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WBCD breast cancer database classification applying artificial metaplasticity neural network

EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9573-9579页

作者： Marcano-Cedeno, A. Quintanilla-Dominguez, J. Andina, D. Tech Univ Madrid Grp Automat Signals & Commun Madrid Spain

The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. These techniques can help doctors form a second opinion and make a better diagnosis. In this paper we present a novel improvement in neural network training for pattern classification. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon's information theory. During the training phase the Artificial metaplasticity Multilayer Perceptron (AMMLP) algorithm gives priority to updating the weights for the less frequent activations over the more frequent ones. In this way metaplasticity is modeled artificially. AMMLP achieves a more effcient training, while maintaining MLP performance. To test the proposed algorithm we used the Wisconsin Breast Cancer Database (WBCD). AMMLP performance is tested using classification accuracy, sensitivity and specificity analysis, and confusion matrix. The obtained AMMLP classification accuracy of 99.26%, a very promising result compared to the backpropagation algorithm (BPA) and recent classification techniques applied to the same database. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Neural network backpropagation algorithm Pattern recognition Metaplasticity Breast cancer AMMLP

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Application of neural networks to the modelling of some constitutive laws

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NEURAL NETWORKS 1999年第2期12卷 371-392页

作者： Pernot, S Lamarque, CH Ecole Natl Travaux Publ Etat DGCB LGM URA CNRS 1652 F-69518 Vaulx En Velin France

This study investigates the modelling of constitutive laws of materials by neural networks. Material behaviour is no longer represented mathematically but is described by neuronal modelling. The main aim is to build a neural network directly from experimental results (the learning phase). We give several examples of constitutive laws (Hooke, Sargin, etc.) using a backpropagation algorithm. Then we show that abilities of adjustment, memorisation and anticipation of neural networks permit us to develop a method of classification of constitutive laws. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： constitutive laws materials neural networks perceptron modelling backpropagation algorithm

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An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期44卷 10477-10493页

作者： Al-Andoli, Mohammed Nasser Tan, Shing Chiang Sim, Kok Swee Goh, Pey Yun Lim, Chee Peng Multimedia Univ Fac Engn & Technol Cyberjaya Malaysia Multimedia Univ Fac Informat Sci & Technol Cyberjaya Malaysia Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic Australia

Malicious software, or malware, has posed serious and evolving security threats to Internet users. Many anti-malware software packages and tools have been developed to protect legitimate users from these threats. However, legacy anti-malware methods are confronted with millions of potential malicious programs. To combat these threats, intelligent anti-malware systems utilizing machine learning (ML) models are useful. However, most ML models have limitations in performance since the training depth is usually limited. The emergence of Deep Learning (DL) models allow more training possibilities and improvement in performance. DL models often use gradient descent optimization, i.e., the Back-Propagation (BP) algorithm;therefore, their training and optimization procedures suffer from local sub-optimal solutions. In addition, DL-based malware detection methods often entail single classifiers. Ensemble learning overcomes the shortcomings of individual techniques by consolidating their strengths to improve the performance. In this paper, we propose an ensemble DL classifier stacked with the Fuzzy ARTMAP (FAM) model for malware detection. The stacked ensemble method uses several heterogeneous deep neural networks as the base learners. During the training and optimization process, these base learners adopt a hybrid BP and Particle Swarm Optimization algorithm to combine both local and global optimization capabilities for identifying optimal features and improving the classification performance. FAM is selected as a meta-learner to effectively train and combine the outputs of the base learners and achieve robust and accurate classification. A series of empirical studies with different benchmark data sets is conducted. The results ascertain that the proposed ensemble method is effective and efficient, outperforming many other compared methods.

关键词： Ensemble learning fuzzy ARTMAP deep learning malware detection particle swarm optimization backpropagation algorithm

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Broadband active noise control using a neural network

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1998年第8期E81D卷 855-861页

作者： Chen, GK Chiueh, TD Chen, JH Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.

关键词： adaptive noise control (ANC) multilayer perceptrons (MLPs) neural network backpropagation algorithm

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Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2009年第1期23卷 13-26页

作者： Yetilmezsoy, Kaan Sapci-Zengin, Zehra Yildiz Tech Univ Fac Civil Engn Dept Environm Engn TR-34349 Istanbul Turkey

A three-layer Artificial Neural Network (ANN) model (9:12:1) for the prediction of Chemical Oxygen Demand Removal Efficiency (CODRE) of Upflow Anaerobic Sludge Blanket (UASB) reactors treating real cotton textile wastewater diluted with domestic wastewater was presented. To validate the proposed method, an experimental study was carried out in three lab-scale UASB reactors to investigate the treatment efficiency on total COD reduction. The reactors were operated for 80 days at mesophilic conditions (36-37.5C) in a temperature-controlled water bath with two hydraulic retention times (HRT) of 4.5 and 9.0 days and with organic loading rates (OLR) between 0.072 and 0.602 kg COD/m3p/day. Five different dilution ratios of 15, 30, 40, 45 and 60% with domestic wastewater were employed to represent seasonal fluctuations, respectively. The study was undertaken in a pH range of 6.20-8.06 and an alkalinity range of 1,350-1,855 mg/l CaCO3. The concentrations of volatile fatty acids (VFA) and total suspended solids (TSS) were observed between 420 and 720 mg/l CH3COOH and 68-338 mg/l, respectively. In the study, a wide range of influent COD concentrations (CODi) between 651 and 4,044 mg/l in feeding was carried out. CODRE of UASB reactors being output parameter of the conducted anaerobic treatment was estimated by nine input parameters such as HRT, pH, CODi concentration, operating temperature, alkalinity, VFA concentration, dilution ratio (DR), OLR, and TSS concentration. After backpropagation (BP) training combined with principal component analysis (PCA), the ANN model predicted CODRE values based on experimental data and all the predictions were proven to be satisfactory with a correlation coefficient of about 0.8245. In the ANN study, the Levenberg-Marquardt algorithm (LMA) was found as the best of 11 BP algorithms. In addition to determination of the optimal ANN structure, a linear-nonlinear study was also employed to investigate the effects of input variables on CODRE values

关键词： Textile wastewater Anaerobic treatment COD removal efficiency Neural network backpropagation algorithm Modeling

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MODELING OF PAVEMENT ROUGHNESS UTILIZING ARTIFICIAL NEURAL NETWORK APPROACH FOR LAOS NATIONAL ROAD NETWORK

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JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2022年第4期28卷 261-277页

作者： Gharieb, Mohamed Nishikawa, Takafumi Nakamura, Shozo Thepvongsa, Khampaseuth Nagasaki Univ Grad Sch Engn 1-14 Bunkyo Machi Nagasaki 8528521 Japan Natl Univ Laos Fac Engn Lao Thai Rd Sisatanak Vientiane Laos

The International Roughness Index (IRI) has become the reference scale for assessing pavement roughness in many highway agencies worldwide. This research aims to develop two Artificial Neural Network (ANN) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Laos Pavement Management System (PMS) database for National Road Network (NRN). The final database consisted of 269 and 122 observations covering 1850 km of DBST NRN and 718 km of AC NRN, respectively. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The obtained data were randomly divided into training (70%), validation (15%), and testing (15%) datasets. The statistical evaluation results of the training dataset reveal that both ANN models (DBST and AC) have good prediction ability with high values of coefficient of determination (R-2 = 0.96 and 0.94) and low values of Mean Absolute Error (MAE = 0.23 and 0.19) and Mean Squared Percentage Error (RMSPE = 7.03 and 9.98). Eventually, the goodness of fit of the proposed ANN models was compared with the Multiple Linear Regression (MLR) models previously developed under the same conditions. The results show that ANN models yielded higher prediction accuracy than MLR models.

关键词： International Roughness Index (IRI) Laos pavement management system (PMS) artificial neural network (ANN) backpropagation algorithm double bituminous surface treatment (DBST) asphalt concrete (AC) pavement age cumulative equivalent single-axle load (CESAL) pavement performance model

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Simulated annealing and weight decay in adaptive learning: The SARPROP algorithm

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1998年第4期9卷 662-668页

作者： Treadgold, NK Gedeon, TD Univ New S Wales Sch Engn & Comp Sci Dept Informat Engn Sydney NSW 2052 Australia

A problem with gradient descent algorithms is that they can converge to poorly performing local minima. Global optimization algorithms address this problem, but at the cost of greatly increased training times. This work examines combining gradient descent with the global optimization technique of simulated annealing (SA). Simulated annealing in the form of noise and weight decay is added to resiliant backpropagation (RPROP), a powerful gradient descent algorithm for training feedforward neural networks. The resulting algorithm, SARPROP, is shown through various simulations not only to be able to escape local minima, but is also able to maintain, and often improve the training times of the RPROP algorithm. In addition, SARPROP may be used with a restart training phase which allows a more thorough search of the error surface and provides an automatic annealing schedule.

关键词： adaptive backpropagation algorithm gradient descent neural network RPROP simulated annealing weight decay

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Image noise types recognition using convolutional neural network with principal components analysis

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IET IMAGE PROCESSING 2017年第12期11卷 1238-1245页

作者： Khaw, Hui Ying Soon, Foo Chong Chuah, Joon Huang Chow, Chee-Onn Univ Malaya Dept Elect Engn Fac Engn Kuala Lumpur 50603 Malaysia

This study presents a model to effectively recognise image noise of different types and levels: impulse, Gaussian, Speckle and Poisson noise, and a mixture of multiple types of the noise. To classify image noise type, the convolutional neural network (CNN) method with backpropagation algorithm and stochastic gradient descent optimisation techniques are implemented. In order to reduce the training time and computational cost of the algorithm, the principal components analysis (PCA) filters generating strategy is deployed to obtain data adaptive filter banks. The authors validated their designed CNN with PCA for noise types recognition model with degraded images containing noise of single and combination of multiple types, with a total of 11,000 and 1650 datasets for training and testing purposes, respectively. The variety and complexity of data have never been addressed before in any other research work. The capability of their intelligent system in handling images degraded under this complicated environment has surpassed human-eye performance in noise types recognition. The authors' experiments have proven the reliability of the proposed noise types recognition model by having achieved an overall average accuracy of 99.3% while recognising eight classes of noise.

关键词： image classification Gaussian noise impulse noise principal component analysis speckle backpropagation stochastic programming gradient methods channel bank filters image filtering adaptive filters image noise types recognition model convolutional neural network principal components analysis Poisson noise speckle Gaussian noise impulse noise image noise type classification CNN method backpropagation algorithm stochastic gradient descent optimisation techniques computational cost reduction training time reduction PCA filter generating strategy data adaptive filter banks data complexity intelligent system human-eye performance

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On the use of neural networks to evaluate groundwater levels in fractured media

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JOURNAL OF HYDROLOGY 2005年第1-4期307卷 92-111页

作者： Lallahem, S Mania, J Hani, A Najjar, Y Univ Lille Ecole Polytech Dept Geotech & Genie Civil CNRSUMR 8107LML F-59655 Villeneuve Dascq France Kansas State Univ Dept Civil Engn Manhattan KS 66506 USA

This paper evaluates the feasibility of using artificial neural network (ANN) methodology for estimating the groundwater level in some piezometers implanted in unconfined chalky aquifer of Northern France. These aquifers are the most susceptible to depletion and contamination, with the recharge rate and dominant processes determining their level of vulnerability. Groundwater level simulation in a chalky media is regarded as a difficult subject in hydrogeology due to the complexity of the physical processes involved, the variability of piezometry in space and time and the aquifer response in fissured and matrix blocks. In the present project, after a detailed geologic and hydrogeologic study of the sector, we simulated the groundwater level by Neural Network. The first objective was to determine the most influential parameters which impact groundwater level in fissured chalky media. The second objective was to investigate the effect of temporal and spatial information by considering current and past data sets along with the use of a variety of piezometer readings. The third objective was to simulate the groundwater level in a selected piezometer. The reasonably good ANN-based simulations revealed the merit of using ANNs and specifically Multi Layer Perceptron (MLP) models. The proposed ANN methodology using minimal lag and number of hidden nodes, along with the optimal number of spatial and temporal variables consistently produced the best performing network-based simulation models. (c) 2004 Elsevier B.V. All rights reserved.

关键词： ANN chalk groundwater fractured media backpropagation algorithm

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Application of neural networks to software quality modeling of a very large telecommunications system

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1997年第4期8卷 902-909页

作者： Khoshgoftaar, TM Allen, EB Hudepohl, JP Aud, SJ NORTEL RES TRIANGLE PK NC USA

Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

关键词： backpropagation algorithm classification discriminant analysis fault-prone modules neural network principal components analysis software metrics

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