The proliferation of malicious URLs has become a significant threat to internet security, encompassing SPAM, phishing, malware, and defacement attacks. Traditional detection methods struggle to keep pace with the evolving nature of these threats. Detecting malicious URLs in real-time requires advanced techniques capable of handling large datasets and identifying novel attack patterns. The challenge lies in developing a robust model that combines efficient feature extraction with accurate classification. We propose a hybrid machine learning approach combining Self-Organizing Map based Radial Movement Optimization (SOM-RMO) for feature extraction and Radial Basis Function Network (RBFN) based Tabu Search for classification. SOM-RMO effectively reduces dimensionality and highlights significant features, while RBFN, optimized with Tabu Search, classifies URLs with high precision. The proposed model demonstrates superior performance in detecting various malicious URL attacks. On a benchmark dataset, our approach achieved an accuracy of 96.5%, precision of 95.2%, recall of 94.8%, and an F1-score of 95.0%, outperforming traditional methods significantly.

In this study, we propose a multi branched network approach to predict the dynamics of a physics attractor characterized by intricate and chaotic behavior. We introduce a unique neural network architecture comprised of Radial Basis Function (RBF) layers combined with an attention mechanism designed to effectively capture nonlinear inter-dependencies inherent in the attractor's temporal evolution. Our results demonstrate successful prediction of the attractor's trajectory across 100 predictions made using a real-world dataset of 36,700 time-series observations encompassing approximately 28 minutes of activity. To further illustrate the performance of our proposed technique, we provide comprehensive visualizations depicting the attractor's original and predicted behaviors alongside quantitative measures comparing observed versus estimated outcomes. Overall, this work showcases the potential of advanced machine learning algorithms in elucidating hidden structures in complex physical systems while offering practical applications in various domains requiring accurate short-term forecasting capabilities.

This short paper is a fast proof-of-concept that the 3-order B-splines used in Kolmogorov-Arnold Networks (KANs) can be well approximated by Gaussian radial basis functions. Doing so leads to FastKAN, a much faster implementation of KAN which is also a radial basis function (RBF) network.

In this paper we present upper bounds for the learning rates for hybrid models that employ a combination of both self-organized and supervised to build receptive field representations the hidden units. The learning performance in such networks with nearest neighbor heuristic can be improved upon by multiplying the individual receptive field widths by a suitable overlap factor. We present results indicat!ng optimal values for such overlap factors. We also present a new algorithm for determining receptive field centers. This method negotiates more hidden units in the regions of the input space as a function of the output and is conducive to better learning when the number of patterns (hidden units) is small.

Three methods for improving the performance of (gaussian) radial basis function (RBF) networks were tested on the NETtaik task. In RBF, a new example is classified by computing its Euclidean distance to a set of centers chosen by unsupervised methods. The application of supervised learning to learn a non-Euclidean distance metric was found to reduce the error rate of RBF networks, while supervised learning of each center's vari(cid:173) ance resulted in inferior performance. The best improvement in accuracy was achieved by networks called generalized radial basis function (GRBF) networks. In GRBF, the center locations are determined by supervised learning.

A high performance speaker-independent isolated-word hybrid speech rec(cid:173) ognizer was developed which combines Hidden Markov Models (HMMs) and Radial Basis Function (RBF) neural networks. In recognition ex(cid:173) periments using a speaker-independent E-set database, the hybrid rec(cid:173) ognizer had an error rate of 11.5% compared to 15.7% for the robust unimodal Gaussian HMM recognizer upon which the hybrid system was based. These results and additional experiments demonstrate that RBF networks can be successfully incorporated in hybrid recognizers and sug(cid:173) gest that they may be capable of good performance with fewer parameters than required by Gaussian mixture classifiers. A global parameter opti(cid:173) mization method designed to minimize the overall word error rather than the frame recognition error failed to reduce the error rate. A hybrid isolated-word speech recognizer was developed which combines neural network and Hidden Markov Model (HMM) approaches.

Bumptrees are geometric data structures introduced by Omohundro (1991) to provide efficient access to a collection of functions on a Euclidean space of interest. We describe a modified bumptree structure that has been employed as a neural network classifier, and compare its performance on several classification tasks against that of radial basis function networks and the standard mutIi-Iayer perceptron.

In this paper we apply the method of complexity regularization to de(cid:173) rive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function network. Our approach differs from the previous complexity regularization neural network function learning schemes in that we operate with random covering numbers and 11 metric entropy, making it po sibleto consider much broader families of activa(cid:173) tion functions, namely functions of bounded variation. Some constraints previously imposed on the network parameters are also eliminated this way. The network is trained by means of complexity regularization in(cid:173) volving empirical risk minimization. Bounds on the expected risk in tenns of the sample size are obtained for a large class of loss functions.

The mortality related to cervical cancer can be substantially re(cid:173) duced through early detection and treatment. However, cur(cid:173) rent detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, non(cid:173) invasively and quantitatively probes the biochemical and morpho(cid:173) logical changes that occur in pre-cancerous tissue. RBF ensemble algorithms based on such spectra provide automated, and near real(cid:173) time implementation of pre-cancer detection in the hands of non(cid:173) experts. The results are more reliable, direct and accurate than those achieved by either human experts or multivariate statistical algorithms.

We extend radial basis function (RBF) networks to the scenario in which multiple correlated tasks are learned simultaneously, and present the cor- responding learning algorithms. We develop the algorithms for learn- ing the network structure, in either a supervised or unsupervised manner. Training data may also be actively selected to improve the network's gen- eralization to test data. Experimental results based on real data demon- strate the advantage of the proposed algorithms and support our conclu- sions.