规则抽取的Fuzzy ARTMAP方法

着重阐述了如何使用有教师监督的自组织神经网络－模糊自适应共振映射网络（Ｆｕｚｚｙ ＡＲＴＭＡＰ）从例子中抽取知识规则。叙述了规则抽取中的两个细节：网络修剪，即删除那些对网络抽取规则贡献不大的节点及其相连的权值；权值的量化，以使系统最终能释译成一套可使用的规则。本文对Ｆｕｚｚｙ ＡＲＴＭＡＰ网络作了改进和简化，并用于医学上心电图（ＥＣＧ）信号中室性早搏（ＰＶＣ）诊断规则的自动获取，取得了比较满意的结     

Comparing the online learning capabilities of Gaussian ARTMAP and Fuzzy ARTMAP for building energy management systems

Recently, there has been a growing interest in the application of Fuzzy ARTMAP for use in building energy management systems or EMS. However, a number of papers have indicated that there are important weaknesses to the Fuzzy ARTMAP approach, such as sensitivity to noisy data and category proliferation. Gaussian ARTMAP was developed to help overcome these weaknesses, raising the question of whether Gaussian ARTMAP could be a more effective approach for building energy management systems? This paper aims to answer this question. In particular, our results show that Gaussian ARTMAP not only has the capability to address the weaknesses of Fuzzy ARTMAP but, by doing this, provides better and more efficient EMS controls with online learning capabilities.     

A decision support tool for allocating hospital bed resources and determining required acuity of care

Limitations in health care funding require physicians and hospitals to find effective ways to utilize resources. Neural networks provide a method for predicting resource utilization of costly resources used for prolonged periods of time. Injury severity knowledge is used to determine the acuity of care required for each patient and length of stay is used to determine duration of inpatient hospitalization. Neural networks perform well on these medical domain problems, predicting total length of stay within 3 days for pediatric trauma (population mean and S.D. 4.37±45.12) and within 4 days for acute pancreatitis patients (7.75±79.19).     

A Fast Simplified Fuzzy ARTMAP Network

We present an algorithmic variant of the simplified fuzzy ARTMAP (SFAM) network, whose structure resembles those of feed-forward networks. Its difference with Kasuba's model is discussed, and their performances are compared on two benchmarks. We show that our algorithm is much faster than Kasuba's algorithm, and by increasing the number of training samples, the difference in speed grows enormously.The performances of the SFAM and the MLP (multilayer perceptron) are compared on three problems: the two benchmarks, and the Farsi optical character recognition (OCR) problem. For training the MLP two different variants of the backpropagation algorithm are used: the BPLRF algorithm (backpropagation with plummeting learning rate factor) for the benchmarks, and the BST algorithm (backpropagation with selective training) for the Farsi OCR problem.The results obtained on all of the three case studies with the MLP and the SFAM, embedded in their customized systems, show that the SFAM's convergence in fast-training mode, is faster than that of MLP, and online operation of the MLP is faster than that of the SFAM. On the benchmark problems the MLP has much better recognition rate than the SFAM. On the Farsi OCR problem, the recognition error of the SFAM is higher than that of the MLP on ill-engineered datasets, but equal on well-engineered ones. The flexible configuration of the SFAM, i.e. its capability to increase the size of the network in order to learn new patterns, as well as its simple parameter adjustment, remain unchallenged by the MLP.     

Fuzzy c-means based support vector machines classifier for perfume recognition

Identification of more than three perfumes is very difficult for the human nose. It is also a problem to recognize patterns of perfume odor with an electronic nose that has multiple sensors. For this reason, a new hybrid classifier has been presented to identify type of perfume from a closely similar data set of 20 different odors of perfumes. The structure of this hybrid technique is the combination of unsupervised fuzzy clustering c-mean (FCM) and supervised support vector machine (SVM). On the other hand this proposed soft computing technique was compared with the other well-known learning algorithms. The results show that the proposed hybrid algorithm’s accuracy is 97.5% better than the others.     

An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN-based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data – ‘Noon Data’ – which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.     

Fuzzy neural network-based rescheduling decision mechanism for semiconductor manufacturing

Most semiconductor manufacturing systems (SMS) operate in a highly dynamic and unpredictable environment. The production rescheduling strategy addresses uncertainty and improves SMS performance. The rescheduling framework of SMS is presented as layered scheduling strategies with an optimization rescheduling decision mechanism. A fuzzy neural network (FNN) based rescheduling decision model is implemented which can rapidly choose an optimized rescheduling strategy to schedule the semiconductor wafer fabrication lines according to current system disturbances. The mapping between the input of FNN, such as disturbances, system state parameters, and the output of FNN, optimal rescheduling strategies, is constructed. An example of a semiconductor fabrication line in Shanghai is given. The experimental results demonstrate the effectiveness of proposed FNN-based rescheduling decision mechanism approach over the alternatives such as back-propagation neural network (BPNN) and multivariate regression (MR).     

A fuzzy information-based approach for breast cancer risk factors assessment

The aim of this study is to define the risk factors that are effective in Breast Cancer (BC) occurrence, and to construct a supportive model that will promote the cause-and-effect relationships among the factors that are crucial to public health. In this study, we utilize Rule-Based Fuzzy Cognitive Map (RBFCM) approach that can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause-and-effect relationships among the concepts to model the behavior of any system. In this study, a decision-making system is constructed to evaluate risk factors of BC based on the information from oncologists. To construct causal relationship, the weight matrix of RBFCM is determined with the combination of the experts’ experience, expertise and views. The results of the proposed methodology will allow better understanding into several root causes, with the help of which, oncologists can improve their prevention and protection recommendation. The results showed that Social Class and Late Maternal Age can be seen as important modifiable factors; on the other hand, Benign Breast Disease, Family History and Breast Density can be considered as important factors as non-modifiable risk factors. This study is somehow weighing the interrelations of the BC risk factors and is enabling us to make a sensitivity analysis between the scenario studies and BC risk factors. A soft computing method is used to simulate the changes of a system over time and address “what if” questions to compare between different case studies.     

Fuzzy logic-based decision support system for automating ergonomics risk assessments

Workers in the modular construction industry are frequently exposed to ergonomic risks, which may lead to injuries and lower productivity. In light of this, researchers have proposed a number of ergonomics risk assessment methods to identify design flaws in work systems, thereby reducing ergonomic discomfort and boosting workplace productivity. However, organizations often disregard ergonomics risk assessments due to a lack of convenient tools and knowledge. Therefore, this study proposes a fuzzy logic-based decision support system to help practitioners to automatically and comprehensively assess the ergonomic performance of work systems. For comprehensive assessment of ergonomic risk, the proposed decision support system considers physical, environmental, and sensory factors. Specifically, the decision support system comprises eight fuzzy expert systems that output a composite risk score, called an “ergonomic risk indicator”, that indicates the overall level of ergonomic risk present in a given work system. The performance of the proposed decision support system is then evaluated using a real-world case study in a modular construction facility by comparing the results of the decision support system with the facility's occupational injury reports. The results prove the effectiveness of the decision support system. Overall, the decision support system is capable of generating a composite risk score, the ergonomic risk indicator, and the proposed high-level architecture and design represent significant contributions for the enhancement of health and safety in the modular construction industry.     

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

One of the major design problems in the context of manufacturing systems is the well-known Buffer Allocation Problem (BAP). This problem arises from the cost involved in terms of space requirements on the production floor and the need to keep in mind the decoupling impact of buffers in increasing the throughput of the line. Production line designers often need to solve the Buffer Allocation Problem (BAP), but this can be difficult, especially for large production lines, because the task is currently highly time consuming. Designers would be interested in a tool that would rapidly provide the solution to the BAP, even if only a near optimal solution is found, especially when they have to make their decisions at an operational level (e.g. hours). For decisions at a strategic level (e.g. years), such a tool would provide preliminary results that would be useful, before attempting to find the optimal solution with a specific search algorithm.     

An improved random forest-based rule extraction method for breast cancer diagnosis

Breast cancer has been becoming the main cause of death in women all around the world. An accurate and interpretable method is necessary for diagnosing patients with breast cancer for well-performed treatment. Nowadays, a great many of ensemble methods have been widely applied to breast cancer diagnosis, capable of achieving high accuracy, such as Random Forest. However, they are black-box methods which are unable to explain the reasons behind the diagnosis. To surmount this limitation, a rule extraction method named improved Random Forest (RF)-based rule extraction (IRFRE) method is developed to derive accurate and interpretable classification rules from a decision tree ensemble for breast cancer diagnosis. Firstly, numbers of decision tree models are constructed using Random Forest to generate abundant decision rules available. And then a rule extraction approach is devised to detach decision rules from the trained trees. Finally, an improved multi-objective evolutionary algorithm (MOEA) is employed to seek for an optimal rule predictor where the constituent rule set is the best trade-off between accuracy and interpretability. The developed method is evaluated on three breast cancer data sets, i.e., the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, Wisconsin Original Breast Cancer (WOBC) dataset, and Surveillance, Epidemiology and End Results (SEER) breast cancer dataset. The experimental results demonstrate that the developed method can primely explain the black-box methods and outperform several popular single algorithms, ensemble learning methods, and rule extraction methods from the view of accuracy and interpretability. What is more, the proposed method can be popularized to other cancer diagnoses in practice, which provides an option to a more interpretable, more accurate cancer diagnosis process.     

Toward breast cancer diagnosis based on automated segmentation of masses in mammograms

This work explores the use of characterization features extracted based on breast-mass contours obtained by automated segmentation methods, for the classification of masses in mammograms according to their diagnosis (benign or malignant). Two sets of mass contours were obtained via two segmentation methods (a dynamic-programming-based method and a constrained region-growing method), and simplified versions of these contours (modeling the contours as ellipses) were employed to extract a set of six features designed for characterization of mass margins (contrast between foreground region and background region, coefficient of variation of edge strength, two measures of the fuzziness of mass margins, a measure of spiculation based on relative gradient orientation, and a measure of spiculation based on edge-signature information). Three popular classifiers (Bayesian classifier, Fisher's linear discriminant, and a support vector machine) were then used to predict the diagnosis of a set of 349 masses based on each of said features and some combinations of these. The systems (each system consists of a segmentation method, a featureset, and a classifier) were compared with each other in terms of their performance on the diagnosis of the set of breast masses. It was found that, although there was a percent difference of about 14% in the average segmentation quality between methods, this was translated into an average percent difference of only 4% in the classification performance. It was also observed that the spiculation feature based on edge-signature information was distinctly better than the rest of the features, although it is not very robust to changes in the quality of the segmentation. All systems were more efficient in predicting the diagnosis of benign masses than that of the malignant masses, resulting in low sensitivity and high specificity values (e.g. 0.6 and 0.8, respectively) since the positive class in the classification experiments is the set of malignant masses. It was concluded that features extracted from automated contours can contribute to the diagnosis of breast masses in screening programs by correctly identifying a majority of benign masses.     

Development of decision support knowledge based system for tool wear diagnosis

This paper deals with the problems faced by small and medium sized metal cutting industries, with the perspective of tool monitoring. In a small or medium size metal cutting industry employing major metal cutting process, one of the primary problem is that of tool monitoring and wear diagnosis. The problem is of immediate concern especially in those industries where the processes or operations employed are flexible and production depends entirely on orders from customers. Due to a flexible manufacturing setup, almost all major metal cutting processers need to be carried out. However, it becomes increasingly difficult for such small or medium size metal cutting industries to employ skilled manpower for each operation as well as expert technicians to supervise the operation, and carry out fault diagnosis and tool monitoring. Also, the problem associated with tool monitoring is that human operator carrying out the monitoring has to rely either on observation such as ceasing of tool, rise in temperature, generation of fumes, noisy operation, vibrations, considerable change in shape etc, or by monitoring the quality of the finished product. Also, there can be instances where the operator does notice a symptom but does not have the expertise to identify the cause of the trouble. Errors in tool monitoring can lead to considerable damage both to the machine as well as the workpiece. On the other hand, if the tool is replaced before it reaches its useful life expectancy, it leads to unnecessary additional cost. A Decision Support Knowledge Based System (DSKBS) has therefore been developed in this paper with the above considerations. The DSKBS provides the user with a friendly environment to diagnose a particular tool wear and obtain the necessary repair or replacement instructions. The goal is to increase productivity, decrease cost of operation and enhance total quality and reliability of the operation.     

Discovering interobserver variability in the cytodiagnosis of breast cancer using decision trees and Bayesian networks

We evaluate the performance of two decision tree procedures and four Bayesian network classifiers as potential decision support systems in the cytodiagnosis of breast cancer. In order to test their performance thoroughly, we use two real-world databases containing 692 cases and 322 cases collected by a single observer and 19 observers, respectively. The results show that, in general, there are considerable differences in all tests (accuracy, sensitivity, specificity, PV+, PV− and ROC) when a specific classifier uses the single-observer dataset compared to those when this same classifier uses the multiple-observer dataset. These results suggest that different observers see different things: a problem known as interobserver variability. We graphically unveil such a problem by presenting the structures of the decision trees and Bayesian networks resultant from running both databases.     

A GAs based approach for mining breast cancer pattern

Data mining usually means the methodologies and tools for the efficient new knowledge discovery from databases. In this paper, a genetic algorithms (GAs) based approach to assess breast cancer pattern is proposed for extracting the decision rules including the predictors, the corresponding inequality and threshold values simultaneously so as to building a decision-making model with maximum prediction accuracy. Early many studies of handling the breast cancer diagnostic problems used the statistical related techniques. As the diagnosis of breast cancer is highly nonlinear in nature, it is hard to develop a comprehensive model taking into account all the independent variables using conventional statistical approaches. Recently, numerous studies have demonstrated that neural networks (NNs) are more reliable than the traditional statistical approaches and the dynamic stress method. The usefulness of using NNs have been reported in literatures but the most obstacle is the in the building and using the model in which the classification rules are hard to be realized. We compared our results against a commercial data mining software, and we show experimentally that the proposed rule extraction approach is promising for improving prediction accuracy and enhancing the modeling simplicity. In particular, our approach is capable of extracting rules which can be developed as a computer model for prediction or classification of breast cancer potential like expert systems.     

Unified methodology of neural analysis in decision support systems built for pharmaceutical technology

The objective of this study was to create universal methodology of artificial neural networks (ANNs) application in construction of decision support systems designed for various dosage forms. Two different dosage forms (solid dispersions and microemulsions) were modeled with use of the same methodology, software and hardware environments. Completely different models prepared confirmed their generalization ability both for solid dosage forms (solid dispersions) and liquid dosage forms (microemulsions). ME_expert and SD_expert systems basing on the neural expert committees were created. In the pilot study their application allowed for appropriate choice of qualitative and quantitative composition of particular pharmaceutical formulation. It was also proposed that ME_expert and SD_expert might provide in silico formulation procedures. Unified methodology of neural modeling in pharmaceutical technology was confirmed to be effective in providing valuable tools for pharmaceutical product development.     

Bridging challenges of clinical decision support systems with a semantic approach. A case study on breast cancer

The integration of Clinical Decision Support Systems (CDSS) in nowadays clinical environments has not been fully achieved yet. Although numerous approaches and technologies have been proposed since 1960, there are still open gaps that need to be bridged. In this work we present advances from the established state of the art, overcoming some of the most notorious reported difficulties in: (i) automating CDSS, (ii) clinical workflow integration, (iii) maintainability and extensibility of the system, (iv) timely advice, (v) evaluation of the costs and effects of clinical decision support, and (vi) the need of architectures that allow the sharing and reusing of CDSS modules and services. In order to do so, we introduce a new clinical task model oriented to clinical workflow integration, which follows a federated approach. Our work makes use of the reported benefits of semantics in order to fully take advantage of the knowledge present in every stage of clinical tasks and the experience acquired by physicians. In order to introduce a feasible extension of classical CDSS, we present a generic architecture that permits a semantic enhancement, namely Semantic CDSS (S-CDSS). A case study of the proposed architecture in the domain of breast cancer is also presented, pointing some highlights of our methodology.     

Fuzzy logic based decision support for mass evacuations of cities prone to coastal or river floods

Facing climate change and more frequent extreme weather conditions, coastal floods and inundations will become more severe. Evacuation can be an efficient solution to secure people's safety in a major disaster. The main difficulty in making an evacuation decision is the imprecise, incomplete and spatially varying nature of the crisis information. In this paper, a fuzzy-logic based method combined with Geographic Information System is proposed to analyze evacuation decision making scenarios. The method can handle qualitative and quantitative data at the same time, avoid sudden changes of decisions affected by uncertainties, and evaluate the spatial necessity to evacuate to support evacuation decision making. The method has been tested at the city of Bordeaux in France. The maps produced representing the need to evacuate can help decision makers better understand evacuation decision situation in terms of local impacts and crisis management anticipation.     

Ultrasound based application for intraglandular mapping of breast cancer

Purpose

Breast cancer is the most common malignant tumor among women worldwide. Breast cancer is one of the few cancers that can be early detected, and the survival rate of the women whose breast cancers are detected on their initial stage is virtually 100%. At the present time, ultrasound (US) is the most important imaging test together with the mammogram for the diagnostic evaluation of the breast. Recent studies have shown that ultrasound, in addition to mammography, helps doctors to spot significantly more cancers compared with mammograms alone.This work intends to standardize the process of the US breast examination, the storage and marking of the US images and their subsequent visualization and comparison.

Methods

It presents an innovative technique for the intraglandular mapping of breast cancer in a 3D scene. An anatomical based model of the breast is used for storage of the US images. Hardware equipment needed for the breast examination is described. Soft application programmed on Apple tools is fully described. The database for the storage is presented.

Results

First clinical applications of the presented tool are reported. Currently, the system is being distributed free of charge to clinical personal in order to evaluate its benefits.

Conclusions

A first version of an application to standardize the process of the US breast examination is presented. First reports show the feasibility of the system to be applied on clinics.     

A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique

This paper presents a novel intelligent diagnosis method based on multiple domain features, modified distance discrimination technique and improved fuzzy ARTMAP (IFAM). The method consists of three steps. To begin with, time-domain, frequency-domain and wavelet grey moments are extracted from the raw vibration signals to demonstrate the fault-related information. Then through the modified distance discrimination technique some salient features are selected from the original feature set. Finally, the optimal feature set is input into the IFAM incorporated with similarity based on the Yu’s norm in the classification phase to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearing, and the test results show that the IFAM identify the fault categories of rolling element bearing more accurately and has a better diagnosis performance compared to the FAM. Furthermore, by the application of the bootstrap method to the diagnosis results it can testify that the IFAM has more capacity of reliability and robustness.