基于特征层密集连接与注意力机制的宽度学习系统及其在锌浮选过程的应用

本文针对宽度学习存在计算消耗资源大、计算过程冗余数据较多等问题,提出了一种基于特征层密集连接与注意力机制的宽度学习系统(DCA-BLS),并利用其建立锌浮选过程快粗槽底流品位在线预测模型.首先将宽度学习系统的特征层不同窗口进行密集连接,引入弹性网络进行稀疏化处理,利用注意力机制处理特征节点,获得不同特征节点的权值,再将加权后的特征节点与输入的数据相结合,共同作为增强层节点的输入,使模型更为紧凑.在公共数据集和锌泡沫浮选数据上将DCA-BLS与其他预测算法进行了对比实验,实验结果表明,本文提出的方法训练时间短,且相较于其他所比较方法具有更高的准确率.     

A context layered locally recurrent neural network for dynamic system identification

This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered locally recurrent neural network (CLLRNN) for dynamic system identification. The CLLRNN is a dynamic neural network which appears in effective in the input–output identification of both linear and nonlinear dynamic systems. The CLLRNN is composed of one input layer, one or more hidden layers, one output layer, and also one context layer improving the ability of the network to capture the linear characteristics of the system being identified. Dynamic memory is provided by means of feedback connections from nodes in the first hidden layer to nodes in the context layer and in case of being two or more hidden layers, from nodes in a hidden layer to nodes in the preceding hidden layer. In addition to feedback connections, there are self-recurrent connections in all nodes of the context and hidden layers. A dynamic backpropagation algorithm with adaptive learning rate is derived to train the CLLRNN. To demonstrate the superior properties of the proposed architecture, it is applied to identify not only linear but also nonlinear dynamic systems. The efficiency of the proposed architecture is demonstrated by comparing the results to some existing recurrent networks and design configurations. In addition, performance of the CLLRNN is analyzed through an experimental application to a dc motor connected to a load to show practicability and effectiveness of the proposed neural network. Results of the experimental application are presented to make a quantitative comparison with an existing recurrent network in the literature.     

A hybrid approach to medical decision support systems: combining feature selection, fuzzy weighted pre-processing and AIRS

This paper presents a hybrid approach based on feature selection, fuzzy weighted pre-processing and artificial immune recognition system (AIRS) to medical decision support systems. We have used the heart disease and hepatitis disease datasets taken from UCI machine learning database as medical dataset. Artificial immune recognition system has shown an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, and liver disorders classification. The proposed approach consists of three stages. In the first stage, the dimensions of heart disease and hepatitis disease datasets are reduced to 9 from 13 and 19 in the feature selection (FS) sub-program by means of C4.5 decision tree algorithm (CBA program), respectively. In the second stage, heart disease and hepatitis disease datasets are normalized in the range of [0,1] and are weighted via fuzzy weighted pre-processing. In the third stage, weighted input values obtained from fuzzy weighted pre-processing are classified using AIRS classifier system. The obtained classification accuracies of our system are 92.59% and 81.82% using 50-50% training-test split for heart disease and hepatitis disease datasets, respectively. With these results, the proposed method can be used in medical decision support systems.     

A fuzzy classification model for myocardial infarction risk assessment

The use of data mining approaches for analyzing patients trace in different medical databases has become an important research field especially with the evolution of these methods and their contributions in medical decision support. In this paper, we develop a new clinical decision support system (CDSS) to diagnose Coronary Artery Diseases (CAD). According to CAD experts, Angiography is most accurate CAD diagnosis technique. However, it has many aftereffects and is very costly. Existing studies showed that CAD diagnosis requires heterogeneous patients traces from medical history while applying data mining techniques to achieve high accuracy. In this paper, an automatic approach to design CDSS for CAD assessment is proposed. The proposed diagnosis model is based on Random Forest algorithm, C5.0 decision tree algorithm and Fuzzy modeling. It consists of two stages: first, Random Forest algorithm is used to rank the features and a C5.0 decision tree based approach for crisp rule generation is developed. Then, we created the fuzzy inference system. The generation of fuzzy weighted rules is carried out automatically from the previous crisp rules. Moreover, a critical issue about the CDSS is that some values of the features are missing in most cases. A new method to deal with the problem of missing data, which allows evaluating the similarity despite the missing information, was proposed. Finally, experimental results underscore very promising classification accuracy of 90.50% while optimizing training time using UCI (the University of California at Irvine) heart diseases datasets compared to the previously reported results.     

Effective diagnosis of heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis.     

Diagnosis of valvular heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the valvular heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS Base Software 9.1.3 for diagnosing of the valvular heart disease. A neural networks ensemble method is in the centre of the proposed system. The ensemble-based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with proposed tool. We obtained 97.4% classification accuracy from the experiments made on data set containing 215 samples. We also obtained 100% and 96% sensitivity and specificity values, respectively, in valvular heart disease diagnosis.     

HSVNN: an efficient medical data classification using dimensionality reduction combined with hybrid support vector neural network

Early diagnosis and therapy are the most essential strategies to prevent deaths from diseases, such as cancer, brain tumors, and heart diseases. In this regard, information mining and artificial intelligence approaches have been valuable tools for providing useful data for early diagnosis. However, high-dimensional data can be challenging to examine, practically difficult to visualize, and costly to measure and store. Transferring a high-dimensional portrayal of the data to a lower-dimensional one without losing important information is the focal issue of dimensionality reduction. Therefore, in this study, dimensionality reduction-based medical data classification is presented. The proposed methodology consists of three modules: pre-processing, dimension reduction using an adaptive artificial flora (AAF) algorithm, and classification. The important features are selected using the AAF algorithm to reduce the dimension of the input data. From the results, a dimension-reduced dataset is obtained. The reduced data are then fed as input to the hybrid classifier. A hybrid support vector neural network is proposed for classification. Finally, the effectiveness of the proposed method is analyzed in terms of different metrics, namely accuracy, sensitivity, and specificity. The proposed method is implemented in MATLAB.

     

Usage of class dependency based feature selection and fuzzy weighted pre-processing methods on classification of macular disease

In this paper, we propose a new feature selection method called class dependency based feature selection for dimensionality reduction of the macular disease dataset from pattern electroretinography (PERG) signals. In order to diagnosis of macular disease, we have used class dependency based feature selection as feature selection process, fuzzy weighted pre-processing as weighted process and decision tree classifier as decision making. The proposed system consists of three parts. First, we have reduced to 9 features number of features of macular disease dataset that has 63 features using class dependency based feature selection, which is first developed by ours. Second, the macular disease dataset that has 9 features is weighted by using fuzzy weighted pre-processing. And finally, decision tree classifier was applied to PERG signals to distinguish between healthy eye and diseased eye (macula diseases). The employed class dependency based feature selection, fuzzy weighted pre-processing and decision tree classifier have reached to 96.22%, 96.27% and 96.30% classification accuracies using 5–10–15-fold cross-validation, respectively. The results confirmed that the medical decision making system based on the class dependency based feature selection, fuzzy weighted pre-processing and decision tree classifier has potential in detecting the macular disease. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.     

Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic

The objective of the present study is to develop/establish a web-based medical diagnostic support system (MDSS) by which health care support can be provided for people living in rural areas of a country. In this respect, this research provides a novel approach for medical diagnosis driven by integrating fuzzy and intuitionistic fuzzy (IF) frameworks. Subsequently, based on the proposed approach a web-based MDSS is developed. The proposed MDSS comprises of a knowledge base (KB) and intuitionistic fuzzy inference system (IFIS). Based on the observation that medical data cannot be described with both precision and certainty, a medical KB is constructed in the form of a set of if-then decision rules by employing both fuzzy and IF logics. After constructing the medical KB, a new set of patients is considered for diagnosing the diseases. For each patient, linguistic values of the patients’ symptoms are considered as inputs of the proposed IFIS and modeled by using the generalized triangular membership functions. Subsequently, integrated fuzzy and IF rule-based inference system is used to find a valid conclusion for the new set of patients. In a nutshell, in this paper fuzzy rule-based and IFS based inference systems are combined for better and more realistic representation of uncertainty of the medical diagnosis problem and for more accurate diagnostic result. The method is composed of following four steps: (1) the modeling of antecedent part of the rules, which consist of linguistic assessments of the patients’ symptoms provided by the doctors/medical experts with their corresponding confidence levels, by using generalized fuzzy numbers; (2) the modeling of consequent part, which reveals the degree of association and the degree of non-association of diseases into the patient, by using IFSs; (3) the use of IF aggregation operator in inference process; (4) the application of relative closeness function to find the final crisp output for a given diagnosis. Finally, the applicability of the proposed approach is illustrated with a suitable case study. This article has also justified the proposed approach by using similarity measurement.     

LBA：轻量级区块链架构

针对现有区块链架构在面临垂直结构型的轻量级应用时存在着结构冗余、性能不足的缺点,提出一种轻量级区块链架构(lightweight blockchain architecture,LBA).在数据层,提出按行存储机制提高数据读写效率,并增加基于MongoDB的存储方式,支持海量数据的线下隔离存储.在共识层,设计分层共识机制,将节点按事务粒度划分为决策层和执行层,减少参与共识的节点数量,提升共识效率.在网络层,提出分布式结构化网络模型,提高网络中数据的广播效率.在应用层,设计自定义事务接口,支持不同场景下的区块链系统功能定制,采用自定义的有限状态机取代图灵机,提高智能合约的可控性和降低复杂度.实验仿真结果表明,LBA架构能有效提高区块链系统中的数据存储效率、网络通信和共识效率,适用于区块链的轻量级中小型应用.     

Online discovery of Heart Rate Variability patterns in mobile healthcare services

Recent years, advances in day-to-day wearable sensors have led to the development of low powered physiological sensor platforms, which can be integrated in body area networks, a new enabling technology for real-time health monitoring. The bottleneck in health state awareness is the algorithm that has to interpret the sensor data. Nowadays Coronary Heart Disease (CHD) is still the leading cause of death. Many classification techniques such as decision tree and neural networks proposed for an early detection of individual at risk for CHD are not able to continuously detect heart state based on sensor data stream. In this study, we propose an online three-layer neural network to recognize Heart Rate Variability (HRV) patterns related to CHD risk in consideration of daily activities. ECG sensor data is preprocessed using Poincaré plot encoding. Incremental learning is utilized to train the network with new data without forgetting the previously learned patterns. The algorithm is named Poincaré-based HRV patterns discovering Incremental Artificial neural Network (PHIAN). When a sample is presented, the nodes in the hidden layer of PHIAN compete for determining the node with the highest similarity to the input. Error variables associated with the neuron units are used as criteria for new node insertion in hopes of allowing the network to learn new patterns and reducing classification error. However, the node insertion has to be stopped in the overlapping decision areas. We suppose that the overlaps between classes have lower probability than the centric part of the classes. Therefore, after a period of learning we remove the nodes with no neighbor. Plus, the error probability density is taken into account instead of input probability density. Finally, the predictive capability of PHIAN is compared with three previous classification models, namely Self-Organizing Map (SOM), Growing Neural Gas (GNG), and Multilayer Perceptron (MLP) in terms of classification error and network structure. The results show that PHIAN outperforms the existing techniques. Our proposed model can be efficiently applied to early detection of abnormal conditions and prevent the abnormal becoming serious.     

Covering-based rough set classification system

Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

     

Extraction of Disease Factors from Medical Texts

Knowledge about specific diseases is often evolving but is embedded in medical texts. We propose a technique that employs term proximity information to improve the extraction of the disease factors, which are concept terms related to specific diseases in the medical texts. The disease factors are a core knowledge base for many information systems for healthcare decision support and education. In two case studies on a broad range of diseases, the proposed technique significantly further enhances a good extraction technique to rank the diagnosis factors.     

Complementary case-based reasoning and competitive fuzzy cognitive maps for advanced medical decisions

This paper presents a new hybrid modeling methodology suitable for complex decision making processes. It extends previous work on competitive fuzzy cognitive maps for medical decision support systems by complementing them with case based reasoning methods. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable advanced medical decision support systems that are suitable to handle situations where the decisions are not clearly distinct. The methodology developed here is applied successfully to model and test two decision support systems, one a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments and the other for decision making choices in external beam radiation therapy.     

基于小波包分形的电力电子故障诊断方法

针对现有电力电子故障诊断的问题,提出了一种基于小波包分形的电力电子故障的诊断方法.首先使用3层小波包对电路输出电压波形进行分解、消噪和重构等预处理,以获得不同频带的重构信号,然后再计算各个重构信号的分形维度,提取出相应的故障特征;最后以此作为输入数据来训练支持向量机,实现电力电子故障的诊断.以三相整流电路为例进行实验,实验结果表明该方法能有效地提取电力电子故障诊断的故障特征,提高故障诊断的准确率,可有效地应用于电力电子系统的故障诊断.     

基于6LoWPAN和模糊控制的实时输液监护系统

为满足更人性化的监护需求,减少患者家属的陪同时间和降低医护人员的工作难度,提出基于6 LoWPAN和模糊控制决策的实时输液监护系统设计方案,采用分层结构,将系统节点分为液滴检测节点、PTC加热器节点、HK 2000G心率传感器节点、SMTIR9902温度传感器节点、数据中转节点、网关节点和模糊控制终端节点7类.采用6LoWPAN技术,网关节点可方便地接入 IPv6互联网,便于数据的采集和传输.通过本系统,医护人员可在本地或远程获取病人输液及生命体征数据,方便及时更换吊瓶、拔针或追踪治疗,为病人提供更舒适、便捷和安全的医疗服务.     

A medical decision support system for disease diagnosis under uncertainty

This paper presents a decision support system (DSS) modeled by a fuzzy expert system (FES) for medical diagnosis to help physicians make better decisions. The proposed system collects comprehensive information about a disease from a group of experts. To this aim, a cross-sectional study is conducted by asking physicians’ expertise on all symptoms relevant to a disease. A fuzzy rule based system is then formed based on this information, which contains a set of significant symptoms relevant to the suspected disease. Linguistic fuzzy values are assigned to model each symptom. The input of the system is the severity level of each symptom reported by patients. The proposed FES considers two approaches to account for uncertain inputs from patients. Two case studies on kidney stone and kidney infection were conducted to demonstrate how the proposed method could be used. A group of patients were used to validate the effectiveness of the proposed expert system. The results show that the proposed fuzzy expert system is capable of diagnosing diseases with a high degree of accuracy and precision comparing to a couple of machine learning methods.     

Subsethood-product fuzzy neural inference system (SuPFuNIS)

A new subsethood-product fuzzy neural inference system (SuPFuNIS) is presented in this paper. It has the flexibility to handle both numeric and linguistic inputs simultaneously. Numeric inputs are fuzzified by input nodes which act as tunable feature fuzzifiers. Rule based knowledge is easily translated directly into a network architecture. Connections in the network are represented by Gaussian fuzzy sets. The novelty of the model lies in a combination of tunable input feature fuzzifiers; fuzzy mutual subsethood-based activation spread in the network; use of the product operator to compute the extent of firing of a rule; and a volume-defuzzification process to produce a numeric output. Supervised gradient descent is employed to train the centers and spreads of individual fuzzy connections. A subsethood-based method for rule generation from the trained network is also suggested. SuPFuNIS can be applied in a variety of application domains. The model has been tested on Mackey-Glass time series prediction, Iris data classification, Hepatitis medical diagnosis, and function approximation benchmark problems. We also use a standard truck backer-upper control problem to demonstrate how expert knowledge can be used to augment the network. The performance of SuPFuNIS compares excellently with other various existing models.     

A Framework for Coordinated Control of Multiagent Systems and Its Applications

In this paper, a framework is proposed for the distributed control and coordination of multiagent systems (MASs). In the proposed framework, the control of MASs is regarded as achieving decentralized control and coordination of agents. Each agent is modeled as a coordinated hybrid agent, which is composed of an intelligent coordination layer and a hybrid control layer. The intelligent coordination layer takes the coordination input, plant input, and workspace input. In the proposed framework, we describe the coordination mechanism in a domain-independent way, i.e., as simple abstract primitives in a coordination rule base for certain dependence relationships between the activities of different agents. The intelligent coordination layer deals with the planning, coordination, decision making, and computation of the agent. The hybrid control layer of the proposed framework takes the output of the intelligent coordination layer and generates discrete and continuous control signals to control the overall process. To verify the feasibility of the proposed framework, experiments for both heterogeneous and homogeneous MASs are implemented. The proposed framework is applied to a multicrane system, a multiple robot system, and a MAS consisting of an overhead crane, a mobile robot, and a robot manipulator. It is demonstrated that the proposed framework can model the three MASs. The agents in these systems are able to cooperate and coordinate to achieve a global goal. In addition, the stability of systems modeled using the proposed framework is also analyzed.     

Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy

Listening via stethoscope is a preferential method, being used by physicians for distinguishing normal and abnormal cardiac systems. On the other hand, listening with stethoscope has a number of constraints. The interpretation of various heart sounds depends on physician’s ability of hearing, experience, and skill. Such limitations may be reduced by developing biomedical-based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary, and mitral stenosis heart valve diseases via stethoscope. Developed system comprises of three stages. In the first stage, for feature extraction, obtained heart sound signals were separated to its sub-bands using discrete wavelet transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of three types of heart sound signals were used as input patterns of the adaptive neuro-fuzzy inference system (ANFIS) classifiers. Developed method reached 98.33% classification accuracy, and it was showed that purposed method is effective for detection of heart valve diseases.