Early prostate cancer diagnosis by using artificial neural networks and support vector machines

The aim of this study is to design a classifier based expert system for early diagnosis of the organ in constraint phase to reach informed decision making without biopsy by using some selected features. The other purpose is to investigate a relationship between BMI (body mass index), smoking factor, and prostate cancer. The data used in this study were collected from 300 men (100: prostate adenocarcinoma, 200: chronic prostatism or benign prostatic hyperplasia). Weight, height, BMI, PSA (prostate specific antigen), Free PSA, age, prostate volume, density, smoking, systolic, diastolic, pulse, and Gleason score features were used and independent sample t-test was applied for feature selection. In order to classify related data, we have used following classifiers; scaled conjugate gradient (SCG), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and Levenberg–Marquardt (LM) training algorithms of artificial neural networks (ANN) and linear, polynomial, and radial based kernel functions of support vector machine (SVM). It was determined that smoking is a factor increases the prostate cancer risk whereas BMI is not affected the prostate cancer. Since PSA, volume, density, and smoking features were to be statistically significant, they were chosen for classification. The proposed system was designed with polynomial based kernel function, which had the best performance (accuracy: 79%). In Turkish Family Health System, family physician to whom patients are applied firstly, would contribute to extract the risk map of illness and direct patients to correct treatments by using expert system such proposed.     

A quasi-static analysis for a class of induced-current EIT systems using discrete coils

A discrete coil EIT system is investigated for the general case of an eccentric circular inhomogeneity. The solution methodology of the forward problem of this system is explained. An optimization procedure using this forward problem solution is developed to find optimum currents that maximize the distinguishability. For an eccentric inhomogeneity problem, it is shown that the coil currents can be optimized to focus the current density in a region of interest. Optimum coil currents under limited peak coil currents constraint and limited total power constraint are obtained. Representative examples that demonstrate the performance of the system are presented.     

Role of Learning Algorithm in Neural Network-Based Backcalculation of Flexible Pavements

Nondestructive testing (NDT) methods are widely used for the performance evaluation of flexible pavements. Falling weight deflectometer (FWD), which measures time-domain deflections resulting from applied impulse loads, is the most popular technique among all NDT methods. The evaluation of the FWD data requires the inversion of mechanical pavement properties using a backcalculation tool that includes both a forward pavement response model and an optimization algorithm. Neural networks (NNs) have also emerged as alternative tools that can be employed for pavement backcalculation problems relative to their real-time processing abilities. However, there have been no comprehensive analyses in previous studies that focus on the learning algorithm and the architecture of a NN model, which considerably affect backcalculation results. In this study, 284 different NN models were developed using synthetic training and testing databases obtained by layered elastic theory. Results indicated that both the learning algorithm and network architecture play important roles in the performance of the NN based backcalculation process.     

Utilizing Genetic Algorithms to Optimize Membership Functions for Fuzzy Weighted Association Rules Mining

It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining. In general, it is unrealistic that experts can always provide such sets. And finding the most appropriate fuzzy sets becomes a more complex problem when items are not considered to have equal importance and the support and confidence parameters required for the association rules mining process are specified as linguistic terms. Existing clustering based automated methods are not satisfactory because they do not consider the optimization of the discovered membership functions. In order to tackle this problem, we propose Genetic Algorithms (GAs) based clustering method, which dynamically adjusts the fuzzy sets to provide maximum profit based on user specified linguistic minimum support and confidence terms. This is achieved by tuning the base values of the membership functions for each quantitative attribute with respect to two different evaluation functions maximizing the number of large itemsets and the average of the confidence intervals of the generated rules. To the best of our knowledge, this is the first effort in this direction. Experiments conducted on 100 K transactions from the adult database of United States census in year 2000 demonstrate that the proposed clustering method exhibits good performance in terms of the number of produced large itemsets and interesting association rules.     

Microchannel bioreactors for bioartificial liver support

An extracorporeal bioartificial liver (BAL) device containing viable hepatocytes has the potential to provide temporary hepatic support to liver failure patients, serving as a bridge to transplantation while awaiting a suitable donor. In some patients, providing temporary hepatic support may be sufficient to allow adequate regeneration of the host liver, thereby eliminating the need for a liver transplant. Although the BAL device is a promising technology for the treatment of liver failure, there are several technical challenges that must be overcome in order to develop systems with sufficient processing capacity and of manageable size. In this study, the authors describe the critical issues involved in developing a BAL device. They also discuss their experiences in hepatocyte culture optimization within the context of a microchannel flat-plate BAL device.     

Experimental and numerical analysis of sodium-carbonate salt gradient solar-pond performance under simulated solar-radiation

The objective of this study is to investigate experimentally and theoretically whether sodium carbonate (Na₂CO₃) salt is suitable for establishing a salinity gradient in a salt-gradient solar-pond (SGSP). For this purpose, a small-scale prismatic solar-pond was constructed. Experiments were conducted in the laboratory under the incident radiation from two halogen-lamps acting as a solar simulator. Furthermore, a one-dimensional transient mathematical model that describes the heat and mass transfer behaviour of the SGSP was developed. The differential equations obtained were solved numerically using a finite-difference method. It was found from the experiments that the density gradient, achieved using sodium carbonate salt, can suppress convection from the bottom to the surface of the pond.     

ON TEMPORAL DEDUCTIVE DATABASES

This article introduces a temporal deductive database system featuring a logic programming language and an algebraic front-end. The language, called Temporal DATALOG, is an extension of DATALOG based on a linear-time temporal logic in which the flow of time is modeled by the set of natural numbers. Programs of Temporal DATALOG are considered as temporal deductive databases, specifying temporal relationships among data and providing base relations to the algebraic front-end. The minimum model of a given Temporal DATALOG program is regarded as the temporal database the program models intensionally. The algebraic front-end, called TRA, is a point-wise extension of the relational algebra upon the set of natural numbers. When needed during the evaluation of TRA expressions, slices of temporal relations over intervals can be retrieved from a given temporal deductive database by bottom-up evaluation strategies.
A modular extension of Temporal DATALOG is also proposed, through which temporal relations created during the evaluation of TRA expressions may be fed back to the deductive part for further manipulation. Modules therefore enable the algebra to have full access to the deductive capabilities of Temporal DATALOG and to extend it with nonstandard algebraic operators. This article also shows that the temporal operators of TRA can be simulated in Temporal DATALOG by program clauses.     

A class of adaptive directional image smoothing filters

The gray level distribution around a pixel of an image usually tends to be more coherent in some directions compared to other directions. The idea of adaptive directional filtering is to estimate the direction of higher coherence around each pixel location and then to employ a window which approximates a line segment in that direction. Hence, the details of the image may be preserved while maintaining a satisfactory level of noise suppression performance. In this paper we describe a class of adaptive directional image smoothing filters based on generalized Gaussian distributions. We propose a measure of spread for the pixel values based on the maximum likelihood estimate of a scale parameter involved in the generalized Gaussian distribution. Several experimental results indicate a significant improvement compared to some standard filters.     

Thermal performance of a solar cooker integrated vacuum-tube collector with heat pipes containing different refrigerants

A solar cooking system using vacuum-tube collectors with heat pipes containing a refrigerant as working fluid has been fabricated, and its performance has been analysed experimentally. The experiments were conducted during clear days in July and August of 2002 in Elazı , Turkey under similar meteorological conditions for three refrigerants and water. Detailed temperature distributions and their time dependences were measured. The maximum temperature obtained in a pot containing 7 l of edible oil was 175 °C. Also, the cooker was successfully used to cook several foods. The cooking processes were performed with the cooker in 27–70 min periods.