improve patient outcomes. Objective: The present study sought to extend the efficacy of a therapist-assisted Internet treatment program for panic disorder, Panic Online, by determining whether comparable outcomes could be achieved and maintained when Panic Online was supported by either GPs or psychologists. Methods: Via a natural groups design, 96 people with a primary diagnosis of panic disorder (with or without agoraphobia) completed the Panic Online program over 12 weeks with the therapeutic assistance of their GP (n = 53), who had received specialist training in cognitive behavioral therapy, or a clinical psychologist (n = 43). Participants completed a clinical diagnostic telephone interview, conducted by a psychologist, and a set of online questionnaires to assess panic-related symptoms at three time periods (pretreatment, posttreatment, and 6 month follow-up). Results: Both treatments led to clinically significant improvements on measures of panic and panic-related symptomatology from pretreatment to posttreatment. Both groups were shown to significantly improve over time. Improvements for both groups were maintained at follow-up; however, the groups did differ significantly on two quality of life domains: physical (F1,82 = 9.13, P = .00) and environmental (F1,82 = 4.41, P = .04). The attrition rate was significantly higher among those being treated by their GP (
- Authors: Shandley, Kerrie , Austin, David , Klein, Britt , Pier, Ciaran , Schattner, Peter , Pierce, David , Wade, Victoria
- Date: 2008
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 10, no. 2 (2008), p. 1-15
- Full Text:
- Reviewed:
- Description: Background: Mental illness is an escalating concern worldwide. The management of disorders such as anxiety and depression largely falls to family doctors or general practitioners (GPs). However, GPs are often too time constrained and may lack the necessary training to adequately manage the needs of such patients. Evidence-based Internet interventions represent a potentially valuable resource to reduce the burden of care and the cost of managing mental health disorders within primary care settings and, at the same time, improve patient outcomes. Objective: The present study sought to extend the efficacy of a therapist-assisted Internet treatment program for panic disorder, Panic Online, by determining whether comparable outcomes could be achieved and maintained when Panic Online was supported by either GPs or psychologists. Methods: Via a natural groups design, 96 people with a primary diagnosis of panic disorder (with or without agoraphobia) completed the Panic Online program over 12 weeks with the therapeutic assistance of their GP (n = 53), who had received specialist training in cognitive behavioral therapy, or a clinical psychologist (n = 43). Participants completed a clinical diagnostic telephone interview, conducted by a psychologist, and a set of online questionnaires to assess panic-related symptoms at three time periods (pretreatment, posttreatment, and 6 month follow-up). Results: Both treatments led to clinically significant improvements on measures of panic and panic-related symptomatology from pretreatment to posttreatment. Both groups were shown to significantly improve over time. Improvements for both groups were maintained at follow-up; however, the groups did differ significantly on two quality of life domains: physical (F1,82 = 9.13, P = .00) and environmental (F1,82 = 4.41, P = .04). The attrition rate was significantly higher among those being treated by their GP (χ 2 1 = 4.40, P = .02, N = 96). Conclusions: This study provides evidence that Internet-based interventions are an effective adjunct to existing mental health care systems. Consequently, this may facilitate and enhance the delivery of evidence-based mental health treatments to increasingly large segments of the population via primary care systems and through suitably trained health professionals.
- Description: Background: Mental illness is an escalating concern worldwide. The management of disorders such as anxiety and depression largely falls to family doctors or general practitioners (GPs). However, GPs are often too time constrained and may lack the necessary training to adequately manage the needs of such patients. Evidence-based Internet interventions represent a potentially valuable resource to reduce the burden of care and the cost of managing mental health disorders within primary care settings and, at the same time, improve patient outcomes. Objective: The present study sought to extend the efficacy of a therapist-assisted Internet treatment program for panic disorder, Panic Online, by determining whether comparable outcomes could be achieved and maintained when Panic Online was supported by either GPs or psychologists. Methods: Via a natural groups design, 96 people with a primary diagnosis of panic disorder (with or without agoraphobia) completed the Panic Online program over 12 weeks with the therapeutic assistance of their GP (n = 53), who had received specialist training in cognitive behavioral therapy, or a clinical psychologist (n = 43). Participants completed a clinical diagnostic telephone interview, conducted by a psychologist, and a set of online questionnaires to assess panic-related symptoms at three time periods (pretreatment, posttreatment, and 6 month follow-up). Results: Both treatments led to clinically significant improvements on measures of panic and panic-related symptomatology from pretreatment to posttreatment. Both groups were shown to significantly improve over time. Improvements for both groups were maintained at follow-up; however, the groups did differ significantly on two quality of life domains: physical (F1,82 = 9.13, P = .00) and environmental (F1,82 = 4.41, P = .04). The attrition rate was significantly higher among those being treated by their GP (
A fully automated CAD system using multi-category feature selection with restricted recombination
- Ghosh, Ranadhir, Ghosh, Moumita, Yearwood, John, Mukherjee, Subhasis
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
- Full Text:
- Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
- Description: 2003005429
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
- Full Text:
- Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
- Description: 2003005429
A smart proxy for a next generation web services transaction
- Pradhan, Sunam, Zaslavsky, Arkady
- Authors: Pradhan, Sunam , Zaslavsky, Arkady
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 646-651
- Full Text:
- Description: In this paper, we propose and describe sProxy - smart proxy, a software tool in Web Services transaction. sProxy acts as a gateway between transaction management systems and Web services which implements a key abstraction of proxy management systems. This enables to perform transactions in the loosely coupled environment i.e. loose coupling among services. Proxies are useful to invoke Web services to allow an easy programming model that facilitates the serialization and transmission of service invocations. Our proposed model supports relaxation of traditional ACID properties with existing commit and recovery protocols. The model works on non-ACID type of transactions which encapsulates Web services. It also uses multithreading proxies to check and update transaction simultaneously. The proposed model solves the current problems with distributed computational activities which involves both transactions and Web Services. The proposed model is more abstract and generic as demonstrated in the paper.
- Description: 2003005442
- Authors: Pradhan, Sunam , Zaslavsky, Arkady
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 646-651
- Full Text:
- Description: In this paper, we propose and describe sProxy - smart proxy, a software tool in Web Services transaction. sProxy acts as a gateway between transaction management systems and Web services which implements a key abstraction of proxy management systems. This enables to perform transactions in the loosely coupled environment i.e. loose coupling among services. Proxies are useful to invoke Web services to allow an easy programming model that facilitates the serialization and transmission of service invocations. Our proposed model supports relaxation of traditional ACID properties with existing commit and recovery protocols. The model works on non-ACID type of transactions which encapsulates Web services. It also uses multithreading proxies to check and update transaction simultaneously. The proposed model solves the current problems with distributed computational activities which involves both transactions and Web Services. The proposed model is more abstract and generic as demonstrated in the paper.
- Description: 2003005442
Image retrieval based on fuzzy mapping of image database and fuzzy similarity distance
- Authors: Kulkarni, Siddhivinayak
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 812-817
- Full Text:
- Description: The on-line image retrieval process consists of a query example image, given by the user as an input, from which low-level image features are extracted. These image features are used to find images in the database which are most similar to the query image. A drawback, however, is that these low level image features are often too restricted to describe images on a conceptual or semantic level. The gap between the high level query from the user and low level features extracted by a computer is known as the semantic gap. Translating or converting the question posed by a human to the low level features seen by the computer illustrates the problem in bridging the semantic gap. This paper proposes a novel fuzzy approach for mapping the fuzzy database while extracting the colour features from image and assigning the weights to this fuzzy content when calculating the similarity between the query image and the images in database. Number of experiments was conducted on a small colour image database and promising results were obtained.
- Description: 2003005444
- Authors: Kulkarni, Siddhivinayak
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 812-817
- Full Text:
- Description: The on-line image retrieval process consists of a query example image, given by the user as an input, from which low-level image features are extracted. These image features are used to find images in the database which are most similar to the query image. A drawback, however, is that these low level image features are often too restricted to describe images on a conceptual or semantic level. The gap between the high level query from the user and low level features extracted by a computer is known as the semantic gap. Translating or converting the question posed by a human to the low level features seen by the computer illustrates the problem in bridging the semantic gap. This paper proposes a novel fuzzy approach for mapping the fuzzy database while extracting the colour features from image and assigning the weights to this fuzzy content when calculating the similarity between the query image and the images in database. Number of experiments was conducted on a small colour image database and promising results were obtained.
- Description: 2003005444
Narrative-based interactive learning environments from modelling reasoning
- Yearwood, John, Stranieri, Andrew
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2007
- Type: Text , Journal article
- Relation: Educational Technology and Society Vol. 10, no. 3 (2007), p. 192-208
- Full Text:
- Reviewed:
- Description: Narrative and story telling has a long history of use in structuring, organising and communicating human experience. This paper describes a narrative based interactive intelligent learning environment which aims to elucidate practical reasoning using interactive emergent narratives that can be used in training novices in decision making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the Generic/Actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training and positive learning outcomes are reported. © International Forum of Educational Technology & Society (IFETS).
- Description: C1
- Description: 2003002522
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2007
- Type: Text , Journal article
- Relation: Educational Technology and Society Vol. 10, no. 3 (2007), p. 192-208
- Full Text:
- Reviewed:
- Description: Narrative and story telling has a long history of use in structuring, organising and communicating human experience. This paper describes a narrative based interactive intelligent learning environment which aims to elucidate practical reasoning using interactive emergent narratives that can be used in training novices in decision making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the Generic/Actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training and positive learning outcomes are reported. © International Forum of Educational Technology & Society (IFETS).
- Description: C1
- Description: 2003002522
Using links to aid web classification
- Xie, Wei, Mammadov, Musa, Yearwood, John
- Authors: Xie, Wei , Mammadov, Musa , Yearwood, John
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 981-986
- Full Text:
- Description: In this paper, we will present a new approach of using link information to improve the accuracy and efficiency of web classification. However, different from others, we only use the mappings between linked documents and their own class or classes. In this case, we only need to add a few features called linked-class features into the datasets. We apply SVM and BoosTexter for classification. We show that the classification accuracy can be improved based on mixtures of ordinary word features and out-linked-class features. We analyze and discuss the reason of this improvement.
- Description: 2003005438
- Authors: Xie, Wei , Mammadov, Musa , Yearwood, John
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 981-986
- Full Text:
- Description: In this paper, we will present a new approach of using link information to improve the accuracy and efficiency of web classification. However, different from others, we only use the mappings between linked documents and their own class or classes. In this case, we only need to add a few features called linked-class features into the datasets. We apply SVM and BoosTexter for classification. We show that the classification accuracy can be improved based on mixtures of ordinary word features and out-linked-class features. We analyze and discuss the reason of this improvement.
- Description: 2003005438
Visual tools for analysing evolution, emergence, and error in data streams
- Hart, Sol, Yearwood, John, Bagirov, Adil
- Authors: Hart, Sol , Yearwood, John , Bagirov, Adil
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 987-992
- Full Text:
- Description: The relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities. Tools are needed to assess and diagnose important trends and investigate drift evolution parameters. In this paper, we present two new and novel visualisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional groupings over time. Through the novel representations presented here, spatial interactions between temporal divisions can be diagnosed and overall distribution patterns identified. It provides a means of evaluating in non-constrained capacity, commonly constrained evolutionary problems.
- Description: 2003005432
- Authors: Hart, Sol , Yearwood, John , Bagirov, Adil
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 987-992
- Full Text:
- Description: The relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities. Tools are needed to assess and diagnose important trends and investigate drift evolution parameters. In this paper, we present two new and novel visualisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional groupings over time. Through the novel representations presented here, spatial interactions between temporal divisions can be diagnosed and overall distribution patterns identified. It provides a means of evaluating in non-constrained capacity, commonly constrained evolutionary problems.
- Description: 2003005432
Decisions surrounding adverse drug reaction prescribing : Insights from consumers and implications for decision support
- O'Brien, Michelle, Yearwood, John
- Authors: O'Brien, Michelle , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 57-71
- Full Text:
- Reviewed:
- Description: This paper presents findings from case studies of health consumers who each suspect they may have experienced an adverse drug reaction (ADR). These case studies are part of a larger study involving consumer/doctor decisions surrounding suspected adverse drug reactions and prescribing. Decision support to assist with the diagnosis and management of ADRs has, to date, primarily focused on providing in-time information to prescribers about factors that pertain to the consumer and the medications they are taking. Decision support that includes consumers usually targets treatment decisions. The results of this paper indicate the prescriber is only one decision contributor in a rich tapestry of decision contributors and decision types, and consumer decision types are significantly broader than treatment decisions. The results provide guidance for the development of decision support within this domain.
- Description: C1
- Description: 2003001435
- Authors: O'Brien, Michelle , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 57-71
- Full Text:
- Reviewed:
- Description: This paper presents findings from case studies of health consumers who each suspect they may have experienced an adverse drug reaction (ADR). These case studies are part of a larger study involving consumer/doctor decisions surrounding suspected adverse drug reactions and prescribing. Decision support to assist with the diagnosis and management of ADRs has, to date, primarily focused on providing in-time information to prescribers about factors that pertain to the consumer and the medications they are taking. Decision support that includes consumers usually targets treatment decisions. The results of this paper indicate the prescriber is only one decision contributor in a rich tapestry of decision contributors and decision types, and consumer decision types are significantly broader than treatment decisions. The results provide guidance for the development of decision support within this domain.
- Description: C1
- Description: 2003001435
Predicting Australian stock market index using neural networks exploiting dynamical swings and intermarket influences
- Pan, Heping, Tilakaratne, Chandima, Yearwood, John
- Authors: Pan, Heping , Tilakaratne, Chandima , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 43-55
- Full Text:
- Reviewed:
- Description: This paper presents a computational approach for predicting the Australian stock market index AORD using multi-layer feed-forward neural networks front the time series data of AORD and various interrelated markets. This effort aims to discover an effective neural network, or a set of adaptive neural networks for this prediction purpose, which can exploit or model various dynamical swings and inter-market influences discovered from professional technical analysis and quantitative analysis. Within a limited range defined by our empirical knowledge, three aspects of effectiveness on data selection are considered: effective inputs from the target market (AORD) itself, a sufficient set of interrelated markets,. and effective inputs from the interrelated markets. Two traditional dimensions of the neural network architecture are also considered: the optimal number of hidden layers, and the optimal number of hidden neurons for each hidden layer. Three important results were obtained: A 6-day cycle was discovered in the Australian stock market during the studied period; the time signature used as additional inputs provides useful information; and a basic neural network using six daily returns of AORD and one daily, returns of SP500 plus the day of the week as inputs exhibits up to 80% directional prediction correctness.
- Description: C1
- Description: 2003001440
- Authors: Pan, Heping , Tilakaratne, Chandima , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 43-55
- Full Text:
- Reviewed:
- Description: This paper presents a computational approach for predicting the Australian stock market index AORD using multi-layer feed-forward neural networks front the time series data of AORD and various interrelated markets. This effort aims to discover an effective neural network, or a set of adaptive neural networks for this prediction purpose, which can exploit or model various dynamical swings and inter-market influences discovered from professional technical analysis and quantitative analysis. Within a limited range defined by our empirical knowledge, three aspects of effectiveness on data selection are considered: effective inputs from the target market (AORD) itself, a sufficient set of interrelated markets,. and effective inputs from the interrelated markets. Two traditional dimensions of the neural network architecture are also considered: the optimal number of hidden layers, and the optimal number of hidden neurons for each hidden layer. Three important results were obtained: A 6-day cycle was discovered in the Australian stock market during the studied period; the time signature used as additional inputs provides useful information; and a basic neural network using six daily returns of AORD and one daily, returns of SP500 plus the day of the week as inputs exhibits up to 80% directional prediction correctness.
- Description: C1
- Description: 2003001440