Recently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to use a feature selection algorithm in data mining and machine learning tasks, since it helps to reduce the data dimensionality and to increase the algorithm performance such as a pixel classification algorithm. Therefore, in this paper we propose a comparative study of two conventional feature selection algorithms, genetic algorithm and ReliefF algorithm, using a classical pixel-labeling scheme based on analyzing and selecting texture features. The two assessed feature selection algorithms in this study have been applied on a training set of the HBR dataset in order to deduce the most selected texture features of each analyzed texture-based feature set. The evaluated feature sets in this study consist of numerous state-of-the-art texture features (Tamura, local binary patterns, gray-level run-length matrix, auto-correlation function, gray-level co-occurrence matrix, Gabor filters, Three-level Haar wavelet transform, three-level wavelet transform using 3-tap Daubechies filter and three-level wavelet transform using 4-tap Daubechies filter). In our experiments, a public corpus of historical document images provided in the context of the historical book recognition contest (HBR2013 dataset: PRImA, Salford, UK) has been used. Qualitative and numerical experiments are given in this study in order to provide a set of comprehensive guidelines on the strengths and the weaknesses of each assessed feature selection algorithm according to the used texture feature set.

Computer aided diagnosis of breast cancer is becoming increasingly a necessity given the exponential growth of performed mammograms. In particular, the breast mass diagnosis and classification arouse nowadays a great interest. Indeed, the complexity of processed mass shapes and the difficulty to distinguish between them require the use of appropriate descriptors. In this paper, suitable characterization methods for breast pathologies are proposed and the study of different classification methods is addressed. In order to analyze the mass shapes, a segmentation is performed manually. Once the images are segmented, a study of various descriptors proposed in the literature is conducted. In order to compare different approaches of characterization, a comparative study is performed. The descriptors commonly used in the breast cancer field are compared to test their ability to characterize the breast lesions. Obtained results show that statistical approaches of texture provides the best classification result.

The evolution of digital technology and the desire to maintain an easy access to scanned documents implies the interest of Document Image Retrieval (DIR). In this paper, we propose a fuzzy graph-based document retrieval approach for determining similarity between document images for content-based document retrieval. To model the structure of document images, we have opted to use Attributed Relational Graphs (ARGs). For each document region (text, graphic, etc) we associate a node which is characterized by a set of fuzzy membership degrees reflecting low-level properties (texture, shape, color, etc). We use this fuzzy description in order to guarantee more robustness against the eventual segmentation errors which may be occurred after the segmentation of document regions. ARGs edges represent spatial relationships between regions which have common boundaries. Finally, we have developed a tree-search based optimal matching algorithm, which allows the search for document according to its structure. The database used for experiments is composed of segmented Coran document images from the National Library of Tunisia. Different weights are assigned for regions and edges according to their relative importance. The results obtained demonstrate the effectiveness of fuzzy graph-based document retrieval. Such an approach is very useful for several applications in many fields.

The objective of this paper is to design and develop an approach to estimate the Remaining Useful Life (RUL) of an industrial equipment evolving in a Cyber-Physical System (CPS). To do so, this work aims to predict failures and malfunctions of an industrial equipment, as well as evaluating all the main underlying causes. The system will also identify the actions to be taken in order to maintain the system at a certain level of performance and guarantee that it will operate in the most efficient way, which implies a study of all aspects: reliability, availability and maintenance. Existing works are often limited to the estimation phase of the RUL, with only a few case studies (such as ball bearings and Proof Of Concept(PoC)) available and a significant lack of data. The innovation of this research work will be to design and develop diagnostic and prognostic approaches based on Deep Learning (DL)(e.g. Boltzmann Machine, Recurrent Networks and Echo State Network (ESN)) for the maintenance and health management of industrial equipment evolving in a CPS.