The heritage of the human peoples remains a beacon of data, after renewed awareness of them, open to their propositions, which are born from the womb of intertwined elements of customs, knowledge, arts, and literature ... Misleading the reception process, it may be something intriguing to identify with caution with the text sample selected by the current search procedures prepared according to the adoption of a descriptive therapist in a three-dimensional approach, as well as a comprehensive textual reference for that specific communication dimension within Anthology Andalusian - Moroccan poetry in the book of pleasure in the perception of the five senses Tahdib ibn Manzoor (d. 711 e) from the origin of Tivashi (d. 651 e) Tagged: Separat

Polyvinyl chloride (PVC) finds use in various industries due to its versatility but is not thermostable, serving as a barrier to causing degradation and release of hydrochloric acid (HCl) under heat stress conditions. Lead and organotin compounds have traditionally been used for stabilizer purpose to discourage such degradation but pose serious ecological as well as health risks. As a counter measure, bio-based stabilizers from plant oils have become popular as a green and sustainable alternative. This review looks back at recent developments in plant oil-based stabilizers for PVC, focusing on the mechanisms of stabilization, sustainability, and cost-effectiveness. We review plant oils such as tung oil, castor oil, and soybean oil,

The current study aims to investigate the effect of the interaction between the use of the improve strategy in teaching mathematics and the level of academic achievement on the acquisition of algebraic concepts and habits of mind among tenth-grade students in Oman. The study adopted the experimental method, based on a quasi-experimental design with two groups: experimental and control groups and pre-post-measurement. The study sample consisted of (28) 10th-grade students as an experimental group and 26 of 10th-grade students as a control group in Al-Tufail bin Amr School in South Al Batinah. The differences in the pretest and posttest gains were analyzed using mean, standard deviation, ANCOVA, t-test, effect size (eta-square), and two-wa

This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe

A study on the treatment and reuse of oily wastewater generated from the process of fuel oil treatment of gas turbine power plant was performed. The feasibility of using hollow fiber ultrafiltration (UF) membrane and reverse osmosis (RO) membrane type polyamide thin-film composite in a pilot plant was investigated. Three different variables: pressure (0.5, 1, 1.5 and 2 bars), oil content (10, 20, 30 and 40 ppm), and temperature (15, 20, 30 and 40 ᵒC) were employed in the UF process while TDS was kept constant at 150 ppm. Four different variables: pressure (5, 6, 7 and 8 bar), oil content (2.5, 5, 7.5 and 10 ppm), total dissolved solids (TDS) (100, 200,300 and 400 ppm), and temperature (15, 20, 30 and 40 ᵒC) were mani

Arabic text classification is a challenging task because of the complex morphology of the language, the existence of different writing forms and a multitude of dialects, which can result in sparser common text representations. While transformer models such as AraBERT have obtained superior results on many Arabic NLP tasks, their high computational requirements make them difficult to deploy in environments with limited hardware resources. In some cases this can also make the model less practical for researchers working with basic computer systems. This study focuses on a more practical issue: how much accuracy a simple classifier may lose when the amount of required computation is reduced. We use a combined TF-IDF representation based on bo

Accurate emotion categorization is an important and challenging task in computer vision and image processing fields. Facial emotion recognition system implies three important stages: Prep-processing and face area allocation, feature extraction and classification. In this study a new system based on geometric features (distances and angles) set derived from the basic facial components such as eyes, eyebrows and mouth using analytical geometry calculations. For classification stage feed forward neural network classifier is used. For evaluation purpose the Standard database "JAFFE" have been used as test material; it holds face samples for seven basic emotions. The results of conducted tests indicate that the use of suggested distances, angles

Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.