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Reinforcement Learning based Intelligent System for Personalized Exam Schedule

Barone Marco, Ciaschi Matteo, Ullah Zaib, Piccardi Armando

Annals of Computer Science and Information Systems

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2024

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Vol. 39

549-–553

EN

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.

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Efficiency and Reliability of Avalanche Consensus Protocol in Vehicular Communication Networks

Ullah Saeed, Ullah Zaib, Waqas Abdullah

Annals of Computer Science and Information Systems

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2024

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Vol. 39

695--700

EN

An vehicular communication networks, security issues such as privacy preservation, secure authentication, and threats from insiders and compromised authorities pose significant challenges due to the centralized nature of existing systems. Addressing these concerns, we propose a blockchain based system that decentralizes control and enhances security using the Avalanche consensus protocol, known for its innovation and scalability. Our proposed system achieves a substantial throughput, with PBFT registering 12.8 transactions per second (TPS) and Avalanche demonstrating an impressive 1007 TPS for 100 validators. In terms of delay, PBFT experiences 6.61 seconds, whereas Avalanche achieves a remarkably low delay of just 1 millisecond, both with 100 validators. These findings underscore the superiority of our proposed system, offering heightened security, privacy, and transaction throughput essential for future vehicular communication systems.

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Disease Diagnosis On Ships Using Hierarchical Reinforcement Learning

Batool Farwa, Hasan Tehreem, Tretola Giancarlo, Ullah Zaib, Abbas Musarat

Annals of Computer Science and Information Systems

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2024

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Vol. 39

555-–559

EN

Every year about 30 million people travel by ships world wide often in extreme weather conditions and also in polluted environment due to ship's fuel combustion and many other factors that impacts the health of both passengers and crew staff so there is a need of medical staff but that's not always available so we introduce an a model based on Reinforcement learning(RL) that is used as the key approach in dialogue system.We incorporates 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 two stage hierarchical strategy.We used the hierarchical structure with double layer policies for automatic disease diagnosis.Double layer means it splits the task into sub-tasks named as high-state strategy and low level strategy.It has a component called user simulator that communicates with patient for symptom collection low level agent inquire symptoms.Once its done collecting it sends results to high level agent which activates the D-classifier for last diagnosis.When its done its send back by user simulator to patients to verify diagnosis made.Every single diagnosis made has its own reward that trains the system.