Drones play a vital role in the fundamental aspects of Industry 4.0 by converting conventional warehouses into intelligent ones, particularly in the realm of barcode scanning. Various potential issues frequently arise during barcode scanning by drones, specifically when the drone camera has difficulty obtaining distinct images due to certain factors, such as distance, capturing the image whilst flying, noise in the environment and different barcode dimensions. In addressing these challenges, this study proposes an approach that combines a proportional–integral–derivative (PID) controller with image processing techniques. The PID controller is responsible for continuously monitoring the camera’s input, detecting the difference

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

Background: Large amounts of oily wastewater and its derivatives are discharged annually from several industries to the environment. Objective: The present study aims to investigate the ability to remove oil content and turbidity from real oily wastewater discharged from the wet oil's unit (West Qurna 1-Crude Oil Location/ Basra-Iraq) by using an innovated electrocoagulation reactor containing concentric aluminum tubes in a monopolar mode. Methods: The influences of the operational variables (current density (1.77-7.07 mA/cm2) and electrolysis time (10-40 min)) were studied using response surface methodology (RSM) and Minitab-17 statistical program. The agitation speed was taken as 200 rpm. Energy and electrodes consumption had been studi

This work focuses on the use of biologically produced activated carbon for improving the physi-co-chemical properties of water samples obtained from the Tigris River. An eco-friendly and low-cost activated carbon was prepared from the Alhagi plant using potassium hydroxide (KOH) as an impregnation agent. The prepared activated carbon was characterised using Fourier-transform infrared spectroscopy to determine the functional groups that exist on the raw material (Alhagi plant) and Alhagi activated carbon (AAC). Scanning electron microscope–energy-dispersive X-ray spectroscope was also used to investigate the surface shape and the elements that compose the powder. Brunauer–Emmett–Teller surface area analysis was used to evaluate the spe

This study was conducted in the poultry field of the Department of Animal Production/ College of Agricultural Engineering Sciences / University of Baghdad for the period from 42 days. Aiming to know the effect of using shrimp waste powder (Metapenaeus Affinis) and enzyme in broilers diet on physiological and microbial performance and indicators of fat oxidation in meat. 250 one-day-old ROSS308 chicks were used. The chicks were fed on diets containing shrimp waste treated with enzyme and not treated with protease enzyme by 0,4,6 %. The experiment included five treatments, with 5 replicates for each treatment, and each replicate contained 10 birds. The results showed a significant decrease (P≤0.05) in the concentration of ALT and AS