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The cobalt‐zinc ferrite (CZF) nanomaterials were prepared by citrate‐gel method, and further calcined at 600°C. The single‐phase cubic spinel structure of CZF was confirmed using the X‐ray diffraction pattern. The average crystallite size was found to be in the range of 22‐29 nm. The surface morphology was examined using the scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The average particle size of Co0.6Zn0.4Fe2O4 was determined to be 19 nm using TEM study which is supporting the average crystallite size measured from the X‐ray diffraction studies. The Fourier‐transform infrared spectra revealed the two strong absorption bands in the series of ferrites between 4500 and 500 cm−1 and these are responsible for the characteristic of spinel ferrites. The presence of elements Cu, Zn, and Co of CZF was confirmed by the elemental spectral signals of energy dispersive spectroscopy. At room temperature, the magnetic measurements of pure ZnFe2O4 and Co0.6Zn0.4Fe2O4 were evaluated based on hysteresis curves (M‐H curves). The results expressed that the addition of nonmagnetic Zn2+ ions increases the magnetic behavior in the mixed CZF samples. The antimicrobial activity of the ZnFe2O4 and Co0.6Zn0.4Fe2O4 nanoferrites was tested against harmful microbes.

This paper describes an approach of using genetic algorithms (GAS) for image segmentation. The approach proposes to solve the problem based on the idea of using genetic programming to discover effective problem-speciJic filters capable of highly and selectively emphasizing some characteristics of the image. Genetic Algorithms work on the principle of simulation of the evolution of individual structures via processes such as selection, mutation, and reproduction. Implementation of image segmenlation using GAS involves rdentibing a suitable binary coding strategy, defining a fitness evaluation function, designing a 'population ' (set of chromosomes), defining genetically inspired operators such as crossover and mutation to evolve new population and deciding the termination $ the evolutionary search for the optimal solution. Segmentation is a non polynomial type of problem. This paper describes the use of genetic algorithms in the randomized search of solution to the segmentation problem, with the initial population built using the state space techniques and then evolved using genetic operators and the fitness function. Accuracy of the results is found to vary with the fitness function. The result is got in the binary form due the coding strategy adapted. A mask is then implemented to extract and reconstruct the segmented image from the original image.

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