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The segregation among benign and malignant nasopharyngeal carcinoma (NPC) from endoscopic images is one of the most challenging issues in cancer diagnosis because of the many conceivable shapes, regions, and image intensities, hence, a proper scientific technique is required to extract the features of cancerous NPC tumors. In the present research, a neural network-based automated discrimination system was implemented for the identification of malignant NPC tumors. In the proposed technique, five different types of qualities, such as local binary pattern, gray-level co-occurrence matrix, histogram of oriented gradients, fractal dimension, and entropy, were first determined from the endoscopic images of NPC tumors and then the following steps were executed: (1) an enhanced adaptive approach was employed as the post-processing method for the classification of NPC tumors, (2) an assessment foundation was created for the automated identification of malignant NPC tumors, (3) the benign and cancerous cases were discriminated by using region growing method and artificial neural network (ANN) approach, and (4) the efficiency of the outcomes was evaluated by comparing the results of ANN. In addition, it was found that texture features had significant effects on isolating benign tumors from malignant cases. It can be concluded that in our proposed method texture features acted as a pointer as well as a help instrument to diagnose the malignant NPC tumors. In order to examine the accuracy of our proposed approach, 159 abnormal and 222 normal cases endoscopic images were acquired from 249 patients, and the classifier yielded 95.66% precision, 95.43% sensitivity, and 95.78% specificity. [ABSTRACT FROM AUTHOR]

Nonparametric mathematical models have gained a very massive attention in the last two decades in solving regression problem. The application of soft computing methodologies produced a very remarkable assistance to human abilities especially in solving nonlinear and non-stationary engineering problems. The current article investigates the utility of k-nearest neighbor (k-nn) approach in predicting ultimate bearing capacity of shallow foundation. The inspected application involves an experimental data set of foundation dimension and soil properties that suggested and calculated via manual computational methods. The predictive model is established using dimensional shallow foundation, and soil properties are an inputs variable, whereas the bearing capacity is the output variable. For the purpose of comparison and evaluating the modeling accuracy, multiple linear regression (MLR) model is chosen to diagnose the result accuracies. Couple of statistical indicators are utilized to exhibit the performance criteria of the predictive model including coefficient of determination (r2), degree of agreement (d), root-mean-square error (RMSE) and mean absolute percentage error (MAPE). The results exhibited a very representable and high accuracies of the investigated k-nn model vis-à-vis MLR. For instance, the RMSE and MAPE were enhanced by 24 and 17%, respectively. In addition, the findings indicated that k-nn provides an accurate and reliable alternative predictive model to the manual computational methods. [ABSTRACT FROM AUTHOR]

In this paper, we have successfully presented a fuzzy Petri net (FPN) model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition (HFT) to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network. [ABSTRACT FROM AUTHOR]