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The instrument provides significantly valuable diagnostic parameters in detecting acute Plasmodium vivax malaria; however, it is not very useful for acute falciparum malaria infection. It is suggested that the laboratories using the hematology analyzers should be aware of such specific parameters, even in the absence of a clinical request.

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Plant species detection aims at the automatic identification of plants. Although a lot of aspects like leaf, flowers, fruits, seeds could contribute to the decision, but leaf features are the most significant. As a plant leaf is always more accessible as compared to other parts of the plants, it is obvious to study it for plant identification. The present paper introduced a novel plant species classifier based on the extraction of morphological features using a Multilayer Perceptron with Adaboosting. The proposed framework comprises pre-processing, feature extraction, feature selection, and classification. Initially, some pre-processing techniques are used to set up a leaf image for the feature extraction process. Various morphological features, i.e., centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. Different classifiers, i.e., k-NN, Decision Tree and Multilayer perceptron are employed to test the accuracy of the algorithm. AdaBoost methodology is explored for improving the precision rate of the proposed system. Experimental results are obtained on a public dataset (FLAVIA) downloaded from http://flavia.sourceforge.net/. A precision rate of 95.42% has been achieved using the proposed machine learning classifier, which outperformed the state-ofthe-art algorithms.

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Bacterial Foraging Optimization (BFO) is optimization technique proposed by K. M. Passino in 2002 To tackle complex search problems of the real world, scientists have been drawing inspiration from nature and natural creatures for years. Bacterial Foraging Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. A Color images Quantization is necessary if the display on which a specific image is presented works with less colors than the original image. While a lot of color reduction techniques exist in the literature, they are mainly designed for image compression as they tend to alter image color structure and distribution, the researchers are always finding alternative strategies for color quantization so that they may be prepared to select the most appropriate technique for the color quantization. The objective of this research work, is to implement a new algorithm for Color Image Quantization based on Bacteria Foraging Optimization. To compare the designed algorithm with other swarm intelligence techniques and to validate the proposed work. The proposed algorithm is then applied to commonly used images including the phantom images. The conducted experiments indicate that proposed algorithm generally results in a significant improvement of image quality compared to other well-known approaches.