Students’ feedback is crucial for educational institutions to assess the performance of their teachers, most opinions are expressed in their native language, especially for people in south Asian regions. In Pakistan, people use Roman Urdu to express their reviews, and this applied in the education domain where students used Roman Urdu to express their feedback. It is very time-consuming and labor-intensive process to handle qualitative opinions manually. Additionally, it can be difficult to determine sentence semantics in a text that is written in a colloquial style like Roman Urdu. This study proposes an enhanced word embedding technique and investigates the neural word Embedding (Word2Vec and Glove) to determine which perfo

In this study, gold nanoparticles were synthesized in a single step biosynthetic method using aqueous leaves extract of thymus vulgaris L. It acts as a reducing and capping agent. The characterizations of nanoparticles were carried out using UV-Visible spectra, X-ray diffraction (XRD) and FTIR. The surface plasmon resonance of the as-prepared gold nanoparticles (GNPs) showed the surface plasmon resonance centered at 550[Formula: see text]nm. The XRD pattern showed that the strong four intense peaks indicated the crystalline nature and the face centered cubic structure of the gold nanoparticles. The average crystallite size of the AuNPs was 14.93[Formula: see text]nm. Field emission scanning electron microscope (FESEM) was used to s

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

The water quality index is the most common mathematical way of monitoring water characteristics due to the reasons for the water parameters to identify the type of water and the validity of its use, whether for drinking, agricultural, or industrial purposes. The water arithmetic indicator method was used to evaluate the drinking water of the Al-Muthana project, where the design capacity was (40000) m3/day, and it consists of traditional units used to treat raw water. Based on the water parameters (Turb, TDS, TH, SO4, NO2, NO3, Cl, Mg, and Ca), the evaluation results were that the quality of drinking water is within the second category of the requirements of the WHO (86.658%) and the first category of the standard has not

Loanwords are the words transferred from one language to another, which become essential part of the borrowing language. The loanwords have come from the source language to the recipient language because of many reasons. Detecting these loanwords is complicated task due to that there are no standard specifications for transferring words between languages and hence low accuracy. This work tries to enhance this accuracy of detecting loanwords between Turkish and Arabic language as a case study. In this paper, the proposed system contributes to find all possible loanwords using any set of characters either alphabetically or randomly arranged. Then, it processes the distortion in the pronunciation, and solves the problem of the missing lette

This research takes up address the practical side by taking case studies for construction projects that include the various Iraqi governorates, as it includes conducting a field survey to identify the impact of parametric costs on construction projects and compare them with what was reached during the analysis and the extent of their validity and accuracy, as well as adopting the approach of personal interviews to know the reality of the state of construction projects. The results showed, after comparing field data and its measurement in construction projects for the sectors (public and private), the correlation between the expected and actual cost change was (97.8%), and this means that the data can be adopted in the re

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob