Taoufik Yeferny

Sofian Hamad, Taoufik Yeferny

Advancements in artificial intelligence (AI), speech recognition systems (ASR), and machine learning have enabled the development of intelligent computer programs called chatbots. Many chatbots have been proposed to provide different services in many areas such as customer service, sales and marketing. However, the use of chatbot as advisers in the field of information security is not yet considered. Furthermore, people, especially normal users who have no technical background, are unaware about many of aspects in information security. Therefore, in this paper we proposed a chatbot that acts as an adviser in information security. The proposed adviser uses a knowledge base with json file. Having such chatbot provides many features including raising the awareness in field of information security by offering accurate advice, based on different opinions from information security expertise, for many users on different. Furthermore, this chatbot is currently deployed through Telegram platform, which is one of widely used social network platforms. The deployment of the proposed chatbot over different platforms is considered as the future work.

Achraf Ben Miled, Taoufik Yeferny, Amira ben Rabeh

Alzheimer's disease is a neurogenerative disease that alters memories, cognitive functions leading to death. Early diagnosis of the disease, by detection of the preliminary stage, called Mild Cognitive Impairment (MCI), remains a challenging issue. In this respect, we introduce, in this paper, a powerful classification architecture that implements the pre-trained network AlexNet to automatically extract the most prominent features from Magnetic Resonance Imaging (MRI) images in order to detect the Alzheimer's disease at the MCI stage. The proposed method is evaluated using a big database from OASIS Database Brain. Various sections of the brain: frontal, sagittal and axial were used. The proposed method achieved 96.83% accuracy by using 420 subjects: 210 Normal and 210 MRI