International Journal of Computational Intelligence and Applications

Signature-based intrusion detection systems look for known, suspicious patterns in the input data. In this paper we explore compression of labeled empirical data using threshold-based clustering with regularization. The main target of clustering is to compress training dataset to the limited number of signatures, and to minimize the number of comparisons that are necessary to determine the status of the input event as a result. Essentially, the process of clustering includes merging of the clusters which are close enough. As a consequence, we will reduce original dataset to the limited number of labeled centroids. In a complex with k-nearest-neighbor (kNN) method, this set of centroids may be used as a multi-class classifier. Clearly, different attributes have different importance depending on the particular training database and given cost matrix. This importance may be regulated in the definition of the distance using linear weight coefficients. The paper introduces special procedure to estimate above weight coefficients.

The experiments on the KDD-99 intrusion detection dataset have confirmed the effectiveness of the proposed methods.

Advances in fluorescent probing and microscopic imaging technology provide important tools for biomedical research in studying the structures and functions of cells and molecules. Such studies require the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poor reproducibility. In this paper, we present and combine several advanced computational, probabilistic, and fuzzy-set methods for the computerized classification of cell nuclei in different mitotic phases. We tested our proposed methods with real image sequences recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective for the task of classification.

In this paper, we apply a new neural network model, namely shunting inhibitory convolutional neural networks, or SICoNNets for short, to the problem of handwritten digit recognition. This type of networks has a generic and flexible architecture, where the processing is based on the physiologically plausible mechanism of shunting inhibition. A hybrid first-order training method, called QRProp, is developed based on the three training algorithms Rprop, Quickprop, and SuperSAB. The MNIST database is used to train and evaluate the performance of SICoNNets in handwritten digit recognition. A network with 24 feature maps and 2722 free parameters achieves a recognition accuracy of 97.3%.

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

The present paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically co-evolve a population of nonlinear transformations on the input data to be classified, and map them to a new space with reduced dimension in order to get a maximum inter-classes discrimination. It is much easier to classify the new samples from the transformed data. Contrary to the existing GP-classification techniques, the proposed one uses a dynamic repartition of the transformed data in separated intervals, the efficiency of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were performed using the Fisher's Iris dataset. After that, the KDD'99 Cup dataset was used to study the intrusion detection and classification problem. The results demonstrate that the proposed genetic approach outperforms the existing GP-classification methods, and provides improved results compared to other existing techniques.

In this paper we propose a new appearance based system which consists of two stages: visual speech feature extraction and classification, followed by recognition of the extracted feature, thereby the result is a complete lip-reading system. This lip-reading system employs our Hyper Column Model (HCM) approach to extract and classify the visual features and uses the Hidden Markov Model (HMM) for recognition. This paper addresses mainly the first stage; i.e. feature extraction and classification. We investigate the HCM performance to achieve feature extraction and classification and then compare the performance when replacing HCM with Fast Discrete Cosine Transform (FDCT). Unlike FDCT, HCM could extract the entire features without any loss. Also the experiments have shown that HCM is generally better than FDCT and provides a good distribution of the phonemes in the feature space for recognition purposes. For fair comparison, two databases are exploited with three different sets of resolution for each database. One of these two databases is designed to include shifted and scaled objects. Experiments reveal that HCM is capable of recovering and dealing with such image restrictions whereas the effectiveness of FDCT drops drastically especially for new subjects.

General functional decomposition is mainly perceived as a logic synthesis method for implementing Boolean functions into FPGA-based architectures. However it also has important applications in many other fields of modern engineering and science.

In this paper, advantages of functional decomposition are demonstrated on "real life" examples. Application of decomposition-based methods in other fields of modern engineering is presented. In the case of decision tables, application of decomposition methods leads to significant benefits in the analysis process of data dependencies, especially in cases when the input decision tables are unmanageably large. Experimental results demonstrate that it can help implementing sequential machines using flip-flops or ROM memory. It also can be efficiently used as multilevel logic synthesis method for VLSI technology.