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At least 595 records · Page 33Linked to original sources
PubMed2006

In vitro calculus detection with a moved smart ultrasonic device.

STUDY OBJECTIVE: The objective of subgingival instrumentation of periodontally diseased root surfaces is to remove the adhering microbial biofilm and calcified deposits. Recently, we have described an automated calculus detection system under static conditions. Clinically however, the tip of the system has to be moved over tooth surfaces. It was thus necessary to study the entire system in motion. METHODS: The detection device is based on a conventional dental piezoelectric ultrasonic handpiece with a conventional scaler insert. The impulse response of the mechanical oscillation system is analysed by a fuzzy logic-based computerized algorithm, which classifies various surfaces. The present study investigates dental surface recognition properties of the new system with the tip being moved over teeth surfaces in vitro. Following a training set of 7977 measurements (3960 calculus, 4017 cement) on 200 extracted teeth, 1363 measurements were conducted on 34 teeth unknown to the system. RESULTS: The surfaces cementum and calculus were correctly classified in 78% within the training set and in 81% within the set unknown, with a kappa value of 0.68. CONCLUSION: It was shown that this method of automatic recognition of tooth surfaces is able to distinguish between different tooth surfaces in vitro independently from tip movements.

Dental Calculus↗
PubMedDate not supplied

The fuzzy medical group in the centre for computational intelligence.

In this paper, five ongoing or completed research projects in medicine using fuzzy sets and logic are summarized. They are, a lightweight fuzzy process for diagnosis using fuzzy symptoms, prediction of pulmonary embolisms from linguistic descriptions of perfusion and ventilation scans, application of the fuzzy ART/MAP and MinMax/MAP neural network models to radiographic image classification, the development of a fuzzy expert system for the analysis of umbilical cord blood, modeling nursing intuition using type 2 fuzzy sets. These projects use a variety of fuzzy methods including clustering, simple set aggregation and type 2 inferencing to achieve their aims. The ongoing research projects reflect an interest in using type 2 fuzzy sets for dealing with vagueness and linguistic knowledge which is commonly found in medical areas where perceptions rather than measurements are the norm.

Artificial Intelligence↗
PubMed2001

A general framework for the fusion of anatomical and functional medical images.

The collection of various data coming from anatomical and functional imagery is becoming very common for the study of a given pathology, and their aggregation generally allows for a better medical decision in clinical studies. A fusion process is described in this article for the modeling of this aggregation. The process is illustrated in the case of anatomical and functional images of the brain, but the general principle may be extended to other organs. The whole three-step fusion process based on possibilistic logic is detailed and a new class of fusion operator is introduced. The use of fuzziness in the process in general and in the operator in particular allows for the management of uncertainty and imprecision inherent to the images. The fusion process is illustrated in two clinical cases: the study of Alzheimer's disease by MR/SPECT fusion and the study of epilepsy by MR/SPECT/PET fusion. Results are presented and evaluated, and a preliminary clinical validation is achieved. The assessment of the method is encouraging, allowing its application on several clinical problems.

Adult↗
PubMed2026

A reinforcement learning-enhanced fuzzy multi-objective equilibrium optimization framework for multiple sequence alignment.

Multiple sequence alignment (MSA) is a fundamental task in bioinformatics, underpinning comparative genomics, structural analysis, and evolutionary inference. However, MSA remains a challenging multi-objective optimization problem due to the need to simultaneously maximize alignment accuracy, preserve conserved regions, and control gap proliferation, particularly in large and heterogeneous sequence collections. In this work, we propose MOFSACEO-MSA, a novel hybrid optimization framework for multiple sequence alignment that integrates a fuzzy multi-objective evaluation scheme with the Equilibrium Optimizer (EO) and a Soft Actor-Critic (SAC)-based adaptive control mechanism. The proposed framework formulates MSA as a dynamic multi-objective optimization problem, in which alignment quality is assessed using complementary residue-level and column-level criteria, including Sum-of-Pairs score, column conservation, entropy, and gap statistics. Fuzzy membership functions are employed to harmonize competing objectives into a unified optimization landscape, while EO provides robust global exploration. To further enhance adaptability, SAC dynamically regulates key EO parameters during the search process, enabling an effective balance between exploration and exploitation across datasets of varying size and heterogeneity. Extensive experiments werew conducted on diverse biological sequence datasets, with a primary focus on RNA benchmarks, including structured families from Rfam, large-scale repositories from RNAcentral and GenBank, and organism-specific tRNA datasets from GtRNAdb. Comparative evaluations against classical alignment tools (ClustalW, MAFFT, MUSCLE, PRANK, KAlign, and T-Coffee), metaheuristic methods (SAGA, Sequoya and EAFSA), and a reinforcement learning-based approach (RLALIGN) demonstrate that MOFSACEO-MSA consistently achieves competitive or superior Sum-of-Pairs scores while significantly reducing gap proportions and maintaining compact alignment lengths. Notably, the proposed framework exhibits improved robustness on large and highly heterogeneous datasets, where existing methods often suffer from excessive gap insertion or unstable convergence. Overall, MOFSACEO-MSA provides a flexible and extensible optimization paradigm that effectively bridges evolutionary search and reinforcement learning for high-quality multiple sequence alignment, with demonstrated effectiveness on challenging RNA alignment tasks.

Sequence Alignment↗
PubMed1996

Incorporation of the Arden Syntax within the reimplementation of a closed-loop decision support system.

The initial implementation of a clinical decision support system, although extremely successful and popular with users, suffered from response time and operational support difficulties. As part of the redesign of the system, which uses closed-loop rules to propose laboratory and other investigations, the decision was taken to move to Arden Syntax for the expression of such rules, although in some areas the current standard was felt to be either inadequate or inelegant. Such limitations have been overcome by the development of local extensions to the Arden Syntax, which are described and where appropriate proposed as possible enhancements of the standard. The use of common UNIX tools in the creation of a compiler which converts Arden Medical Logic Modules to M (MUMPS) code is also discussed.

Decision Making, Computer-Assisted↗
PubMed2006

Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation.

This study compares the performance of neurofuzzy logic and neural networks using two software packages (INForm and FormRules) in generating predictive models for a published database for an immediate release tablet formulation. Both approaches were successful in developing good predictive models for tablet tensile strength and drug dissolution profiles. While neural networks demonstrated a slightly superior capability in predicting unseen data, neurofuzzy logic had the added advantage of generating rule sets representing the cause-effect relationships contained in the experimental data.

Fuzzy Logic↗
PubMed2002

tacg--a grep for DNA.

BACKGROUND: Pattern matching is the core of bioinformatics; it is used in database searching, restriction enzyme mapping, and finding open reading frames. It is done repeatedly over increasingly long sequences, thus codes must be efficient and insensitive to sequence length. Such patterns of interest include simple motifs with IUPAC degeneracies, regular expressions, patterns allowing mismatches, and probability matrices. RESULTS: I describe a small application which allows searching for all the above pattern types individually, which further allows these atomic motifs to be assembled into logical rules for more sophisticated analysis. CONCLUSION: tacg is small, portable, faster and more capable than most alternatives, relatively easy to modify, and freely available in source code.

Algorithms↗
PubMed1995

Using data preprocessing and single layer perceptron to analyze laboratory data.

During daily work in hospitals a large amount of clinical data is produced each day. Totally computerized patient records are not yet widely used but a large part of essential information is already stored on computer files. These include laboratory test results, diagnoses, codes for operations, codes of histopathological diagnoses and maybe even the patient's medication. Accordingly, these databases include much clinical knowledge that would be useful for clinicians. Laboratories try to support clinicians by producing reference values for laboratory tests. It is, of course, necessary information but, however, it does not give very much information about the weight of evidence that an abnormal laboratory test will give in special clinical settings. We have developed a software package - DiagaiD - in order to build a smart link between patient databases and clinicians. It utilizes neural network-based machine learning techniques and can produce decision support which meets the special needs of clinicians. From example cases it can learn clinically relevant transformations from original numeric values to logical values. By using data transformation together with a single layer perceptron it is possible to build nonlinear models from a set of preclassified example cases. In this paper, we use two small datasets to show how this scheme works in the diagnosis of acute appendicitis and in the diagnosis of myocardial infarction. Results are compared with those obtained using logistic regression or backpropagation neural networks. The performance of our neuro-fuzzy tool seemed to be slightly better in these two materials but the differences did not reach statistical significance.

Appendicitis↗
PubMed2005

Synthesizing cellular intelligence and artificial intelligence for bioprocesses.

Microbial processes operated under realistic conditions are difficult to describe by mechanistic models, thereby limiting their optimization and control. Responses of living cells to their environment suggest that they possess some "innate intelligence". Such responses have been modeled by a cybernetic approach. Furthermore, the overall behavior of a bioreactor containing a population of cells may be described and controlled through artificial intelligence methods. Therefore, it seems logical to combine cybernetic models with artificial intelligence to evolve an integrated intelligence-based strategy that is physiologically more faithful than the current approaches. This possibility is discussed, together with practical considerations favoring a hybrid approach that includes some mathematical modeling.

Artificial Intelligence↗
PubMed2005

The fuzzy clustering analysis based on AFS theory.

In the framework of axiomatic fuzzy sets theory, we first study how to impersonally and automatically determine the membership functions for fuzzy sets according to original data and facts, and a new algorithmic framework of determining membership functions and their logic operations for fuzzy sets has been proposed. Then, we apply the proposed algorithmic framework to give a new clustering algorithm and show that the algorithm is feasible. A number of illustrative examples show that this approach offers a far more flexible and effective means for the intelligent systems in real-world applications. Compared with popular fuzzy clustering algorithms, such as c-means fuzzy algorithm and k-nearest-neighbor fuzzy algorithm, the new fuzzy clustering algorithm is more simple and understandable, the data types of the attributes can be various data types or subpreference relations, even descriptions of human intuition, and the distance function and the class number need not be given beforehand.

Algorithms↗
PubMed1996

On the mental representation of conditional sentences.

Four experiments are reported which attempt to externalize subjects' mental representation of conditional sentences, using novel research methods. In Experiment 1, subjects were shown arrays of coloured shapes and asked to rate the degree to which they appeared to be true of conditional statements such as "If the figure is green then it is a triangle". The arrays contained different distributions of the four logically possible cases in which the antecedent or consequent is true or false: TT, TF, FT, and FF. For example, a blue triangle would be FT for the conditional quoted above. In Experiments 2 to 4, subjects were able to construct their own arrays to make conditional either true or false with any distribution of the four cases they wished to choose. The presence and absence of negative components was varied, as was the form of the conditional, being either "if then" as above or "only if": "The figure is green only if it is a triangle". The first findings was that subjects represent conditional in fuzzy way: conditional that include some counter-example TF cases (Experiment 1) may be rated as true, and such cases are often included when subjects construct an array to make the rule true (Experiments 2 to 4). Other findings included a strong tendency to include psychologically irrelevant FT and FF cases in constructed arrays, presumably to show that conditional statements only apply some of the time. A tendency to construct cases in line with the "matching bias" reported on analogous tasks in the literature was found, but only in Experiment 4, where the number of symbols available to construct each case was controlled. The findings are discussed in relation to the major contemporary theories of conditional reasoning based upon inference rules and mental models, neither of which can account for all the results.

Adult↗
PubMedDate not supplied

Disease cluster statistics for imprecise space-time locations.

Health professionals are investigating an increasing number of possible disease clusters, and statistical tests play an important role in cluster description and analysis. Existing cluster statistics assume precise data, when in reality health events are often imprecise (for example, place of residence is known only to the census district or zip code) and uncertain (for example, 'I first became ill sometime in 1985'). This incompatibility--precise methods used to analyse imprecise data--is largely ignored, resulting in test statistics of unknown accuracy. Most cluster statistics can be written as the cross-product of two matrices where one matrix reflects nearest-neighbour, distance or adjacency relationships and the second matrix is health related (for example, case-control identities). This paper explores a general approach to clustering, which incorporates uncertainty regarding space-time locations into these nearest neighbour, distance or adjacency relationships. Because the approach is general it can be used with almost all existing cluster tests, and, because it accounts for imprecise location data, it is suited to the 'real-world' nature of disease cluster investigations.

Bias↗
PubMedDate not supplied

A k nearest neighbour test for space-time interaction.

This paper describes a k nearest neighbour statistic sensitive to the pattern of cases expected of space-time clusters of health events. The Knox and Mantel tests are frequently used for space-time clustering but have two disadvantages. First, the selection of critical space-time distances for the Knox test and of a data transformation for the Mantel test is subjective. Second, the Mantel statistic is the sum of the products of space and time distances, is linear in form, and is not sensitive to non-linear associations between small space and time distances expected of contagious processes. The k nearest neighbour statistic is the number of case pairs that are k nearest neighbours in both space and time, and is evaluated under the null hypothesis of independent space and time nearest neighbour relationships. The test was applied to simulated and real data and compared to the Knox and Mantel tests using statistical power comparisons. The k nearest neighbour test proved sensitive to the space-time interaction pattern expected of disease clusters, does not require parameters (such as critical distances) to be estimated from the data, and may be used to test hypotheses about the spatial and temporal scale of the cluster process. The method addresses significant weaknesses in existing space-time cluster tests and should prove useful in the quantification and evaluation of clusters of human health events. Additional research is needed to further document the power of the test under different cluster processes.

Censuses↗
PubMed1999

Multi-criteria decision making--an approach to setting priorities in health care.

The objective of this paper is to present a multi-criteria decision making (MCDM) approach to support public health decision making that takes into consideration the fuzziness of the decision goals and the behavioural aspect of the decision maker. The approach is used to analyse the process of health technology procurement in a University Hospital in Rio de Janeiro, Brazil. The method, known as TODIM, relies on evaluating alternatives with a set of decision criteria assessed using an ordinal scale. Fuzziness in generating criteria scores and weights or conflicts caused by dealing with different viewpoints of a group of decision makers (DMs) are solved using fuzzy set aggregation rules. The results suggested that MCDM models, incorporating fuzzy set approaches, should form a set of tools for public health decision making analysis, particularly when there are polarized opinions and conflicting objectives from the DM group.

Brazil↗
PubMed2000

Data model for the elimination of matrix effects in enzyme-based flow-injection systems.

This contribution presents a new conceptional enzyme-based flow injection analysis (FIA) system for the process and quality control of food processing and biotechnological systems. It provides the determination of different analytes in distinct process media on the base of a common experimental set-up. In contrast to known comparable systems, analysis is performed without the commonly used sample preparation and dilution steps. Instead, the adaptation to the necessary measurement range is realized by optimization of intrinsic system parameters. The central principle of the work presented is the elimination of occurring interferences by the heterogeneous matrix of the process sample. Based on a particular injection mode, the application of dehydrogenases as indicator enzymes and a specially developed data model using cognitive methods, cross sensitivities of the detector as well as disturbed reaction rates of the enzymes could be almost completely compensated. Two applications are presented, the analysis of ethanol in non-alcoholic beer and the online determination of D-/L-lactate during a lactic acid fermentation, which reveal the advantage of the developed system.

Alanine Transaminase↗
PubMed2002

Hybrid model building methodology using unsupervised fuzzy clustering and supervised neural networks.

This paper suggests a model building methodology for dealing with new processes. The methodology, called Hybrid Fuzzy Neural Networks (HFNN), combines unsupervised fuzzy clustering and supervised neural networks in order to create simple and flexible models. Fuzzy clustering was used to define relevant domains on the input space. Then, sets of multilayer perceptrons (MLP) were trained (one for each domain) to map input-output relations, creating, in the process, a set of specified sub-models. The estimated output of the model was obtained by fusing the different sub-model outputs weighted by their predicted possibilities. On-line reinforcement learning enabled improvement of the model. The determination of the optimal number of clusters is fundamental to the success of the HFNN approach. The effectiveness of several validity measures was compared to the generalization capability of the model and information criteria. The validity measures were tested with fermentation simulations and real fermentations of a yeast-like fungus, Aureobasidium pullulans. The results outline the criteria limitations. The learning capability of the HFNN was tested with the fermentation data. The results underline the advantages of HFNN over a single neural network.

Biotechnology↗
PubMed1995

Image processing algorithms for the automated micronucleus assay in binucleated human lymphocytes.

The frequency of micronuclei in binucleated lymphocytes (cytochalasin B assay) may serve as a biological dosimeter after radiation exposure. The automation of the micronucleus assay in binucleated human lymphocytes has been considerably advanced in recent years. In our studies for this purpose the detection of binucleated cells (BNCs) and the scoring of micronuclei (MN) was divided into two parts. First, detection of BNCs was feasible with low microscopic magnification (x 100). The positions of classified BNCs were stored. Second, after an automatic change of microscope objective, the stored BNCs were automatically analyzed in sequence at high microscopic magnification (x 630) for occurrence of MN. For both phases of image analysis we used empirical methods based on mathematical morphology. The system is able to recognize nearly 65% of BNCs with false positive decisions of 6% and about 75% of the MN with false positive decisions of 7%.

Algorithms↗