The use of Bayesian approach has the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and ensemble trees for explanatory variables are all together and at every stage on the other. In addition to obtaining the subsequent information at each node in the construction of these classification tree. Although bayesian estimates is generally accurate, but it seems that the logistic model is still a good competitor in the field of binary responses through its flexibility and mathematical representation. So is the use of three research methods data processing is carried out, namely: logistic model, and model classification regression tree, and bayesian regression tree mode

Astronomy image is regarded main source of information to discover outer space, therefore to know the basic contain for galaxy (Milky way), it was classified using Variable Precision Rough Sets technique to determine the different region within galaxy according different color in the image. From classified image we can determined the percentage for each class and then what is the percentage mean. In this technique a good classified image result and faster time required to done the classification process.

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences

A nonlinear filter for smoothing color and gray images
corrupted by Gaussian noise is presented in this paper. The proposed
filter designed to reduce the noise in the R,G, and B bands of the
color images and preserving the edges. This filter applied in order to
prepare images for further processing such as edge detection and
image segmentation.
The results of computer simulations show that the proposed
filter gave satisfactory results when compared with the results of
conventional filters such as Gaussian low pass filter and median filter
by using Cross Correlation Coefficient (ccc) criteria.

The study aimed to evaluating the inhibitory activity of apigenin extracted from Salvia officinalis leaves on the growth of L20B cancer cell in vitro, and through two incubation periods; 48 and 72 hours. Accordingly, eight concentrations (1.56, 3.13, 6.25, 12.5, 25.0, 50.0, 100.0 and 200.0 micromol) of apigenin and similar concentrations of vitamin C and carbon tetrachloride (CCl4) were tested. The apigenin revealed its significant inhibitory potentials against the growth of L20B cell line, especially at the low concentrations (1.56, 3.13 and 6.25 micromol) and at 72 incubation period in comparison with vitamin C and CCl4.

Victorian age is known as a time of perpetual change. It is a time of new industrialization, increased urbanization and new technology. Therefore, one of the strongest undercurrents of this is the position of women. Woman at that time was viewed as “an angel in the house” especially this who belongs to the upper-middle class. She is described in such term because she spends much time in her domestic domain. Therefore, she should be passive, obedient and dependent. Despite the Victorian society contains several classes; women are defined under two labels: the angel and the demon or the whore. The angel woman can be any woman from the lower middle class to the aristocracy, while the whore refers to any working class woman. The differen