This study explores the challenges in Artificial Intelligence (AI) systems in generating image captions, a task that requires effective integration of computer vision and natural language processing techniques. A comparative analysis between traditional approaches such as retrieval- based methods and linguistic templates) and modern approaches based on deep learning such as encoder-decoder models, attention mechanisms, and transformers). Theoretical results show that modern models perform better for the accuracy and the ability to generate more complex descriptions, while traditional methods outperform speed and simplicity. The paper proposes a hybrid framework that combines the advantages of both approaches, where conventional methods prod

The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

Synthesis three organic inhibitors for carbon steel corrosion: 2-(propylthio)-1H-benzo[d]imidazole (PTBI), 2-(allylthio)- 1H-benzo[d]imidazole (ATBI) and 2-(prop-2-ynylthio)-1H-benzo[d]imidazole (YTBI) were prepared from reaction of 2-mercapto benzimidazole with different alkyl halide. The melting point and TLC were used to confirm the purity of the inhibitors as well as using the [FTIR, 1H-NMR and 13C-NMR] for the identify structures. The synthesized inhibitors were examined by potentiostatic polarization measurement as corrosion inhibitors of carbon steel in acidic media [1M H2SO4 ].The polarization measurement results showed that the mixed type inhibitors. In addition, the efficiency of inhibitors (YTBI) were studied at different con

    This research study the effect of surface modification and copper (Cu) plating carbon fiber (CF) surface on the thermal stability and wettability of carbon fiber (CF)/epoxy (EP) composites. The TGA result indicates that the thermal-stability of carbon fiber may be enhanced after Cu coating CF. TGA curve showed that the treatment temperature was enhanced thermal stability of Ep/CF, this is due to the oxidation during heating. The Cu plating increased the thermal conductivity, this increase might be due to reduce in contact resistance at the interface due to chemical modification and copper plating and tunneling resistance.

   The increase of surface polarity after coating cause decreas