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Mr. Dr. Health-Assistant Chatbot Hossain, Md Meem; Krishna Pillai, Salini; Dansy, Sholestica Elmie; Bilong, Aldrin Aran; Panessai, Ismail Yusuf
International Journal of Artificial Intelligence Vol 8 No 2: December 2021
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-0802.301

Abstract

Research says 60% of visits to a doctor are for simple small-scale diseases, 80% of which can be diagnosed at home using simple check-up. These diseases mostly include common cold and cough, headache, abdominal pains etc. Whereas, chat-bots in healthcare are highly in demand, which functioning can offer various services from symptom checking and appointment scheduling. Therefore, the purpose of the research aims to design, develop and evaluate a health-assistant Chat-bot Application entitled “MR.Dr.” that helps users to ask any personal query related to healthcare without physically available to the hospital. MR.Dr. is evaluated in term of usability. 30 respondents attended the survey of usability evaluation. In the system usability scale MR.Dr. achieved 87.6 % rating which means Grade A (excellent). User's feedback level was pretty satisfying where 24/7 service is the highest one.
A Deep Reinforcement Learning Agent for Snake Game Hossain, Md Meem; Fakokunde, Akinwumi; Olaolu, Omololu Isaac
International Journal of Artificial Intelligence Vol 10 No 2: December 2023
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01002.565

Abstract

After watching AlphaGo a Netflix documentary which presents how AlphaGo is an AI computer game developed by deep-mind technologies based on deep reinforcement learning (DRL). Since then, my interest in reinforcement learning has been growing. In this project, I will apply reinforcement learning to develop an agent to play snake game. Where Deep learning will implement a neural Network to help the agent (snake) to learn what action must take to get a state. If we describe deep reinforcement learning (DRL) model where agent interacts with an environment and chooses an action. Based on action, agents receive feedback from the environment as states (or perceives) and rewards. A state = an array with 11 input values, each input values represent a neural network that provides an output of 3 values, each one represents three possible actions the agent (snake) can take (Straight, Right Turn and Left Turn).