A CAD system using clustering and novel feature extraction technique
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at CISTM 2005, Gurgaon, India : 24th - 26th July, 2005
- Full Text: false
- Reviewed:
- Description: Many previous efforts have utilized many different approaches for recognition in breast cancer detection using various ANN classifier-modelling techniques. Most of the previous work was concentred mostly on the classification of the damaged areas with the help of doctor’s suggestion. Doctors use to mark the suspicious areas area in the mammogram and the classifier only extract those marked areas and tries to classify it. An intelligent automatic diagnosis system can be very helpful for radiologist in diagnosing Breast cancer. In this research we are applying a local search gradient free clustering algorithm to find out the suspicious / damaged area. We compare our results with the doctor’s marking. Also it has been observed that, beyond a certain point, the inclusion of additional features leads to a worse rather than better performance. Moreover, the choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and a necessary number of samples. A common problem with the multi-category feature classification is the conflict between the categories. None of the feasible solutions allow simultaneous optimal solution for all categories. In order to find an optimal solution the search space can be divided based on an individual category in each sub region and finally merging them through decision spport system. Combining the feature selection with the classifier has been a major challenge for the researchers. A similar technique employed in both the levels often worsens their performance. Some preliminary studies has revealed that while using traditional canonical GA has been a good choice for feature selection modules, however under perform for the classifier level module. An evolutionary based algorithm for the classifier level provides a much better solution for this purpose. In this paper we propose a hybrid canonical based feature extraction technique with a combination of evolutionary algorithm based classifier using a feed forward MLP model.
- Description: E1
- Description: 2003001369
A fully automated breast cancer recognition system using discrete-gradient based clustering and multi category feature selection
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Advanced Computational Intelligence and Intelligent Informatics Vol. 9, no. 3 (2005), p. 244-256
- Full Text: false
- Reviewed:
- Description: Advances in machine intelligence have provided a whole new window of opportunities in medical research. Building a fully automated computer aided diagnostic system for digital mammograms is just one of them. Given some success with semi-automated systems earlier, a fully automated CAD system is just another step forward. A proper combination of a feature selection model and a classifier for those areas of a mammogram marked by radiologists has been very successful. However a fully automated system with only two modules is a time consuming process as the suspicious areas in a mammogram can be quite small when compared to the whole image. Thus an additional clustering process can help in reducing the time complexity of the overall process. In this paper we propose a fast clustering process to identify suspicious areas. Another novelty of this paper is a multi-category feature selection approach. The choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and the required number of samples. In this paper we propose a hybrid canonical based feature extraction technique as a combination of an evolutionary algorithm based classifier with a feed forward MLP model.
- Description: C1
- Description: 2003001358
A fully automated CAD system using multi-category feature selection with restricted recombination
- 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 Hybrid algorithm for estimation of the parameters of Hidden Markov Model based acoustic modeling of speech signals using constraint-based genetic algorithm and expectation maximization
- Authors: Ghosh, Ranadhir , Huda, Shamsul , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the Workshop in Learning Algorithms for Pattern Recognition, in conjunction with the 18th Australian Joint Conference on Artificial Intelligence, Sydney : 5th December, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001368
A hybrid approach for feature and architecture selection in online handwriting recognition
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at RASC 2004: Fifth International Conference on Recent Advances in Soft Computing, Nottingham, United Kingdom : 16th - 18th December, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000870
A hybrid clustering algorithm using two level of abstraction
- Authors: Ghosh, Ranadhir , Mammadov, Musa , Ghosh, Moumita , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at Fuzzy Logic, Soft Computing, and Computational Intelligence, 11th International Fuzzy Systems Association World Congress, Beijing, China : 28th - 31st July, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001360
A hybrid evolutionary algorithm for multi category feature selection in breast cancer recognition
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the Second International Conference on Software Computing and Intelligent Systems, Yokahama, Japan : 21st - 22nd September, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000869
A hybrid neural learning algorithm combining evolutionary algorithm with discrete gradient method
- Authors: Ghosh, Ranadhir , Yearwood, John , Bagirov, Adil
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the Second International Conference on Software Computing and Intelligent Systems, Yokahama, Japan : 21st October, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000860
A hybrid neural learning algorithm using evolutionary learning and derivative free local search method
- Authors: Ghosh, Ranadhir , Yearwood, John , Ghosh, Moumita , Bagirov, Adil
- Date: 2006
- Type: Text , Journal article
- Relation: International Journal of Neural Systems Vol. 16, no. 3 (2006), p. 201-213
- Full Text: false
- Reviewed:
- Description: In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models. © World Scientific Publishing Company.
- Description: C1
- Description: 2003001712
A hybrid question answering schema using encapsulated semantics in lexical resources
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Artificial Intelligence, AI 2006: Advances in Artificial Intelligence, Hobart : 4th December, 2006 p. 1276-1280
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001531
A modular framework for multi category feature selection in digital mammography
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ESANN 2004 Proceedings: European Symposium on Artificial Neural Networks, Bruges, Belguim : 28/04/2004 Vol. Elsevier, p. 175-180
- Full Text:
- Reviewed:
- Description: Many existing researches utilized many different approaches for recognition in digital mammography using various ANN classifier-modeling techniques. Different types of feature extraction techniques are also used. It has been observed that, beyond a certain point, the inclusion of additional features leads to a worse rather than better performance. Moreover, the choice of features to represent the patterns affects several aspects of pattern recognition problem such as accuracy, required learning time and necessary number of samples. A common problem with the multi category feature classification is the conflict between the categories. None of the feasible solutions allow simultaneous optimal solution for all categories. In order to find an optimal solutions the searching space can be divided based on individual category in each sub region and finally merging them through decision spport system. In this paper we propose a canonical GA based modular feature selection approach combined with standard MLP.
- Description: E1
- Description: 2003000872
A rule based plagiarism detection using decision tree
- Authors: Ghosh, Moumita , Ghosh, Ranadhir , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the CIMCA 2004: International Conference on Computational Intelligence for Modelling, Control and Automation, Gold Coast, Queensland : 12th July, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000858
A semantic approach to boost passage retrieval effectiveness for question answering
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Computer Science 2006 Twenty-Ninth Australian Computer Science Conference, Hobart : 16th January, 2006 p. 95-101
- Full Text:
- Reviewed:
- Description: In the current state of the rapid growth of information resources and the huge number of requests submitted by users to existing information retrieval systems; recently, Question Answering systems have attracted more attention to meet information needs providing users with more precise and focused retrieval units. As one of the most challenging and important processes of such systems is to retrieve the best related text excerpts with regard to the questions, we propose a novel approach to exploit not only the syntax of the natural language of the questions and texts, but also the semantics relayed beneath them via a semantic question rewriting and passage retrieval task. The semantic structure used to address the surface mismatch of the semantically related passages and queries is FrameNet which is a lexical resource for English constituted based on frame semantics. We have run our proposed approach on a subset of the TREC 2004 factoid questions to retrieve passages containing correct answers from the AQUAINT collection and we have obtained promising results.
- Description: E1
- Description: 2003001803
A semantic method to information extraction for decision support systems
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference proceedings
- Full Text: false
- Description: In this paper, we describe a novel schema for a more semantic text mining process which results in more comprehensive decision making activity by decision support systems via providing more effective and accurate textual information. The utility of two semantic lexical resources; Frame Net and Word Net, in extracting required text snippets from unstructured free texts yields a better and more accurate information extraction process to deliver more precise information either to a DSS or to a decision maker. We explain how the usage of these lexical resources could elevate a focused text mining process which could be applied to an information provider system in a decision support paradigm. The preliminary results obtained after a starter experiment show that the hybrid information extraction schema performs well on some semantic failure situations.
- Description: 2003010644
A variable initialization approach to the EM algorithm for better estimation of the parameters of hidden Markov Model based acoustic modeling of speech signals
- Authors: Huda, Shamsul , Ghosh, Ranadhir , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Artificial Intelligence, Advances in Data Mining, Applications in Medicine, Web Mining, Marketing, Image and Signal Mining Conference 2006, Leipzig, Germany : 14th July, 2006 p. 416-430
- Full Text: false
- Reviewed:
- Description: The traditional method for estimation of the parameters of Hidden Markov Model (HMM) based acoustic modeling of speech uses the Expectation-Maximization (EM) algorithm. The EM algorithm is sensitive to initial values of HMM parameters and is likely to terminate at a local maximum of likelihood function resulting in non-optimized estimation for HMM and lower recognition accuracy. In this paper, to obtain better estimation for HMM and higher recognition accuracy, several candidate HMMs are created by applying EM on multiple initial models. The best HMM is chosen from the candidate HMMs which has highest value for likelihood function. Initial models are created by varying maximum frame number in the segmentation step of HMM initialization process. A binary search is applied while creating the initial models. The proposed method has been tested on TIMIT database. Experimental results show that our approach obtains improved values for likelihood function and improved recognition accuracy.
- Description: E1
- Description: 2003001542
A within-frame ontological extension on FrameNet : Application in predicate chain analysis and question answering
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 20th Australian Joint Conference on Artificial Intelligence, AI 2007: Advances in Artificial Intelligence, Gold Coast, Queensland : 2nd-6th December 2007 p. 404-414
- Full Text: false
- Description: An ontological extension on the frames in FrameNet is presented in this paper. The general conceptual relations between frame elements, in conjunction with existing characteristics of this lexical resource, suggest more sophisticated semantic analysis of lexical chains (e.g. predicate chains) exploited in many text understanding applications. In particular, we have investigated its benefit for meaning-aware question answering when combined with an inference strategy. The proposed knowledge representation mechanism on the frame elements of FrameNet has been shown to have an impact on answering natural language questions on the basis of our case analysis.
- Description: 2003005507
An evolutionary neural learning algorithm for offline cursive handwriting words with hamming network lexicon
- Authors: Ghosh, Moumita , Ghosh, Ranadhir , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at Seventeenth International Florida Artificial Intelligence Research Symposium Conference, FLAIRS 2004, Miami Beach, USA : 15th May, 2004
- Full Text: false
- Reviewed:
- Description: In this paper we incorporate a hybrid evolutionary method, which uses a combination of genetic algorithm and matrix based solution method such as QR factorization. A heuristic segmentation algorithm is initially used to over segment each word. Then the segmentation points are passed through the rule-based module to discard the incorrect segmentation points and include any missing segmentation points. Following the segmentation the connected contour is extracted between two correct segmentation points. The contour is passed through the feature extraction module that extracts the angular features of the contour, after which the EALS-BT algorithm finds the architecture and the weights for the classifier network. These recognized characters are grouped into words and passed to a variable length lexicon that retrieves words that has highest confidence value. Hamming neural network is used as a lexicon that rectifies the word misrecognized by the classifier. We have used CEDAR benchmark dataset and UCI Machine Learning repository (Upper case) to test the train and test the system
- Description: E1
- Description: 2003000865
An experiment in task decomposition and ensembling for a modular artificial neural network
- Authors: Ferguson, Brent , Ghosh, Ranadhir , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at Innovations in Applied Artificial Intelligence: 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Ottawa, Canada : 17th May, 2004
- Full Text:
- Reviewed:
- Description: Modular neural networks have the possibility of overcoming common scalability and interference problems experienced by fully connected neural networks when applied to large databases. In this paper we trial an approach to constructing modular ANN's for a very large problem from CEDAR for the classification of handwritten characters. In our approach, we apply progressive task decomposition methods based upon clustering and regression techniques to find modules. We then test methods for combining the modules into ensembles and compare their structural characteristics and classification performance with that of an ANN having a fully connected topology. The results reveal improvements to classification rates as well as network topologies for this problem.
- Description: E1
- Description: 2003000852
Applying anatomical therapeutic chemical (ATC) and critical term ontologies to Australian drug safety data for association rules and adverse event signalling
- Authors: Saunders, Gary , Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Conferences in Research and Practice in Information Technology, Advances in Ontologies 2005: Proceedings of the Australasian Ontology Workshop AOW 2005 Vol. 58, no. (2005), p. 93-98
- Full Text:
- Reviewed:
- Description: C1
- Description: 2003001450
Comparative analysis of genetic algorithm vs. evolutionary algorithm for hybrid models with discrete gradient method for artificial neural network
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Bagirov, Adil
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the 11th International Fuzzy Systems Associations World Congress, IFSA 2005, Beijing, China, Volume III, Beijing, China : 28th - 31th July, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001359