The study includes preparation and characterisation of mixed azo-linked Schiff-base ligands and their complexes. The starting material was isolated from the mixing of 2-amino pyridine diazonium salt with 2-amino-phenole and 4-amino-3- hydroxy-1-naphthalene sulfonic acid respectively in 1:1 mole ratio in water. In this work, the formation of azo-linked Schiffbase ligands are reported. Ligands of the azo-linked Schiff-base was achieved by the reaction of starting material with 4- (dimethylamino) benzaldehyde) (HL1 and HL2). The complexes were prepared by mixing the azo-linked Schiff-base ligands with the metal salts; CoII, NiII and CdII in a 1:1:1 mole ratio. Ligands and complexes were characterised by analytical and spectroscopic analyses including; microanalysis, chloride content, thermal analysis, magnetic susceptibility for complexes, conductance, FTIR, UV-Vis and 1H-NMR spectroscopy. Physico-chemical techniques indicated complexes demonstrated six coordinate structures in the solid and solution sate. Biological activity of the ligands and their metal complexes were screened for their antimicrobial activity against four bacterial species (Escherichia coli and Enterobacter (G-)), (Bacillus stubtilis and Staphylococcus aureus (G+)).
The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med
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Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a
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Data steganography is a technique used to hide data, secret message, within another data, cover carrier. It is considered as a part of information security. Audio steganography is a type of data steganography, where the secret message is hidden in audio carrier. This paper proposes an efficient audio steganography method that uses LSB technique. The proposed method enhances steganography performance by exploiting all carrier samples and balancing between hiding capacity and distortion ratio. It suggests an adaptive number of hiding bits for each audio sample depending on the secret message size, the cover carrier size, and the signal to noise ratio (SNR). Comparison results show that the proposed method outperforms state of the art methods
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