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

2-amino-4-(4-chloro phenyl)-1,3-thiazole (1) was synthesized by refluxing thiourea with para-chloro phenacyl bromide in absolute methanol. The condensation of amine compound (1)  with phenylisothiocyanate in the presence of pyridine will  produce 1-(4-(4-chlorophenyl)thiazol-2-yl)-3-phenylthiourea(2), which is  upon treatment with 2,4 dinitrophenyl hydrazine by conventional method, afforded 1- ( 4 - ( 4 – chlorophenyl ) thiazol – 2 – yl ) – 3 - phenylhydrazonamide,N' - ( 2 , 4 -dinitrophenyl) ,(3).The characterization of the titled compounds were performed utilizing FTIR spectroscopy, ¹HNMR and CHNS elemental analysis, and by me

In the presence of deep submicron noise, providing reliable and energy‐efficient network on‐chip operation is becoming a challenging objective. In this study, the authors propose a hybrid automatic repeat request (HARQ)‐based coding scheme that simultaneously reduces the crosstalk induced bus delay and provides multi‐bit error protection while achieving high‐energy savings. This is achieved by calculating two‐dimensional parities and duplicating all the bits, which provide single error correction and six errors detection. The error correction reduces the performance degradation caused by retransmissions, which when combined with voltage swing reduction, due to its high error detection, high‐energy savings are achieved. The res

Exploring the antibacterial potential of neem oil (Azadirachta indica) in combination with gentamicin (GEN) against pathogenic molds, especially Pseudomonas aeruginosa, has drawn concern due to the quest for natural treatment options against incurable diseases. Prospective research directions include looking for natural cures for many of the currently incurable diseases available now. microbial identification system, were used to identify the isolates. The research utilized a range of methods, such as the diffusion agar well (AWD) assays, TEM (transmission electron microscopy) analysis, minimum inhibitory concentration (MIC) assays, and real-time PCR (RT-qPCR) to analyze bacterial expression and the antibacterial action of neem oil (Azadira