Personalized learning has been proving to be useful concept in the learning of a student.Artificial Intelligence (AI) which has revolutionized many aspects of our lives has also been glowingly used in the education sector.One of the fascinating AI technique, the Reinforcement Learning (RL) is considered as the perfect tool to develop personalized solution in the education.RL algorithms have the ability to take into account personal characteristics of each student.This work presents the development of personalized exam scheduler using RL.The intelligent examination scheduler consider several parameters for training such as age, academic year, past education performance, discipline, number of courses, and gap between two exams.The trained RL agent then able to provide examination schedule to a student depending on a student personal record, interests and abilities.The preliminary results are encouraging and more research would bring useful contribution of AI in various aspects of learning process of a student.

Every year about 30 million people travel by ship worldwide often in extreme weather conditions and polluted environments and many other factors that impact the health of passengers and crew staff.Such issues require medical staff for passenger health care.We introduce a model based on Reinforcement learning(RL) which is used in the dialogue system.We incorporate the Hierarchical reinforcement learning (HRL) model with the layers of Deep Q-Network for dialogue oriented diagnosis system.Policy learning is integrated as policy gradients are already defined.We created a two-stage hierarchical strategy.We used the hierarchical structure with double-layer policies for automatic disease diagnosis.A double layer means it splits the task into sub-tasks named high-state strategy and low-level strategy.It has a user simulator component that communicates with the patient for symptom collection low-level agents inquire about symptoms.Once it's done collecting it sends results to the high-level agent which activates the D-classifier for the last diagnosis.When it's done its sent back by the user simulator to patients to verify the diagnosis made.Every single diagnosis made has its reward that trains the system

In vehicular communication networks, centralized systems face significant security challenges, including privacy preservation, secure authentication, threats from compromised authorities, latency, and throughput.We propose a blockchainbased system that decentralizes control, enhances throughput, and optimizes latency.By leveraging the Avalanche consensus protocol, our solution assures efficient, secure, and robust communication within vehicular networks, mitigating risks associated with centralized control.Our proposed system achieves a substantial throughput, with the Practical Byzantine Fault Tolerance (PBFT) protocol registering 12.8 transactions per second (TPS), and the Avalanche protocol demonstrates an impressive 1007 TPS for 100 validators.Regarding the delay, PBFT experiences 6.61 seconds, whereas Avalanche protocol achieves a remarkably low delay of just one millisecond, both with 100 validators.These findings highlight the superiority of our proposed system in terms of low latency, and enhanced transaction throughput, essential for future vehicular communication systems.

A Wireless Sensor Network (WSN) is composed of distributed sensors with limited processing capabilities and energy restrictions. These unique attributes pose new challenges amongst which prolonging the WSN lifetime is one of the most important. Clustering is an energy efficient routing technique that has been widely applied to report data from the WSN nodes to a centralised Base Station. A plethora of different clustering protocols have been proposed. Some protocols are based on equal-sized clusters while others use clusters of unequal size. Some others make use of rotation techniques to reduce the amount of cluster head elections. When different clustering approaches are presented different simulation settings are used. In this paper we perform a comparison study of HEED based clustering protocols that are HEED, UHEED, RUHEED and a novel variation of R-HEED that is ER-HEED. We have considered the same network model, the same energy consumption model and we have compared the lifetime of the protocols by considering various case studies. Our comparison study shows that the selection of the protocol to be used depends on the case study and the WSN lifetime measure that is considered.