This paper is an attempt to introduce AUBUE - ADAPTIVE User-INTERFACE Based on Users' Emotions- in which users' emotions are detected through the users` interactions with the keyboard; then a graphical user INTERFACE’s color is adapted in accordance with the users' emotion. The emotions of joy, distress, and anger from the Ortony, Clore, and Collins (OCC) emotion model are utilized to replicate the emotional state of the user. After detection of emotions and their intensities, the current mood of the user is updated, and the appropriate colors for the background of the graphical user INTERFACE are chosen with regards to the user's current mood. For evaluation of the AUBUE, we implemented a game for guessing picture name. The game included four elements: (1) logging the demographic information, (2) type speed estimator, (3) guessing picture name, and (4) help. An evaluation was accomplished in two modes: in mode 1, the background color is static, and in mode 2 where the background's color based on users' emotion is dynamic. The results are represented based on the demographic factors and categorized in four groups: English level, English typing level, gaming experience and gender.

Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce ADAPTIVE GDD (AGDD), which eliminates the inappropriate effect of clustered samples by ADAPTIVEly updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

The increasing use of unmanned aerial vehicles (UAVs) or drones in different civil and military operations has attracted the attentions of many researchers and science communities. One of the most notable challenges in this field is supervising and controlling a group or a team of UAVs by a single user. Thereupon, we propose a new intelligent ADAPTIVE INTERFACE (IAI) to overcome this challenge. Our proposed IAI is not only empowered by comprehensive IAI architecture but also has some notable features like presenting a single-display user INTERFACE for controlling a UAV team, leveraging the user cognitive model to deliver the right information at the right time, supporting the user by the system behavior explanation, and guiding and helping the user to choose the right decisions. Finally, we examine the developed IAI with the contribution of eleven volunteers and in three different scenarios. The results obtained show the power of the proposed IAI to reduce the workload and to increase the user's situation awareness level, and as a result, to promote the mission completion percentage.

Objectives: Accurate estimation of post-operative clinical parameters in scoliosis correction surgery is crucial. Different studies have been carried out to investigate scoliosis surgery results, which were costly, time-consuming, and with limited application. This st udy aims to estimate post-operative main thoracic cobb and thoracic kyphosis angles in adolescent idiopathic scoliosis patients using an ADAPTIVE neuro-fuzzy INTERFACE system. Methods: Distinct pre-operative clinical indices of fifty-five patients (e. g., thoracic cobb, kyphosis, lordosis, and pelvic incidence) were taken as the inputs of the ADAPTIVE neuro-fuzzy INTERFACE system in four categorized groups, and post-operative thoracic cobb and kyphosis angles were taken as the outputs. To evaluate the robustness of this ADAPTIVE system, the predicted values of post-operative angles were compared with the measured indices after the surgery by calculating the root mean square errors and clinical corrective deviation indices, including the relative deviation of post-operative angle prediction from the actual angle after the surgery. Results: The group with inputs for main thoracic cobb, pelvic incidence, thoracic kyphosis, and T1 spinopelvic inclination angles had the lowest root mean square error among the four groups. The error values were 3. 0°,and 6. 3°,for the post-operative cobb and thoracic kyphosis angles, respectively. Moreover, the values of clinical corrective deviation indices were calculated for four sample cases, including 0. 0086 and 0. 0641 for the cobb angles of two cases and 0. 0534 and 0. 2879 for thoracic kyphosis of the other two cases. Conclusion: In all scoliotic cases, the post-operative cobb angles were lesser than the pre-operative ones,however, the post-operative thoracic kyphosis might be lesser or higher than the pre-operative ones. Therefore, the cobb angle correction is in a more regular pattern and is more straightforward to predict cobb angles. Consequently, their root-mean-squared errors become lesser values than thoracic kyphosis.

Background and Objectives: Developing of techniques for regional flood frequency estimation in ungauged sites is one of the foremost goals of contemporary hydrology. The flood frequency evaluation for ungauged catchments is usually approached by deriving suitable statistical relationships (models) between flood statistics and basins characteristics. Already, several equations have been presented to estimate the flood frequency in different areas such as Karkheh basin. However, due to the complexity of this phenomenon, the relationships have not been capable to simulate the flood frequency with desired accuracy. Accordingly, in this study, in addition to the regression method has been used in the previous studies, the ANN and ANFIS models are applied. In fact, these are a type of black box models without any knowledge of processes within the system, in which inputs are converted into outputs (or output). This situation indicates that this type of new models is actually similar to the regression relations, however, there is further flexibility in adjusting the weights and thus can be used as an replacement to multivariate regressions.Materials and Methods: The study area, including 33 hydrometry stations, is located in the west of Iran. In this study, 27 of the stations for calibration and 6 of the stations for validation were used. To approach a unique model, return period was taken into account as the independent factor.Results: For achieving the best ANN and ANFIS system, different combinations of physiographic with return periods, as input data, has been used. To find the important input factors of the models, sensitivity analysis has been performed in SPSS software. Accordingly, the most important independent variables were including: Return period, area, height, main stream length and slope. In the ANN model, different combinations of these inputs were compared together. It should be noted that for optimizing the connecting weights among different layers of ANN, Genetic algorithms have been used. Consequently, the best selected network is Feed-forward with the structure of 5-10-1 and R2=0.95. In the ANFIS system, with increasing the number of input variables for each of the four membership function, including Triangular, Gaussian, Gaussian2 and trapezoidal, simulation accuracy increases. The best simulation is a triangular function with RMSE=0.1514, R2=0.97 and the number of rules is 243. Finally, by comparing models, The ANFIS model was selected as the best model. The ANFIS has the best accuracy especially in high return period.Conclusion: Where the sub-basins are small and their flood in different return periods is less than 1000 m3/s, the regression model makes a good accordance with real flood. The ANN model has also good performance in low discharges. The regression presents its forecast in the framework of formulas and it is better and more practical for engineers. Generally, The ANFIS model is the best model for all ranges of the discharge and the best tool for prediction enormous flood in Karkheh basin.

Short-term runoff forecasting is very important due to direct relationship between mangers approach with loss of life by flood. In this study, daily rainfall-runoff modeling was carried out in Hajighoshan watershed using artificial neural networks (ANNs) and ADAPTIVE neuro-fuzzy INTERFACE system (ANFIS) with different inputs(current day rainfall; current rainfall and pervious day rainfall; current rainfall, pervious day rainfall and two previous day) methods. Also, the different functions i.e. Gaussian, Gaussian 2, Triangular, Gaussian Bell shape were used to ANFIS and number of neurons at hidden layer of ANNs were changed between 2 to 10 neurons. Root mean squared error (RMSE), mean absolute error (MAE) and correlation coefficient (R) statistics are employed to evaluate the performance of the ANNs and ANFIS models for runoff forecasting. Based on the results of test stage, ANFIS with RMSE=7.11, MAE=2.18 and R=0.60 is superior to rainfall-runoff modeling than the ANN with RMSE=6.03, MAE=1.97 and R=0.39.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust ADAPTIVE control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust ADAPTIVE control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

The discharge or runoff which ousts from a watershed is important, because its deficiency leads to financial losses and its excesses cause damage in lives and property as flood. In this research by using Artificial Neural Network Multi-layer Perceptron (MLP) and ADAPTIVE Neuro-fuzzy INTERFACE system (ANFIS) and multiple regression method were simulated rainfall- runoff process on daily basis in the Khorramabad watershed. For inputs, different combinations of precipitation inputs including current rainfall, pervious day rainfall and two previous days were used. Inputs membership function for ANFIS model in this research is: the trapezoid, triangular, Gaussian and Gaussian type 2. ML P model using in this research was evaluated with one hidden layer and the number of variables neurons. The results showed that ADAPTIVE Neuro-fuzzy INTERFACE system (ANFIS) compared to multi-layer perceptron model (MLP) and multiple regression model has better performance. Also, by increasing in the number of inputs, involvement pervious day rainfall and two previous days, all three models performance will be better.