AI clustering techniques: a new approach in horizontal fragmentation of classes with complex attributes and methods in object oriented databases.
An agglomerative method for automatic forming of hierarchical classification
An automatic hybrid method for retinal blood vessel extraction
The extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper presents a novel hybrid automatic approach for the extraction of retinal image vessels. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. In mathematical morphology, the retinal image is smoothed and strengthened so that the blood vessels are enhanced and the background information...
An experimental evaluation of two approaches to mining context based sequential patterns
An experimental speaker-independent system for isolated word recognition implemented for romanian language.
Application of Neural-Net Computing to Transient Stability Assessment and Enhancement of Electric Power Systems
Application of the Constrained Edit Distance Algorithm to Search Procedures
Applications of the Moore-Penrose inverse in digital image restoration.
Approximation of phenol concentration using novel hybrid computational intelligence methods
Automatic speech signal segmentation based on the innovation adaptive filter
Bivariate hahn moments for image reconstruction
Brèves communications. Formalisation d'un outil pour la description d'images
Cascading classifiers
We propose a multistage recognition method built as a cascade of a linear parametric model and a -nearest neighbor (-NN) nonparametric classifier. The linear model learns a “rule” and the -NN learns the “exceptions” rejected by the “rule.” Because the rule-learner handles a large percentage of the examples using a simple and general rule, only a small subset of the training set is stored as exceptions during training. Similarly during testing, most patterns are handled by the rule -learner and...
Classification and clustering of printed mathematical symbols with improved backpropagation and self-organizing map.
Classification of reduction invariants with improved backpropagation.
Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems
Intrusion detection is a critical component of security information systems. The intrusion detection process attempts to detect malicious attacks by examining various data collected during processes on the protected system. This paper examines the anomaly-based intrusion detection based on sequences of system calls. The point is to construct a model that describes normal or acceptable system activity using the classification trees approach. The created database is utilized as a basis for distinguishing...
Clustering of vaguely defined objects
This paper is concerned with the clustering of objects whose properties cannot be described by exact data. These can only be described by fuzzy sets or by linguistic values of previously defined linguistic variables. To cluster these objects we use a generalization of classic clustering methods in which instead of similarity (dissimilarity) of objects, used fuzzy similarity (fuzzy dissimilarity) to define the clustering of fuzzy objects.
Combining adaptive vector quantization and prototype selection techniques to improve nearest neighbour classifiers
Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbour (NN) classification rules both to improve its accuracy (editing) and to alleviate its computational burden (condensing). Methods based on selecting/discarding prototypes and methods based on adapting prototypes have been separately introduced to deal with this problem. Different approaches to this problem are considered in this paper and their main advantages and drawbacks are pointed out along with some...
Combining biometric fractal pattern and particle swarm optimization-based classifier for fingerprint recognition.
Comment on “Convergence properties of adaptive threshold elements...”