HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first

As they are the smallest functional parts of the muscle, motor units (MUs) are considered as the basic building blocks of the neuromuscular system. Monitoring MU recruitment, de-recruitment, and firing rate (by either invasive or surface techniques) leads to the understanding of motor control strategies and of their pathological alterations. EMG signal decomposition is the process of identification and classification of individual motor unit action potentials (MUAPs) in the interference pattern detected with either intramuscular or surface electrodes. Signal processing techniques were used in EMG signal decomposition to understand fundamental and physiological issues. Many techniques have been developed to decompose intramuscularly detec

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

   The removal of Anit-Inflammatory drugs, namely; Acetaminophen (ACTP), from wastewater by bulk liquid membrane (BLM) process using Aliquat 336 (QCl) as a carrier was investigated. The effects of several parameters on the extraction efficiency were studied in this research, such as the initial feed phase concentration (10-50) ppm of ACTP, stripping phase (NaCl) concentration (0.3,0.5,0.7 M), temperature (30-50^oC), the volume ratio of feed phase to membrane phase (200-400ml/80ml), agitation speed of the feed phase (75-125 rpm), membrane stirring speed (0, 100, 150 rpm), carrier concentration (1, 5, 9 wt%), the pH of feed (2, 4, 6, 8, 10), and solvent type (CCl₄ and n-Heptane). The study shows that high ext

Room temperature ionic liquids show potential as an alternative to conventional organic membrane solvents mainly due to their properties of low vapour pressure, low volatility and they are often stable. In the present work, the technical feasibilities of room temperature ionic liquids as bulk liquid membranes for phenol removal were investigated experimentally. In this research several hydrophobic ionic liquids were synthesized at laboratory. These ionic liquids include (1-butyl-3-methylimidazolium bis (trifluoromethylsulfonyl) imide[Bmim][NTf₂], 1-Hexyl-3-methylimidazolium bis (trifluoromethylsulfonyl) imide[Hmim][NTf₂], 1-octyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide[Omim][NTf₂],1‐butyl