Browsing USN Open Archive by Author "Alam, M. Afshar"

Now showing items 1-4 of 4

    • Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification 

      Siddiqui, Farheen; Mohammad, Awwab; Alam, M. Afshar; Naaz, Sameena; Agarwal, Parul; Sohail, Shahab Saquib; Madsen, Dag Øivind (Peer reviewed; Journal article, 2023)
      BACKGROUND: Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to ...

    • Human-Centric Aggregation via Ordered Weighted Aggregation for Ranked Recommendation in Recommender Systems 

      Sohail, Shahab Saquib; Aziz, Asfia; Ali, Rashid; Hasan, S. Hamid; Madsen, Dag Øivind; Alam, M. Afshar (Peer reviewed; Journal article, 2023)
      In this paper, we propose an approach to recommender systems that incorporates human-centric aggregation via Ordered Weighted Aggregation (OWA) to prioritize the suggestions of expert rankers over the usual recommendations. ...

    • The scholarly footprint of ChatGPT: a bibliometric analysis of the early outbreak phase 

      Farhat, Faiza; Silva, Emmanuel Sirimal; Hassani, Hossein; Madsen, Dag Øivind; Sohail, Shahab Saquib; Himeur, Yassine; Alam, M. Afshar; Zafar, Aasim (Peer reviewed; Journal article, 2023)
      This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study zooms in on the early outbreak phase from when ChatGPT was ...

    • Toward optimizing scientific workflow using multi-objective optimization in a cloud environment 

      Ghafir, Shabina; Alam, M. Afshar; Siddiqui, Farheen; Naaz, Sameena; Sohail, Shahab Saquib; Madsen, Dag Øivind (Peer reviewed; Journal article, 2024)
      Scientific workflows are a common and critical part of scientific computing, involving complex computations and oversized and distributed computing resources. Efficient workflow execution requires scheduling algorithms ...