Fuzzy Temporal Rules Research Articles

IoT enabled healthcare system for predicting the diseases using feature optimization, decision tree, neural network, and fuzzy temporal rules

SummaryDiabetes, cancer, and heart diseases are identified to be fatal for human beings due to the lack of awareness, erratic food habits, lack of physical work and so forth. Many disease prediction systems are introduced by various researchers. However, early diagnosis of the diseases and monitoring systems are not working effectively and it increases the death rate. Recently, Internet of Things (IoT) is incorporated to collect the instant data from patient and the system makes effective decisions. Many IoT enabled healthcare systems are available for predicting the specific diseases but such effective healthcare system for measuring the severity of diabetes, cancer and heart diseases is scarce. For this purpose, a new neuro‐fuzzy model is used in the proposed IoT enabled healthcare system for disease prediction, which combines fuzzy rules with temporal features. First, it categorizes the available features of the patient records. Second, a new temporal decision tree is introduced for learning the patient records carefully and predicting the record by using the available data. Third, the fuzzy temporal decision tree calculates the relationship between the available features and also selects them as contributed features. In addition, some of the features are identified as not useful for making a final decision, and these are to be removed. Fourth, the fuzzy rules are to be generated by using the Mamdani fuzzy membership function. Finally, a new framework is proposed which is the combination of neuro‐fuzzy temporal networks, fuzzy temporal decision trees, and fuzzy temporal rules. The proposed model is evaluated by conducting experiments and proved as better than the existing systems in terms of prediction accuracy, error rate, and efficiency.

RETRACTED ARTICLE: Intelligent fuzzy rule-based approach with outlier detection for secured routing in WSN

In wireless sensor networks (WSNs), energy optimization and the provision of security are the major design challenges. Since the wireless sensor devices are energy constrained, the issue of high energy consumption by the malicious nodes must be addressed well in order to enhance the network performance by making increased network lifetime, reduced energy consumption and delay. In the past, many researchers worked in the provision of new techniques for providing improved security to WSN in order to enhance the reliability in the routing process. However, most of the existing routing techniques are not able to achieve the required security through the use of intelligent techniques for safeguarding the sensor nodes from malicious attacks. In order to address these problems, a new fuzzy temporal clustering-based secured communication model with trust analysis and outlier detection has been developed in this research work. For this purpose, a new fuzzy temporal rule-based cluster-based routing algorithm with trust modelling and outlier detection for monitoring the nodes participating in the communication has been proposed. In addition, a fuzzy temporal rule- and distance-based outlier detection algorithm is also proposed in this paper for distinguishing the malicious nodes from other nodes within each cluster of the network and has been used in the secured routing algorithm. The proposed secure routing algorithm uses the temporal reasoning tasks of explanation-based learning and prediction as well as spatial constraints for making efficient routing decisions through the application of trust and key management techniques for performing effective authentication of nodes and thereby isolating the malicious nodes from communication through outlier detection. By applying these two proposed algorithms for communication in the proposed work, it is proved through experiments that the proposed secure routing algorithm and the outlier detection algorithm are able to perform secured and reliable routing through genuine cluster head nodes more effectively. Moreover, these two algorithms provide improved quality of service with respect to the reliability of communication, packet delivery ratio, reduction in end-to-end delay and reduced energy consumption.

A Temporal Neuro-Fuzzy System for Estimating Remaining Useful Life in Preheater Cement Cyclones

Fault prognosis in industrial plants is a complex problem, and time is an important factor for the resolution of this problem. The main indicator for the task of fault prognosis is the estimate of remaining useful life (RUL), which essentially depends on the predicted time to failure. This paper introduces a temporal neuro-fuzzy system (TNFS) for performing the fault prognosis task and exactly estimating the RUL of preheater cyclones in a cement plant. The main component of the TNFS is a set of temporal fuzzy rules that have been chosen for their ability to explain the behavior of the entire system, the components’ degradation, and the RUL estimation. The benefit of introducing time in the structure of fuzzy rules is that a local memory of the TNFS is created to capture the dynamics of the prognostic task. More precisely, the paper emphasizes improving the performance of TNFSs for prediction. The RUL estimation process is broken down into four generic processes: building a predictive model, selecting the most critical parameters, training the TNFS, and predicting RUL through the generated temporal fuzzy rules. Finally, the performance of the proposed TNFS is evaluated using a real preheater cement cyclone dataset. The results show that our TNFS produces better results than classical neuro-fuzzy systems and neural networks.

An Intelligent Risk Prediction System for Breast Cancer Using Fuzzy Temporal Rules

Online prediction of risk on breast cancer is a challenging task in the area of health care during the past decade. Since the existing statistical and data mining methods have limitations with respect to the prediction of breast cancer, there is a need for proposing more effective predictive models which can predict the breast cancer more effectively. In this paper, we propose a new intelligent online risk prediction model for predicting the breast cancer using fuzzy temporal rules more accurately. Moreover, this intelligent system determines the contributing attributes from the dataset using intelligent fuzzy temporal rules and also performs prediction by applying fuzzy rule-based classification with temporal constraints. Moreover, the rules are validated using a domain expert and the experiments conducted in this work using questionnaire, rule-based classification and consultation with domain expert have proved that the proposed system provides more accurate results for risk prediction than the other existing systems.

An Intelligent Hybrid Mechanism to Predict the Risk of Cardio Vascular Disease

Background/Objectives: The objective is to generate rules to predict the severity of Cardiac disease by applying Rough Set Theory, Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN) and Fuzzy Logic (FL). Methods/ Statistical Analysis: In order to achieve the aim, the Rough Set Theory is hybrided with PSO to give Intelligent Hybrid Quick Reduct Particle Swarm Optimization (IHQRPSO) Algorithm which explore the search space and find the optimal data. The optimal data set is then classified by using ANN as disease and undiseased patients and finally the rules are framed using FL to predict the severity of disease. Findings: By using the IHQRPSO Algorithm, the attribute number has been reduced from 13 to 5. The optimal data set is then trained by ANN which used Back propagation Learning algorithm for classification. The classified unhealthy patients are then categorized to risk level such as mild, low and severe based on the rules obtained from Fuzzy Inference System. A total of 63 rules is framed. The whole Hybrid mechanism is then compared with the existing work which used Genetic Algorithm and Fuzzy Temporal Rule Mining Algorithm for prediction. While comparing the proposed hybrid mechanism with existing one, the prediction accuracy has been increased to a considerable level. Applications/Improvements: The Performance of the algorithm is evaluated in terms of accuracy. The accuracy was 95.25% while using the proposed algorithm where it was around 85 while using the existing work.

An intelligent neuro fuzzy temporal knowledge representation model for mining temporal patterns

Representation of temporal knowledge and analysis of temporal data is becoming a good practice for effective classification and prediction. Various semantic levels on knowledge representation schemes have been measured for temporal data. The existing Fuzzy Cognitive Maps FCMs facilitate modeling dynamic systems for knowledge representation and reasoning under uncertainty. However, the FCMs are constructed manually and are constrained by the human experts' validation for assessing its reliability and they are lacking in considering temporal features necessary for reasoning in medical applications. This paper proposes a new temporal mining system known as Fuzzy Temporal Cognitive Map FTCM, which defines a complete discrete temporal extension and fuzzy inference mechanism of FCM. In FTCM, the temporal dependencies of concepts during a particular time interval are measured. This work aims to reduce the complexities of dynamic modeling of a complex causal system by proposing a four layer fuzzy neural network to construct FTCM from the temporal data. In this proposed model, a fuzzy temporal mutual subsethood operator is used to measure the activation spread in the FTCM for automatic quantification of causalities. This FTCM is designed for a set of temporal clinical records, which can be further used for inferencing and prediction in medical diagnosis by generating a set of fuzzy temporal rules using Allen's temporal relationships and fuzzy temporal rules.

A DYNAMIC TEMPORAL NEURO FUZZY INFERENCE SYSTEM FOR MINING MEDICAL DATABASES

The analysis and representation of temporal data ar e becoming increasingly important in many areas of research and application. The existing Fuzzy Cognit ive Maps (FCMs) are efficient modeling method for knowledge representation and fuzzy reasoning in time series analysis. In the past, it was used to repr esent a complex causal system as a collection of concepts a nd causal relationships among concepts. However, most of the FCMs available now are constructed manually and are constrained with human experts’ interventio n for assessing its reliability. This study proposes a new temporal mining system to discover temporal dependencies between the concepts of a complex causal system by building a Fuzzy Temporal Cognitive Map (FTCM) by extending the FCM. For this purpose, a four-layer fuzzy temporal neural network is proposed and implemented by the automatic creation of the conventional FTCMs from the given data. This FTCM is generated from the medical temporal database records of diabetic patients where the medical diagnosis is performed by converting the fuzzy cogn etive maps into a fuzzy temporal rule based inferen ce system using Allen’s temporal relationships and fuz zy temporal rules.

A Rough Set based Fuzzy Inference System for Mining Temporal Medical Databases

The main objective of this research work is to construct a Fuzzy Temporal Rule Based Classifier that uses fuzzy rough set and temporal logic in order to mine temporal patterns in medical databases. The lower approximation concepts and fuzzy decision table with the fuzzy features are used to obtain fuzzy decision classes for building the classifier. The goals are pre-processing for feature selection, construction of classifier, and rule induction based on increment rough set approach. The features are selected using Hybrid Genetic Algorithm. Moreover the elementary sets are obtained from lower approximations are categorized into the decision classes. Based on the decision classes a discernibility vector is constructed to define the temporal consistency degree among the objects. Now the Rule Based Classifier is transformed into a temporal rule based fuzzy inference system by incorporating the Allen’s temporal algebra to induce rules. It is proposed to use incremental rough set to update rule induction in dynamic databases. Ultimately these rules are categorized as rules with range values to perform prediction effectively. The efficiency of the approach is compared with other classifiers in order to assess the accuracy of the fuzzy temporal rule based classifier. Experiments have been carried out on the diabetic dataset and the simulation results obtained prove that the proposed temporal rule-based classifier on clinical diabetic dataset stays as an evidence for predicting the severity of the disease and precision in decision support system.

A Fuzzy Temporal Rule-based System for handling the Nursing Process on mobile devices

Nowadays, mobile devices offer new methods to retrieve information everywhere. In the nursing area, this technology is highly suitable due to the ubiquity in this profession. In this paper, we present a new version of Medea, a mobile system that provides storing the evolution of patients from handheld devices using the nursing viewpoint. Medea is relied on the Need Theory of Virginia Henderson, as patient valuation model, and the nursing taxonomies of NANDA, NIC and NOC, as patient care plan. In addition, the vital signs of patients have been integrated in the mobile application. The main complementary innovation of Medea is an expert system based on fuzzy temporal rules, which suggests diagnoses, interventions and outcomes from the patient valuation and the vital signs. This model takes into account the importance of the data and temporal dimension in the patient symptoms. Furthermore, the complexity of the nursing domain has been synthesized in a few concepts, which make possible the creation of rules in an easy way.

A Descriptive Framework for the Multidimensional Medical Data Mining and Representation

Problem statement: Association rule mining with fuzzy logic was explored by research for effective datamining and classification. Approach: It was used to find all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Results: In this study, we propose new rule mining technique using fuzzy logic for mining medical data in order to understand and better serve the needs of Multidimensional Breast cancer Data applications. Conclusion: The main objective of multidimensional Medical data mining is to provide the end user with more useful and interesting patterns. Therefore, the main contribution of this study is the proposed and implementation of fuzzy temporal association rule mining algorithm to classify and detect breast cancer from the dataset.

People detection through quantified fuzzy temporal rules

The knowledge about the position and movement of people is of great importance in mobile robotics for implementing tasks such as navigation, mapping, localization, or human–robot interaction. This knowledge enhances the robustness, reliability and performance of the robot control architecture. In this paper, a pattern classifier system for the detection of people using laser range finders data is presented. The approach is based on the quantified fuzzy temporal rules (QFTRs) knowledge representation and reasoning paradigm, that is able to analyze the spatio-temporal patterns that are associated to people. The pattern classifier system is a knowledge base made up of QFTRs that were learned with an evolutionary algorithm based on the cooperative-competitive approach together with token competition. A deep experimental study with a Pioneer II robot involving a five-fold cross-validation and several runs of the genetic algorithm has been done, showing a classification rate over 80%. Moreover, the characteristics of the tests represent complex and realistic conditions (people moving in groups, the robot moving in part of the experiments, and the existence of static and moving people).

Landmark Detection in Mobile Robotics Using Fuzzy Temporal Rules

Detection of landmarks is essential in mobile robotics for navigation tasks like building topological maps or robot localization. Doors are one of the most common landmarks since they show the topological structure of indoor environments. In this paper, the novel paradigm of fuzzy temporal rules is used for detecting doors from the information of ultrasound sensors. This paradigm can be used both to model the necessary knowledge for detection and to consider the temporal variation of several sensor signals. Experimental results using a Nomad 200 mobile robot in a real environment produce 91% of doors were correctly detected, which show the reliability and robustness of the system.

A fuzzy temporal rule-based velocity controller for mobile robotics

This paper describes a velocity controller implemented on a Nomad 200 mobile robot. The controller has been developed for wall-following behaviour, and its design is modularized into two blocks: angular and linear velocity control. A simple design and implementation was made for the former, with the aim of focusing the design efforts on the linear velocity control block, in order to remark the usefulness of this task. The latter has been implemented using an explicit model for knowledge representation and reasoning called fuzzy temporal rules (FTRs). This model enables to explicitly incorporate time as a variable, due to which the evolution of variables in a temporal reference can be described. Using this mechanism we obtain linear velocity values that are adapted to each different circumstance, and thus a higher average velocity as well as smoother and more robust behaviours are achieved.

Fuzzy temporal rules for mobile robot guidance in dynamic environments

The paper describes a fuzzy control system for the avoidance of moving objects by a robot. The objects move with no type of restriction, varying their velocity and making turns. Due to the complex nature of this movement, it is necessary to realize temporal reasoning with the aim of estimating the trend of the moving object. A new paradigm of fuzzy temporal reasoning, which we call fuzzy temporal rules (FTRs), is used for this control task. The control system has over 117 rules, which reflects the complexity of the problem to be tackled. The controller has been subjected to an exhaustive validation process and examples are shown of the results obtained.

Enhanced fuzzy temporal rules and their projection onto fuzzy Petri nets

In this paper, an enhanced model of fuzzy temporal knowledge bases (FTKBs) formed by fuzzy temporal rules is introduced, and its projection onto the formalism of fuzzy Petri nets (FPNs) is presented. The main objective is to enhance the expressiveness of the rules making up the FTKB, regarding a previous proposal, 1 at the same time as keeping the computation cost within the limits that allow us to work on-line in dynamic environments. This is attained by means of a partial compaction of the rules, prior to their on-line execution. The projection of the FTKB over an FPN makes its design, analysis, and execution easier, by explicitly bringing out all its variables and relationships.