Soil texture is variable through space and controls most of the soil’s Physico-chemical, biological and hydrological characteristics and governs agricultural production and yield. Therefore, determining its variability and generating accurate soil texture maps have a key role in soil management and sustainable agriculture. The purpose of this study is to introduce a numerical algorithm named Least Square Support Vector Machine for Regression (LS-SVR) as a predictive model in Digital Soil mapping (DSM) of soil texture fractions and evaluating its performances based on modeling evaluation criteria. In this study, the soil texture data of 49 soil profiles in Tabriz plain, Iran, was used. The important covariates were selected using Genetic algorithm (GA). The model evaluation results based on ME, MAE, RMSE, and R2 indicate the high performance of LS-SVR in predicting soil texture components. The prediction RMSE for sand, silt, and clay was 6.82, 5.08 and 6.06, respectively. Silt prediction had the highest ME and the lowest MAE and RSME values. The algorithm simulated the complex spatial patterns of soil texture fractions and provided high accuracy predictions and maps. Therefore, the LS-SVR algorithm has the capability to be used as predictive models in soil texture digital mapping. This study highlighted the potential of the LS-SVR algorithm in high precision s

To reproduce an image, it is necessary to map out of gamut colors of the image to destination gamut. It is clear that the best color gamut mapping introduces the perceptually closest image to the original one. In this study, a new color gamut mapping is purposed by the aid of Genetic algorithm (GA). The color difference between the original and mapped images based on SLAB formula was chosen as fitness function. The proposed algorithm was applied in CIELAB color space and special genetic operators were developed to meet the aim of gamut mapping. To increase the rate of convergence and have a faster algorithm, one of the initial population chromosomes can be obtained from the result of clipping method. The results showed that the new method introduces smaller color difference between the reproduced and original images in comparison with the common clipping method. The other advantage of the genetic color gamut mapping is that any new criterion for color image difference can be easily used as a fitness function. In addition, by this method the final colors are not restricted to the gamut surface and they may be included into the gamut.

The purpose of this study is to introduce a method for improving the accuracy of mapping mangrove covers by integrating CART algorithm and vegetation indices using Landsat 8 imagery. In this study, 7 vegetation indices were calculated including DVI, NDVI, NDII, IPVI, MNDWI, SAVI, and OSAVI. The important indicators and thresholds were identified by the CART algorithm in R-software and then the Mangrove cover besides surrounding areas was mapped using the decision tree technique. The results of accuracy assessment based on comparing control points which recorded by GPS with SPOT 6/7 images showed that the resulted map had a general accuracy of 80. 97% and a kappa of 0. 74. Using moderate resolution satellite imageries for mapping mangrove is difficult due to background reflectance effect (e. g. water and soil) and mixed pixels considering environmental conditions. Results demonstrated that this approach could produce information about mangroves for decision makers in order to conservation and management planning.

One of the evaluation methods for image steganography is preserving cover image quality and algorithm imperceptibility. Placing hidden information should be done in such a way that there is minimal change in quality between the cover image and the coded image (stego image). The quality of the stego image is mainly influenced by the replacement method and the amount of hidden information or the replacement capacity. This can be treated as an optimization problem and a quality function can be considered for optimization. The variables of this function are the mappings applied to the cover image and the hidden information and location of the information. In the proposed method, by genetic algorithm and using the two concepts of targeted search and aimless search, the appropriate location and state for placement in the least significant bits of the cover image are identified. In this method, hidden information can be extracted completely and without error. This feature is important for management systems and cloud networks that use steganography to store information. Finally, the proposed method is tested and the results are compared with other methods in this field. The proposed method, in addition to maintaining the stego image quality, which is optimized based on PSNR, has also shown good performance in examining histogram and NIQE statistical criteria.

In network-on-chip implementation, mapping can be considered as an important step in application implementation. The tasks of an application are often represented in the form of a core graph. The cores establish a link between themselves using a communication platform and often the network on the chip. For finding proper mapping for an application, developers have proposed various algorithms. In most cases, due to the complexity, exact search methods are used to find the mapping. However, these methods are suitable for networks with small dimensions. As the size of the network increases, the search time also increases exponentially. This article, from the perspective of a heuristic approach, uses the harmony search method to decide when to connect cores to routers. Our approach uses an improved version of the harmony search algorithm with a focus on reducing power consumption and delay. algorithm complexity analysis reveals a more appropriate solution compared to similar algorithms with respect to application traffic pattern. Compared to similar methods, the algorithm achieves 39. 98% less delay and 61. 11% saving in power consumption.

The Sensitivity of Quantitative electroencephalogram (Brain mapping) was Compared with Transient ischemic Attack (TIA) and computerized tomography (C.T. Scan) Finding. Twenty Patients were tested with Quantitative EEG (Brain mapping) within 24h from the onset of neurological symptoms where as CT Scan was performed within 24h from the onset of attack. EEG was recorded from 16 electrodes placed upon the Scale to the international 10-20 system. Spectral analysis was carried out for each channel. Absolute and Relative power were calculated for Delta - Theta -Alpha and Beta frequency bands and such data were successively represented in color coded maps. The results indicated that EEG mapping showed focal abnormality in delta band in 17 of 20 cases.      

Potential mapping of Permeability is a crucial factor in determining the productivity of an oil and gas reservoirs. Accurately estimating permeability is essential for optimizing production and reducing operational costs. In this study, we utilized the CUDNNLSTM algorithm to estimate reservoir permeability. The drilling core data were divided into a training pool and a validation pool, with 80% of the data used for training and 20% for validation. Based on the high variation permeability along the formation, we developed the CUDNNLSTM algorithm for estimating permeability. First, due to the highly dispersed signals from the sonic, density, and neutron logs, which are related to permeability, we adjusted the algorithm to train for 1000 epochs. However, once the validation loss value reached 0.0158, the algorithm automatically stopped the training process at epoch number 500. Within 500 epochs of the algorithm, we achieved an impressive accuracy of 98.42%. Using the algorithm, we estimated the permeabilities of the entire set of wells, and the results were highly satisfactory. The CUDNNLSTM algorithm due to the large number of neurons and the ability to solve high-order equations on the GPU is a powerful tool for accurately estimating permeability in oil and gas reservoirs. Its ability to handle highly dispersed signals from various logs makes it a valuable asset in optimizing production and reducing operational costs, because it is much cheaper than the cost of core extraction and has very high accuracy.

Recognition and study of snow reservoirs as the supplier of the base flow of rivers and main outset of freshwater resources in snowy and high basins play an important role in planning and management of water resources usage. However, one of the main problems in snow phenomenon recognition using optical satellite images is to separate clouds and snow. To supper this problem, we use the fact that the cloud does not have a stable geolocation compared to snow. A temporal filter is designed by the combination of Modis Terra and Aqua to remove the cloud pixels. Moreover, different spectral behavior of the cloud in different wavelengths makes it possible to separate it from the snow. A normalized difference cloud index is defined using Modis data to detect and remove the cloud pixels from the image. The pixel-based method is used to extract the snow coverage map of the Northen area of the Fars province using the daily Modis data spanning between 1392 and 1393. In order to evaluate the final results, the data from 14 ground stations as well as Landsat8 OLI image are used as ground truth. The accuracy of 100% was achieved using the first method while the accuracy of the second method by corresponding the pixels of snow coverage maps is estimated as 98.58%. According to the results and accomplished evaluations, the snow maps generated using the threshold-based method without or with the cloud coverage removed by the application of the proposed method has a high precision. The results can then be easily used in the snowmelt run-off modeling in the water resource and reservoirs management.

Introduction Introduction and Objectives: Cutaneous Leishmaniasis (CL) is a vector-borne disease, endemic of the Middle East. The spread of CL is highly associated with the socio-ecological interactions of vectors, hosts and environmental conditions. CL is the most frequent vector-borne disease in Iran and especially in the north-eastern province, Golestan, which has long been known as one of the most important endemic areas for CL dispersion. Therefore, Golestan province was selected as the study area of this research. The main objectives of the study are to analyze annual spatial distribution of CL, investigate the relations between environmental/climate factors and incidence rate of CL and also provide a model to predict CL distribution at rural district level in Golestan province. Materials and methods Data: CL incidences, census data, environmental and climate factors have been used in this study to provide a model and produce a map to predict the CL distribution. The CL incidences are continuously recorded by the Center for Disease Control and Prevention (CDC) of Golestan province. The population and census data for 2013-2015 period were obtained from Iranian Statistical Center. Environmental and climate data such as vegetation, average humidity, average temperature, precipitation, number of rainy days, number of freezing days, maximum wind speed and evaporation rate were used as parameters affecting the model. Methodology The statistical and geo-statistical analyses were used to investigate the relation between environmental/climate factors and CL incidence rate, and to investigate the existence of spatial autocorrelation between CL cases, respectively. Additionally, Multilayer perceptron (MLP) neural network was used to model the relation between the distribution of CL incidences with environmental/climate factors, and also to generate the risk maps of CL. MLP is a type of neural network which consists of multiple layers of neurons or processing elements connected in a feed forward fashion. It encompasses three types of layers: input, hidden, and output. It has a unidirectional flow of information. Generally, information flow starts from input layer, goes through hidden layer, and then to output layer, which provides the response of the network to the input stimuli. In this type of network, there are generally three distinct types of neurons in layers. The input layer contains some neurons as the input variables. The hidden neurons, which are contained in one or more hidden layers, process and encode information within the network. The hidden layer receives, processes, and passes the input data to the output layer. Number of hidden layers and number of neurons within each layer affect the accuracy and functionality of the network. The output layer contains target output vector. In this study, effective parameters along with CL incidence rate of 2013-2014 were fed to the MLP as training data. The trained MLP was used afterward to generate the risk map of 2015 and test accuracy of the model. In order to determine the optimal parameters of the MLP, the grid-search and cross-validation techniques were used on 25% of the training dataset in the training phase. The performance of MLP was investigated using the root mean square error (RMSE), mean absolute percentage error (MAPE) and area under curve (AUC) of receiver operating characteristic (ROC) measures. Sensitivity analysis was also used to determine most effective variables regarding predictive mapping of CL distribution Results and Discussion Results of global Moran’ s I index indicated that there is spatial autocorrelation among CL cases, and also distribution of CL cases in Golestan province in each 3 years is clustered. Moreover, statistical analyses showed that majority of the incidences belonged to rural districts of Gonbad-Kavos and Maraveh-Tappeh. Based on the results of statistical analyses (including Pearson correlation and Spearman rank correlation), positive correlations were observed between the CL incidence rate and average temperature, maximum wind speed and evaporation. In addition, negative correlation was found between the CL incidence rate and average humidity, precipitation, number of rainy days, number of freezing days and vegetation. According to the results of evaluation criteria including RMSE, MAPE and AUC, the trained MLP model was able to generate risk maps of CL in 2013-2015 for each rural district with acceptable accuracy. Additionally, results of sensitivity analysis indicate that vegetation and average humidity are the most influencing variables in the incidence of CL and in predictive mapping of CL distribution in Golestan province. Conclusion and Future works In this study, the global Moran’ s I index indicated the presence of spatial autocorrelation among CL cases, and clustered distribution of disease in the study area. The statistical analyses showed that environmental and climate factors greatly affect the spatial distribution of CL. The MLP method, used to generate CL distribution risk maps, was able to generate the study area risk maps with acceptable accuracy. Results highlight the potential high risk areas requiring special plans and resources for monitoring and control of the disease. As a future work, we suggest that the effects of other environmental and socio-economic parameters should be evaluated to improve the accuracy of the model. It is also recommended that other methods such as regression and other neural network techniques be used to generate CL risk maps.