Fuzzy rough granular neural networks, fuzzy granules, and classification

AbstractWe introduce a fuzzy rough granular neural network (FRGNN) model based on the multilayer perceptron using a back-propagation algorithm for the fuzzy classification of patterns. We provide the development strategy of the network mainly based upon the input vector, initial connection weights determined by fuzzy rough set theoretic concepts, and the target vector. While the input vector is described in terms of fuzzy granules, the target vector is defined in terms of fuzzy class membership values and zeros. Crude domain knowledge about the initial data is represented in the form of a decision table, which is divided into subtables corresponding to different classes. The data in each decision table is converted into granular form. The syntax of these decision tables automatically determines the appropriate number of hidden nodes, while the dependency factors from all the decision tables are used as initial weights. The dependency factor of each attribute and the average degree of the dependency factor of all the attributes with respect to decision classes are considered as initial connection weights between the nodes of the input layer and the hidden layer, and the hidden layer and the output layer, respectively. The effectiveness of the proposed FRGNN is demonstrated on several real-life data sets

Side chain variations radically alter the diffusion of poly(2-alkyl-2-oxazoline) functionalised nanoparticles through a mucosal barrier

Functionalised nanomaterials are gaining popularity for use as drug delivery vehicles and, in particular, mucus penetrating nanoparticles may improve drug bioavailability via the oral route. To date, few polymers have been investigated for their muco-penetration, and the effects of systematic structural changes to polymer architectures on the penetration and diffusion of functionalised nanomaterials through mucosal tissue have not been reported. We investigated the influence of poly(2-oxazoline) alkyl side chain length on nanoparticle diffusion; poly(2-methyl-2-oxazoline), poly(2-ethyl-2-oxazoline), and poly(2-n-propyl-2-oxazoline) were grafted onto the surface of thiolated silica nanoparticles and characterised by FT-IR, Raman and NMR spectroscopy, thermogravimetric analysis, and small angle neutron scattering. Diffusion coefficients were determined in water and in a mucin dispersion (using Nanoparticle Tracking Analysis), and penetration through a mucosal barrier was assessed using an ex vivo fluorescence technique. The addition of a single methylene group in the side chain significantly altered the penetration and diffusion of the materials in both mucin dispersions and mucosal tissue. Nanoparticles functionalised with poly(2-methyl-2-oxazoline) were significantly more diffusive than particles with poly(2-ethyl-2-oxazoline) while particles with poly(2-n-propyl-2-oxazoline) showed no significant increase compared to the unfunctionalised particles. These data show that variations in the polymer structure can radically alter their diffusive properties with clear implications for the future design of mucus penetrating systems

POZylation: a new approach to enhance nanoparticle diffusion through mucosal barriers

The increasing use of nanoparticles in the pharmaceutical industry is generating concomitant interest in developing nanomaterials that can rapidly penetrate into, and permeate through, biological membranes to facilitate drug delivery and improve the bioavailability of active pharmaceutical ingredients. Here, we demonstrate that the permeation of thiolated silica nanoparticles through porcine gastric mucosa can be significantly enhanced by their functionalization with either 5 kDa poly(2-ethyl-2-oxazoline) or poly(ethylene glycol). Nanoparticle diffusion was assessed using two independent techniques; Nanoparticle Tracking Analysis, and fluorescence microscopy. Our results show that poly(2-ethyl-2-oxazoline) and poly(ethylene glycol) have comparable abilities to enhance diffusion of silica nanoparticles in mucin dispersions and through the gastric mucosa. These findings provide a new strategy in the design of nanomedicines, by surface modification or nanoparticle core construction, for enhanced transmucosal drug delivery

A novel fuzzy rough granular neural network for classification

A novel fuzzy rough granular neural network (NFRGNN) based on the multilayer perceptron using backpropagation algorithm is described for fuzzy classification of patterns. We provide a development strategy of knowledge extraction from data using fuzzy rough set theoretic techniques. Extracted knowledge is then encoded into the network in the form of initial weights. The granular input vector is presented to the network while the target vector is provided in terms of membership values and zeros. The superiority of NFRGNN is demonstrated on several real life data sets

Fuzzy rough granular self organizing map

A fuzzy rough granular self organizing map (FRGSOM) is proposed for clustering patterns from overlapping regions using competitive learning of the Kohonen's self organizing map. The development strategy of the FRGSOM is mainly based on granular input vector and initial connection weights. The input vector is described in terms of fuzzy granules low, medium or high, and the number of granulation structures depends on the number of classes present in the data. Each structure is developed by a user defined α-value, labeled according to class information, and presented to a decision system. This decision system is used to extract domain knowledge in the form of dependency factors using fuzzy rough sets. These factors are assigned as the initial connection weights of the proposed FRGSOM, and then the network is trained through competitive learning. The effectiveness of the FRGSOM is shown on different real life data sets

Fuzzy rough granular self-organizing map and fuzzy rough entropy

A fuzzy rough granular self-organizing map (FRGSOM) involving a 3-dimensional linguistic vector and connection weights, defined in an unsupervised manner, is proposed for clustering patterns having overlapping regions. Each feature of a pattern is transformed into a 3-dimensional granular space using a π-membership function with centers and scaling factors corresponding to the linguistic terms low, medium or high. The three-dimensional linguistic vectors are then used to develop granulation structures, based on a user defined α-value. The granulation structures are labeled with integer values representing the crisp decision classes. These structures are presented in a decision table, which is used to determine the dependency factors of the conditional attributes using the concept of fuzzy rough sets. The dependency factors are used as initial connection weights of the proposed FRGSOM. The FRGSOM is then trained through a competitive learning of the self-organizing map. We also propose a new “fuzzy rough entropy measure”, based on the resulting clusters and using the concept of fuzzy rough sets. The effectiveness of the FRGSOM and the utility of “fuzzy rough entropy” in evaluating cluster quality are demonstrated on different real life datasets, including microarrays, with varying dimensions

Granular neural networks, pattern recognition and bioinformatics

This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing