Artificial Intelligence is revolutionizing the world. In the last decades, it is applied in almost all fields especially in medical prediction. Researchers in artificial intelligence have exploited predictive approaches in the medical sector for its vital importance in the process of decision making. Medical prediction aims to estimate the probability of developing a disease, to predict survivability and the spread of a disease in an area. Prediction is at the core of modern evidence-based medicine, and healthcare is one of the largest and most rapidly growing segments of AI. Application of technologies such as genomics, biotechnology, wearable sensors, and AI allows to: In this research, an overview of applied methods on the management of diseases is presented. The most used methods are Artificial Intelligence methods such as machine learning and deep learning techniques which have improved diagnosis and prognosis efficiency.

The rapid advancement of technology holds great promise for various types of users, clients, or service providers. Intelligent robots, whether virtual or physical, can simplify the reservation process. With the development of machines and processing tools, natural language processing (NLP) and natural language understanding (NLU) have emerged to help people comprehend spoken language through machines. In order to facilitate seamless human-machine interaction, we aim to address customer needs through a chatbot. The objective of this paper is to incorporate sentiment analysis techniques with deep learning algorithms to cater to customers’ needs during message exchanges. This study aims to create an intelligent chatbot to engage customers during their routine operations and offer support. In addition, it offers to companies a manner to detect sarcastic messages. The proposed chatbot utilizes deep learning techniques to predict users’ intentions based on the questions asked and provide a helpful and convenient answer. A new chatbot for the customer is presented to overcome with challenges related to a wrong statement like sarcastic one and feedback towards user messages. A comparison between deep and transfer learning gives a new insight to include sentiments and sarcasm detection in the conversion process.

Nowadays, service composition is one of the major problems in the Cloud due to the exceptional growth in the number of services deployed by providers. Recently, atomic services have been found to be unable to deal with all client requirements. Traditional service composition gives the clients a composite service without non-functional parameters. To respond to both functional and non-functional parameters, we need a service composition. Since web services cannot communicate with each other or participate dynamically to handle changes service parameters in service composition, this issue has led us to use a dynamic entity represented by an agent based on dynamic architecture. This work proposes an agent-based architecture with a new cooperation protocol that can offer an automatic and adaptable service composition by providing a composite service with the maximum quality of service. The implementation of this model has been provided in order to evaluate the authors' system. The obtained results demonstrate the effectiveness of their proposed system.

Nowadays, cloud computing is regarded as a new paradigm in the field of information technology. Since the emergence of this paradigm, most of corporations have started using it through the deployment or the use of its services. Furthermore, service deployment in the cloud forms its kernel function. To ensure a good function of service deployment we need to combine a set of services. In fact, to find a limited number candidate services constitutes a problem due to the absence of a unified service description. In this paper, we propose a solution to solve the deployment problem by considering it as a service composition taking into account the quality of service. Since we have many atomic services, this paper transforms this composition problem into an optimisation problem. Moreover, to reduce the number of service candidates we propose a new service description language to assist the discovery process. The implementation of this model has been provided in order to evaluate our system. The obtained results demonstrate the effectiveness of our proposed system.