Pub Date : 1900-01-01DOI: 10.1109/ANZIIS.2001.974097
I. Smith, R. Lister, M. Ray, G. Hawson
Excessive post-operative bleeding occurs in approximately one out of eight patients who undergo heart bypass surgery. Earlier workers have identified laboratory parameters that are correlated with post-operative blood loss but these correlations are not strong enough to be clinically useful. This paper describes a predictor that combines several of these parameters using Naive Bayesian Reasoning, to produce a clinically useful predictor of blood loss.
{"title":"Naive Bayesian prediction of bleeding after heart by-pass surgery","authors":"I. Smith, R. Lister, M. Ray, G. Hawson","doi":"10.1109/ANZIIS.2001.974097","DOIUrl":"https://doi.org/10.1109/ANZIIS.2001.974097","url":null,"abstract":"Excessive post-operative bleeding occurs in approximately one out of eight patients who undergo heart bypass surgery. Earlier workers have identified laboratory parameters that are correlated with post-operative blood loss but these correlations are not strong enough to be clinically useful. This paper describes a predictor that combines several of these parameters using Naive Bayesian Reasoning, to produce a clinically useful predictor of blood loss.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129490158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ANZIIS.2001.974111
H. Sato, Y. Mitsukura, M. Fukumi, N. Akamatsu
Interestingly, in order to achieve a new Human Interface such that digital computers can deal with the KASEI information, the study of the KANSEI information processing recently has been approached. In this paper, we propose a new classification method of emotional speech by analyzing feature parameters obtained from the emotional speech and by learning them using neural networks, which is regarded as a KANSEI information processing. In the present research, KANSEI information is usually human emotion. The emotion is classified broadly into four patterns such as neutral, anger, sad and joy. The pitch as one of feature parameters governs voice modulation, and can be sensitive to change of emotion. The pitch is extracted from each emotional speech by the cepstrum method. Input values of neural networks (NNs) are then emotional pitch patterns, which are time-varying. It is shown that NNs can achieve classification of emotion by learning each emotional pitch pattern by means of computer simulations.
{"title":"Emotional speech classification with prosodic prameters by using neural networks","authors":"H. Sato, Y. Mitsukura, M. Fukumi, N. Akamatsu","doi":"10.1109/ANZIIS.2001.974111","DOIUrl":"https://doi.org/10.1109/ANZIIS.2001.974111","url":null,"abstract":"Interestingly, in order to achieve a new Human Interface such that digital computers can deal with the KASEI information, the study of the KANSEI information processing recently has been approached. In this paper, we propose a new classification method of emotional speech by analyzing feature parameters obtained from the emotional speech and by learning them using neural networks, which is regarded as a KANSEI information processing. In the present research, KANSEI information is usually human emotion. The emotion is classified broadly into four patterns such as neutral, anger, sad and joy. The pitch as one of feature parameters governs voice modulation, and can be sensitive to change of emotion. The pitch is extracted from each emotional speech by the cepstrum method. Input values of neural networks (NNs) are then emotional pitch patterns, which are time-varying. It is shown that NNs can achieve classification of emotion by learning each emotional pitch pattern by means of computer simulations.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"51 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114038297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ANZIIS.2001.974087
A. Garliauskas
The expressed layered structures in the cereberal and cerebellar cortices of the brain are attributed to most animals while the human and some primate neostriatum neurons are laid out as clustered higher and lower cell density mosaics. These ordered structures are probably formed by a self-organizing mechanism. which is widely discussed in the present paper. Considering theoretical principles and neuronal networks, the N-shaped current-voltage relation was included in the model and its influence on the stability and conditions of self-organization discussed. The formation of ordered structures was founded in vicinity of the equilibrium point. The concomitant computational experiment is made.
{"title":"Self-organization of mosaics in artificial neural networks for the visual cortex of the brain","authors":"A. Garliauskas","doi":"10.1109/ANZIIS.2001.974087","DOIUrl":"https://doi.org/10.1109/ANZIIS.2001.974087","url":null,"abstract":"The expressed layered structures in the cereberal and cerebellar cortices of the brain are attributed to most animals while the human and some primate neostriatum neurons are laid out as clustered higher and lower cell density mosaics. These ordered structures are probably formed by a self-organizing mechanism. which is widely discussed in the present paper. Considering theoretical principles and neuronal networks, the N-shaped current-voltage relation was included in the model and its influence on the stability and conditions of self-organization discussed. The formation of ordered structures was founded in vicinity of the equilibrium point. The concomitant computational experiment is made.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"3 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125687865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ANZIIS.2001.974101
M. C. Orr, Duc-Son Pham, B. Lithgow, R. Mahony
An algorithm for the analysis of speech utilising the time frequency properties of wavelets is introduced. The extracted wavelet coefficients are analysed using two techniques, firstly a covariance matrix is generated to provide information about speaker characteristics. Second, the kurtosis of the wavelet coefficients is used to facilitate the detection of multiple speakers. Preliminary results show that some phonetic information, such as articulation placement and identification of voiced/unvoiced sections, can be extracted from the kurtosis analysis.
{"title":"Speech perception based algorithm for the separation of overlapping speech signal","authors":"M. C. Orr, Duc-Son Pham, B. Lithgow, R. Mahony","doi":"10.1109/ANZIIS.2001.974101","DOIUrl":"https://doi.org/10.1109/ANZIIS.2001.974101","url":null,"abstract":"An algorithm for the analysis of speech utilising the time frequency properties of wavelets is introduced. The extracted wavelet coefficients are analysed using two techniques, firstly a covariance matrix is generated to provide information about speaker characteristics. Second, the kurtosis of the wavelet coefficients is used to facilitate the detection of multiple speakers. Preliminary results show that some phonetic information, such as articulation placement and identification of voiced/unvoiced sections, can be extracted from the kurtosis analysis.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122433779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ANZIIS.2001.974089
N. Pah, D. Kumar
To determine the status of a muscle, surface electromyography (SEMG) is a useful tool being non-invasive and easy to record. Clinicians are able to classify the signal visually but because of the large number of parameters of the signal, automatic classification becomes difficult. This paper reports our efforts at using Wavelet Transforms to process the signal before using Neural Networks for classification. The paper reports that by using specific wavelets for transform and at specific levels of decomposition, the features of the signal correlating with muscle status were highlighted and classification of this data using neural networks gave excellent results.
{"title":"Classification of electromyograph for localised muscle fatigue using neural networks","authors":"N. Pah, D. Kumar","doi":"10.1109/ANZIIS.2001.974089","DOIUrl":"https://doi.org/10.1109/ANZIIS.2001.974089","url":null,"abstract":"To determine the status of a muscle, surface electromyography (SEMG) is a useful tool being non-invasive and easy to record. Clinicians are able to classify the signal visually but because of the large number of parameters of the signal, automatic classification becomes difficult. This paper reports our efforts at using Wavelet Transforms to process the signal before using Neural Networks for classification. The paper reports that by using specific wavelets for transform and at specific levels of decomposition, the features of the signal correlating with muscle status were highlighted and classification of this data using neural networks gave excellent results.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123792640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ANZIIS.2001.974078
B. Piggott, A. Smith, M. Fisher, R. Aldridge
This paper describes the use of image processing tools to study applied stress on biological cells. The cells investigated were taken from onion epidermal layers. These were chosen because they are all similar in size and shape i.e. almost long thin rectangles with well-defined intercellular walls. The paper describes samples preparation, the way in which data is captured and how it is processed to obtain local strain values on a cell by cell basis. The paper concludes with a discussion of the importance of the observations and how the data can be further improved.
{"title":"The use of image processing in observing the effect of applied stress on onion epidermal cellular structures","authors":"B. Piggott, A. Smith, M. Fisher, R. Aldridge","doi":"10.1109/ANZIIS.2001.974078","DOIUrl":"https://doi.org/10.1109/ANZIIS.2001.974078","url":null,"abstract":"This paper describes the use of image processing tools to study applied stress on biological cells. The cells investigated were taken from onion epidermal layers. These were chosen because they are all similar in size and shape i.e. almost long thin rectangles with well-defined intercellular walls. The paper describes samples preparation, the way in which data is captured and how it is processed to obtain local strain values on a cell by cell basis. The paper concludes with a discussion of the importance of the observations and how the data can be further improved.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132791878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ANZIIS.2001.974056
G. Arulampalam, A. Bouzerdoum
Shunting inhibitory artificial neural networks (SIANNs) are biologically inspired networks in which the neurons interact among each other via a nonlinear mechanism called shunting inhibition. Since they are high-order networks, SIANNs are capable of producing complex, nonlinear decision boundaries. In this article, feedforward SIANNs are applied to several medical diagnosis problems and the results are compared with those obtained using multilayer perceptrons (MLPs). First, the structure of feedforward SIANNs is presented. Then, these networks are applied to some standard medical classification problems, namely the Pima Indians diabetes and Wisconsin breast cancer classification problems. The SIANN performance compares favourably with that of MLPs. Moreover, some problems with the diabetes data set are addressed and a reduction in the number of inputs is investigated.
{"title":"Application of shunting inhibitory artificial neural networks to medical diagnosis","authors":"G. Arulampalam, A. Bouzerdoum","doi":"10.1109/ANZIIS.2001.974056","DOIUrl":"https://doi.org/10.1109/ANZIIS.2001.974056","url":null,"abstract":"Shunting inhibitory artificial neural networks (SIANNs) are biologically inspired networks in which the neurons interact among each other via a nonlinear mechanism called shunting inhibition. Since they are high-order networks, SIANNs are capable of producing complex, nonlinear decision boundaries. In this article, feedforward SIANNs are applied to several medical diagnosis problems and the results are compared with those obtained using multilayer perceptrons (MLPs). First, the structure of feedforward SIANNs is presented. Then, these networks are applied to some standard medical classification problems, namely the Pima Indians diabetes and Wisconsin breast cancer classification problems. The SIANN performance compares favourably with that of MLPs. Moreover, some problems with the diabetes data set are addressed and a reduction in the number of inputs is investigated.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115763904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ANZIIS.2001.974074
Z. Les, R. Tadeusiewicz, M. Les
Image analysis and recognition applied in medical engineering requires specific methods of shape analysis and representation that need to be learnt. In this paper the method of knowledge generation as a part of a shape understanding method is proposed. The knowledge generation method used in the system of shape understanding is related to hierarchically organised knowledge of the shape classes. The system of shape understanding that is able to perform different tasks of shape analysis and recognition, based on the ability of the system to understand the different concepts of shape at the different levels of cognition, is proposed. The system consists of different types of experts that perform different processing and reasoning tasks and is designed to perform the visual diagnosis in medical applications.
{"title":"Shape understanding: knowledge generation and learning","authors":"Z. Les, R. Tadeusiewicz, M. Les","doi":"10.1109/ANZIIS.2001.974074","DOIUrl":"https://doi.org/10.1109/ANZIIS.2001.974074","url":null,"abstract":"Image analysis and recognition applied in medical engineering requires specific methods of shape analysis and representation that need to be learnt. In this paper the method of knowledge generation as a part of a shape understanding method is proposed. The knowledge generation method used in the system of shape understanding is related to hierarchically organised knowledge of the shape classes. The system of shape understanding that is able to perform different tasks of shape analysis and recognition, based on the ability of the system to understand the different concepts of shape at the different levels of cognition, is proposed. The system consists of different types of experts that perform different processing and reasoning tasks and is designed to perform the visual diagnosis in medical applications.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123054606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ANZIIS.2001.974113
N. Ma, D.K. Kumar, N. Pah
Muscles are responsible for movement of the limbs. Muscle contraction is accompanied by electrical activity that is measurable and is the electromyography (EMG) recording. Due to the complex nature of the signal, detailed analysis and classification is often difficult, especially if the EMG relates to movement. This paper reports the research to determine features of the multi-channel EMG signal recording that correlate with the movement of the hand of the subjects. Different processing techniques are reported. It demonstrates integral of the RMS of the signal correlates best with the movement.
{"title":"Classification of hand direction using multi-channel electromyography by neural network","authors":"N. Ma, D.K. Kumar, N. Pah","doi":"10.1109/ANZIIS.2001.974113","DOIUrl":"https://doi.org/10.1109/ANZIIS.2001.974113","url":null,"abstract":"Muscles are responsible for movement of the limbs. Muscle contraction is accompanied by electrical activity that is measurable and is the electromyography (EMG) recording. Due to the complex nature of the signal, detailed analysis and classification is often difficult, especially if the EMG relates to movement. This paper reports the research to determine features of the multi-channel EMG signal recording that correlate with the movement of the hand of the subjects. Different processing techniques are reported. It demonstrates integral of the RMS of the signal correlates best with the movement.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130563105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ANZIIS.2001.974105
Zou Qingsong, K. C. Keong, Ng Wan Sing, Chen Yintao
In this paper, we present a new image segmentation approach for MRI of the head, which is a semi-automatic process. Unlike automatic segmentation or manual segmentation, the semi-automatic segmentation approach is a robust and interactive segmentation process. This approach carries out 3D volume data segmentation based on 2D image slices. By utilising the user-provided image mask, including areas of interest or structural information, the semi-automatic segmentation process can generate a new segmented volume dataset and structural information. The object based volume visualization method can use this segmented dataset and structural information to perform structure based manipulation and visualization, which cannot be achieved using a normal volume rendering method.
{"title":"MRI head segmentation for object based volume visualization","authors":"Zou Qingsong, K. C. Keong, Ng Wan Sing, Chen Yintao","doi":"10.1109/ANZIIS.2001.974105","DOIUrl":"https://doi.org/10.1109/ANZIIS.2001.974105","url":null,"abstract":"In this paper, we present a new image segmentation approach for MRI of the head, which is a semi-automatic process. Unlike automatic segmentation or manual segmentation, the semi-automatic segmentation approach is a robust and interactive segmentation process. This approach carries out 3D volume data segmentation based on 2D image slices. By utilising the user-provided image mask, including areas of interest or structural information, the semi-automatic segmentation process can generate a new segmented volume dataset and structural information. The object based volume visualization method can use this segmented dataset and structural information to perform structure based manipulation and visualization, which cannot be achieved using a normal volume rendering method.","PeriodicalId":383878,"journal":{"name":"The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126440760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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