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Search Results: 1 - 10 of 6343 matches for " Mohammed Nasser "
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Recurrent Support and Relevance Vector Machines Based Model with Application to Forecasting Volatility of Financial Returns  [PDF]
Altaf Hossain, Mohammed Nasser
Journal of Intelligent Learning Systems and Applications (JILSA) , 2011, DOI: 10.4236/jilsa.2011.34026
Abstract: In the recent years, the use of GARCH type (especially, ARMA-GARCH) models and computational-intelligence-based techniques—Support Vector Machine (SVM) and Relevance Vector Machine (RVM) have been successfully used for financial forecasting. This paper deals with the application of ARMA-GARCH, recurrent SVM (RSVM) and recurrent RVM (RRVM) in volatility forecasting. Based on RSVM and RRVM, two GARCH methods are used and are compared with parametric GARCHs (Pure and ARMA-GARCH) in terms of their ability to forecast multi-periodically. These models are evaluated on four performance metrics: MSE, MAE, DS, and linear regression R squared. The real data in this study uses two Asian stock market composite indices of BSE SENSEX and NIKKEI225. This paper also examines the effects of outliers on modeling and forecasting volatility. Our experiment shows that both the RSVM and RRVM perform almost equally, but better than the GARCH type models in forecasting. The ARMA-GARCH model is superior to the pure GARCH and only the RRVM with RSVM hold the robustness properties in forecasting.
Effects of Chemical Reaction on the Unsteady Free Convection Flow past an Infinite Vertical Permeable Moving Plate with Variable Temperature  [PDF]
Fayza Mohammed Nasser El-Fayez
Journal of Surface Engineered Materials and Advanced Technology (JSEMAT) , 2012, DOI: 10.4236/jsemat.2012.22016
Abstract: Analytical solutions for the effect of chemical reaction on the unsteady free convection flow past an infinite vertical permeable moving plate with variable temperature has been studied. The plate is assumed to move with a constant velocity in the direction of fluid flow. The highly nonlinear coupled differential equations governing the boundary layer flow, heat and mass transfer are solved using two-term harmonic and non-harmonic functions. The parameters that arise in the perturbation analysis are Prandtl number (thermal diffusivity), Schmidt number (mass diffusivity), Grashof number (free convection), modified Grashof number, Chemical reaction parameter (rate constant), Skin friction coefficient and Sherwood number (wall mass transfer coefficient). The study has been compared with available exact solution in the literature and they are found to be in good agreement. It is observed that: The concentration increases during generative reaction and decreases in destructive reaction. The concentration increases with decreasing Schmidt number. The effect of increasing values of K leads to a fall in velocity profiles. The velocity decreases with increasing values of the Schmidt number. An increase in modified Grashof number leads to an increase in velocity profiles. The skin friction increases with decreasing Schmidt number. In generative reaction the skin friction decreases and in destructive reaction the skin friction increases.
Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS)  [PDF]
Md. Al Mehedi Hasan, Mohammed Nasser, Biprodip Pal, Shamim Ahmad
Journal of Intelligent Learning Systems and Applications (JILSA) , 2014, DOI: 10.4236/jilsa.2014.61005

The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with many features. To get rid of this problem, several types of intrusion detection methods have been proposed and shown different levels of accuracy. This is why the choice of the effective and robust method for IDS is very important topic in information security. In this work, we have built two models for the classification purpose. One is based on Support Vector Machines (SVM) and the other is Random Forests (RF). Experimental results show that either classifier is effective. SVM is slightly more accurate, but more expensive in terms of time. RF produces similar accuracy in a much faster manner if given modeling parameters. These classifiers can contribute to an IDS system as one source of analysis and increase its accuracy. In this paper, KDD’99 Dataset is used and find out which one is the best intrusion detector for this dataset. Statistical analysis on KDD’99 dataset found important issues which highly affect the performance of evaluated systems and results in a very poor evaluation of anomaly detection approaches. The most important deficiency in the KDD’99 dataset is the huge number of redundant records. To solve these issues, we have developed a new dataset, KDD99Train+ and KDD99Test+, which does not include any redundant records in the train set as well as in the test set, so the classifiers will not be biased towards more frequent records. The numbers of records in the train and test sets are now reasonable, which make it affordable to run the experiments on the complete set without the need to randomly select a small portion. The findings of this paper will be very useful to use SVM and RF in a more meaningful way in order to maximize the performance rate and minimize the false negative rate.

Expression of e-cadherin, n-cadherin and snail and their correlation with clinicopathological variants: an immunohistochemical study of 132 invasive ductal breast carcinomas in Egypt
ElMoneim, Hanan Mohamed Abd;Zaghloul, Nasser Mohammed;
Clinics , 2011, DOI: 10.1590/S1807-59322011001000015
Abstract: objective: to evaluate the expression of the cell adhesion molecules e-cadherin and n-cadherin and the transcription factor snail in invasive ductal breast carcinomas and to determine their relationships with clinicopathological features. methods: immunohistochemistry was used to examine e-cadherin, n-cadherin, and snail protein expression in 132 invasive breast carcinomas. results: the expression of e-cadherin was decreased (negative or weak) in 37.1% of invasive carcinomas, while n-cadherin and snail overexpression were detected in 51.9% and 40.9% of carcinomas, respectively. low e-cadherin expression was significantly correlated with poorly differentiated carcinoma (53.1%), positive node status (80.9%), poor nottingham prognostic index (64.7%), and the presence of estrogen and progesterone receptors. overexpression of n-cadherin and snail were also significantly correlated with poorly differentiated carcinoma, positive node status, and poor nottingham prognostic index but were correlated with the absence of hormone receptors. loss of e-cadherin immunoexpression was strongly associated with the presence of membranous n-cadherin (87.8%) and nuclear snail (69.4%). conclusion: loss of e-cadherin and overexpression of n-cadherin and snail in breast carcinomas may play a central role in the development of invasive ductal breast carcinoma. these biomarkers may provide a valuable reference for the study of invasive ductal carcinoma progression and to characterize the biological behavior of the tumor. in the future, increased n-cadherin and decreased e-cadherin expression may be used as indicators of the progression and prognosis of invasive ductal carcinoma.
Text Realization Image Steganography
Dr. Mohammed Nasser Hussein Al-Turfi
International Journal of Engineering , 2012,
Abstract: In this paper the steganography strategy is going to be implemented but in a different wayfrom a different scope since the important data will neither be hidden in an image nortransferred through the communication channel inside an image, but on the contrary, a wellknown image will be used exists on both sides of the channel and a text message containsimportant data will be transmitted. With the suitable operations, we can re-mix and re-makethe source image.MATLAB7 is the program where the algorithm implemented on it, where the algorithm showshigh ability for achieving the task to different type and size of images. Perfect reconstructionwas achieved on the receiving side. But the most interesting is that the algorithm that dealswith secured image transmission transmits no images at all.
A New Singular Value Decomposition Based Robust Graphical Clustering Technique and Its Application in Climatic Data
Nishith Kumar,Mohammed Nasser,Subaran Chandra Sarker
Journal of Geography and Geology , 2011, DOI: 10.5539/jgg.v3n1p227
Abstract: An attempt is made to study mathematical properties of singular value decomposition (SVD) and its data exploring capacity and to apply them to make exploratory type clustering for 10 climatic variables and thirty weather stations in Bangladesh using a newly developed graphical technique. Findings in SVD and Robust singular value decomposition (RSVD) based graphs are compared with that of classical K-means cluster analysis, its robust version, partition by medoids (PAM) and classical factor analysis, and the comparison clearly demonstrates the advantage of SVD over its competitors. Lastly the method is tested on well known Hawkins-Bradu-Kass (1984) data.
A Comparative Study of Kernel and Robust Canonical Correlation Analysis
Ashad M. Alam,Mohammed Nasser,Kenji Fukumizu
Journal of Multimedia , 2010, DOI: 10.4304/jmm.5.1.3-11
Abstract: A number of measures of canonical correlation coefficient are now used in multimedia related fields like object recognition, image segmentation facial expression recognition and pattern recognition in the different literature. Some robust forms of classical canonical correlation coefficient are introduced recently to address the robustness issue of the canonical coefficient in the presence of outliers and departure from normality. Also a few number of kernels are used in canonical analysis to capture nonlinear relationship in data space, which is linear in some higher dimensional feature space. But not much work has been done to investigate their relative performances through i) simulation from the view point of sensitivity, breakdown analysis as well as ii) using real data sets. In this paper an attempt has been made to compare performances of kernel canonical correlation coefficients (Gaussian function, Laplacian function and Polynomial function) with that of robust and classical canonical correlation coefficient measures using simulation with five sample sizes (50, 500, 1000, 1500 and 2000), influence function, breakdown point along with several real data and a multi-modal data sets, focusing on the specific case of segmented images with associated text. We investigate the bias, mean square error(MISE), qualitative robustness index (RI), sensitivity curve of each estimator under a variety of situations and also employ box plots and scatter plots of canonical variates to judge their performances. We have observed that the class of kernel estimators perform better than the class of classical and robust estimators in general and the kernel estimator with Laplacian function has shown the best performance for large sample size and break down is high in case of nonlinear data.
Crop Water Requirements in Egypt Using Remote Sensing Techniques  [PDF]
Mohammed A. El-Shirbeny, Abd-Elraouf M. Ali, Nasser H. Saleh
Journal of Agricultural Chemistry and Environment (JACEN) , 2014, DOI: 10.4236/jacen.2014.32B010

The common Soil in Egypt is clay soil so common irrigation system is tradition surface irrigation with 60% irrigation efficiency. Agricultural sector consumes more than 80% of water resources under surface irrigation (tradition methods). In arid and semi-arid regions consumptive use is the best index for irrigation requirements. A large part of the irrigation water applied to farm land is consumed by Evapotranspiration (ET). Irrigation water consumption under each of the physical and climatic conditions for large scale will be easier with remote sensing techniques. In Egypt, Agricultural cycle is often tow agricultural seasons yearly; summer and winter. Common summer crops are Maize, Rice and Cotton while common winter crops are Clover and Wheat. Landsat8 bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate Normalized Deference Vegetation Index (NDVI) and monitoring cultivated areas. The cultivated land area was 3,277,311 ha in August 2013. In this paper Kc = 2 * NDVI ? 0.2 represents the relation between crop coefficient (Kc) and NDVI. Kc and Reference evapotranspiration (ETo) used to estimate ETc in Egypt. The main objective of this paper is studying the potential crop Evapotranspiration in Egypt using remote sensing techniques.

Feature Selection for Intrusion Detection Using Random Forest  [PDF]
Md. Al Mehedi Hasan, Mohammed Nasser, Shamim Ahmad, Khademul Islam Molla
Journal of Information Security (JIS) , 2016, DOI: 10.4236/jis.2016.73009
Abstract: An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various ir-relevant and redundant features and results in increased processing time and low detection rate. Therefore, feature selection should be treated as an indispensable pre-processing step to improve the overall system performance significantly while mining on huge datasets. In this context, in this paper, we focus on a two-step approach of feature selection based on Random Forest. The first step selects the features with higher variable importance score and guides the initialization of search process for the second step whose outputs the final feature subset for classification and in-terpretation. The effectiveness of this algorithm is demonstrated on KDD’99 intrusion detection datasets, which are based on DARPA 98 dataset, provides labeled data for researchers working in the field of intrusion detection. The important deficiency in the KDD’99 data set is the huge number of redundant records as observed earlier. Therefore, we have derived a data set RRE-KDD by eliminating redundant record from KDD’99 train and test dataset, so the classifiers and feature selection method will not be biased towards more frequent records. This RRE-KDD consists of both KDD99Train+ and KDD99Test+ dataset for training and testing purposes, respectively. The experimental results show that the Random Forest based proposed approach can select most im-portant and relevant features useful for classification, which, in turn, reduces not only the number of input features and time but also increases the classification accuracy.
Qualitative Robustness in Estimation
Mohammed Nasser,Nor Aishah Hamzah,Md. Ashad Alam
Pakistan Journal of Statistics and Operation Research , 2012, DOI: 10.1234/pjsor.v8i3.532
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