The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

Background: For patients with coronavirus disease(COVID-19), continuous positive airway pressure (CPAP) has been considered as a useful treatment. The goal of CPAP therapy is to enhance oxygenation, relieve breathing muscle strain, and maybe avoid intubation. If applied in a medical ward with a multidisciplinary approach, CPAP has the potential to reduce the burden on intensive care units. Methods: Cross-sectional design was conducted in the ALSHEFAA center for crises in Baghdad. Questionnaire filled by 80 nurses who work in Respiratory Isolation Unit who had chosen by non-probability (purposive) selection collected the data. Then the researcher used an observational checklist to evaluate nurses’ practice. The data was analyzed us

Many industrial systems involve multiple criteria and objectives, and they are very complex problems in computational science, such as task scheduling. We propose bi-criteria and bi-objective scheduling problems, which are solved by two nature-inspired evolutionary algorithms, such as Simulated Annealing (SA) and Bee Algorithm (BA). This problem is characterized by scheduling a batch of tasks on multiple machines, and it is fundamental because the solution should focus on the simultaneous optimization of two conflicting objectives: the makespan minimization and the total tardiness minimization. This problem is NP-Hard, and therefore, two evolutionary methods were used to search for solutions intelligently in this huge, very complex

In this paper, a dynamic investigation is done for strip, rectangular and square machine foundation at the top surface of two-layer dry sand with various states (i.e., loose on medium sand and dense on medium sand). The dynamic investigation is performed numerically using finite element programming, PLAXIS 3D. The soil is expected as a versatile totally plastic material that complies with the Mohr-Coulomb yield criterion. A harmonic load is applied at the base with an amplitude of 6 kPa at a frequency of (2 and 6) Hz, and seismic is applied with acceleration – time input of earthquake hit Halabjah city north of Iraq. A parametric study is done to evaluate the influence of changing L/B ratio (Length=12,6,3 m and width=3 m), type of sand

In this study, the response and behavior of machine foundations resting on dry and saturated sand was investigated experimentally. In order to investigate the response of soil and footing to steady state dynamic loading, a physical model was manufactured. The manufactured physical model could be used to simulate steady state harmonic load at different operating frequencies. Total of (84) physical models were performed. The parameters that were taken into considerations include loading frequency, size of footing and different soil conditions. The footing parameters were related to the size of the rectangular footing and depth of embedment. Two sizes of rectangular steel model footing were used (100 200 12.5 mm) and (200 400 5.0 mm).

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The current study was designed to investigate the presence of aflatoxin M1 in 25 samples of pasteurized canned milk which collected randomly from some Iraqi local markets using ELISA technique. Aflatoxin M1 was present in 21 samples, the concentration of aflatoxin M1 ranged from (0.25-50 ppb). UV radiation (365nm wave length) was used for detoxification of aflatoxin M1 (sample with highest concentration /50 ppb of aflatoxin M1 in two different volumes ((25 & 50 ml)) for two different time (15 & 30 min) and 30, 60, 90 cm distance between lamp and milk layer were used for this purpose). Results showed that distance between lamp and milk layer was the most effective parameter in reduction of aflatoxin M1, and whenever the distance increase the