Analysis of Completion Time at Information Technology Department using Artificial Neural Network

This paper aims to perform modeling of Taiwanese farm and ecotourism data using compositional models as a probabilistic approach and to compare its results with the performance of an artificial neural network approach. Authors use probabilistic compositional models together with the artificial neural network as a classifier and compare the accuracy of both approaches. The probabilistic model structure is learned using hill climbing algorithm, and the weights of multilayer feedforward artificial neural network are learned using an R implementation of H2O library for deep learning. In case of both approaches, we employ a non-exhaustive cross-validation method and compare the models. The comparison is augmented by the structure of the compositional model and basic characterization of artificial neural network. As expected, the compositional models show significant advantages in interpretability of results and (probabilistic) relations between variables, whereas the artificial neural network provides more accurate yet “black-box” model.

In this ever-changing world, the function of information technology (IT) department is becoming increasingly important. The objective of this study is to construct an approach based on balanced scorecard (BSC) and artificial neural network (ANN) for evaluating an IT department in the manufacturing industry. A novel hybrid particle swarm optimization (HPSO)-based learning method is utilized in the ANN. The reliability of the ANN model is tested by a practical example. The results show that the ANN model learned by HPSO algorithm has relative high accuracy and is better than the back propagation algorithm. The empirical findings suggest that the ANNs model can be a persuasive analytical tool for the performance evaluation of the IT department.

An approach based on artificial neural networks is proposed for the analysis of electromechanical modes in Taiwan power system. To evaluate the dynamic performance of a power system in system operation and planning, the dominant eigenvalues for the worst-damped electromechanical mode must be computed. A multilayer feedforward artificial neural network is developed. It is well known that eigenvalues are complicated functions of many system variables such as bus loads, line flows, generation schedule, bus voltages etc. An important procedure in neural network design is to select those features which most affect system eigenvalues. A clustering artificial neural network is thus designed for feature selection. To demonstrate the effectiveness of the approach, results from eigenvalue analyses of Taiwan power system are reported.

This paper presents a method for incorporating a priori information about an uncertain nonlinear system into the structure of a multilayer feedforward artificial neural network. Known information is incorporated into the activation function of the network output layer. An algorithm is derived for backpropagating the error and updating adjustable parameters within this layer that is consistent with existing supervised learning techniques. The developed technique is applied to the identification of a dynamic system and compared with conventional feedforward artificial neural network identifier. Results exhibit an improvement in the quality of the identification model and an increase in the rate of convergence. As a practical application, a prior information is utilized for identification of switched reluctance motor characteristics on the basis of experimental measurements. The results further demonstrate that artificial neural networks employing a priori information converge faster, require fewer adjustable weights, and more accurately predict the system of interest.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Heat treated cast aluminum components like engine blocks and cylinder heads can develop significant amount of residual stress and distortion particularly with water quench. To incorporate the influence of residual stress and distortion in cast aluminum product design, a rapid simulation approach has been developed based on artificial neural network and component geometry characteristics. Multilayer feed-forward artificial neural network (ANN) models were trained and verified using FEA residual stress and distortion predictions together with part geometry information such as curvature, maximum dihedral angle, topologic features including node's neighbors, as well as quench parameters like quench temperature and quench media.&lt;/div&gt;&lt;/div&gt;

An inverse analysis using artificial intelligence based on the guided ultrasonic waves is proposed for effective identification of damage in thick steel beams for the purpose of structural health monitoring applications. Parameterized modeling for finite element analysis is applied to constitute the damage parameter database cost-effectively. For signal processing and feature extraction, wavelet transform is employed. A novel feature extraction technique, damage characteristic points, is applied to constitute the database for pattern recognition procedures. Using the extracted metrics, a multilayer feedforward artificial neural network under supervision of an error-backpropagation algorithm is developed and trained. The generalization performance of the artificial neural network has been examined experimentally. Results illustrate that the proposed metrics together with artificial neural network technique are powerful tools for effective identification of damage in the case of thick structures.

This paper is concerned with optical character recognition (OCR) system for Bangla conjunctive characters. A method is proposed giving emphasis on the identification of the characters using the proposed methodology. Here generalization is achieved by preprocessing the characters before presenting them to the system for classification. The pre-processing systematically functions isolating the characters from BMP images, as well as noise removal, scaling and binary image conversion. The method uses a flexible matching between sample data and training data components applying Multi-layered Feed-forward Artificial Neural Network. A number of parameters of Neural Network are estimated so that the system performance is improved in comparison with normal training. Classification of character is defined as error minimization among the possible training set. Also, a measure of the amount of distortion for this training is given. Application of Artificial Neural Network in character recognition has made the system faster with optimum performance.

This paper presents the development of several multilayer feed-forward artificial neural network (ANN) models aimed at the simulation and parametrization of semiconductor optical amplifiers (SOAs). In the direct approach, it is assumed that the SOA physical parameters are known, and the ANNs can reproduce the results of detailed SOA models up to 20 times faster. In the inverse approach, it is assumed that the SOA is a grey-box with a known model but unknown parameters, and the ANNs infer the SOA model parameters from the input-output data. These results provide efficient surrogate models to represent SOAs in future digital twins for on-line optimization of ultra-wide band transmission systems that employ the SOA as the repeater element.

Gray layer technology represents a novel method for incorporating prior information about an uncertain nonlinear system into the structure of a multilayer feedforward artificial neural network (ANN). A prescriptive technique is developed that sets or constrains the weights of a particular layer, designated the gray layer, according to a known or partially known function that is related in some manner to the system of interest. An algorithm is derived for backpropagating the error through the gray layer and adjusting the parameters of the gray layer that is consistent with other ANN learning techniques. Gray layer technology is applied to the identification of two different partially known dynamic systems. Results demonstrate a significant improvement in the quality of the identification model and an increase in the rate of convergence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Hypergame is a typical framework to deal with decision situations with multiple conflicting players who perceive the conflict differently and subjectively. In the face of opponent players' different outcome preferences information over the outcome space perceived by different decision makers using fuzzy linguistic values, a concept of fuzzy relational degree between different fuzzy numbers sequences is first presented in this paper, and based on fuzzy relational degree analysis, the weight of each decision maker can be evaluated and the integrated fuzzy outcome preferences can be obtained. Then, a multi-layer feedforward artificial neural network (ANN) is designed and trained to learn the criterions of comparing trapezoidal fuzzy numbers. Using the trained ANN, the integrated fuzzy outcome preferences can be ranked automatically. At last, a numerical example is provided to illustrate the proposed method.

Evaluation of neural network based simulation models for the prediction of site specific soil properties of the large agricultural areas will provide important technological benefits for the efficient use of land resources and Agricultural Decision Making System. In this study, an artificial intelligence model was investigated for simulation of site specific iron (Fe) and zinc (Zn) levels in the soils of apple orchard by using a feedforward multilayered Artificial Neural Network (ANN). The measurements of Fe and Zn were made for the topsoil (0-25 cm) and subsoil samples (25-50 cm) collected from forty five different coordinates within intervals of 20 x 10 m based on a grid sampling system in the east and north directions. The measured coordinate values were reflected to the input layer of the developed two input one output feedforward multilayer of the ANN model. Respectively, the measured Fe and Zn values were reflected to its output layer, and then the training stage was started. In the training, back propagation algorithms were used to get the most suitable NN structure for the prediction. As a result of varied trainings, the ANN having two hidden layers and ten neurons produced the best estimation values for site specific Fe and Zn estimations. The most suitable prediction values (R2 = 0.98 and R2 = 0.97, P<0.01) were obtained from the ANN with 10.10.1 structure for site specific Fe and Zn levels in the topsoil. When the simulated values obtained from the ANN model were compared with the measured values, it was observed that successful results were achieved based on varied training values. Hence, the ANN based model should be calibrated for varied training conditions depending on different soil conditions to get more reliable results. Key words: Articifial neural network, neural structures, iron, zinc, apple orchard.

The purpose of this study is to develop new multilayer feed-forward artificial neural network (ANN)-based maximal oxygen uptake (VO2max) prediction models by using submaximal treadmill exercise and nonexercise data. Using 10-fold cross validation on the dataset, standard error of estimate (SEE) and multiple correlation coefficient (R) of the models are calculated. It is shown that the models including submaximal, standard nonexercise and questionnaire variables yield higher R and lower SEE than the ones including submaximal and standard nonexercise variables only. The results of ANN-based models are also compared with the ones obtained by Multiple Linear Regression (MLR) and Support Vector Machines (SVM). It is shown that ANN-based models perform better than MLR and SVM-based models for predicting VO2max.

Artificial neural network (ANN) modelling of crystallisation temperatures of nickel–phosphorus deposits

The present study was designed for the experimental and modeling investigations on thermodynamic analysis of drying onion slices using four microwave power levels (100, 350, 500, and 750 W) and the four samples thicknesses (2.5, 5, 7.5, and 10 mm). A multilayer feedforward artificial neural network was employed in order to predict energy and exergy performance of the dryer and the prediction success were compared using three evaluation criteria. The average values for energy efficiency and specific energy loss ranged from 13.52% to 37.94% and from 1.35 to 7.43 MJ/kgwater, respectively. Findings showed that the exergy efficiency changed from 11.79% to 30.84%. In addition, the statistical analysis revealed that higher microwave power levels and the thinner samples significantly (p &lt; .05) enhanced both the energy and exergy efficiencies. The obtained results for exergy improvement potential (accounted for 37.83%–69.41% from the total exergy inlet) indicated that the drying process has good potential for exergy performance improvement. Based on the modeling outcomes, the energy efficiency was well predicted by an artificial neural network with a topology of 3–18–18–1 and LM training algorithm and threshold function of Tan–Tan–Lin. However, the best topology for the exergy efficiency prediction had 3–20–16–1 structure, LM training algorithm and Log–Log–Lin transfer function (R2 of .94).Practical ApplicationsIn this study, onion slices were dried in a domestic microwave oven and the influence of the power level and thickness of the samples on energy and exergy parameters was investigated. In addition to experimental evaluation, the multi‐layer feed‐forward neural network can be used to model and predict changes in energy efficiency and exergy during the process.

Artificial neural network modelling of crystallization temperatures of the Ni–P based amorphous alloys