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
An intelligent offline handwriting recognition system using evolutionary neural learning algorithm and rule based over segmented data points
- Authors: Ghosh, Ranadhir , Ghosh, Moumita
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 73-86
- Full Text: false
- Reviewed:
- Description: In this paper we propose a novel technique of using a hybrid evolutionary method, which uses a combination of genetic algorithm and matrix based solution methods such as QR factorization. The training of the model is based on a layer based hierarchical structure for the architecture and the weights for the Artificial Neural Network classifier. The architecture for the classifier is found using a binary search type procedure. The hierarchical structured algorithm (EALS-BT) is also a hybrid, because it combines the Genetic Algorithm based method with the Matrix based solution method for finding weights. 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 contour is extracted between two correct segmentation points. The contour is passed through the feature extraction module that extracts the angular features, 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 have the highest confidence value.
- Description: C1
- Description: 2003001367
Fusion strategies for neural learning algorithms using evolutionary and discrete gradient approaches
- Authors: Ghosh, Ranadhir , Yearwood, John , Ghosh, Moumita , Bagirov, Adil
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at AIA 2005: International Conference on Artificial Intelligence and Applications, Innsbruck, Austria : 14th - 16th February, 2006
- Full Text: false
- Reviewed:
- Description: In this paper we investigate 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.
- Description: E1
- Description: 2003001365
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 high-throughput glasshouse bioassay to detect crown rot resistance in wheat germplasm
- Authors: Mitter, V. , Zhang, M. C. , Liu, C. J. , Ghosh, Ranadhir , Ghosh, Moumita , Chakraborty, Sukumar
- Date: 2006
- Type: Text , Journal article
- Relation: Plant Pathology Vol. 55, no. 3 (2006), p. 433-441
- Full Text: false
- Reviewed:
- Description: A high-throughput and reliable seedling bioassay to screen wheat germplasm for crown rot resistance was developed. Single wheat seedlings were grown in square seedling punnets in a glasshouse and inoculated with a monoconidial Fusarium pseudograminearum isolate 10 days after emergence. The punnets were laid horizontally on their side and a 10-
- Description: C1
Comparative analysis of genetic algorithm, simulated annealing and cutting angle method for artificial neural networks
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Bagirov, Adil
- Date: 2005
- Type: Text , Journal article
- Relation: Machine Learning and Data Mining in Pattern Recognition, Proceedings Vol. 3587, no. (2005), p. 62-70
- Full Text: false
- Reviewed:
- Description: Neural network learning is the main essence of ANN. There are many problems associated with the multiple local minima in neural networks. Global optimization methods are capable of finding global optimal solution. In this paper we investigate and present a comparative study for the effects of probabilistic and deterministic global search method for artificial neural network using fully connected feed forward multi-layered perceptron architecture. We investigate two probabilistic global search method namely Genetic algorithm and Simulated annealing method and a deterministic cutting angle method to find weights in neural network. Experiments were carried out on UCI benchmark dataset.
- Description: C1
- Description: 2003003398
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 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
Solving Euclidian travelling salesman problem using discrete-gradient based clustering and kohonen neural network
- Authors: Ghosh, Moumita , Ugon, Julien , Ghosh, Ranadhir , Bagirov, Adil
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ICOTA6: 6th International Conference on Optimization - Techniques and Applications, Ballarat, Victoria : 9th December, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000864
A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier
- Authors: Ghosh, Moumita , Ghosh, Ranadhir , Verma, Brijesh
- Date: 2004
- Type: Text , Journal article
- Relation: International Journal of Pattern Recognition and Artificial Intelligence Vol. 18, no. 7 (Nov 2004), p. 1267-1283
- Full Text: false
- Reviewed:
- Description: In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing- Author
- Description: C1
- Description: 2003000867
Determining regularization parameters for derivative free neural learning
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Bagirov, Adil
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at 4th International Conference, MLDM 2005: Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany : 9th-11th July 2005 p. 71-79
- Full Text: false
- Description: Derivative free optimization methods have recently gained a lot of attractions for neural learning. The curse of dimensionality for the neural learning problem makes local optimization methods very attractive; however the error surface contains many local minima. Discrete gradient method is a special case of derivative free methods based on bundle methods and has the ability to jump over many local minima. There are two types of problems that are associated with this when local optimization methods are used for neural learning. The first type of problems is initial sensitivity dependence problem- that is commonly solved by using a hybrid model. Our early research has shown that discrete gradient method combining with other global methods such as evolutionary algorithm makes them even more attractive. These types of hybrid models have been studied by other researchers also. Another less mentioned problem is the problem of large weight values for the synaptic connections of the network. Large synaptic weight values often lead to the problem of paralysis and convergence problem especially when a hybrid model is used for fine tuning the learning task. In this paper we study and analyse the effect of different regularization parameters for our objective function to restrict the weight values without compromising the classification accuracy.
- Description: 2003001362
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 feature extraction technique for online handwriting recognition
- Authors: Verma, Brijesh , Lu, Jenny , Ghosh, Moumita , Ghosh, Ranadhir
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at 2004 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary : 25th July, 2004
- Full Text: false
- Reviewed:
- Description: The paper presents a feature extraction technique for online handwriting recognition. The technique incorporates many characteristics of handwritten characters based on structural, directional and zoning information and combines them to create a single global feature vector. The technique is independent to character size and it can extract features from the raw data without resizing. Using the proposed technique and a Neural Network based classifier, many experiments were conducted on UNIPEN benchmark database. The recognition rates are 98.2% for digits, 91.2% for uppercase and 91.4% for lowercase.
- Description: E1
- Description: 2003000868
Hybridization of neural learning algorithms using evolutionary and discrete gradient approaches
- Authors: Ghosh, Ranadhir , Yearwood, John , Ghosh, Moumita , Bagirov, Adil
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Computer Science Vol. 1, no. 3 (2005), p. 387-394
- Full Text: false
- Reviewed:
- Description: In this study we investigated 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 study 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.
- Description: C1
- Description: 2003001357
On recognition of handwritten devanagari numerals
- Authors: Ghosh, Ranadhir , Bhattacharya, Ujjwal , Chaudhuri, Bidyut , Ghosh, Moumita
- 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, Australia : 5th December, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001371
Application of derivative free methods for production optimization
- Authors: Bagirov, Adil , Mason, T. L. , Ghosh, Moumita
- Date: 2006
- Type: Text , Journal article
- Relation: Applied and Computational Mathematics Vol. 5, no. 1 (2006), p. 94-105
- Full Text: false
- Reviewed:
- Description: Continuous gas lift is a high value optimization proposition, where high-pressure gas is injected at various depths, into oil production well to lightened the fluid column and so improve production and recovery. Gas lift optimization models as a surrogate for optimization planning, are usually nonconvex and even nonsmooth. Moreover, in many situations the objective and/or constraint functions in these problems are not known analytically. Most of traditional methods of optimization cannot be applied to solve such problems. Derivative free methods seem to be better choice for solving such problems. In this paper, we compare two different derivative free methods, our variant of the discrete gradient method and the generalized descent method for solving nonlinear gas lift optimization problems. We consider two different gas lift optimization problems. The objective functions in these problems are separable, nonsmooth and nonconvex. Although both algorithms produce satisfactory results, however the discrete gradient method better deals with noisy data and produces better results.
- Description: C1
- Description: 2003001714
A novel approach for structural feature extraction : Contour vs. direction
- Authors: Verma, Brijesh , Blumenstein, Michael , Ghosh, Moumita
- Date: 2004
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 25, no. 9 (2004), p. 975-988
- Full Text: false
- Reviewed:
- Description: The paper presents a novel approach for extracting structural features from segmented cursive handwriting. The proposed approach is based on the contour code and stroke direction. The contour code feature utilises the rate of change of slope along the contour profile in addition to other properties such as the ascender and descender count, start point and end point. The direction feature identifies individual line segments or strokes from the character's outer boundary or thinned representation and highlights each character's pertinent direction information. Each feature is investigated employing a benchmark database and the experimental results using the proposed contour code based structural feature are very promising. A comparative evaluation with the directional feature and existing transition feature is included. © 2004 Elsevier B.V. All rights reserved.
- Description: C1
- Description: 2003002951
Offline handwriting recognition using evolutionary neural learning algorithm based on rule based over segmented data points
- Authors: Ghosh, Moumita , Ghosh, Ranadhir
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the 11th Conference of the International Graphonomics Society IGS2003 - Connecting Sciences Using Graphonomic Research, Arizona, USA : 2nd May, 2003 p. 73-87
- Full Text: false
- Reviewed:
- Description: In this paper we propose a novel technique of using a hybrid evolutionary method, which uses a combination of genetic algorithm and matrix based solution methods such as QR factorization. The training of the model is based on a layer based hierarchical structure for the architecture and the weights for the Artificial Neural Network classifier. The architecture for the classifier is found using a binary search type procedure. The hierarchical structured algorithm (EALS-BT) is also a hybrid, because it combines the Genetic Algorithm based method with the Matrix based solution method for finding weights. 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 contour is extracted between two correct segmentation points. The contour is passed through the feature extraction module that extracts the angular features, 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 have the highest confidence value. ACM Classification: I.4 (Image Processing and Computer Vision)
- Description: E1
- Description: 2003000420
Two level clustering using SOM and dynamical systems
- Authors: Ghosh, Ranadhir , Mammadov, Musa , Ghosh, Moumita , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ICOTA6: 6th International Conference on Optimization - Techniques and Applications, Ballarat, Victoria : 9th December, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000871
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