NONLINEAR ADAPTIVE SIGNAL PROCESSING

Nonlinear techniques for signal processing and recognition have the promise of achieving systems which are superior to linear systems in a number of ways such as better performance in terms of accuracy, fault tolerance, resolution, highly parallel architectures and cloker similarity to biological intelligent systems. The nonlinear techniques proposed are in the form of multistage neural networks in which each stage can be a particular neural network and all the stages operate in parallel. The specific approach focused upon is the parallel, self-organizing, hierarchical neural networks (PSHNN\u27s). A new type of PSHNN is discussed such that the outputs are allowed to be continuous-valued. The perfo:rmance of the resulting networks is tested in problems of prediction of speech and of chaotic time-series. Three types of networks in which the stages are learned by the delta rule, sequential least-squares, and the backpropagation (BP) algolrithm, respectively, are described. In all cases studied, the new networks achieve better performarnce than linear prediction. This is shown both theoretically and experimentally. A revised BP algorithm is discussed for learning input nonlinearities. The advantage of the revised BP algorithm is that the PSHNN with revised BP stages can be extended to use the sequential leastsquares (SLS) or the least mean absolule value rule (LMAV) in the last stage. A forward-backward training algorithm for parallel, self-organizing hierarchical neural networks is described. Using linear algebra, it is shown that the forward-backward training of an n-stage PSHNN until convergence is equivalent to the pseudo-inverse solution for a single, total network designed in the leastsquares sense with the total input vector consisting of the actual input vector and its additional nonlinear transformations. These results are also valid when a single long input vector is partitioned into smaller length vectors. A number of advantages achieved are small modules for easy and fast learning, parallel implementation of small modules during testing, faster convergence rate, better numerical error-reduction, and suitability for learning input nonlinear transformations by the backpropagation algorithm. Better performance in terms of deeper minimum of the error function and faster convergence rate is achieved when a single BP network is replaced by a PSHNN of equal complexity in which each stage is a BP network of smaller complexity than the single BP network

Drug/nondrug classification with consensual Self-Organising Map and Self-Organising Global Ranking algorithms

PubMedID: 20063467In this paper, a special consensual approach is discussed for separating the druglike compounds from the non-druglike compounds. It involves a group decision to produce a consensus of multiple classification results obtained with a single classification algorithm. The individual results are obtained with either the Self Organising Global Ranking (SOGR) or Self Organising Map (SOM). The main difference between the proposed algorithm and SOM is the neighbourhood concept. The constructed consensual model has a preprocessing unit which consists of transformation of input patterns by random matrices and median filtering to generate independent errors for a single type of classifier, and a postprocessing unit for consensus. The confirmed drugs were classified with a consensual accuracy of 90.63% while nondrugs resulted in 80.44% accuracy. The SOGR results were better than the SOM algorithm results. © 2008 Inderscience Enterprises Ltd

Consensual classification of drug and nondrug compounds

PubMedID: 20054990A special consensual approach is discussed for separating a molecular group with a proven pharmacological activity from another molecular group without any activity. It is mainly a group decision to produce a consensus of multiple classification results obtained with a single classification algorithm. For this purpose, the constructed model has a preprocessing unit which consists of transformation of input patterns by random matrices and median filtering to generate independent errors for a single type of classifier and postprocessing for consensus. The neural network based consensus classifier operating with MOE descriptors was applied to a set of 641 chemical structures. The confirmed drugs were classified with an accuracy of 86.54% while nondrugs resulted in 82.67% accuracy. © 2008 Inderscience Enterprises Ltd

Hemoglobin secondary structure prediction with four kernels on support vector machines

2005 ICSC Congress on Computational Intelligence Methods and Applications --15 December 2005 through 17 December 2005 -- Istanbul --Secondary structure prediction of proteins has increasingly been a central research area in bioinformatics. In this paper, support vector machines (SVM) are discussed as a method for the prediction of hemoglobin secondary structures. Different sliding window sizes and different kernels of SVM are comparatively investigated in terms of accuracy of prediction of hemoglobin secondary structure. For this purpose, the training and testing data were obtained from the Protein Data Bank, US with Database of Secondary Structures of Protein (DSSP). The results of prediction with different SVM kernels and different window sizes were found to be in the range of 5.93 - 15.90, 67.76-70.05, 69.77 - 73.25, and 74.42 - 77.64 % for linear kernel, sigmoid kernel, polynomial kernel and Gaussian radial basis kernel, respectively

Body mass index and complications following major gastrointestinal surgery: A prospective, international cohort study and meta-analysis

Aim Previous studies reported conflicting evidence on the effects of obesity on outcomes after gastrointestinal surgery. The aims of this study were to explore the relationship of obesity with major postoperative complications in an international cohort and to present a metaanalysis of all available prospective data. Methods This prospective, multicentre study included adults undergoing both elective and emergency gastrointestinal resection, reversal of stoma or formation of stoma. The primary end-point was 30-day major complications (Clavien–Dindo Grades III–V). A systematic search was undertaken for studies assessing the relationship between obesity and major complications after gastrointestinal surgery. Individual patient meta-analysis was used to analyse pooled results. Results This study included 2519 patients across 127 centres, of whom 560 (22.2%) were obese. Unadjusted major complication rates were lower in obese vs normal weight patients (13.0% vs 16.2%, respectively), but this did not reach statistical significance (P = 0.863) on multivariate analysis for patients having surgery for either malignant or benign conditions. Individual patient meta-analysis demonstrated that obese patients undergoing surgery formalignancy were at increased risk of major complications (OR 2.10, 95% CI 1.49–2.96, P < 0.001), whereas obese patients undergoing surgery for benign indications were at decreased risk (OR 0.59, 95% CI 0.46–0.75, P < 0.001) compared to normal weight patients. Conclusions In our international data, obesity was not found to be associated with major complications following gastrointestinal surgery. Meta-analysis of available prospective data made a novel finding of obesity being associated with different outcomes depending on whether patients were undergoing surgery for benign or malignant disease