Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

The feline calicivirus (FCV) is a highly contagious and infectious virus that infects cats and causes moderate to stringent respiratory infections and oropharyngeal illness. It is prevalent in shelters and birthplace colonies and frequently infects kitten cats. 50 distinct cats were involved in the research, with samples acquired between October 2020 and January 2021. Swabs were taken from the oropharynx and conjunctiva, conditional on the signs of FCV disease septicity, to inspect viral nucleic acid from collecting samples, then extract the RNA from the swabs and turn it into a cDNA particle, and finally distinguishing the open reading frame nucleic acid gene 2 using a primer special for feline calicivirus, All specimens were taken

In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used

The idea of ​​carrying out research on incomplete data came from the circumstances of our dear country and the horrors of war, which resulted in the missing of many important data and in all aspects of economic, natural, health, scientific life, etc.,. The reasons for the missing are different, including what is outside the will of the concerned or be the will of the concerned, which is planned for that because of the cost or risk or because of the lack of possibilities for inspection. The missing data in this study were processed using Principal Component  Analysis and self-organizing map methods using simulation. The variables of child health and variables affecting children's health were taken into account: breastfeed

Many researchers have tackled the shear behavior of Reinforced Concrete (RC) beams by using different kinds of strengthening in the shear regions and steel fibers. In the current paper, the effect of multiple parameters, such as using one percentage of Steel Fibers (SF) with and without stirrups, without stirrups and steel fibers, on the shear behavior of RC beams, has been studied and compared by using Finite Element analysis (FE). Three-dimensional (3D) models of (RC) beams are developed and analyzed using ABAQUS commercial software. The models were validated by comparing their results with the experimental test. The total number of beams that were modeled for validation purposes was four. Extensive pa

In this paper a theoretical attempt is made to determine whether changes in the aorta diameter at different location along the aorta can be detected by brachial artery measurement.  The aorta is divided into six main parts, each part with 4 lumps of 0.018m length. It is assumed that a desired section of the aorta has a radius change of 100,200, 500%. The results show that there is a significant change for part 2 (lumps 5-8) from the other parts. This indicates that the nearest position to the artery gives the significant change in the artery wave pressure while other parts of the aorta have a small effect.

Vol. 6, Issue 1 (2025)

Characterization of the heterogonous reservoir is complex representation and evaluation of petrophysical properties and application of the relationships between porosity-permeability within the framework of hydraulic flow units is used to estimate permeability in un-cored wells. Techniques of flow unit or hydraulic flow unit (HFU) divided the reservoir into zones laterally and vertically which can be managed and control fluid flow within flow unit and considerably is entirely different with other flow units through reservoir. Each flow unit can be distinguished by applying the relationships of flow zone indicator (FZI) method. Supporting the relationship between porosity and permeability by using flow zone indictor is ca

Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy

Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings