In this paper, a fast lossless image compression method is introduced for compressing medical images, it is based on splitting the image blocks according to its nature along with using the polynomial approximation to decompose image signal followed by applying run length coding on the residue part of the image, which represents the error caused by applying polynomial approximation. Then, Huffman coding is applied as a last stage to encode the polynomial coefficients and run length coding. The test results indicate that the suggested method can lead to promising performance.

Hand gestures are currently considered one of the most accurate ways to communicate in many applications, such as sign language, controlling robots, the virtual world, smart homes, and the field of video games. Several techniques are used to detect and classify hand gestures, for instance using gloves that contain several sensors or depending on computer vision. In this work, computer vision is utilized instead of using gloves to control the robot's movement. That is because gloves need complicated electrical connections that limit user mobility, sensors may be costly to replace, and gloves can spread skin illnesses between users. Based on computer vision, the MediaPipe (MP) method is used. This method is a modern method that is discover

Background: Thymus vulgaris is a plant rich in essential oils acclaimed for the management of oxidative stress and inflammation in the organs. Meanwhile, the heavy metal lead is widely distributed in nature and continued exposure to lead acetate causes reduced fertility.Objectives: The present study aimed to investigate the effects of T. vulgaris on ovarian and uterine structural and functional characteristics in female rats exposed to lead acetate. Methods: Three groups of 18 mature Wistar albino female rats (Rattus norvegicus), 15 weeks old and weighing between 200 and 210 g, were established and handled for 60 days as follows: Group A (control group) received 0.5 mL of distilled water (DW) daily; group B received 5 mg/kg body weight (BW

The process of composting which involves the treatment of organic wastes to obtain a stable clean product, has become an increasingly desirable choice, at any scale from home to large waste treatment plants. Composting serves as an alternative to landfilling for managing biodegradable waste while also increasing or preserving soil organic matter, decreasing solid waste, and reducing disposal costs. The compost product improves the condition of the soil, reduces erosion, and helps reduce plant diseases without having adverse impacts on the environment. This study aims to manufacture high-quality compost as a solid waste management method by converting organic waste into a useful resource for the community in a financially and ecologi

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

Spatial data observed on a group of areal units is common in scientific applications. The usual hierarchical approach for modeling this kind of dataset is to introduce a spatial random effect with an autoregressive prior. However, the usual Markov chain Monte Carlo scheme for this hierarchical framework requires the spatial effects to be sampled from their full conditional posteriors one-by-one resulting in poor mixing. More importantly, it makes the model computationally inefficient for datasets with large number of units. In this article, we propose a Bayesian approach that uses the spectral structure of the adjacency to construct a low-rank expansion for modeling spatial dependence. We propose a pair of computationally efficient estimati

The problem of Bi-level programming is to reduce or maximize the function of the target by having another target function within the constraints. This problem has received a great deal of attention in the programming community due to the proliferation of applications and the use of evolutionary algorithms in addressing this kind of problem. Two non-linear bi-level programming methods are used in this paper. The goal is to achieve the optimal solution through the simulation method using the Monte Carlo method using different small and large sample sizes. The research reached the Branch Bound algorithm was preferred in solving the problem of non-linear two-level programming this is because the results were better.

In this paper a modified approach have been used to find the approximate solution of ordinary delay differential equations with constant delay using the collocation method based on Bernstien polynomials.