Browsing by Author "Sulaiman, Riza"

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Citation Count: 0
An end-to-end categorizing strategy for green energy sources: Picture q-rung orthopair fuzzy EXPROM-II: MADA approach
(Elsevier, 2024) Parthasarathy, Thirumalai Nallasivan; Narayanamoorthy, Samayan; Sulaiman, Riza; Elamir, Amir Mohamed; Ahmadian, Ali; Kang, Daekook
In today's world, people's need for sustainable energy is crucial. In India, people utilizing 73% of electricity from fossil fuels is the biggest hazard to the environment. In order to partly gratify the nation, policymakers have decided to invest in sustainability renewable energy sources rather than non-renewable resources for the production of energy. There five distinct kinds of green energy sources. The research key contribution is selecting the best renewable energy sources among the five different categories. Therefore, here we form a mathematical fuzzy model to identify the decision received from hesitating knowledge in the form of an Picture Q -rung orthopair fuzzy set (PQROFS) is engaged to handle the uncertain data in decision makers' thoughts. A gathering of relevant judgment Multi -Attribute Decision Analysis (MADA) is employed. Attributes play a prominent role in DM for ranking the alternatives. Here we have twelve attributes and five alternatives. An objective CRiteria Importance Through Intercriteria Correlation (CRITIC) method is employed for attributes priority determination and EXtension of the PROMethee-II (EXPROM-II) method an method is determining the alternative from the preference between each alternative and novel methodology. Ultimately, to validate the proposed results, compared the proposed results with other existing MCDA method results.
Citation Count: 0
A novel approach for self-driving car in partially observable environment using life long reinforcement learning
(Elsevier, 2024) Quadir, Md Abdul; Jaiswal, Dibyanshu; Mohan, Senthilkumar; Innab, Nisreen; Sulaiman, Riza; Alaoui, Mohammed Kbiri; Ahmadian, Ali
Despite ground-breaking advancements in robotics, gaming, and other challenging domains, reinforcement learning still faces significant challenges in solving dynamic, open-world problems. Since reinforcement learning algorithms usually perform poorly when exposed to new tasks outside of their data distribution, continuous learning algorithms have drawn significant attention. In parallel with work on lifelong learning algorithms, there is a need for challenging environments, properly planned trials, and metrics to measure research success. In this context, a Deep Asynchronous Autonomous Learning System (DAALS) is proposed in this paper for training a selfdriving car in a partially observable environment for real-world scenarios in a continuous state-action space. To cater to three different use cases, three different algorithms were used. To train their agents for learning and upgrading discrete state policies, DAALS used the Asynchronous Advantage Stager Reviewer (AASR) algorithm. To train its agent for continuous state spaces, DAALS also uses an Extensive Deterministic Policy Gradient (EDPG) algorithm. To train the agent in a lifelong form of learning for partially observable environments, DAALS uses a Deep Deterministic Policy Gradient Novel Lifelong Learning Algorithm (DDPGNLLA). The system offers flexibility to the user to train the agents for both discrete and continuous state-action spaces. Compared to previous models in continuous state-action spaces, Deep deterministic policy gradient lifelong learning algorithm outperforms previous models by 46.09%. Furthermore, the Deep Asynchronous Autonomous System tends to outperform all previous reinforcement learning algorithms, making our proposed approach a real-world solution. As DAALS has tested on number of different environments it provides the insights on how modern Artificial Intelligence (AI) solutions can be generalized making it one of the better solutions for AI general domain problems.