Synthesized and extending the Bidentate Schiff base complexes using multilayer feedforward neural network

Complexes of Pd(II) and Ni(II) have been synthesized with general composition ML2X2 (M =Pd(II), Ni(II); L = benzylsalicylideneimine and X = OCH3, F). All synthesized compoundshave been characterized using elemental analysis, magnetic susceptibility measurements,infrared and NMR spectral studies that led to the conclusion that the ligands act as bidentatemanner to form square planar geometry for all complexes. As an extending work, the modeldevelopment of these complexes using multilayer feedforward neural network wereperformed. NiL1d, PdL1d, NiL1c and PdL1c were fed to the training network as inputs andbacteria as output. Levenberg Marquardt training algorithm was used during the networktraining with 10 nodes in hidden layer. The results of testing network showed that theregression, R is 1, indicating that the developed model is good. This is supported by the small mean square error (MSE) is 1.948x10-28 at epochs 5. The finding in this study is significant, thus contributed to the design of antibacterial agent especially to the bidentate Schiff base complexes.Keywords: Schiff base, palladium(II), nickel(II), antibacterial, regression, neural network

Deep Divergence-Based Approach to Clustering

A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of research is in its infancy, and how to design and optimize suitable loss functions to train deep neural networks for clustering is still an open question. Our contribution to this emerging field is a new deep clustering network that leverages the discriminative power of information-theoretic divergence measures, which have been shown to be effective in traditional clustering. We propose a novel loss function that incorporates geometric regularization constraints, thus avoiding degenerate structures of the resulting clustering partition. Experiments on synthetic benchmarks and real datasets show that the proposed network achieves competitive performance with respect to other state-of-the-art methods, scales well to large datasets, and does not require pre-training steps