Many undergraduate learners at English departments who study English as a foreign language are unable to speak and use language correctly in their post -graduate careers. This problem can be attributed to certain difficulties, which they faced throughout their education years that hinder their endeavors to learn. Therefore, this study aims to discover the main difficulties faced by EFL students in language learning and test the difficulty variable according to gender and college variables then find suitable solutions for enhancing learning. A questionnaire with 15 items and 5 scales were used to help in discovering the difficulties. The questionnaire was distributed to the selected sample of study which consists of 90 (male and female) stud

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

This paper aims to study the effects of the long term solar activity on the critical frequencies of ionospheric F1 layer over Baghdad city, during the solar cycle 22, within (1988- 1995). It is found that the critical frequency of this layer is closely related to the sunspots number during the years of the solar cycle 22, at a middle latitude region of the world. The study discussed the effect of sunspot numbers and solar events on the electron densities of F1 layer, which is the most important ionospheric parameter.

Introduction: Infection control or hospital-acquired infections are the major concern of the health care system and agencies. Critical care nurses are on the first-line contact with the patients, so on, they are most vulnerable to acquired infections. It is really important to regularly check their knowledge and practices concerning infection control. Objectives: The study aims to identify the impact of years’ experience on nurses’ knowledge and practices concerning infection control in three hospitals and center (Baghdad teaching hospital, Ibn Al-Nafees hospital, and Ibn al-Bitar center) Methodology: Cross-sectional study was conducted, the study starting from 4th of July 2020 to 13th of November 2020. Non-probability (purposive) sampl

In this work, the impact of different geomagnetic storm events on the plasma-sphere layer (ionosphere layer) over the northern and southern hemisphere regions was investigated during solar cycle 23. To grasp the influence of geomagnetic storms on the behavior and variation of the critical frequency parameter of the F2 ionospheric layer (foF2), five geomagnetic storms (classified as great, severe, and strong), with Disturbance storm time (Dst) values <-100 nT were chosen. Four stations located in different mid-latitude regions in northern and southern hemispheres were designated, the northern stations are: Millstone Hill (42.6° N, 288.50° W) and Rome (41.90° N, 12.50° E) and the southern stations are: Port Stanley (-51.60° S,

The determination of critical micelle concentration of selected non-ionic surfactants (Tween 20,40 and 80) have been investigated using magnetic water(MW)as an aqueous medium.Conductometry technique is used to determine critical micelle concentration.The effect of alcohol addition and temperature variation at the range(293.15 -303.15K) are also pursued. It is concluded that the process of micellization is spontaneous and endothermic because of the observed free energy of micellization (ΔG^om) , enthalpy change of micellization  (ΔH^om), and entropy change of micellization (ΔS^om) for the system was also studied.The properties of the non-ionic surfactants were studied, both in absence and presence of