Natural settings make it challenging to identify facial expressions since head position, illumination level, and ‎‎occlusion vary. Thus, developing a more generic model without front-facing images alone is quite crucial. This ‎research proposes a facial expression ‎recognition model based on pre-trained deep convolutional neural networks ‎with transfer learning. The model was trained ‎on several cases to classify face expressions into seven ‎classifications efficiently. The proposed system used the EfficientNetB0 model ‎that has one dense dropout layer. The model first rescales and norms the input dataset in the input ‎layer that takes images of a larger resolution to get better results. After entering 7 blocks sequential

Sliver / Sliver chloride is as old used from human but the sliver / sliver chloride nanoparticles have only recently been recogenized. They have used in medicin and agiculture. In the present study have been investigation the effecte biosynthesis Sliver / Sliver chloride nanoparticles as antibacterial by demonstrated that Ag / AgCl NPs arrest the growth of many bacterial: S.typhimurium, k. pneumonia. S. aureus, L.monocytogenes, B. Anthracis, E. coli, C. frundi, S. Pneumonia, P. Aeruginosa. The elements compestion and crystallization panal of biosynthesized nanoparticles were chracterazated by FTIR, XRD and SEM. From XRD, It is confirmed the synthesized nanoparticles contain Sliver / Sliver chloride elements. Synthesized Ag / AgCl NPs showed

Quick and accurate quaternary mixture resolution of furosemide (FURO), carbamazepine (CARB), diazepam (DIAZ) and carvedilol (CARV) by using derivative spectrophotometric method was performed. FURO and CARV were determined by means of first (D1), second (D2), third (D3) and fourth (D4) derivative spectrophotometric methods, CARB was determined by using D1, D2, D3 derivatives, while D1 and D2 were used for the determination of DIAZ. The recommended methods were verified using laboratory prepared mixtures and then successfully applied for the pharmaceutical formulations analysis of the cited drugs. The results obtained revealed the efficiency of the proposed methods as quantitative tool of analysis of the quaternary mixture with no requirement

A simple, accurate and sensitive spectrophotometric way is used to determine Bisacodyl in pure and pharmaceutical preparations. The proposed method depends on using 2,4-Dinitrophenylhydrazine as chromogenic reagent . The method was based on the oxidative coupling reaction of Bisacodyl with 2,4-Dinitrophenylhydrazine with Sodium periodate in the presence of sodium hydroxide as alkaline media to form red water soluble dye product , that has a maximum absorption at ?max 522nm . Beer ,s law is obeyed in the concentration of (2.00–20.00) ?g.ml -1 .The molar absorptivity is (6505) L.mol-1.cm-1,a sandall sensitivity of(0.0555) ?g.cm-2), correlation coefficient of (0.9970) , Limitof detection (LOD) (0.0312 ?g.ml-1), limit of Quantitation (LOQ) (

The study of the dynamic behavior of packed distillation column was studied by frequency response analysis using Matlab program. A packed distillation column (80 mm diameter) (2000 mm height) filled with glass packing (Raschig Rings 10mm), packing height (1500 mm) has been modified for separation of methanol-water mixture (60 vol%). The column dynamic behavior was studied experimentally under different step changes in, feed rate (±30%), reflux rate (±22%), and reboiler heat duty (±150%), the top and bottom concentration of methanol were measured. A frequency response analysis for the above step response was carried out using Bode diagram, the log modulus and the phase angle were  used to analyze the process model. A Matlab progra

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo