Productivity growth and efficiency measurements in fuzzy environments with an application to health care

Health care organizations must continuously improve their productivity to sustain long-term growth and profitability. Sustainable productivity performance is mostly assumed to be a natural outcome of successful health care management. Data envelopment analysis (DEA) is a popular mathematical programming method for comparing the inputs and outputs of a set of homogenous decision making units (DMUs) by evaluating their relative efficiency. The Malmquist productivity index (MPI) is widely used for productivity analysis by relying on constructing a best practice frontier and calculating the relative performance of a DMU for different time periods. The conventional DEA requires accurate and crisp data to calculate the MPI. However, the real-world data are often imprecise and vague. In this study, the authors propose a novel productivity measurement approach in fuzzy environments with MPI. An application of the proposed approach in health care is presented to demonstrate the simplicity and efficacy of the procedures and algorithms in a hospital efficiency study conducted for a State Office of Inspector General in the United States. © 2012, IGI Global.

Unsupervised KDD to creatively support managers' decision making with fuzzy association rules:a distribution channel application

To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of marketing management support systems has paralleled this evolution in informational problems faced by managers, leading to a growth in the study (and use) of artificial intelligence and soft computing methodologies. Here, we present and implement a novel intelligent system that incorporates fuzzy logic and genetic algorithms to operate in an unsupervised manner. This approach allows the discovery of interesting association rules, which can be linguistically interpreted, in large scale databases (KDD or Knowledge Discovery in Databases.) We then demonstrate its application to a distribution channel problem. It is shown how the proposed system is able to return a number of novel and potentially-interesting associations among variables. Thus, it is argued that our method has significant potential to improve the analysis of marketing and business databases in practice, especially in non-programmed decisional scenarios, as well as to assist scholarly researchers in their exploratory analysis. © 2013 Elsevier Inc.

Contradiction versus Selfcontradiction in Fuzzy Logic

* This work is partially supported by CICYT (Spain) under project TIN 2005-08943-C02-001 and by UPM-CAM (Spain) under project R05/11240.

A Geometrical Interpretation to Define Contradiction Degrees between Two Fuzzy Sets

For inference purposes in both classical and fuzzy logic, neither the information itself should be contradictory, nor should any of the items of available information contradict each other. In order to avoid these troubles in fuzzy logic, a study about contradiction was initiated by Trillas et al. in [5] and [6]. They introduced the concepts of both self-contradictory fuzzy set and contradiction between two fuzzy sets. Moreover, the need to study not only contradiction but also the degree of such contradiction is pointed out in [1] and [2], suggesting some measures for this purpose. Nevertheless, contradiction could have been measured in some other way. This paper focuses on the study of contradiction between two fuzzy sets dealing with the problem from a geometrical point of view that allow us to find out new ways to measure the contradiction degree. To do this, the two fuzzy sets are interpreted as a subset of the unit square, and the so called contradiction region is determined. Specially we tackle the case in which both sets represent a curve in [0,1]2. This new geometrical approach allows us to obtain different functions to measure contradiction throughout distances. Moreover, some properties of these contradiction measure functions are established and, in some particular case, the relations among these different functions are obtained.

About the Multi Criteria Ranging Problem in the Fuzzy Environment

Decision making and technical decision analysis demand computer-aided techniques and therefore more and more support by formal techniques. In recent years fuzzy decision analysis and related techniques gained importance as an efficient method for planning and optimization applications in fields like production planning, financial and economical modeling and forecasting or classification. It is also known, that the hierarchical modeling of the situation is one of the most popular modeling method. It is shown, how to use the fuzzy hierarchical model in complex with other methods of Multiple Criteria Decision Making. We propose a novel approach to overcome the inherent limitations of Hierarchical Methods by exploiting multiple criteria decision making.

Intelligent System for Computer Aided Assembly Process Planning

This paper presents the concepts of the intelligent system for aiding of the module assembly technology. The first part of this paper presents a project of intelligent support system for computer aided assembly process planning. The second part includes a coincidence description of the chosen aspects of implementation of this intelligent system using technologies of artificial intelligence (artificial neural networks, fuzzy logic, expert systems and genetic algorithms).

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO)

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment. The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks. The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.

Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Implementação de uma arquitetura fuzzy neural em hardware com treinamento online

Computational Intelligence Methods have been expanding to industrial applications motivated by their ability to solve problems in engineering. Therefore, the embedded systems follow the same idea of using computational intelligence tools embedded on machines. There are several works in the area of embedded systems and intelligent systems. However, there are a few papers that have joined both areas. The aim of this study was to implement an adaptive fuzzy neural hardware with online training embedded on Field Programmable Gate Array – FPGA. The system adaptation can occur during the execution of a given application, aiming online performance improvement. The proposed system architecture is modular, allowing different configurations of fuzzy neural network topologies with online training. The proposed system was applied to: mathematical function interpolation, pattern classification and selfcompensation of industrial sensors. The proposed system achieves satisfactory performance in both tasks. The experiments results shows the advantages and disadvantages of online training in hardware when performed in parallel and sequentially ways. The sequentially training method provides economy in FPGA area, however, increases the complexity of architecture actions. The parallel training method achieves high performance and reduced processing time, the pipeline technique is used to increase the proposed architecture performance. The study development was based on available tools for FPGA circuits.

Sistema de contagem com morfologia matemática fuzzy

Mathematical Morphology presents a systematic approach to extract geometric features of binary images, using morphological operators that transform the original image into another by means of a third image called structuring element and came out in 1960 by researchers Jean Serra and George Matheron. Fuzzy mathematical morphology extends the operators towards grayscale and color images and was initially proposed by Goetherian using fuzzy logic. Using this approach it is possible to make a study of fuzzy connectives, which allows some scope for analysis for the construction of morphological operators and their applicability in image processing. In this paper, we propose the development of morphological operators fuzzy using the R-implications for aid and improve image processing, and then to build a system with these operators to count the spores mycorrhizal fungi and red blood cells. It was used as the hypothetical-deductive methodologies for the part formal and incremental-iterative for the experimental part. These operators were applied in digital and microscopic images. The conjunctions and implications of fuzzy morphology mathematical reasoning will be used in order to choose the best adjunction to be applied depending on the problem being approached, i.e., we will use automorphisms on the implications and observe their influence on segmenting images and then on their processing. In order to validate the developed system, it was applied to counting problems in microscopic images, extending to pathological images. It was noted that for the computation of spores the best operator was the erosion of Gödel. It developed three groups of morphological operators fuzzy, Lukasiewicz, And Godel Goguen that can have a variety applications

Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.

Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.

X-ray diffraction analysis, binocular component analysis and radiocarbon dating of sediment cores from the NW Iberian shelf system

Based on a well-established stratigraphic framework and 47 AMS-14C dated sediment cores, the distribution of facies types on the NW Iberian margin is analysed in response to the last deglacial sea-level rise, thus providing a case study on the sedimentary evolution of a high-energy, low-accumulation shelf system. Altogether, four main types of sedimentary facies are defined. (1) A gravel-dominated facies occurs mostly as time-transgressive ravinement beds, which initially developed as shoreface and storm deposits in shallow waters on the outer shelf during the last sea-level lowstand; (2) A widespread, time-transgressive mixed siliceous/biogenic-carbonaceous sand facies indicates areas of moderate hydrodynamic regimes, high contribution of reworked shelf material, and fluvial supply to the shelf; (3) A glaucony-containing sand facies in a stationary position on the outer shelf formed mostly during the last-glacial sea-level rise by reworking of older deposits as well as authigenic mineral formation; and (4) A mud facies is mostly restricted to confined Holocene fine-grained depocentres, which are located in mid-shelf position. The observed spatial and temporal distribution of these facies types on the high-energy, low-accumulation NW Iberian shelf was essentially controlled by the local interplay of sediment supply, shelf morphology, and strength of the hydrodynamic system. These patterns are in contrast to high-accumulation systems where extensive sediment supply is the dominant factor on the facies distribution. This study emphasises the importance of large-scale erosion and material recycling on the sedimentary buildup during the deglacial drowning of the shelf. The presence of a homogenous and up to 15-m thick transgressive cover above a lag horizon contradicts the common assumption of sparse and laterally confined sediment accumulation on high-energy shelf systems during deglacial sea-level rise. In contrast to this extensive sand cover, laterally very confined and maximal 4-m thin mud depocentres developed during the Holocene sea-level highstand. This restricted formation of fine-grained depocentres was related to the combination of: (1) frequently occurring high-energy hydrodynamic conditions; (2) low overall terrigenous input by the adjacent rivers; and (3) the large distance of the Galicia Mud Belt to its main sediment supplier.