Creativity, imagination, and ingenuity are connected with instructing students in literary tasks. The purpose of the study is to determine what teaching activities are most Productive in helping students to comprehend literature. The research design used in the study is descriptive one. The researcher created a survey questionnaire that teachers could complete. It includes twelve learning activities related to teaching literature, such as: conceptual mapping of a poem, short story, or novel; observing and analyzing videos; collaborative discussions about the chosen text; and topical discussions with a partner ,composition of a poem ,analytic writings; playing a role; narrating stories with visual assistance, teacher-student exchanges; attending lectures; private orally presenting; poems arrangement; and, evaluated spoken word in literary studies. The responders include six language literature professors at the College of Education Ibn Rushd for Human Sciences and 175 second-stage English department students. The most engaging and productive learning activities were identified by interpreting the data using an average mean and a modified Likert scale. Students were found to find playing a role, reflective writing, and discussion groups to be very engaging and productive. The teacher-student conversation was being equally very engaging and productive. Upon conducting a study among language teachers, the researcher discovered that graded spoken recitation is the least engaging yet most successful method. Based on the study's findings, recommendations and ideas for more research are made.
Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye
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Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.
In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation
... Show MoreDrilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.
In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation
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The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra
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Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d
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this study aimed to study the effect of Cordia myxa extract on the growth and activities of the following types of bacteria : Escherichia coli , Pseudomonas aeruginosa, Proteus Spp., Klebsiella pneumoniae , Staphylococcus aureus, Streptococcus pyogenes , Bacillus subtilus, and the yeast Candida albicans .the results showed an inhibitory effect of the methanol extract on both the growth and activity of the tested microbes .this was reflected by the minimum inhibitory concentration ( MIC ) of different type of bacteria and the yeast.
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