The using of the parametric models and the subsequent estimation methods require the presence of many of the primary conditions to be met by those models to represent the population under study adequately, these prompting researchers to search for more flexible models of parametric models and these models were nonparametric models.

    In this manuscript were compared to the so-called Nadaraya-Watson estimator in two cases (use of fixed bandwidth and variable) through simulation with different models and samples sizes.  Through simulation experiments and the results showed that for the first and second models preferred NW with fixed bandwidth fo

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are

The current study is the identification and isolation dermatophyte species in clinical isolates by both Sabouraud’s Dextrose Agar (SDA) and on Dermatophyte Test Medium (DTM). Clinical specimens of hair, nails and skin scales were collected from patients with dermatophytosis and submitted to direct microscopic examination after immersion in 20% of potassium hydroxide solution. The clinical specimens were cultured on SDA containing chloramphenicol and cycloheximide, and on DTM. Tinea corporis showed the highest prevalent dermatophyte infection among patients (26.7%), followed by Tinea pedis (23.3%), whereas Tinea manuum exhibited the lowest fungal infection (6.7 %). Rural areas revealed the highest prevalence of dermatophyte in

The need for image compression is always renewed because of its importance in reducing the volume of data; which in turn will be stored in less space and transferred more quickly though the communication channels.
In this paper a low cost color image lossy color image compression is introduced. The RGB image data is transformed to YUV color space, then the chromatic bands U & V are down-sampled using dissemination step. The bi-orthogonal wavelet transform is used to decompose each color sub band, separately. Then, the Discrete Cosine Transform (DCT) is used to encode the Low-Low (LL) sub band. The other wavelet sub bands are coded using scalar Quantization. Also, the quad tree coding process was applied on the outcomes of DCT and

In this paper a dynamic behavior and control of  a jacketed continuous stirred tank reactor (CSTR)  is developed using different control strategies, conventional feedback control (PI and PID), and neural network (NARMA-L2, and NN Predictive) control. The dynamic model for CSTR process is described by a first order lag system with dead time.

The optimum tuning of control parameters are found by two different methods; Frequency Analysis Curve method (Bode diagram) and Process Reaction Curve using the mean of Square Error (MSE) method. It is found that the Process Reaction Curve method is better than the Frequency Analysis Curve method and PID feedback controller is better than PI feedback controller.

The results s

     In this work, the methods (Moments, Modified Moments, L-Moments, Percentile, Rank Set sampling and Maximum Likelihood) were used to estimate the reliability function and the two parameters of the Transmuted Pareto (TP) distribution. We use simulation to generate the required data from three cases this indicates  sample size , and it replicates  for the real value for parameters, for reliability times values  we take .

Results were compared by using mean square error (MSE), the result appears as follows :

The best methods are Modified Moments, Maximum likelihood and L-Moments in first case, second case and third case respectively.

Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential f

In this paper, a robust adaptive sliding mode controller is designed for a mobile platform trajectory tracking.  The mobile platform is an example of a nonholonomic mechanical system. The presence of holonomic constraints reduces the number of degree of freedom that represents the system model, while the nonholonomic constraints reduce the differentiable degree of freedom. The mathematical model was derived here for the mobile platform, considering the existence of one holonomic and two nonholonomic constraints imposed on system dynamics. The partial feedback linearization method was used to get the input-output relation, where the output is the error functions between the position of a certain point on the platform

Pattern matching algorithms are usually used as detecting process in intrusion detection system. The efficiency of these algorithms is affected by the performance of the intrusion detection system which reflects the requirement of a new investigation in this field. Four matching algorithms and a combined of two algorithms, for intrusion detection system based on new DNA encoding, are applied for evaluation of their achievements. These algorithms are Brute-force algorithm, Boyer-Moore algorithm, Horspool algorithm, Knuth-Morris-Pratt algorithm, and the combined of Boyer-Moore algorithm and Knuth–Morris– Pratt algorithm. The performance of the proposed approach is calculated based on the executed time, where these algorithms are applied o

Intrusion detection systems detect attacks inside computers and networks, where the detection of the attacks must be in fast time and high rate. Various methods proposed achieved high detection rate, this was done either by improving the algorithm or hybridizing with another algorithm. However, they are suffering from the time, especially after the improvement of the algorithm and dealing with large traffic data. On the other hand, past researches have been successfully applied to the DNA sequences detection approaches for intrusion detection system; the achieved detection rate results were very low, on other hand, the processing time was fast. Also, feature selection used to reduce the computation and complexity lead to speed up the system