The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

The coordination ability of the azo-Schiff base 2-[1,5-Dimethyl-3-[2-(5-methyl-1H-indol-3-yl)-ethyl imino]-2-phenyl-2,3-dihydro-1H-pyrazol-4-ylazo]-5- hydroxy-benzoic acid has been proven in complexation reactions with Co(II), Ni(II), Cu(II), Pd(II) and Pt(II) ions. The free ligand (LH) and its complexes were characterized using elemental analysis, determination of metal concentration, magnetic susceptibility, molar conductivity, FTIR, Uv-Vis, (1H, 13C) NMR spectra, mass spectra and thermal analysis (TGA). The results confirmed the coordination of the ligand through the nitrogen of the azomethine, Azo group (Azo) and the carboxylate ion with the metal ions. The activation thermodynamic parameters, such as ΔE*, ΔH*, ΔS*, ΔG*and K are cal

Industrial characteristics calculations concentrated on the physical properties for break down voltage in sf6, cf4 gases and their mixture with different concentrations are presented in our work. Calculations are achieved by using an improved modern code simulated on windows technique. Our results give rise to a compatible agreement with the other experimental published data.