• Neural Collaborative Filtering and Autoencoder Enabled Deep Learning Models for Recommender Systems 

  Arnold, Kwofie (AUST, 2022-11-15)

  Finding important and useful information is getting harder as much more information is available online. The challenge for content producers is to deliver the appropriate content to the appropriate consumers while making ...
  
  
• A Lightweight Convolutional Neural Network for Breast Cancer Detection Using Knowledge Distillation Techniques 

  Modu, Falmata (AUST, 2023-01-23)

  The second most heterogeneous cancer ever discovered is Breast Cancer (BC). BC is a disease that develops from malignant tumors when the breast cells begin to grow abnor-mally. Although it grows in the breast, it can spread ...
  
  
• Algorithms For Approximation of J-Fixed Points of Nonexpansive - Type Maps, Zeros of Monotone Maps, Solutions of Feasibility and Variational Inequality Problems 

  Nnakwe, Monday Ogudu (AUST, 2019-07-05)

  It is well known that many physically significant problems in different areas of research can be transformed at equilibrium state into an inclusion problem of the form 0 ∈ Au, where A is either a multi-valued accretive map ...
  
  
• A Performant Predict Analytics Approach to Recommender Systems Using Deep Learning Methods 

  Obaje, Williams Usman (AUST, 2022-09-05)

  Recently, Massive amounts of data have been generated as a result of the frequency at which the amount of information available digitally is advancing. To enable users to effectively utilise the huge amount of information, ...
  
  
• Cloud Computing Adoption in Nigeria Universities: A Conceptualized Model Based on Stakeholder Perceptions 

  Umar Ibrahim, Hajara (AUST, 2022-12-09)

  Cloud computing is a technology that represents a shift from traditional ownership of infrastructure and other resources to a more scalable pattern in which computing resources are leased online to businesses on a pay-as-you-go ...
  
  

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