• Predicting Cell Cleavage Timings from Time-Lapse Videos of Human Embryos 

      Sharma, Akriti; Ansari, Ayaz Z.; Kakulavarapu, Radhika; Stensen, Mette Haug; Riegler, Michael; Hammer, Hugo Lewi (Peer reviewed; Journal article, 2023)
      Assisted reproductive technology is used for treating infertility, and its success relies on the quality and viability of embryos chosen for uterine transfer. Currently, embryologists manually assess embryo development, ...
    • Prediction of cloud fractional cover using machine learning 

      Svennevik, Hanna; Riegler, Michael A.; Hicks, Steven; Storelvmo, Trude; Hammer, Hugo L. (Big Data and Cognitive Computing;Volume 5 / Issue 4, Peer reviewed; Journal article, 2021-11-03)
      Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming ...
    • Synthesizing a Talking Child Avatar to Train Interviewers Working with Maltreated Children 

      Salehi, Pegah; Hassan, Syed Zohaib; Lammerse, Myrthe; Shafiee Sabet, Saeed; Riiser, Ingvild; Røed, Ragnhild Klingenberg; Sinkerud Johnson, Miriam; Hicks, Steven; Thambawita, Vajira; Powell, Martine; Lamb, Michael E.; Baugerud, Gunn Astrid; Halvorsen, Pål; Riegler, Michael (Big Data and Cognitive Computing;Volume 6 / Issue 2, Peer reviewed; Journal article, 2022-06-01)
      When responding to allegations of child sexual, physical, and psychological abuse, Child Protection Service (CPS) workers and police personnel need to elicit detailed and accurate accounts of the abuse to assist in ...
    • Unraveling the Impact of Land Cover Changes on Climate Using Machine Learning and Explainable Artificial Intelligence 

      Kolevatova, Anastasiia; Riegler, Michael; Cherubini, Francesco; Hu, Xiangping; Hammer, Hugo Lewi (Big Data and Cognitive Computing;Volume 5, Issue 4, Peer reviewed; Journal article, 2021-10-15)
      A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time ...