Artificial neural networks (ANNs) are the models of choice in many data classification tasks. In this study, ANN classification models were used to explore user perceptions about kitchen faucet styles and investigate the relations between the overall preferences and kansei word scores of users. The scores given by consumers were obtained via a two-stage questionnaire mentioned in a previous study by the authors. Through the questionnaire, consumers were asked to give scores after examining three-dimensional (3-D) drawings of new product samples created with the help of industrial product designers. Because it was neither practical nor necessary to develop a prototype or a picture of each of the alternative designs, a fractional factorial experimental design similar to Taguchi's L-16 orthogonal array was used. After completing this preparatory work to develop ANNs and obtain the necessary related data, an analysis of variance (ANOVA) was performed to identify the critical factors that affect the accuracy of the ANN model to be used and determine the best factor levels for the ANN model. A genetic algorithm (GA) was then integrated with the ANN model found to be the best and implemented to determine the optimal levels of the design parameters related to product appearance. Lastly, the product categories were classified as unfavorable or favorable, and three products were derived for each category. In comparison with the previously published papers of the authors, the GA integrated with the ANN model was found to be an effective tool for revealing user perceptions in new product development. In regard to the findings of the present work, it can be said that, this technique can be used as an alternative of several complex analytical approaches, in order to explore users' perceptions.
The nature of clinical diagnosis for psychological disorders are quiet different and difficult than the diagnosis of a disease. Generally they are assessed by screening certain behavioral features shown by the human that makes the differential diagnosis as a challenging task with respect to accuracy. This diagnostic reasoning process again becomes error prone when there are improper, insufficient clinical data and lack of clinical expertise. Thus artificial intelligence based assistances in predicting and assessing psychological disorders have gained much interest. Artificial intelligence based techniques like neural network can simulate expertise for supporting decision making problems in any domain. Childhood autism is a neuro-psychiatric developmental disorder that impairs mainly three functional areas in a child: social, communication and behavior. This article demonstrates the application of a Possibilistic- Linear Vector Quantization (Po- LVQ) neural network for the preliminary screening and grading of childhood autistic disorder. The diagnostic system assesses the grades as: Normal, Mild-Moderate, Moderate-Severe, Severe. It is able to perform with an improved overall accuracy of 95% exactly agreeing to the diagnostic criteria. Results of other performance parameters are also good enough to support the existing works about the applicability of neural network in autism diagnosis. Hence this research proposes a Po-LVQ based assessment support system for the diagnostic confirmation in grading childhood autism, during uncertain diagnosis due to lack of expertise. This helps to reduce the frustration and lengthy delays experiences to parents before obtaining an accurate diagnostic result.
This paper describes a framework for a statistical anomaly prediction system using Quickprop neural network ensemble forecasting model, which predicts unauthorized invasions of users based on previous observations and takes further action before intrusion occurs. This paper investigates a NN ensemble approach to the problem of intrusion prediction and the various architectures are investigated using Quickprop algorithm. This paper focuses on intrusion prediction techniques for preventing intrusions that manifest through anomalous changes in intensity of transactions in a relational database systems at the application level. We present a novel approach to prevent misuse within an information system by gathering and maintaining knowledge of the behavior of the user rather than anticipating attacks by unknown assailants. The experimental study is performed using real data provided by a major Corporate Bank. A comparative evaluation of the two ensemble networks over the individual networks was carried out using a mean absolute percentage error on a prediction data set and a better prediction accuracy has been observed. Furthermore, the performance analysis shows that the model captures well the volatility of the user behavior and has a good forecasting ability.
This paper addresses the problem of probability estimation in Multiclass classification tasks combining two well-known data mining techniques: Support Vector Machines and Neural Networks. We present an algorithm which uses both techniques in a two-step procedure. The first step employs Support Vector Machines within a One-vs-All reduction from multiclass to binary approach to obtain the distances between each observation and the Support Vectors representing the classes. The second step uses these distances as inputs for a Neural Network, built with an entropy cost function and softmax transfer function for the output layer where class membership is used for training. Consequently, this network estimates probabilities of class membership for new observations. A benchmark using different databases demonstrates that the proposed algorithm is highly competitive with the most recent techniques for multiclass probability estimation.
The voice communicaton between technological devices and the operator becomes a stronger challenge as technology becomes more advanced and complex. New applicatons of artificial neural networks are capable of recognition and verification of effects and safety of commands given by the operator of the technological device. In this paper there is a review of the selected issues on estimation of results and safety of the operator‘s commands as well as supervision of the technological process. A view is offered of the complexity of effect analysis and safety assessment of commands given by the operator using neural networks. There is also an intelligent two-way voice communication system between the technological device and the operator presented, which consists of the intelligent mechanisms of operator identification, word and command recognition, command syntax and result analysis, command safety assessment, technological process supervision as well as operator reaction assessment. The first part of the paper introduces a new concept of modern supervising system of the technological process using and intelligent layer of two-way voice communication between the technical device and the operator and discusses the general topics and issues. The second part is devoted to a discussion of more specific topics of the automatic command verification that have led to interesting new approaches and techniques. and Obsahuje seznam literatury
Link-capacity functions are the relationships between the fundamental traffic variables like travel time and the flow rate. These relationships are important inputs to the capacity-restrained traffic assignment models. This study investigates the prediction of travel time as a function of several variables V/C (flow rate/capacity), retail activity, parking, number of bus stops and link type. For this purpose, the necessary data collected in Izmir, Turkey are employed by Artificial Neural Networks (ANNs) and Regression-based models of multiple linear regression (MLR) and multiple non-linear regression (MNLR). In ANNs modelling, 70% of the whole dataset is randomly selected for the training, whereas the rest is utilized in testing the model. Similarly, the same training dataset is employed in obtaining the optimal values of the coefficients of the regression-based models. Although all of the variables are used in the input vector of the models to predict the travel time, the most significant independent variables are found to be V/C and retail activity. By considering these two significant input variables, ANNs predicted the travel time with the correlation coefficient R = 0.87 while this value was almost 0.60 for the regression-based models.
Speaker identification is becoming an increasingly popular technology in today's society. Besides being cost effective and producing a strong return on investment in all the defined business cases, speaker identification lends itself well to a variety of uses and implementations. These implementations can range from corridor security to safer driving to increased productivity. By focusing on the technology and companies that drive today's voice recognition and identification systems, we can learn current implementations and predict future trends.
In this paper one-dimensional discrete cosine transform (DCT) is used as a feature extractor to reduce signal information redundancy and transfer the sampled human speech signal from time domain to frequency domain. Only a subset of these coefficients, which have large magnitude, are selected. These coefficients are necessary to save the most important information of the speech signal, which are enough to recognize the original speech signal, and then these coefficients are normalized globally. The normalized coefficients are fed to a multilayer momentum backpropagation neural network for classification. The recognition rate can be very high by using a very small number of the coefficients which are enough to reflect the specifications of the speaker voice.
An artificial neural network ANN is learned to classify the voices of eight speakers, five voice samples for each speaker are used in the learning phase. The network is tested using other five different samples for the same speakers. During the learning phase many parameters are tested which are: the number of selected coefficients, number of hidden nodes and the value of the momentum parameter. In the testing phase the identification performance is computed for each value of the above parameters.
This article presents an application of evolutionary fuzzy rules to the modeling and prediction of power output of a real-world Photovoltaic Power Plant (PVPP). The method is compared to artificial neural networks and support vector regression that were also used to build predictors in order to analyse a time-series like data describing the production of the PVPP. The models of the PVPP are created using different supervised machine learning methods in order to forecast the short-term output of the power plant and compare the accuracy of the prediction.
Digital Watermarking (DW) based on computational intelligence (CI) is currently attracting considerable interest from the research community. This article provides an overview of the research progress in applying CI methods to the problem of DW. The scope of this review will encompass core methods of CI, including rough sets (RS), fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithms (GA), swarm intelligence (SI), and hybrid intelligent systems. The research contributions in each field are systematically summarized and compared to highlight promising new research directions. The findings of this review should provide useful insights into the current DW literature and be a good source for anyone who is interested in the application of CI approaches to DW systems or related fields. In addition, hybrid intelligent systems are a growing research area in CI.