Social interaction is the platform that enables people to connect and practice language. Active listening stimulates them to understand the language they are speaking. The problem of the study highlights that less attention to listening among speaking, reading, and writing skills causes the weakness of collaborative learning. This paper contributes to characterizing the effectiveness of collaborative learning in developing learner’s listening skills. It aims to underscore the role of target language learners as members of the learning groups and of the teacher in the collaborative learning process. 130 Iraqi EFL teachers from different colleges at the University of Baghdad participated in this study. The scores in the statistical data were measured in the 5 Likert scale using IBM Statistical Package for Social Software (SPSS) version 24. The research findings showed that the correlation between collaborative learning and listening skills significantly developed students' other fundamental language skills. The results showed that great attention is paid to reading and speaking skills while learning collaboratively. An essential limitation of this study is that it needs to address barriers encountered by collaborative learners to practice reflective listening. More research on pronunciation and grammar is necessary for improving listening skills.
Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut
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The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach
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Autorías: Abdulsahıb Mohammed Muneer, Habeeb Sabhan Maytham, Kazim Abed Emad. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 1, 2021. Artículo de Revista en Psyke.
Background: disruptive behavioral disorders among primary school children is oone of the most popular, which has negative social, psychological, educational, and physical repercussions on children and families. Objective: This study sought to determine effect disruptive behavioral disorders quality of learning among school chil dren. Methods: A descriptive cross-sectional design study was conducted at Baquba primary schools in Diyala Governorate, and the study period was extended from October 6th, 2024, to January 15th, 2025. A nonprobability purposive sample was used to include 275 teachers working at selected Baquba primary schools, Iraq. Data were collected using a self-admin istered questionnaire, two components of the st
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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Resulted in scientific and technological developments to the emergence of changes in the educational process and methods of teaching modern formats commensurate with the level of mental retardation. Which called for educational institutions, including the University of Baghdad / College of Fine Arts to urge and guide researchers to study and follow-up of recent developments in the educational process in order to develop in the fine arts in general and technical education in particular being play an important role in achieving educational goals. The educational methods of modern educational require effort-intensive and advanced for the development of technical skills among students, and thus worked researcher to employ computer technology
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Research aims
1. Measuring expectations of self-efficiency at teachers of secondary schools.
2. Measuring biological skills at teachers of secondary schools.
3. There is no sole or gathering forecasting of forecasting expectations of self-efficiency , may forecasts about biological skills at teachers of secondary schools.
To fulfill aims of the research, the researcher managed measuring of expectations of self-efficiency according to point of view of Bandura for this concept in this research, arranging measure of biological skills according to view of global health, the researcher has made to investigate of truth , constant these two measurements, analyzing statistically their two paragraphs on a sample (460) teachers (males
With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se
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