Diabetic retinopathy is a severe retinal disease that can blur or distort the vision of the patient. It is one of the leading causes of blindness. Early detection of diabetic retinopathy can significantly help in the treatment. The recent development in the field of AI and especially Deep learning provides ambitious solutions that can be exploited to predict, forecast and diagnose several diseases in their early phases. This work aims towards finding an automatic way to classify a given set of retina images in order to detect the diabetic retinopathy. Deep learning concepts have been used with a convolutional neural network (CNN) algorithm to build a multi-classification model that can detect and classify disease levels automatically. In this study, a CNN architecture has been applied with several parameters on a dataset of diabetic retinopathy with different structures. At the current stage of this work, obtained results are highly encouraging.

The design and the evaluation of communication protocols in WSNs is a crucial issue. Generally, researchers use simulation methods to evaluate them. However, formal modelling and analysis techniques are an efficient alternative to simulation methods. Indeed, these techniques allow performance evaluation and model verification. In this paper, a formal approach is proposed to modelling and to evaluating the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) MAC protocol with a star topology. Moreover, the proposed approach deals with some properties that are not stated in most existing works. The approach uses Hierarchical Timed Coloured Petri Nets (HTCPNs) formalism to model the protocol and exploits the CPN-Tools to analyse the generated models. HTCPNs provide timed aspect which facilitates the consideration of time constraints inherent to the CSMA/CA protocol.

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.