Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Fuzzy Logic”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 847 records · Page 47Linked to original sources
PubMed2003

A GIS-based fuzzy classification for mapping the agricultural soils for N-fertilizers use.

Special attention should be paid to the choice of the proper N-fertilizer, in order to avoid a further acidification and degradation of acid soils and at the same time to improve nitrogen use efficiency and to limit the nitrate pollution of the ground waters. Therefore, the risk of leaching of the fertilizer and of the acidification of the soils must be considered prior to any N-fertilizer application. The application of N-fertilizers to the soil requires a good knowledge of the soil-fertilizer relationship, which those who are planning the fertilization policy and/or applying it might not have. In this study, a fuzzy classification methodology is presented for mapping the agricultural soils according to the kind and the rate of application of N-fertilizer that should be used. The values of pH, clay, sand and carbonates soil variables are estimated at each point of an area by applying geostatistical techniques. Using the pH values three fuzzy sets: "no-risk-acidification"; "low-risk-acidification"; and "high-risk-acidification" are produced and the memberships of each point to the three sets are estimated. Additionally, from the clay and sand values the membership grade to the fuzzy set "risk-of-leaching" is calculated. The parameters and their values, which are used for the construction of the fuzzy sets, are based on the literature, the existing knowledge and the experimentation, of the soil-fertilizer relationships and provide a consistent mechanism for mapping the soils according to the type of N-fertilizers that should be applied and the rate of applications. The maps produced can easily be interpreted and used by non-experts in the application of the fertilization policy at national, local and farm level. The methodology is presented through a case study using data from the Amfilochia area, west Greece.

Agriculture↗
PubMed2001

Smooth muscle electromyography.

This article addresses smooth muscle physiology, electromechanical and pharmacomechanical coupling, computed-based interpretations, and the role of electromyography in the diagnosis of erectile dysfunction.

Discriminant Analysis↗
PubMed2002

Combining fuzzy querying of imprecise data and predictive microbiology using category-based reasoning for prediction of the possible microbial spoilage in foods: application to Listeria monocytogenes.

Various predictive models of microbial behavior have been created and extensive data collection has been done by numerous private or public laboratories. However, significant differences between predicted and observed values in foods have been observed and need to be stressed, understood and explained as much as possible. In this paper, we present a software tool (currently at the level of a prototype) able: (i) to store in a database all relevant information expressed on one hand as qualitative or quantitative data and on the other hand as precise or imprecise data; (ii) to retrieve the more relevant information from the database using queries where criteria may be expressed as fuzzy values in order to enhance the flexibility of the search: (iii) to compute, in addition to the nearest data, an estimation of searched values using statistical models. The architecture of this software tool is structured as a category-based reasoning system. Example queries about Listeria monocytogenes (L. monocytogenes) illustrate the functionalities of this tool.

Computer Simulation↗
PubMed2002

Optimization of feeding profile for a fed-batch bioreactor by an evolutionary algorithm.

The optimal feeding profile of a fed batch process was designed by means of an evolutionary algorithm. The algorithm chromosomes include the real-valued parameters of a profile function, defined by previous knowledge. Each chromosome is composed of the parameters that define the feeding profile: the feed rates, the singular arc parameters and the switching times between the profile states. The feed profile design was tested on a fed-batch process simulation. The accepted profiles were smooth and similar to those derived analytically in other studies. Two selection functions, roulette wheel and geometric ranking, were compared. In order to overcome the problem of model mismatches, a novel optimization scheme was carried out. During its operation the process was sampled, the model was updated and the optimization procedure was applied. The on-line optimization showed improvement in the objective function for relatively low sample times. Choosing the sampling frequencies depends on the process dynamics and the time required for the measurements and optimization. Further study on experiments of fed-batch process demonstrated the use of complex, non-differentiable model and produced improved process performances using the optimal feeding profile.

Algorithms↗
PubMed2003

Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants.

A new approach to nonlinear modeling and adaptive monitoring using fuzzy principal component regression (FPCR) is proposed and then applied to a real wastewater treatment plant (WWTP) data set. First, principal component analysis (PCA) is used to reduce the dimensionality of data and to remove collinearity. Second, the adaptive credibilistic fuzzy-c-means method is used to appropriately monitor diverse operating conditions based on the PCA score values. Then a new adaptive discrimination monitoring method is proposed to distinguish between a large process change and a simple fault. Third, a FPCR method is proposed, where the Takagi-Sugeno-Kang (TSK) fuzzy model is employed to model the relation between the PCA score values and the target output to avoid the over-fitting problem with original variables. Here, the rule bases, the centers and the widths of TSK fuzzy model are found by heuristic methods. The proposed FPCR method is applied to predict the output variable, the reduction of chemical oxygen demand in the full-scale WWTP. The result shows that it has the ability to model the nonlinear process and multiple operating conditions and is able to identify various operating regions and discriminate between a sustained fault and a simple fault (or abnormalities) occurring within the process data.

Algorithms↗
PubMed1997

Health policy as a fuzzy concept: methodological problems encountered when evaluating health policy reforms in an international perspective.

Investigating health policy reforms at a national level is a troublesome task, since it is difficult to establish exactly when a certain policy change took place and it is also difficult to determine the content of the reform. In this paper three main causes are distinguished that contribute to the 'fuzziness' surrounding reforms. Firstly, ordinary mistakes occur, even experts err. Secondly, in some countries responsibility for (part of the) health care system is delegated to the regional level, causing regional variations which lead to uninterpretable effects at country-level health care system descriptions. Thirdly policy reforms are often not a straightforward process. Implementations can be characterized as a gradual process which can be disturbed at any point in time. For example, the use of framework legislation, effects of public discussions and law enforcement problems contribute to the fuzziness. To strengthen the validity of information from secondary sources (often the best available and linguistically accessible sources) the use of primary sources and expert consultation highly recommended, respectively giving insight in formal rules and initial intentions of policy measures and in the impact of reforms at performance level. The fuzziness can be captured into an index (reform implementation index), containing the gathered information and thus facilitating statistical analyses controlling for process-information.

Belgium↗
PubMed2001

Acute leukemia diagnosis aid using multicriteria fuzzy assignment methodology.

We recently developed a new multicriteria classification method called PROAFTN to help medical diagnosis. The aim of this paper is to present the application of the proposed method for the diagnosis of acute leukemia (AL). The implementation of PROAFTN was carried out on cytological data of 108 cases of AL, using the classification rules of French, American and British hematologists, and was then applied on an independent test set of 83 cases of AL. Forty-seven features, obtained by examining patients' bone-marrow smears with light microscope, defined each case. In order to determine the percentage of correct classifications for each subtype of AL, we compared our results with the clinical diagnosis given previously by the hematologist. 96.4% of the cases were correctly classified by the program, suggesting that the method yields good results in terms of discrimination between AL subtypes.

Algorithms↗
PubMed2005

Cytological image analysis with a genetic fuzzy finite state machine.

The objective of this research is to design a pattern recognition system based on a Fuzzy Finite State Machine (FFSM). We try to find an optimal FFSM with Genetic Algorithms (GA). In order to validate this system, the classifier has been applied to a real problem: distinction between normal and abnormal cells in cytological breast fine needle aspirate images and cytological peritoneal fluid images. The characteristic used in the discrimination between normal and abnormal cells is a texture measurement of the chromatin distribution in cellular nuclei. Furthermore, the effectiveness of this method as a pattern classifier is compared with other existing supervised and unsupervised methods and evaluated with Receiver Operating Curves (ROC) methodology.

Algorithms↗
PubMed1998

Mixed quantitative/qualitative modeling and simulation of the cardiovascular system.

The cardiovascular system is composed of the hemodynamical system and the central nervous system (CNS) control. Whereas the structure and functioning of the hemodynamical system are well known and a number of quantitative models have already been developed that capture the behavior of the hemodynamical system fairly accurately, the CNS control is, at present, still not completely understood and no good deductive models exist that are able to describe the CNS control from physical and physiological principles. The use of qualitative methodologies may offer an interesting alternative to quantitative modeling approaches for inductively capturing the behavior of the CNS control. In this paper, a qualitative model of the CNS control of the cardiovascular system is developed by means of the fuzzy inductive reasoning (FIR) methodology. FIR is a fairly new modeling technique that is based on the general system problem solving (GSPS) methodology developed by G.J. Klir (Architecture of Systems Problem Solving, Plenum Press, New York, 1985). Previous investigations have demonstrated the applicability of this approach to modeling and simulating systems, the structure of which is partially or totally unknown. In this paper, five separate controller models for different control actuations are described that have been identified independently using the FIR methodology. Then the loop between the hemodynamical system, modeled by means of differential equations, and the CNS control, modeled in terms of five FIR models, is closed, in order to study the behavior of the cardiovascular system as a whole. The model described in this paper has been validated for a single patient only.

Central Nervous System↗
PubMed2000

Identification of reliable spike templates in multi-unit extracellular recordings using fuzzy clustering.

A method for extracting single-unit spike trains from extracellular recordings containing the activity of several simultaneously active cells is presented. The technique is particularly effective when spikes overlap temporally. It is capable of identifying the exact number of neurons contributing to a recording and of creating reliable spike templates. The procedure is based on fuzzy clustering and its performance is controlled by minimizing a cluster-validity index which optimizes the compactness and separation of the identified clusters. Application examples with synthetic spike trains generated from real spikes and segments of background noise show the advantage of the fuzzy method over conventional template-creation approaches in a wide range of signal-to-noise ratios.

Action Potentials↗
PubMed2003

Self-organizing neural networks for pharmacophore mapping.

We have shown that the SOM network can be a useful tool in pharmacophore mapping strategy. A possibility for the generation of fuzzy molecular representations together with its ability for discovering such aspects of molecular similarity that can be easily overlooked by a human chemist is an important advantage. The reduction in complexity resulting from the data compression is another one. The main disadvantage of SOM usage is the need for the application of special software packages not usually organized in user friendly toolboxes that can be applied easily. Instead, it needs some experience and time to optimize the parameters controlling the performance of the network.

Fuzzy Logic↗
PubMed1999

A climate-based distribution model of malaria transmission in sub-Saharan Africa.

Malaria remains the single largest threat to child survival in sub-Saharan Africa and warrants long-term investment for control. Previous malaria distribution maps have been vague and arbitrary. Marlies Craig, Bob Snow and David le Sueur here describe a simple numerical approach to defining distribution of malaria transmission, based upon biological constraints of climate on parasite and vector development. The model compared well with contemporary field data and historical 'expert opinion' maps, excepting small-scale ecological anomalies. The model provides a numerical basis for further refinement and prediction of the impact of climate change on transmission. Together with population, morbidity and mortality data, the model provides a fundamental tool for strategic control of malaria.

Africa South of the Sahara↗
PubMed2001

Database mining applied to central nervous system (CNS) activity.

A data set of 389 compounds, active in the central nervous system (CNS) and divided into eight classes according to the receptor type, was extracted from the RBI database and analyzed by Self-Organizing Maps (SOM), also known as Kohonen Artificial Neural Networks. This method gives a 2D representation of the distribution of the compounds in the hyperspace derived from their molecular descriptors. As SOM belongs to the category of unsupervised techniques, it has to be combined with another method in order to generate classification models with predictive ability. The fuzzy clustering (FC) approach seems to be particularly suitable to delineate clusters in a rational way from SOM and to get an automatic objective map interpretation. Maps derived by SOM showed specific regions associated with a unique receptor type and zones in which two or more activity classes are nested. Then, the modeling ability of the proposed SOM/FC Hybrid System tools applied simultaneously to eight activity classes was validated after dividing the 389 compounds into a training set and a test set, including 259 and 130 molecules, respectively. The proper experimental activity class, among the eight possible ones, was predicted simultaneously and correctly for 81% of the test set compounds.

Central Nervous System↗
PubMed2003

Prediction of oral bioavailability by adaptive fuzzy partitioning.

An adaptive fuzzy partition (AFP) algorithm was applied on two bioavailability data sets subdivided into four ranges of activity. A large set of molecular descriptors was tested and the most relevant parameters were selected with help of a procedure based on genetic algorithm concepts and stepwise method. After building several AFP models on a training set, the best ones were able to predict correctly 75% of the validation set compounds. Furthermore, an improvement of about 15% in the validation results was got, on the same data set, as regard to other prediction methods. The importance to work with data sets including a large molecular diversity, and to use tools able to manage it, was also shown. The prediction power was increased up to 25% employing a data set with a better-optimised molecular diversity.

Administration, Oral↗
PubMed2003

Ultrasonic multifeature tissue characterization for prostate diagnostics.

A new system for prostate diagnostics based on multifeature tissue characterization is proposed. Radiofrequency (RF) ultrasonic echo data are acquired during the standard transrectal ultrasound (US) imaging examination. Nine spectral, texture, first order and morphologic parameters are calculated and fed into two adaptive neuro-fuzzy inference systems (FIS) working in parallel. The outputs of the FISs are fed into a postprocessing procedure evaluating contextual information before being combined to form a malignancy map in which areas of high cancer probability are marked in red. The malignancy map is presented to the physician during the examination to improve the early detection of prostate cancer. The system has been evaluated on 100 patients undergoing radical prostatectomy. The ROC curve area using leave-one-out cross-validation over patients is A(Z) = 0.86 when distinguishing between hyperechoic and hypoechoic tumors and normal tissue and A(Z) = 0.84 when distinguishing between isoechoic tumors and healthy tissue, respectively. Tumors that are not visible in the conventional B-mode image can be located. Diagnosis of the prostate carcinoma using multifeature tissue characterization in combination with US imaging allows the detection of tumors at an early stage. Also, biopsy guidance and therapy planning can be improved.

Aged↗
PubMed2000

An early vision-based snake model for ultrasound image segmentation.

Due to the speckles and the ill-defined edges of the object of interest, the classic image-segmentation techniques are usually ineffective in segmenting ultrasound (US) images. In this paper, we present a new algorithm for segmenting general US images that is composed of two major techniques; namely, the early-vision model and the discrete-snake model. By simulating human early vision, the early-vision model can capture both grey-scale and textural edges while the speckle noise is suppressed. By performing deformation only on the peaks of the distance map, the discrete-snake model promises better noise immunity and more accurate convergence. Moreover, the constraint for most conventional snake models that the initial contour needs to be located very close to the actual boundary has been relaxed substantially. The performance of the proposed snake model has been shown to be comparable to manual delineation and superior to that of the gradient vector flow (GVF) snake model.

Algorithms↗
PubMedDate not supplied

Evolution with material symbol systems.

Pattee's semantic closure principle is used to study the characteristics and requirements of evolving material symbols systems. By contrasting agents that reproduce via genetic variation with agents that reproduce via self-inspection, we reach the conclusion that symbols are necessary to attain open-ended evolution, but only if the phenotypes of agents are the result of a material, self-organization process. This way, a study of the inter-dependencies of symbol and matter is presented. This study is based first on a theoretical treatment of symbolic representations, and secondly on simulations of simple agents with matter-symbol inter-dependencies. The agent-based simulations use evolutionary algorithms with indirectly encoded phenotypes. The indirect encoding is based on Fuzzy Development programs, which are procedures for combining fuzzy sets in such a way as to model self-organizing development processes.

Algorithms↗