Sharif Digital Repository

Application of the DOE with the ANN in kinetic study of the ODHP was investigated.•The catalyst of vanadium/graphene synthesized through the hydrothermal technique.•The ANN and RSM's simulations were utilized to generate the extra data points.•Power law models and corresponding parameters determined to describe the reactions.•The optimization conducted in order to minimize the COx production. In the current investigation, an application of the design of experiments (DOE) along with the artificial neural networks (ANN) in a kinetic study of oxidative dehydrogenation of propane (ODHP) reaction over a synthesized vanadium-graphene catalyst at 400-500. °C presented aiming at minimizing the CO. x... 

In this research the application of design of experiment (DOE) coupled with the artificial neural networks (ANN) in kinetic study of oxidative dehydrogenation of propane (ODHP) over a vanadium-graphene catalyst at 400-500 °C and a method of data collection/fitting for the experiments were presented. The proposed reaction network composed of consecutive and simultaneous reactions with kinetics expressed by simple power law equations involving a total of 20 unknown parameters (10 reaction orders and 5 rate constants each expressed in terms of a pre-exponential factors and activation energies) determined through non-linear regression analysis. Because of the complex nature of the system, neural... 

Early diagnosis of any disease at a lower cost is preferable. Automatic medical diagnosis classification tools reduce financial burden on health care systems. In medical diagnosis, patterns consist of observable symptoms and the results of diagnostic tests, which have various associated costs and risks. In this paper, we have experimented and suggested an automated pattern classification method for classifying four diseases into two classes. In the literature on machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variables. There are endeavors to use regression methods to... 

Adjusting control factors (independent variables) to achieve an optimal level of output (response variable) is usually required in many real-world manufacturing problems. Common optimization methods often begin with estimating the relationship between a response and independent variables. Among these techniques, response surface methodology (RSM), due to its simplicity, has recently attracted extensive attention. However, on the one hand, in some cases, the relationship between a response and independent variables is too complex to be estimated using polynomial regression models. On the other hand, solving the obtained optimization model is not easy by exact methods. This paper introduces a... 

Absorption of carbon dioxide (CO2) in aqueous solutions can be improved by the addition of other compounds. However, this requires a large amount of equilibrium data for solubility estimation in a wide ranges of temperature, pressure and concentration. In this paper, a model based on an artificial neural network (ANN) was proposed and developed with mixtures containing monoethanolamine (MEA), diethanolamine (DEA), methyldiethanolamine (MDEA), 2-amino-2-methyl-1-propanol (AMP), methanol, triethanolamine (TEA), piperazine (PZ), diisopropanolamine (DIPA) and tetramethylensulfone (TMS) to predict solubility of CO2 in mixed aqueous solution (especially in binary and ternary mixtures) over wide... 

The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment.Proton nuclear magnetic resonance spectroscopy ( 1H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for... 

By new advancements in vehicle manufacturing, evaluation of vehicle quality assurance has got a more critical issue. Today noise and vibration generated inside and outside the vehicles are more important factors for customers than before. So far several researchers have focused on interior noise transfer path analysis and the results have been published in related papers but each method has its own limitations. In present work, the vibration transfer path analysis and vibration path ranking of a car interior have been performed. As interior vibration is a source of structural borne noise problem, thus, the results of this research can be used to present the structural borne noise state in a... 

Prediction of accident frequency based on traffic and roadway characteristics has been a very significant tool in the field of traffic management. The accident frequencies on 185 roadway segments of the city of Mashhad, Iran, for the year 2007, were used to develop accident prediction models. Negative Binomial Regression, Zero Inated Negative Binomial Regression, Support Vector Machine and Back-Propagation Neural Network models were used to fit the accident data. Both fitting and predicting abilities of the models were evaluated through computing error values. Results show that the NBR model is the most effective model for predicting the number of accidents because of its low prediction and... 

Demand forecasting plays an important role as a decision support tool in power system management, especially in smart grid and liberalized power market. In this paper, a demand forecasting method is presented by using support vector regression (SVR). The proposed method is applied to practical hourly data of the Greater Tehran Electricity Distribution Company. The SVR parameters are selected by using a grid optimization process and an investigation on different kernel functions. Moreover, correlation analysis is used to find exogenous variables. Acceptable accuracy of load prediction is shown by comparing the result of SVR model to that of the artificial neural networks and the actual data,... 

Aim: The aim of this study was to search for metabolic biomarkers of Crohn's disease (CD). Background: Crohn's disease (CD) is a type of inflammatory bowel disease that causes a wide variety of symptoms. CD can influence any part of the gastrointestinal tract from mouth to anus. CD is not easily diagnosed because monitoring tools are currently insufficient. Thus, the discovery of proper methods is needed for early diagnosis of CD. Patients and methods: We utilized metabolic profiling using proton nuclear magnetic resonance spectroscopy (1HNMR) to find the metabolites in serum. Classification of CD and healthy subject was done using partial least squares discriminant analysis (PLS-DA).... 

A feed forward multi-layer perceptron neural network was developed to predict carbon dioxide loading capacity of chemical absorbents over wide ranges of temperature, pressure, and concentration based on the molecular weight of solution. To verify the suggested artificial neural network (ANN), regression analysis was conducted on the estimated and experimental values of CO2 solubility in various aqueous solutions. Furthermore, a comparison was performed between results of the proposed neural network and experimental data that were not previously used for network training, as well as a set of data for binary solutions. Comparison between the proposed multi-layer perceptron (MLP) network and... 

The measurement of PVT properties of natural gas in gas pipelines, gas storage systems, and gas reservoirs require accurate values of compressibility factor. Although equation of state and empirical correlations were utilized to estimate compressibility factor, but the demands for novel, more reliable, and easy-to-use models encouraged the researchers to introduce modern tools such as artificial intelligent systems.This paper introduces Particle swarm optimization (PSO) and Genetic algorithm (GA) as population-based stochastic search algorithms to optimize the weights and biases of networks, and to prevent trapping in local minima. Hence, in this paper, GA and PSO were used to minimize the... 

The fish freshness was evaluated using machine vision technique through color changes of eyes and gills of farmed and wild gilthead sea bream (Sparus aurata), being employed lightness (L*), redness (a *), yellowness (b*), chroma (c *), and total color difference (ΔE) parameters during fish ice storage. A digital color imaging system, calibrated to provide accurate CIELAB color measurements, was employed to record the visual characteristics of eyes and gills. The region of interest was automatically selected using a computer program developed in MATLAB software. L*, b *, and ΔE of eyes increased with storage time, while c* decreased. The a* parameter of fish eyes did not show clear a trend... 

We propose a cost estimation model based on a fuzzy rule backpropagation network, configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach, we determine the optimal path of the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty  

Parkinson's disease (PD) is a neurodegenerative disease, which is not easily diagnosed using clinical tests and the discovery of proper methods would be a major step towards a successful diagnosis. In the present study, we employed metabolic profiling using proton nuclear magnetic resonance spectroscopy to find metabolites in serum, which are helpful for the diagnosis of PD. Classification of PD and healthy subject was done using random forest. Serum levels of selenium measured by atomic absorption spectrometry in PD group were lower than the serum selenium levels in the control group. The metabolites causing selenium changes in PD patients were identified using random forest, and a model... 

Two artificial neural networks (ANNs) are constructed, trained, and tested to map inputs of a complex trunk finite element (FE) model to its outputs for spinal loads and muscle forces. Five input variables (thorax flexion angle, load magnitude, its anterior and lateral positions, load handling technique, i.e., one- or two-handed static lifting) and four model outputs (L4-L5 and L5-S1 disc compression and anterior-posterior shear forces) for spinal loads and 76 model outputs (forces in individual trunk muscles) are considered. Moreover, full quadratic regression equations mapping input-outputs of the model developed here for muscle forces and previously for spine loads are used to compare the... 

Data streams are endless flow of data produced in high speed, large size and usually non-stationary environments. The main property of these streams is the occurrence of concept drifts. Using decision trees is shown to be a powerful approach for accurate and fast learning of data streams. In this paper, we present an incremental regression tree that can predict the target variable of newly incoming instances. The tree is updated in the case of occurring concept drifts either by altering its structure or updating its embedded models. Experimental results show the effectiveness of our algorithm in speed and accuracy aspects in comparison to the best state-of-the-art methods  

Conventional models of cardiovascular system frequently lack required detail and focus primarily on the overall relationship between pressure, flow and volume. This study proposes a localized and regional model of the cardiovascular system. It utilizes noninvasive blood flow and pressure seed data and temporal cardiac muscle regional activity to predict the operation of the heart under normal and congestive heart failure conditions. The analysis considers specific regions of the heart, namely, base, mid and apex of left ventricle. The proposed method of parameter estimation for hydraulic electric analogy model is recursive least squares algorithm. Based on simulation results and comparison... 

Estimating the energy consumption of applications is a key aspect in optimizing embedded systems energy consumption. This paper proposes a simple yet accurate instruction-level energy estimation model for embedded systems. As a case study, the model parameters were determined for a commonly used ARM7TDMI-based microcontroller. The total energy includes the energy consumption of the processor core, Flash memory, memory controller, and SRAM. The model parameters are instructions opcode, number of shift operations, register bank bit flips, instructions weight and their Hamming distance, and different types of memory accesses. Also, the effect of pipeline stalls have been considered. In order to... 

We are concerned with the design of a model and an algorithm for computing the shortest path in a network having various types of fuzzy arc lengths. First, a new technique is devised for the addition of various fuzzy numbers in a path using α-cuts by proposing a least squares model to obtain membership functions for the considered additions. Due to the complexity of the addition of various fuzzy numbers for larger problems, a genetic algorithm is presented for finding the shortest path in the network. For this, we apply a recently proposed distance function for comparison of fuzzy numbers. Examples are worked out to illustrate the applicability of the proposed approach