Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac

The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

Social media is known as detectors platform that are used to measure the activities of the users in the real world. However, the huge and unfiltered feed of messages posted on social media trigger social warnings, particularly when these messages contain hate speech towards specific individual or community. The negative effect of these messages on individuals or the society at large is of great concern to governments and non-governmental organizations. Word clouds provide a simple and efficient means of visually transferring the most common words from text documents. This research aims to develop a word cloud model based on hateful words on online social media environment such as Google News. Several steps are involved including data acq

Biogenic gold nanoparticles (AuNPs) were synthesized using broccoli extract to assess their antioxidant activity, wound-healing potential, and selective anticancer effects. Green synthesis with broccoli offers an environmentally friendly way to produce stable and biocompatible nanomaterials. In this study, Brassica oleracea aqueous extract served as both the reducing and capping agent, producing AuNPs with a characteristic surface plasmon resonance peak at 560 nm and a well-defined cubic crystalline structure confirmed by XRD. TEM analysis showed uniformly dispersed, semi-spherical nanoparticles with an average size of 7.5 ± 3.6 nm. The biosynthesized AuNPs exhibited potent antioxidant activity, achieving 91.2 % DPPH scavenging at 100 µg/

Objectives: The study aim to evaluate nursing performance during nasogastric tube feeding in neonatal intensive care unit. Methodology: A descriptive study was carried out in Neonatal Intensive Care Unit at al–Batool Teaching Hospital, for the purpose of evaluate of quality of nursing performance for premature baby during nasogastric tube feeding in neonatal intensive care unit. The study consumed the period from 4th of December 2017 to the 24nd of April 2018, Non-probability purposive sample of (25) nurses working in the neonatal intensive care unit. The data were collected through the use of Observational instrument which consist of socio-demographic characteristics, quality of nursing care. Results: The study shows that the majority

In this work 2-hydrazino pyrimidine (1) was prepared from 2-mercapto pyrimidine with hydrazine hydrate. Treatment of (1) with active methylene compounds gave 2-(3,5-dimethyl -1 H – Pyrazole-1-yl) pyrimidine , whereas the reaction of (1) with carboxylic anhydride namely maleic anhydride or 1,2,3,6-tetra hydro phthalic anhydride yielded 1-Pyrimidine-2-yl-1,2-dihydro pyridazine-3,6-dione (3) and 2 – Pyrimidin -2-yl -2,3,4 a ,5,8 a – hexahydro phthalazine 1,4 – dione (4) . Reaction of (1) with phenyl isothiocyanate and ethyl chloro acetate afforded 3-Phenyl-1,3-thiazolidine-2,4-dione-2( pyrimidine -2- yl hydrazone (6) Azomethine (7-10) were prepared through condensation of (1) with aromatic aldehydes or ketones, then comp

Erbium, as optical probe, doped silicate sol-gel glass with
different Er concentrations was formed by wet chemical synthesis
method using ethanol, water and tetraethaylorthosilicate
[Si(OC2H5)4] precursor. Erbium ions were incorporated into silica
sol-gel matrix via dissolution of Erbium chloride solution into the
initial Si(OC2H5)4 precursor sol. Aluminum (Al) as a co-dopant was
added to the final precursor in the form of Aluminum chloride
(AlCl3) solution. The prepared samples were analyzed using atomic
absorption analysis, X-ray diffraction and spectroscopic tests. The
experimental results concerned with the transmission spectra suggest
that the final samples have a good transparency and homogeneity.
A

Some new 2,5-disubsituted-1,3,4-oxadiazole derivatives with azo group were synthesized by known reactions sequence . The structure of the synthesized compounds were confirmed by physical and spectral means .