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A multilayer perceptron-based medical decision support system for heart disease diagnosis

EXPERT SYSTEMS WITH APPLICATIONS 2006年第2期30卷 272-281页

作者： Yan, HM Jiang, YT Zheng, J Peng, CL Li, QH Univ Elect Sci & Technol China Life Sci & Technol Inst Chengdu 610054 Peoples R China Univ Nevada Dept Elect & Comp Engn Las Vegas NV 89154 USA Chongqing Univ Bioengn Inst Chongqing 400044 Peoples R China SW Hosp Chongqing 400038 Peoples R China CUNY Queens Coll Dept Comp Sci Flushing NY 11367 USA

The medical diagnosis by nature is a complex and fuzzy cognitive process, and soft computing methods, such as neural networks, have shown great potential to be applied in the development of medical decision support systems (MDSS). In this paper, a multiplayer perceptron-based decision support system is developed to support the diagnosis of heart diseases. The input layer of the system includes 40 input variables, categorized into four groups and then encoded using the proposed coding schemes. The number of nodes in the hidden layer is determined through a cascade learning process. Each of the 5 nodes in the output layer corresponds to one heart disease of interest. In the system, the missing data of a patient are handled using the substituting mean method. Furthermore, an improved back propagation algorithm is used to train the system. A total of 352 medical records collected from the patients suffering from five heart diseases have been used to train and test the system. In particular, three assessment methods, cross validation, holdout and bootstrapping, are applied to assess the generalization of the system. The results show that the proposed MLP-based decision support system can achieve very high diagnosis accuracy (> 90%) and comparably small intervals (< 5%), proving its usefulness in support of clinic decision process of heart diseases. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： medical decision support system multilayer perceptron back propagation algorithm performance of classifier heart disease

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Particle swarm optimization of a neural network model in a machining process

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2014年第3期39卷 533-548页

作者： Garg, Saurabh Patra, Karali Pal, Surjya K. Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Indian Inst Technol Patna Dept Mech Engn Patna 800013 Bihar India Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India

This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network (BPNN). This analysis is carried out following a series of experiments employing high speed steel (HSS) drills for drilling on mild steel workpieces, under different sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as the average flank wear in each case. The results show that the PSO trained ANN not only gives better prediction results and reduced computational times compared to the BPNN, it is also a more robust model, being free of getting trapped in local optimum solutions unlike the latter. Besides, it offers the advantages of a straight-forward logic, simple realization and underlying intelligence.

关键词： Particle swarm optimization artificial neural network back propagation algorithm flank wear drilling

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GENERATING FUZZY MEMBERSHIP FUNCTIONS - A MONOTONIC NEURAL-NETWORK MODEL

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FUZZY SETS AND SYSTEMS 1994年第1期61卷 71-81页

作者： WANG, SH Faculty of Business University of New Brunswick Saint John NB Canada E2L 4L5

A crucial issue in the practical applications of fuzzy sets is to find a fuzzy membership function. This paper suggests that neural networks which use the back propagation learning algorithm under monotonic function c... 详细信息

关键词： FUZZY SETS ULTRAFUZZY NEURAL NETWORKS back propagation algorithm MONOTONICITY

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Research on the Prediction of Drilling Rate in Geological Core Drilling Based on the BP Neural Network

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APPLIED SCIENCES-BASEL 2024年第21期14卷 9959页

作者： Gong, Da Zu, Yutong Zhou, Zheng Jia, Mingrang Liu, Jiachen Hu, Yuanbiao China Univ Geosci Sch Mat Sci & Technol Beijing 100083 Peoples R China

The development of automation and intelligence in geological core drilling is not yet mature. The selection and improvement of drilling parameters rely mainly on experience, and adjustments are often made after drilling by evaluating the core, which introduces a lag and reduces the drilling efficiency. Therefore, this study first establishes a geological core drilling experiment platform to collect drilling data. Through the constructed geological core drilling experimental platform, the practical data at the drill bit can be directly obtained, solving the problem of the data from the surface equipment in the practical drilling differing from the practical data. Second, the back propagation (BP) algorithm is used to perform the ROP prediction, with weight on bit (WOB), torque (TOR), flow rate (Q), and rotation speed (RPM) as input parameters, and rate of penetration (ROP) as the output. Subsequently, correlation analysis is used to perform the feature parameter optimization, and the effects of bit wear and bit cutting depth on the experiment are considered. Finally, comparison with algorithms such as ridge regression, SVM and KNN shows that the ROP prediction model using the BP neural network has the highest prediction accuracy of 94.1%. The results provide a reference for ROP prediction and the automation of geological core drilling rigs.

关键词： geological core drilling back propagation algorithm drilling speed prediction BP neural network ROP

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Evaluation and forecasting of daily groundwater outflow in a small chalky watershed

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HYDROLOGICAL PROCESSES 2003年第8期17卷 1561-1577页

作者： Lallahem, S Mania, J Univ Lille Ecole Polytech Dept Geotech & Genie Civil CNRSLML URA 1441 F-59655 Villeneuve Dascq France

The purpose of this research is to include expert knowledge as one part of the modelling system and therefore offer the chance to create a productive interaction system between expert, mathematical model (MMO8) and artificial neural networks (ANNs). In the present project, the first objective is to determine some parameters by the MMO8 model, introduced as ANN input parameters to forecast spring outflow. The second objective is first to investigate the effect of temporal information by taking current and past data sets and then to forecast spring outflow. The good results obtained reveal the merit of the ANNs-MMO8 combination, and specifically multilayer perceptron (MLP) models. This methodology, for a network with lower, lag and number hidden layer, consistently produced better performance. Copyright (C) 2003 John Wiley Sons, Ltd.

关键词： groundwater neural networks fractured rock chalk stopped training approach back propagation algorithm

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Utilization of artificial neural networks and autoregressive modeling in diagnosing mitral valve stenosis

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COMPUTERS IN BIOLOGY AND MEDICINE 2006年第5期36卷 473-483页

作者： Kara, S Güven, A Okandan, M Dirgenali, F Erciyes Univ Dept Elect Engn Biomed Engn Grp TR-38039 Kayseri Turkey Erciyes Univ Dept Elect Civil Aviat Sch TR-38039 Kayseri Turkey Dept Elect Engn TR-38039 Kayseri Turkey

This research is concentrated on the diagnosis of mitral heart valve stenosis through the analysis of Doppler Signals' AR power spectral density graphic with the help of ANN. Multilayer feedforward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented in the MATLAB environment. Correct classification of 94% was achieved, whereas 4 false classifications have been observed for the test group of 68 subjects in total. The designed classification structure has about 97.3% sensitivity, 90.3% specifity and positive prediction is calculated to be 92.3%. The stated results show that the proposed method can make an effective interpretation. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： mitral valve stenosis artificial neural networks back propagation algorithm Levenberg Marquart method autoregressive modeling

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Prediction of emission characteristics of a diesel engine using experimental and artificial neural networks

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APPLIED NANOSCIENCE 2021年第1期13卷 433-442页

作者： Van Hung, Tran Alkhamis, Hussein H. Alrefaei, Abdulwahed F. Sohret, Yasin Brindhadevi, Kathirvel Van Lang Univ 45 Nguyen Khac Nhu StCo Giang Ward Ho Chi Minh City Vietnam King Saud Univ Coll Sci Dept Bot & Microbiol Riyadh 11451 Saudi Arabia King Saud Univ Coll Sci Dept Zool POB 2455 Riyadh 11451 Saudi Arabia Suleyman Demirel Univ Sch Civil Aviat Dept Airframe & Power Plant Maintenance Isparta Turkey Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

The focus of the study is to investigate effects of corn blends on exhaust emissions using Artificial Neural Network (ANN) approach. A series of experiments were conducted on the water-cooled multi-cylinder engine to calibrate the emissions of CO, THC, and NOx. The biodiesel was prepared using the transesterification process. Furthermore, the MgO nanoparticles of 10, 15, 20 and 30 ppm was added to the corn blends through ultrasonication. The ANN is developed to anticipate the emission characteristics of the compression ignition engine. As engine load increases, the emission of carbon monoxide and total hydrocarbons decreases significantly. On the contrary, the emission of NOx gases spiked at higher load. The ANN back propagation algorithm is developed with four input network and one output network to predict the results. The blends C10, C15, C20, and C30 were studied with the developed ANN by varying the engine load. Besides, the highest and lowest value of mean square errors and correlation coefficient were found for CO, THC, and NOx. Meanwhile, the optimized regression coefficients for the emission parameters ranged between 0.8875 and 0.9858. The predicted correlation coefficients for CO, THC, and NOx were 0.9985, 0.9978 and 0.9986, respectively.

关键词： Diesel engine ANN back propagation algorithm Biodiesel Emission NOx

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Intrusion detection using hierarchical neural networks

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PATTERN RECOGNITION LETTERS 2005年第6期26卷 779-791页

作者： Zhang, CL Jiang, J Kamel, M Univ Waterloo Dept Elect & Comp Engn Pattern Anal & Machine Intelligence Res Grp Waterloo ON N2L 3G1 Canada

Most intrusion detection system (IDS) with a single-level structure can only detect either misuse or anomaly attacks. Some IDSs with multi-level structure or multi-classifier are proposed to detect both attacks, but they are limited in adaptively learning. In this paper, two hierarchical IDS frameworks using Radial Basis Functions (RBF) are proposed. A serial hierarchical IDS (SHIDS) is proposed to identify misuse attack accurately and anomaly attacks adaptively. A parallel hierarchical IDS (PHIDS) is proposed to enhance the SHIDS's functionalities and performance. The experiments show that the two proposed IDSs can detect network intrusions in real-time, train new classifiers for novel intrusions automatically, and modify their structures adaptively after new classifiers are trained. (c) 2004 Elsevier B.V. All rights reserved.

关键词： intrusion detection neural networks back propagation algorithm radius basis functions hierarchical neural network neural network ensembles

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Dual Stage Normalization Approach Towards Classification of Breast Cancer

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IETE JOURNAL OF RESEARCH 2022年第4期68卷 3074-3085页

作者： Aswathy, M. A. Jagannath, M. Vellore Inst Technol Chennai 600127 Tamil Nadu India

Breast cancer is a major concern among women that causes high risk of death. Early diagnosis of such cancer becomes challenging due to alterations in the color of the histopathological breast images. This study uses a publicly available dataset of breast cancer histopathology images. This paper introduces a dual stage normalization approach, to address the color variation problem of biopsy specimen collectively caused by incompatible staining in biopsy process and bizarre imaging quality. The dual stage normalization proposed here consists of a stain normalization unit and a light normalization unit. This system addresses the variations of both imaging and staining of specimen that are caused by a microscopic imaging setup. Later on, eight features have been extracted from the normalized images and used for the classification of breast cancer (benign and malignant). The overall accuracy of the back propagation algorithm (BPA) classifier is obtained as 81.8%. After comparison with other classifier accuracies, BPA classifier is found to be acceptable. Recall and precision values are approximately 89% and 90%, respectively, which is acceptable. The saturation-weighted hue statistics produces balanced and uniform color hues for stain normalization. This statistic is powerful against variations in model parameters and unsusceptible to image subjects and achromatic colors. This normalization technique retains all histological data with an enhanced performance.

关键词： back propagation algorithm biopsy breast cancer histopathology light normalization stain normalization

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Determining the Content of Nitrogen in Rubber Trees by the Method of NIR Spectroscopy

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JOURNAL OF APPLIED SPECTROSCOPY 2017年第4期84卷 627-632页

作者： Tang, R. Chen, K. Jiang, C. Li, Ch Hainan Univ Sch Mech & Elect Engn Haikou 570228 Hainan Peoples R China

Foliar nitrogen (N) is an important index to evaluate the content of nitrogen in rubber trees. Hence, a fast way utilizing the spectroscopy to estimate the nitrogen content level of the rubber tree under natural environment has been preliminarily investigated in this paper. The combined features strategy is adopted in this study. The evaluation model is developed by back propagation (BP) algorithm and Adaboost algorithm. The results of simulation experiment show that satisfactory classification performance has been achieved using the proposed model and the percentage of samples correctly classified by the proposed model in the validation set is 92.98%.

关键词： rubber tree nitrogen spectral discrimination back propagation algorithm Adaboost algorithm

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