@article {43095,
	title = {Predicting an unstable tear film through artificial intelligence},
	journal = {Scientific Reports},
	volume = {12},
	year = {2022},
	month = {12/2022},
	pages = {21416},
	publisher = {Springer Nature},
	abstract = {Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence algorithms perform on clinical data related to dry eye disease. The data was processed and subjected to numerous machine learning classification algorithms with the aim to predict decreased tear film break-up time. Moreover, feature selection techniques (information gain and information gain ratio) were applied to determine which clinical factors contribute most to an unstable tear film. The applied machine learning algorithms outperformed baseline classifications performed with\&nbsp;ZeroR\&nbsp;according to included evaluation metrics. Clinical features such as ocular surface staining, meibomian gland expressibility and dropout, blink frequency, osmolarity, meibum quality and symptom score were recognized as important predictors for tear film instability. We identify and discuss potential limitations and pitfalls.},
	doi = {10.1038/s41598-022-25821-y},
	url = {https://www.nature.com/articles/s41598-022-25821-y},
	author = {Fineide, Fredrik and Stor{\r a}s, Andrea and Chen, Xiangjun and Morten S. {Magn{\o}} and Yazidi, Anis and Riegler, Michael and Tor Paaske {Utheim}}
}
@inproceedings {27639,
	title = {LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification},
	journal = {The Joint International Conference PDCAT-PAAP 2020},
	year = {2020},
	publisher = { Springer},
	abstract = {Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud-hosted infrastructures may face a long waiting time before the training completes or might not be able to train a model at all. Investigating novel ways to reduce the training time could be a potential solution to alleviate this drawback, and thus enabling more rapid development of new algorithms and models. In this paper, we propose LightLayers, a method for reducing the number of trainable parameters in deep neural networks (DNN). The proposed LightLayers consists of LightDense andLightConv2D layer that are as efficient as regular Conv2D and Dense layers, but uses less parameters. We resort to Matrix Factorization to reduce the complexity of the DNN models resulting into lightweight DNNmodels that require less computational power, without much loss in the accuracy. We have tested LightLayers on MNIST, Fashion MNIST, CI-FAR 10, and CIFAR 100 datasets. Promising results are obtained for MNIST, Fashion MNIST, CIFAR-10 datasets whereas CIFAR 100 shows acceptable performance by using fewer parameters.},
	keywords = {CIFAR-10, Convolutional  neural network, Deep  Learning, Fashion MNIST, Lightweight  model, MNIST, Weight decomposition},
	author = {Jha, Debesh and Yazidi, Anis and Riegler, Michael and Johansen, Dag and H{\r a}vard D. {Johansen} and Halvorsen, P{\r a}l}
}
@misc {26918,
	title = {QoE-Based Analysis of Real-Time Adaptive 360-Degree Video Streaming},
	year = {2019},
	address = {Proceedings of the 2019 on Wireless of the Students, by the Students, and for the Students Workshop, New York, USA},
	isbn = {978-1-4503-6929-9},
	doi = {10.1145/3349621.3355728},
	url = {https://doi.org/10.1145/3349621.3355728},
	author = {Midoglu, Cise and Klausen, Magnus and Alay, {\"O}zg{\"u} and Yazidi, Anis and Haugerud, Harek and Griwodz, Carsten}
}
