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.

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

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.      

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.

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.

Introduction: Maps can create a deeper insight of our surrounding environment. The map can also be used in curriculum for its reforming, and developing and ultimately improving teaching-learning process. This systematic review aimed to identify applications and benefits of curriculum mapping.Methods: Seven search engine and websites were searched using the keywords including curriculum mapping, curriculum map (s), medical, curriculum development and planning.1100 article was found and 179 articles were enrolled in the study. Then the relevant article (i.e.36 articles) were selected and analyzed.Results: 112 advantage and 111 applications were found in reviewed articles.32 advantage and 29 applications were listed after removing repetitive cases and merging some cases. The most common mentioned advantage was developing a participation culture (students and teachers participation in curriculum). Most applications of these maps were curriculum monitoring and evaluation from different perspective regarding to the required competencies for students.Conclusion: Various users including curriculum planners, administrators, professors and students use curriculum mapping according to their needs. Regarding the benefits of curriculum mapping and its various applications in the educational system, it is suggested that teachers and administrators in each department, design a curriculum map in a project. In this regard, computer programmers can increase accuracy and speed in the project.

Geochemical exploration data play a vital role in mineral prospectivity modelling (MPM) for discovering unknown mineral deposits. In this study, the improved spatially weighted singularity mapping (SWSM) method is used to improve the practice of identifying geochemical anomalies related to copper mineralization in the Sarduiyeh district, Iran. Then, the random forest algorithm (RF) and geometric average function (GA) are used to integrate the resulting geochemical predictor map with other predictor maps. As demonstrated by the high area under the curve (AUC) values, this approach can effectively delineate prospective areas with RF and GA. However, compared to the GA approach (AUC=0.78), the RF technique (AUC = 0.98) offers superior prediction capabilities due to its enhanced ability to capture spatial correlations between predictive maps and known mineral deposits. The proposed procedure, a hybrid of the improved SWSM and RF has outstanding predictive capabilities for identifying prospective areas. A case in point is the new, highly prospective areas identified in this study, which present priority targets for future exploration in the Sarduiyeh district.