Melissopalynology, or pollen analysis of honey, is one of the areas that benefited greatly from image processing and analysis techniques, where melissopalynology is the science that studies the pollen contained in honey, using a microscopic examination. Nowadays, developing an automatic classification system for pollen identification presents a challenge. This article presents a metaheuristic for image segmentation to detect pollen grains in images. It is a swarm intelligence technique inspired from grey wolf hunting behavior in nature, centered around respecting the hierarchy of a pack. It was tested on a set of microscopic images of pollen grains. To evaluate pollen detection, we represented the detected pollen grains using two methods, grey-level based representations where we kept grey value of each pixel, and a binary mask-based technique, where a pixel could have only two values (1 or 0). Then, we used a convolutional neural network (CNN) technique for image classification to predict the specie of each pollen. The proposed system was tested on a set of microscopic images of pollen grains. The obtained performance measures of the system proved that the system is very successful.

Measuring <span>semantic similarity between terms is a crucial step in information retrieval and integration since it necessitates semantic content matching. Even though several models have been proposed to measure semantic similarity, these models are not able to effectively quantify the weight of relevant items that affect the semantic similarity judgment process. In this study, we present a new method for measuring semantic similarity between cross-ontologies, that consists of hybridizing node-based approaches such as WuP and Reda with the weight of similarity computed using WordNet. The proposed approach has been experimented to show its efficiency with two ontologies, configuration management tool (CMT) and ConfOf, from the conference domaine in the web ontology language (OWL) ontologies benchmark OAEI 2015 and evaluated using two metrics: density and cohesion.</span>

Melissopalynology is a field that studies pollen grain origins to identify their species. It consists of studying either the chemical composition of each grain, or their shapes using microscopic images. This paper presents a system of pollen identification based on the microscopic images, it is divided into two parts, first part is the pollen detection using a thresholding method with simulated annealing algorithm. The second step is the pollen classification, in which we used deep convolutional neural network to extract features from the detected pollen grains and represent them in numerical vectors, therefore, we can use these vectors to classify them based on fully connected neural network, SVM or similarity calculation. The obtained results showed a high efficiency of the neural network in which it could recognize 98.07% of the pollen species compared not just to SVM and similarity methods, but also to works from literature.

In this paper, authors are interested in the problem of lossless compression of unlabeled semi-ordered trees. Semi-ordered trees are a class of trees that present an order between some sibling while some other sibling are unordered. They offer a wide possibility of applications especially for the representation of plants architecture. Authors show that these trees present remarkable compression properties covering those of ordered and unordered trees. To illustrate this approach, authors apply these notions to a particular class of semi-ordered trees which is the most studied branching structure particularly for a botanical motivation, namely axial trees.

Pollen recognition is one of the most active research areas in field of ecological modeling. It is done either via microscopic images analysis of pollen grains, or via chemical components analysis. In this paper, we were interested in pollen images analysis, in which we proposed an approach for image segmentation in order to detect pollen grains in the microscopic images. The approach starts by generating two pixels using genetic algorithms where one pixel of the selected ones is a pollen pixel while the other is background pixels, then we used kmeans algorithm for image pixels clustering to segment the input image, after that we classified the segmented images using machine learning technics, and finally, we used taboo search to save the best pixels chosen by genetic algorithm based on the obtained accuracy as fitness function. The obtained results proved the efficiency of the proposed system where it could recognize 96.4% of the pollen grains.