Now showing items 21-27 of 27

    • Supervised machine learning approach for effective supplier classification 

      Harikrishnakumar, Ramkumar; Dand, Alok; Nannapaneni, Saideep; Krishnan, Krishna K. (IEEE, 2019-12)
      Supplier assessment plays a critical role in the supply chain management, which involves the flow of goods and services from the initial stage (raw material procurement) to the final stage (delivery). Supplier assessment ...
    • A survey of biometric and machine learning methods for tracking students' attention and engagement 

      Villa, Maria; Gofman, Mikhail I.; Mitra, Sinjini; Almadan, Ali; Krishnan, Anoop; Rattani, Ajita (IEEE, 2021-02-23)
      The skills of focusing and paying attention are critical to student learning. According to Piontkowski et al. [40], "Educators often talk about attention as a general mental state in which the mind focuses on some special ...
    • A survey on machine and deep learning models for childhood and adolescent obesity 

      Siddiqui, Hera; Rattani, Ajita; Woods, Nikki Keene; Cure, Laila; Lewis, Rhonda K.; Twomey, Janet M.; Smith-Campbell, Betty; Hill, Twyla J. (IEEE, 2021-11-25)
      Childhood and adolescent obesity is a serious health problem that is on the rise at the global level. Earlier, certain diseases such as Type 2 diabetes, high blood pressure, and heart disease affected only adults, but now ...
    • The effectiveness of Smart Compose: An artificial intelligent system 

      Gnacek, Matt; Doran, Eric; Bommer, Sharon; Appiah-Kubi, Philip (Association for Industry, Engineering and Management Systems (AIEMS), 2020-06)
      The growth of artificial intelligence (AI) technologies in everyday life and manufacturing are expected to reduce the mental workload of a user or human operator and increase their efficiency. In industrial systems, such ...
    • The use of machine learning for electrical component end-of-life predictions 

      Rust, Ryan; Elshennawy, Ahmad (Industry, Engineering & Conference Management Systems Conference, 2021-03)
      Diminishing Manufacturing Sources and Material Shortages (DMSMS), also referred to as obsolescence, is a sector of product sustainment that is receiving more attention as certain technologies continue to have longer and ...
    • Utilizing machine learning to predict offshore wind farm power output for European countries 

      Ozturk, Oktay; Hangun, Batuhan; Shoaeinaeini, Maryam (IEEE, 2022-10-25)
      One might assume that the types of energy resources used by a country and its level of development are related since developed nations focus on using alternative energy sources like the wind to produce green and sustainable ...
    • Wind power prediction in different months of the year using machine learning techniques 

      Pun, Kesh; Basnet, Saurav M.S.; Jewell, Ward T. (IEEE, 2021-04-19)
      Integration of wind power into the grid has been rapidly increasing at both the transmission as well as distribution levels. Wind power generation is variable, nonlinear, and intermittent in nature. The monthly average and ...