Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

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Mixed ligand complexes of bivalent metal ions, viz ; M= Co(II),Ni(II),Cu(II), Zn(II), Cd (II), and Hg(II) of the composition [M(Anth)2(TMP)] in 1:2:1 molar ratio, (where . AnthrH= Anthranilic acid (C7H7NO2) and Trimethoprime (TMP) = (C14H18N4O3) have been synthesized and characterized by repeated melting point determination, Solubility, Molar conductivity (Λm ),determination the percentage of the metal (M%) in the complexes by (AAS), FT-IR, magnetic susceptibility measurements [μeff (BM)] and electronic spectral data. The two ligands and their metal complexes have been screened for their bacterial activity against selected microbial strains (Gram +ve) & (Gram -ve).

Mixed ligand complexes of bivalent metal ions, viz ; M= Co(II),Ni(II),Cu(II), Zn(II), Cd (II), and Hg(II) of the composition [M(Anth)2(TMP)] in 1:2:1 molar ratio, (where . AnthrH= Anthranilic acid (C7H7NO2) and Trimethoprime (TMP) = (C14H18N4O3) have been synthesized and characterized by repeated melting point determination, Solubility, Molar conductivity (Λm ),determination the percentage of the metal (M%) in the complexes by (AAS), FT-IR, magnetic susceptibility measurements [µeff (BM)] and electronic spectral data. The two ligands and their metal complexes have been screened for their bacterial activity against selected microbial strains (Gram +ve) & (Gram -ve).

In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and

The complexes of Schiff base of 4-aminoantipyrine and 1,10-phenanthroline with metal ions Mn (II), Cu (II), Ni (II) and Cd (II) were prepared in ethanolic solution, these complexes were characterized by Infrared , electronic spectra, molar conductance, Atomic Absorption ,microanalysis elemental and magnetic moment measurements. From these studies the tetrahedral geometry structure for the prepared complexes were suggested.The prepared ligand of 4-aminoantipyrine was characterized by using Gc-mass spectrometer .

The Co(II), Ni(II) ,Cu(II), Zn(II) ,Cd(II) and Hg(II) complexes of mixed of amino acid (L-Proline) and Trimethoprim antibiotic were synthesized. The complexes were characterized using solubility, melting point, conductivity measurement ,. and determination the percentage of the metal in the complexes by flame(AAS).Magnetic susceptibility, Spectroscopic Method [FT-IR and UV-Vis]. Draw the proposed structure of the complexes using program , Chem. office 3D(2006). The ligands and there metal complexes were screened for their antimicrobial activity against four bacteria (gram + ve) and (gram-ve){Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Bacillus}.The proposed structure of the complexes using program , Chem office 3D(20