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12
Identification Of Regions Of Interest In Digital Mammograms
 Journal of Intelligent Systems
, 2000
"... The main purpose of this paper is to compare clustering (region growing) and gradient based techniques for detecting regions of interest in digital mammograms. These regions of interest form the basis of applying shape and texture techniques for detecting cancerous masses. In addition, the paper pro ..."
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The main purpose of this paper is to compare clustering (region growing) and gradient based techniques for detecting regions of interest in digital mammograms. These regions of interest form the basis of applying shape and texture techniques for detecting cancerous masses. In addition, the paper proposes a two stage method where gradient based techniques are applied first followed by region growing method which will yield lesser numbers of regions for analysis. For this purpose we first use histogram equalisation and fuzzy enhancement techniques to improve the quality of the images and compare their utility on our mammogram data. Image enhanced mammograms are then subjected to clustering or gradient operations (masking) for segmentation purpose. The segmented image is then analysed for estimating the regions of interest and the results are compared against previously known diagnosis of the radiologist. A total of 30 mammograms from the University of South Florida database were used whe...
2006) Odour Recognition using Electronic Noses in Robotic and Intelligent Systems. Doctoral Dissertation
"... This thesis is about integrating the sense of smell into artificial intelligent systems. In order to endow such systems with olfaction, we use a device called an electronic nose or enose. An enose consists of a number of gas sensors with partial selectivity and a pattern recognition component trai ..."
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This thesis is about integrating the sense of smell into artificial intelligent systems. In order to endow such systems with olfaction, we use a device called an electronic nose or enose. An enose consists of a number of gas sensors with partial selectivity and a pattern recognition component trained to recognize both complex and simple types of odours. Discussed in this thesis are a number of challenges which makes the integration of electronic noses into an intelligent system nontrivial. Challenges unique to the current technological state of odour identification include the characteristics of the gas sensing technologies such as sensitivity and drift, and the limitations of the pattern recognition algorithms to cope with these characteristics. Another challenge general to olfaction is the inherent difficulty in conveying or communicating a perception of an odour to a human user. If we are to consider enoses into today’s and tomorrow’s intelligent systems, it is important to examine the ingredients currently present in
2D Spiral Pattern Recognition with Possibilistic Measures
, 1998
"... The main task for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the xy plane. This problem is of critical importance since it incorporates temporal characteristics often found in realtime applications, i.e. the spiral coils with t ..."
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The main task for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the xy plane. This problem is of critical importance since it incorporates temporal characteristics often found in realtime applications, i.e. the spiral coils with time. Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks. This paper describes a fuzzy approach which outperforms previous work in terms of the recognition rate and the speed of recognition. The paper presents the new approach and results with the validation and test sets. The results show that it is possible to solve the spiral problem in a relatively small amount of time with the fuzzy approach (up to 100% correct classification on the validation and test set; 77.2% correct classification with crossvalidation using the leaveoneout method). Keywords: possibilisti...
Fuzzy Neural Computing of Coffee and Tainted Water Data from an Electronic Nose
 Sensors and Actuators B
, 1996
"... In this paper we compare the ability of a fuzzy neural network and a classical backpropagation network to classify odour samples which were obtained by an electronic nose employing semiconducting oxide conductometric gas sensors. Two different samples sets were analysed: first the aroma of 3 blends ..."
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In this paper we compare the ability of a fuzzy neural network and a classical backpropagation network to classify odour samples which were obtained by an electronic nose employing semiconducting oxide conductometric gas sensors. Two different samples sets were analysed: first the aroma of 3 blends of commercial coffee, and secondly the headspace of 6 different tainted water samples. The two experimental datasets provided an excellent opportunity to test the ability of a fuzzy neural network due to the high level of sensor variability often experienced with this type of sensor. Results are presented on the application of 3 layer fuzzy neural networks to electronic nose data which demonstrate a considerable improvement in performance to a common backpropagation network. 1. Introduction Artificial neural networks (ANNs) have been the subject of considerable research for over twenty years. However, it is during the last decade or so that research interest 1 Sensors and Actuators, vo...
Effect of Noise on Generalisation in Massively Parallel Fuzzy Systems
 Pattern Recognition
, 1998
"... This paper studies the performance of Massively Parallel Fuzzy Systems (MPFS) on the two spiral benchmark. Spiral data is contaminated with five different noise distributions. The recognition rates of the system are reported with varying levels of different types of noise. The behaviour of the syste ..."
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This paper studies the performance of Massively Parallel Fuzzy Systems (MPFS) on the two spiral benchmark. Spiral data is contaminated with five different noise distributions. The recognition rates of the system are reported with varying levels of different types of noise. The behaviour of the system is investigated with additive, multiplicative, cumulative and noncumulative noise. The results show that the MPFS system remains stable to different noise variations and the generalisation error remains consistently low. As the total noise in the system increases, the system witnesses a linear decrease in entropy and the generalisation error is easier to predict. The error rate is found to have two separate patterns of variation for cumulative and noncumulative noise. Keywords: MPFS, noise distribution, possibility, recognition rate, spiral benchmark 2 1 INTRODUCTION Massively Parallel Fuzzy Systems (MPFS) were first proposed by Singh to solve pattern recognition problems in realtime...
Massively Parallel Fuzzy Systems: The Case of Three Spiral Pattern Recognition
 In Proceedings of the IEEE International Conference on Fuzzy Systems FUZZIEEE'98, IEEE
, 1998
"... The main objectives of this paper are: 1) to describe the working of a Massively Parallel Fuzzy System; 2) to test the system on a new benchmark, the three spiral data set; and 3) to describe the behaviour of the system when solving the problem. The described system is aimed at solving pattern recog ..."
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The main objectives of this paper are: 1) to describe the working of a Massively Parallel Fuzzy System; 2) to test the system on a new benchmark, the three spiral data set; and 3) to describe the behaviour of the system when solving the problem. The described system is aimed at solving pattern recognition problems in realtime. Pattern recognition data are subjected to noniterative decision making through the estimation of class membership of test data. This paper describes the performance of this system on the temporal three spiral benchmark. The task is to learn three class data which lies on three distinct spirals that coil around each other and around the origin with time. There are no linear solutions to this problem (33% recognition rate with discriminant analysis). The system under consideration classifies the training set with 100% success and recognises training data with up to 89% (without the use of rejection threshold) and 98% success (with a rejection threshold of =.8)....
Fuzzy Multiobjective Linear Programming Approach for Traveling Salesman Problem
"... Traveling salesman problem (TSP) is one of the challenging reallife problems, attracting researchers of many fields including Artificial Intelligence, Operations Research, and Algorithm Design and Analysis. The problem has been well studied till now under different headings and has been solved with ..."
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Traveling salesman problem (TSP) is one of the challenging reallife problems, attracting researchers of many fields including Artificial Intelligence, Operations Research, and Algorithm Design and Analysis. The problem has been well studied till now under different headings and has been solved with different approaches including genetic algorithms and linear programming. Conventional linear programming is designed to deal with crisp parameters, but information about real life systems is often available in the form of vague descriptions. Fuzzy methods are designed to handle vague terms, and are most suited to finding optimal solutions to problems with vague parameters. Fuzzy multiobjective linear programming, an amalgamation of fuzzy logic and multiobjective linear programming, deals with flexible aspiration levels or goals and fuzzy constraints with acceptable deviations. In this paper, a methodology, for solving a TSP with imprecise parameters, is deployed using fuzzy multiobjective linear programming. An example of TSP with multiple objectives and vague parameters is discussed. 1.
Automated FuzzyClustering for DoctuS Expert System
"... 'Doctus ' 1 is capable of deduction also called rulebased reasoning and of induction, which is the symbolic version of reasoning by cases 2. If connected to databases or data warehouses the inductive reasoning of Doctus is also used for data mining. To handle numerical domains Doctus uses ..."
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'Doctus ' 1 is capable of deduction also called rulebased reasoning and of induction, which is the symbolic version of reasoning by cases 2. If connected to databases or data warehouses the inductive reasoning of Doctus is also used for data mining. To handle numerical domains Doctus uses statistical clustering algorithm. We define the problem in three steps: how to perform a clustering, which is neither rigid nor sensitive to noise, benefiting from the properties of the application domain, reducing the complexity as much as possible, and supplying the decision maker with useful information enabling the possibility of interaction? In this paper we present the conception of Automated FuzzyClustering using triangular and trapezoidal Fuzzysets, which provides overlapping Fuzzyset covering of the domain. I. FUZZY CLUSTERING FOR SYMBOLIC ES – WHY? We investigate the expert systems in supporting the business decision making process. Let’s first examine the domain of the application, to map characteristics that are important to choose the appropriate tool for support. We are dealing with decision making of a leader and of a manager on the expert level of knowledge and higher, who are to considering much of soft information and hard data, and use heuristic processes to take the decisions. First there is a need to discover the properties of the heuristic processes in comparison to other processes: 1. At deterministic processes there is an expected value only with no dispersion. It is determined what output follows a particular input, it will happen in 100 % of repetitions. Small changes on the input will result in small changes on the output, which can be calculated precisely. Deterministic processes can be met e.g. in classical physics (mechanics of not microscopic, but also not astronomy sized bodies). 2. Output of a stochastic process can be described with its expected value and its dispersion, which is smaller at least one order of magnitude. Small changes on the input will result in small changes on the output, which can be 1 www.doctus.info 2 Originally
Classifier Systems Based on Possibility Distributions: A Comparative Study
 Proceedings of the 3rd International conference on neural networks and genetic algorithms (ICANNGA97
, 1997
"... The main aim of this paper is three fold: a) to understand the working of a classifier system based on possibility distribution functions, b) to evaluate its performance against other superior methods such as fuzzy and nonfuzzy neural networks on real data, c) and finally to recommend changes for e ..."
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The main aim of this paper is three fold: a) to understand the working of a classifier system based on possibility distribution functions, b) to evaluate its performance against other superior methods such as fuzzy and nonfuzzy neural networks on real data, c) and finally to recommend changes for enhancing its performance. The paper explains how to construct a possibility based classifier system which is used with conventional errorestimation techniques such as crossvalidation and bootstrapping. The results were obtained on a set of electronic nose data and this performance was compared with earlier published results on the same data using fuzzy and nonfuzzy neural networks. The results show that the possibility approach is superior to the nonfuzzy approach, however, further work needs to be done. 1. Introduction Classifier systems for most pattern recognition problems need to satisfy various criteria to be considered reliable for decision making. These criteria relate not only ...