E-learning has recently become of great importance, especially after the emergence of the Corona pandemic, but e-learning has many disadvantages. In order to preserve education, some universities have resorted to using blended learning. Currently, the Ministry of Higher Education and Scientific Research in Iraq has adopted e-learning in universities and schools, especially in scientific disciplines that need laboratories and a spatial presence. In this work, we collected a dataset based on 27 features and presented a model utilizing a support vector machine with regression that was enhanced with the KNN method, which identifies factors that have a substantial influence on the model for the type of education, whether blended or traditional.
Furthermore, the dataset used was primarily focused on three key factors: personal information, the impact of e-Learning platforms, and the influence of the Corona virus. The attributes that were measured revealed that social status, computer skills, and the basic platform gave the user enough tools to continue the learning process. The size of the classrooms and laboratories that meet the health safety conditions is the most significant. The goal of our work is to discover a model that predicts how blended learning will be used during and after the coronavirus pandemic and to produce a model with minimal errors.
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
Natural bentonite (B) mineral clay was modified by anionic surfactant sodium dodecyl sulfate (SDS) and characterized using different techniques such as: FTIR spectroscopy, scanning electron microscopy (SEM) and X-Ray diffraction (XRD). The bentonite and modified bentonite were used as adsorbents for the adsorption of methyl violet (MV) from aqueous solutions. The adsorption study was carried out at different conditions such as: contact time, pH value and adsorbent weight. The adsorption kinetic described by pseudo– first order and pseudo – second order equilibrium experimental data described by Langmuir, Freundlich and Temkin isotherm models. The thermodynamic parameters standard free energy ( ), standard entropy ( ) standa
... Show MoreNaturally available products have been used widely for centuries in handling human disease. The present study aimed to determine the effect of aluminum potassium sulfate addition into the soft liner on tensile strength and peel bond strength. The effect of aluminum potassium sulfate evaluated by two methods, first one include incorporation of KAL (SO4)2 into soft liner monomer in concentration (2%,3% by wt.) while the second method include immersion of soft liner specimens in solution of KAL(SO4)2 in concentration(5%,10% percent) during time periods (0,10 minutes). In conclusions, the results of current study encourage use KAL (SO4)2 within soft liner material
Rumors are typically described as remarks whose true value is unknown. A rumor on social media has the potential to spread erroneous information to a large group of individuals. Those false facts will influence decision-making in a variety of societies. In online social media, where enormous amounts of information are simply distributed over a large network of sources with unverified authority, detecting rumors is critical. This research proposes that rumor detection be done using Natural Language Processing (NLP) tools as well as six distinct Machine Learning (ML) methods (Nave Bayes (NB), random forest (RF), K-nearest neighbor (KNN), Logistic Regression (LR), Stochastic Gradient Descent (SGD) and Decision Tree (
... Show MoreThe research aims to shed light on the recent experience in the Iraqi business environment, which is the experience of the merger. To evaluate a recent experience in an important sector of the Iraqi business sectors, namely the industrial sector to enable decision-makers to review that experience to judge the extent of its success and address some of the lapses that experience that by measuring synergies can be judged on the success of the merger experience or not. The research community is the governmental industrial sector. The research sample included six cases of merger (14) companies before the merger. The Holt method was used to predict the net sales and total cost values before the merger as if it were not merged. Th
... Show MoreCorrosion behavior of aluminum alloy 7025 was investigated in hydrochloric acid (pH=1) containing 0.6 mol.dm-3 NaCl in the existence and absence of diverse concentrations of sulphamethoxazole as environmentally friendly corrosion inhibitor over the temperature range (298-313)K. Electrochemical polarization method using potentiostatic technique was employed. The inhibition efficiency has been raised with increased sulphamethoxazole concentration but lessened at temperature increases. The highest efficiency value was 96.5 at 298 K and 2 x10-4 mol.dm-3 concentration of sulphamethoxazole. The sulphamethoxazole adsorption was agreed with Langmuir adsorption isotherm. Some thermodynamic parameter (△Gads) and activation energy (Ea) were determin
... Show MoreIn the absence of environmental regulation, food stays to be contaminated with heavy metals, which is becoming a big worry for human health. The present research focusses on the environmental and health effects of irrigating a number of crops grown in the soils surrounding the Al-Rustamia old plant using treated wastewater generated by the plant. The physicochemical properties, alkalinity, and electrical conductivity of the samples were evaluated, and vegetable samples were tested for Cd, Pb, Ni, and Zn, levels, and even the transfer factor (TF) from soils to crops and crop and multi-targeted risk, daily intake (DIM) of metals, and health risk index (HRI) was calculated. The findings found that the average contents of Zn, Pb, Ni, and Cd in
... Show MoreIn this study, some attenuation parameters of gamma shields were studied. This shields consisting of composite materials of Unsaturated polyester as a base material and Nano iron oxide (Fe2O3) and, micro iron (Fe) as reinforcement materials at different percentages (1, 3,5,7and 9)wt%, and with different thickness (1, 1.5, 2, 2.5, 3, 3.5and 4) cm. The results showed that the use of nanoparticles is better than the microparticales in the field of radiation shielding. It has been shown that the values of attenuation parameters of gamma it bitter in the case of nanoparticles than case of the use of micro material.
The present study aims at answering the following questions:.
1-Which is more effective in enriching students. Vocabulary ,the use of short stories or the traditional way?
2-What extent has the use of short stories an effect upon the students. achievement in vocabulary test?
3- Is there any significant difference between the male and female student of the experimental group in vocabulary achievement test?
 
... Show MoreA reliable differential pulse polarographic (DPP) method has been developed and applied for the determination of ibuprofen IBU in dosage form with dropping mercury electrode (DME) versus Ag/AgCl. The best peak was found at cathodic peak of -1.18 V in phosphate buffer at pH=4 and 0.025M of KNO3 as supporting electrolyte. In order to obtaine the highest sensitivity, instrumental and experimental parameters were examined including the type and concentration of supporting electrolyte, pH of buffer solution, pulse amplitude and voltage step time. Diffusion current showed a direct linear relationship to ibuprofen concentration in the range of (5 – 30) μg. mL-1 (2.43× 10-5
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