Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

A hydrophilic interaction chromatography has been investigated to separate 2-deoxycytidine chosen for nucleoside. A small molecule with specific features for human serum samples was 2-deoxycytidine tested. 2-deoxycytidine has been applied to self-made stationary hydrophilic phases (ZIC1 and ZIC5). The deoxycytidine (dCD) retention was investigated with varying concentrations of sodium acetate buffer, acetonitrile%, and pH. The results confirmed the hydrophilicity of 2-deoxycytidine. The exchanger retention mechanism was studied taking into account 2-deoxycytidine used for describing the interaction of hydrophilic and cation. For both ZIC1 and ZIC5 exchangers, we described and discussed the influence of chromatographic conditions (co

The corrosion behavior of bare and chemical conversion coated (through anodizing) aluminum ASA 606 I in stagnant chromic acid solutions . Solutions of 2, 6 & J O wt. % Cr03 at 45°C, have been investigated using polarization technique. The anodizing experiments were conducted under fixed conditions of 35 minute exposure time and 30 volt supplied voltage. The most important feature achieved was the great difference in behavior between the anodic polarization curves for bare and anodized aluminum in different concentrations of chromic acid solutions.

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Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa

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