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
... Show MoreThe drones have become the focus of researchers’ attention because they enter into many details of life. The Tri-copter was chosen because it combines the advantages of the quadcopter in stability and manoeuvrability quickly. In this paper, the nonlinear Tri-copter model is entirely derived and applied three controllers; Proportional-Integral-Derivative (PID), Fractional Order PID (FOPID), and Nonlinear PID (NLPID). The tuning process for the controllers’ parameters had been tuned by using the Grey Wolf Optimization (GWO) algorithm. Then the results obtained had been compared. Where the improvement rate for the Tri-copter model of the nonlinear controller (NLPID) if compared with
Background: Phosphodiesterase-5 (PDE-5) inhibitorsrestore nitric oxide (NO) signaling and may reducecirculating inflammatory markers, and improve metabolicparameters through a number of mechanisms. Dailyadministration of the PDE-5 inhibitor, tadalafil (TAD) mayattenuate inflammation; improve fasting plasma glucose andtriglyceride levels and body weight. This study aims toevaluate the efficacy of low dose PDE-5 inhibitor, tadalafil(TAD) in controlling dysglycemia and body weight in obesediabetic men.Methods: Forty obese men with type 2 diabetes aged 30-50years incorporated in this study, all with A1c of 7-8.5%,attending obesity unit in AL-Kindy college of medicine.Weight, height, BMI, FPG, A1c, cholesterol, TG, HDL andLDL measured for all
... Show MoreThe COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. T
... Show MoreA robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While protecting the video buffer occupancy from exceeding the limited operating level can provide continuous video str
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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
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