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At least 19 recordsLinked to original sources
PubMed2003

The role of soft computing in intelligent machines.

An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.

Algorithms↗
PubMed2005

Advanced soft computing diagnosis method for tumour grading.

OBJECTIVE: To develop an advanced diagnostic method for urinary bladder tumour grading. A novel soft computing modelling methodology based on the augmentation of fuzzy cognitive maps (FCMs) with the unsupervised active Hebbian learning (AHL) algorithm is applied. MATERIAL AND METHODS: One hundred and twenty-eight cases of urinary bladder cancer were retrieved from the archives of the Department of Histopathology, University Hospital of Patras, Greece. All tumours had been characterized according to the classical World Health Organization (WHO) grading system. To design the FCM model for tumour grading, three experts histopathologists defined the main histopathological features (concepts) and their impact on grade characterization. The resulted FCM model consisted of nine concepts. Eight concepts represented the main histopathological features for tumour grading. The ninth concept represented the tumour grade. To increase the classification ability of the FCM model, the AHL algorithm was applied to adjust the weights of the FCM. RESULTS: The proposed FCM grading model achieved a classification accuracy of 72.5%, 74.42% and 95.55% for tumours of grades I, II and III, respectively. CONCLUSIONS: An advanced computerized method to support tumour grade diagnosis decision was proposed and developed. The novelty of the method is based on employing the soft computing method of FCMs to represent specialized knowledge on histopathology and on augmenting FCMs ability using an unsupervised learning algorithm, the AHL. The proposed method performs with reasonably high accuracy compared to other existing methods and at the same time meets the physicians' requirements for transparency and explicability.

Algorithms↗
PubMedDate not supplied

Soft computing in the design of nontoxic chemicals.

This research focuses on the use of soft computing to aid in the development of novel, state-of-the-art, nontoxic dyes which are of commercial importance to the U.S. textile industry. Where appropriate, modern molecular orbital (MO) and density functional (DF) techniques are employed to establish the necessary databases of molecular properties to be used in conjunction with the neural network approach. In this research, we focused on the following: (1) using molecular modeling to establish databases of various molecular properties of azo dyes required as input for our neural network approach; (2) designing and implementing a neural network architecture suitable to process these databases; and (3) investigating combinations of molecular descriptors needed to predict various properties of the azo dyes.

Journal Article↗
PubMed2000

Staging of cervical cancer with soft computing.

This paper describes a way of designing a hybrid decision support system in soft computing paradigm for detecting the different stages of cervical cancer. Hybridization includes the evolution of knowledge-based subnetwork modules with genetic algorithms (GA's) using rough set theory and the Interactive Dichotomizer 3 (ID3) algorithm. Crude subnetworks obtained via rough set theory and the ID3 algorithm are evolved using GA's. The evolution uses a restricted mutation operator which utilizes the knowledge of the modular structure, already generated, for faster convergence. The GA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conventional multilayer perceptron. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for extraction of logical rules and human interpretation of the inferencing procedure.

Algorithms↗
PubMedDate not supplied

Database mining using soft computing techniques. An integrated neural network-fuzzy logic-genetic algorithm approach.

Two different soft computing (SC) techniques (a competitive learning neural network and an integrated neural network-fuzzy logic-genetic algorithm approach) are employed in the analysis of a database subset obtained from the Cambridge Structural Database. The chemical problem chosen for study is relevant to the relationship between various metric parameters in transition metal imido (LnMdNZ, Z = carbon-based substituent) complexes and the chemical consequences of such relationships. The SC techniques confirmed and quantified the suspected relationship between the metal-nitrogen bond length and the metal-nitrogen-substituent bond angle for transition metal imidos: increased metal-nitrogen-carbon angles correlate with shortened metal-nitrogen distances. The mining effort also yielded an unexpected correlation between the NC distance and the MNC angle-shorter NC correlate with larger MNC. A fuzzy inference system is used to construct an MNred-NC-MNC hypersurface. This hypersurface suggests a complicated interdependence among NC, MNred, and the angle subtended by these two bonds. Also, major portions of the hypersurface are very flat, in regions where MNC is approaching linearity. The relationships are also seen to be influenced by whether the imido substituent is an alkyl or aryl group. Computationally, the present results are of particular interest in two respects. First, SC classification was able to isolate an "outlier" cluster. Identification of outliers is important as they may correspond to unreported experimental errors in the database or novel chemical entities, both of which warrant further investigation. Second, the SC database mining not only confirmed and quantified a suspected relationship (MNred versus MNC) within the data but also yielded a trend that was not suspected (NC versus MNC).

Journal Article↗
PubMedDate not supplied

Myocardium extraction in positron emission tomography based on soft computing.

This paper presents an efficient and accurate approach to myocardium extraction in Positron Emission Tomography (PET) images based on a careful application of soft computing techniques. PET images present a noisy background, making the automatic myocardium extraction and uptake quantification a difficult task. In this work a Self Organized Radial Basis Function Network (SRBFN) is designed to focus on the myocardium in an iterative process until the total extraction of the myocardium from the noisy background is achieved. Fuzzy sets and fuzziness measures are used to compute the error of the network. The method was tested on a set of nine images of different patients and its effectiveness is illustrated in two patients showing tracer uptake defects.

Fluorodeoxyglucose F18↗
PubMed2000

Structural analysis of transition metal beta-X substituent interactions. Toward the use of soft computing methods for catalyst modeling

Fuzzy logic and neural network techniques are used to classify intramolecular interactions between transition metals (M) and beta-X substituents in the following structural motif (LnMC(alpha)(A1)(A2)-C(beta)(B1)(B2)X). These interactions are relevant to the direct polymerization of functionalized olefins by Ziegler-Natta (ZN) catalysis. The efficiency and effectiveness of different soft computing techniques are compared. These methods give not only encouraging results with respect to general data mining issues but also insight into the factors that effect interactions between transition metals and beta-X substituents.

Journal Article↗
PubMed2004

Soft-computing base analyses of the relationship between annoyance and coping with noise and odor.

The majority of research on annoyance as an important impact of noise, odor, and other stressors on man, has regarded the person as a passive receptor. It was however recognized that this person is an active participant trying to alter a troubled person-environment relationship or to sustain a desirable one. Coping has to be incorporated. This is of particular importance in changing exposure situations. For large populations a lot of insight can be gained by looking at average effects only. To investigate changes in annoyance and effects of coping, the individual or small group has to be studied. Then it becomes imperative to recognize the inherent vagueness in perception and human behavior. Fortunately, tools have been developed over the past decades that allow doing this in a mathematically precise way. These tools are sometimes referred to by the common label: soft-computing, hence the title of this paper. This work revealed different styles of coping both by blind clustering and by (fuzzy) logical aggregation of different actions reported in a survey. The relationship between annoyance and the intensity of coping it generates was quantified after it was recognized that the possibility for coping is created by the presence of the stressor rather than the actual fact of coping. It was further proven that refinement of this relationship is possible if a person can be identified as a coper. This personal factor can be extracted from a known reaction to one stressor and be used for predicting coping intensity and style in another situation. The effect of coping on a perceived change in annoyance is quantified by a set of fuzzy linguistic rules. This closes the loop that is responsible for at least some of the dynamics of the response to a stressor. This work thus provides all essential building blocks for designing models for annoyance in changing environments.

Adaptation, Psychological↗
PubMedDate not supplied

Computing. Soft cells.

Explore the source record for details and available documents.

Computer-Assisted Instruction↗
PubMedDate not supplied

Determining flexor-tendon repair techniques via soft computing.

An SC-based multi-objective decision-making method for determining the optimal flexor-tendon repair technique from experimental and clinical survey data, and with variable circumstances, was presented. Results were compared with those from the Taguchi method. Using the Taguchi method results in the need to perform ad-hoc decisions when the outcomes for individual objectives are contradictory to a particular preference or circumstance, whereas the SC-based multi-objective technique provides a rigorous straightforward computational process in which changing preferences and importance of differing objectives are easily accommodated. Also, adding more objectives is straightforward and easily accomplished. The use of fuzzy-set representations of information categories provides insight into their performance throughout the range of their universe of discourse. The ability of the technique to provide a "best" medical decision given a particular physician, hospital, patient, situation, and other criteria was also demonstrated.

Cadaver↗
PubMed1995

Soft-copy computed radiography in neonatal and pediatric intensive care units: cost-savings analysis.

PURPOSE: To determine whether soft-copy interpretation of computed radiography (CR) in neonatal and pediatric intensive care units (ICUs) can result in a cost savings compared with screen-film radiography for the radiology department. MATERIALS AND METHODS: The actual cost of CR equipment, maintenance contracts, and the estimated cost for data storage were compared with the potential savings that could result from eliminating film, decreasing the number of librarians and lost radiographs, and improving technologists' efficiency. RESULTS: After the 1st year, net cash flow from use of soft-copy CR interpretation is projected to result in a slight savings for the radiology department but not enough to offset the capital equipment purchase price. CONCLUSION: Soft-copy CR ICU imaging will not result in a cost savings. To have a balanced net cash flow would require either more than doubling potential savings or decreasing capital equipment cost by greater than one-half. The justification for soft-copy CR needs to be judged by its effect on increasing physician efficiency and timely decisions on patient care.

Capital Financing↗
PubMed1997

Accuracy of bedside chest hard-copy screen-film versus hard- and soft-copy computed radiographs in a medical intensive care unit: receiver operating characteristic analysis.

PURPOSE: To compare the clinical diagnostic accuracy of hard-copy readings of screen-film bedside chest radiographs and both hard- and soft-copy readings of bedside chest computed radiographs obtained in a medical intensive care unit. MATERIALS AND METHODS: Two samples of 95 cases were assembled from chest images obtained in 541 patients with either screen-film radiography or computed radiography. The cases were stratified according to the clinical problem for which the examination was ordered; the corresponding diagnosis was verified by a panel of two or three radiologists. Four radiologists blindly read the hard-copy images obtained with screen-film or computed radiography. Six months later, the radiologists read the computed radiographs by using an 8-bit, 1,684 x 2,048-pixel display. The data were analyzed by using multireader-multicase receiver operating characteristic (ROC) analysis of variance. RESULTS: No statistically significant differences in the area under the ROC curve were found between any of the methods. CONCLUSION: The results provide some justification for using bedside chest computed radiography and for reading soft-copy images from a high-quality display.

Female↗
PubMed2006

CI for a DSS to predict nursing coverage in a geriatric care facility.

The vast and growing area of informatics presents the single biggest change in how societies grow, work, and live. Strong emphasis is placed on environment, work safety, disease minimization, illness detection, and care interventions. The informatics in these areas is not adequately used to realize the full benefits to society, but using computational intelligence or soft computing techniques, we obtain a closer acceptance of confidence in the application of informatics to improve quality of life. There is a vast array of applications of informatics in society like bioinformatics, genetic detection, medical diagnosis, manufacturing, environmental responsibility, transportation design, etc. By using neural networks and genetic algorithms, reasoning on the data sources can result in emerging nursing informatics patterns where prediction can become more and more accurate in an acuity system that ultimately gives effective therapeutic care and decreased rates of mortality. In this paper, I propose the use of CI techniques to determine crisp data that can be incorporated into decision-making for nursing care.

Aged↗
PubMed2003

Tools for intelligent control: fuzzy controllers, neural networks and genetic algorithms.

The goal of this expository paper is to bring forth the basic current elements of soft computing (fuzzy logic, neural networks, genetic algorithms and genetic programming) and the current applications in intelligent control. Fuzzy sets and fuzzy logic and their applications to control systems have been documented. Other elements of soft computing, such as neural networks and genetic algorithms, are also treated for the novice reader. Each topic will have a number of relevant references of as many key contributors as possible.

Algorithms↗
PubMedDate not supplied

Defining a neural network controller structure for a rubbertuator robot.

Rubbertuator (Rubber-Actuator) robot arm is a pneumatic robot, unique with its lightweight, high power, compliant and spark free nature. Compressibility of air in the actuator tubes and the elastic nature of the rubber, however, are the two major sources of increased non-linearity and complexity in motion control. Soft computing, exploiting the tolerance of uncertainty and vagueness in cognitive reasoning has been offering easy to handle, robust, and low-priced solutions to several non-linear industrial applications. Nonetheless, the black-box approach in these systems results in application specific architectures with some important design parameters left for fine tuning (i.e. number of nodes in a neural network). In this study we propose a more systematic method in defining the structure of a soft computing technique, namely the backpropagation neural network, when used as a controller for rubbertuator robot systems. The structure of the neural network is based on the physical model of the robot, while the neural network itself is trained to learn the trajectory independent parameters of the model that are essential for defining the robot dynamics. The proposed system performance was compared with a well-tuned PID controller and shown to be more accurate in trajectory control for rubbertuator robots.

Elasticity↗
PubMed1984

[Computer tomography in primary soft tissue tumors].

Computed tomography was performed in 37 patients with primary soft tissue tumours (24 malignant and 13 benign lesions). Except for lipomas it was not possible to relate attenuation values to histologic type or grade of malignancy. Major advantages with CT include the ability to demonstrate infiltration of surrounding structures and to determine tumour size. There is a risk of overestimating the size of malignant tumours because of peritumoural edema.

Abdominal Muscles↗