@article { ToMC2023,
	title = {Bottleneck Identification in Cloudified Mobile Networks based on Distributed Telemetry},
	journal = {Transactions on Mobile Computing},
	year = {2023},
	pages = {1{\textendash}18},
	publisher = {IEEE},
	abstract = {Cloudified mobile networks are expected to deliver a multitude of services with reduced capital and operating expenses. A characteristic example is 5G networks serving several slices in parallel. Such mobile networks, therefore, need to ensure that the SLAs of customised end-to-end sliced services are met. This requires monitoring the resource usage and characteristics of data flows at the virtualised network core, as well as tracking the performance of the radio interfaces and UEs. A centralised monitoring architecture can not scale to support millions of UEs though. This paper, proposes a 2-stage distributed telemetry framework in which UEs act as early warning sensors. After UEs flag an anomaly, a ML model is activated, at network controller, to attribute the cause of the anomaly. The framework achieves 85\% F1-score in detecting anomalies caused by different bottlenecks, and an overall 89\% F1-score in attributing these bottlenecks. This accuracy of our distributed framework is similar to that of a centralised monitoring system, but with no overhead of transmitting UE-based telemetry data to the centralised controller. The study also finds that passive in-band network telemetry has the potential to replace active monitoring and can further reduce the overhead of a network monitoring system.},
	keywords = {Anomaly, Bottleneck, classification, congestion, Mobile Cloud Network, Telemetry},
	issn = {1558-0660},
	doi = {10.1109/TMC.2023.3312051},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10239332},
	author = {Fida, Mah-rukh and Azza Hassan Moh {Ahmed} and Dreibholz, Thomas and Andr{\'e}s Felipe {Ocampo} and Ahmed Mustafa {Elmokashfi} and Foivos Ioannis {Michelinakis}}
}
@inbook {42517,
	title = {Identifying Ionic Channel Block in a Virtual Cardiomyocyte Population Using Machine Learning Classifiers},
	booktitle = {Computational Physiology - Simula Summer School 2021 - Student Reports},
	volume = {12},
	year = {2022},
	month = {05/2022},
	pages = {91 - 109},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	keywords = {action potential, AI, cardiomyocyte, classification},
	isbn = {978-3-031-05163-0},
	issn = {2512-1677},
	doi = {10.1007/978-3-031-05164-710.1007/978-3-031-05164-7_8},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-05164-7_8},
	author = {Singstad, Bj{\o}rn-Jostein and Bendik Steinsv{\r a}g {Dalen} and Sihra, Sandhya and Forsch, Nickolas and Wall, Samuel},
	editor = {Kimberly J {McCabe}}
}
@inproceedings {42519,
	title = {Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain},
	journal = {2021 Computing in Cardiology (CinC)2021 Computing in Cardiology (CinC)},
	year = {2021},
	month = {10/2022},
	publisher = {IEEE},
	address = {Brno, Czech Republic},
	abstract = {Over the last decade, AI has shown its feasibility in classifying heart-related diagnoses from ECGs. Earlier studies have mainly focused on 12 and 2-lead ECGs, but we aim to classify 26 different diagnoses based on 12, 6, 4, 3, and 2-lead ECGs in this study. We trained a supervised model on a dataset containing 88 253 ECGs with 26 different diagnoses used as ground truth. The training and classification steps can be separated into three parts. (1) Pan Tompkins algorithm was used to find peaks and calculate the average heart rate. (2) The average heart rate and the Fourier transformed ECG signal was used to train convolutional neural networks (CNN) system that classified the ECGs with regular or irregular rhythms. 9 out of 26 classes were classified in this step. (3) Finally, CNN models in a classifier chain were trained to classify the remaining 17 diagnoses. The classification results from step 2 and the raw ECG signal were used as input to the classifier chain in step 3. Our team, CardiOUS, achieved a PhysioNet Challenge score of -0.63 for all sets of leads on the hidden test set. Based on the test score, our team placed at 38th out of 39 teams in the official ranking.},
	keywords = {AI, Cardiology, classification, deep learning, ECG},
	isbn = {978-1-6654-7916-5},
	issn = {2325-887X},
	doi = {10.23919/CinC53138.2021.9662750},
	url = {https://ieeexplore.ieee.org/abstract/document/9662750},
	author = {Singstad, Bj{\o}rn-Jostein and Eraraya Morenzo {Muten} and Pal Haugar {Brekke}},
	editor = {Pickett, Christine}
}
@article {28007,
	title = {Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events},
	journal = {International Journal of Semantic Computing},
	volume = {15},
	number = {2},
	year = {2021},
	month = {Jan-06-2021},
	pages = {161 - 187},
	publisher = {World Scientific},
	abstract = {Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.},
	keywords = {3d CNN, classification, Detection, soccer events, spotting},
	issn = {1793-351X},
	doi = {10.1142/S1793351X2140002X},
	url = {https://www.worldscientific.com/doi/abs/10.1142/S1793351X2140002X},
	author = {Olav A. Nerg{\r a}r {Rongved} and Hicks, Steven and Thambawita, Vajira and H{\r a}kon Kvale {Stensland} and Zouganeli, Evi and Johansen, Dag and Midoglu, Cise and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {naseer2020:mode,
	title = {Mode Inference using enhanced Segmentation and Pre-processing on raw Global Positioning System data},
	journal = {Measurement and Control},
	volume = {53},
	year = {2020},
	month = {08/2020},
	pages = {1144 - 1158},
	publisher = {SAGE journals},
	abstract = {Many applications use the Global Positioning System data that provide rich context information for multiple purposes. Easier availability and access of Global Positioning System data can facilitate various mobile applications, and one of such applications is to infer the mobility of a user. Most existing works for inferring users{\textquoteright} transportation modes need the combination of Global Positioning System data and other types of data such as accelerometer and Global System for Mobile Communications. However, the dependency of the applications to use data sources other than the Global Positioning System makes the use of applications difficult if the peer data source is not available. In this paper, we introduce a new generic framework for the inference of transportation mode by only using the Global Positioning System data. Our contribution is threefold. First, we propose a new method for Global Positioning System trajectory data preprocessing using the grid probability distribution function. Second, we introduce an algorithm for the change point{\textendash}based trajectory segmentation, to more effectively identify the single-mode segments from Global Positioning System trajectories. Third, we introduce new statistical-based topographic features that are more discriminative for transportation mode detection. Through extensive evaluation on the large trajectory data GeoLife, our approach shows significant performance improvement in terms of accuracy over state-of-the-art baseline models.},
	keywords = {classification, global positioning system trajectory, Machine learning, statistical analysis, transportation mode},
	issn = {0020-2940},
	doi = {10.1177/0020294020918324},
	url = {https://journals.sagepub.com/doi/10.1177/0020294020918324},
	author = {Nawaz, Asif and Zhiqiu, Huang and Senzhang, Wang and Hussain, Yasir and Naseer, Amara and Izhar, Muhammad and Khan, Zaheer},
	editor = {Onder, Mehmet}
}
@inproceedings {26254,
	title = {Using preprocessing as a tool in medical image detection},
	journal = {MediaEval 2018},
	year = {2018},
	month = {10/2018},
	publisher = {MediaEval},
	address = {Nice, France},
	abstract = {In this paper, we describe our approach to gastrointestinal disease classification for the medico task at MediaEval 2018. We propose multiple ways to inpaint problematic areas in the test and training set to help with classification. We discuss the effect that preprocessing does to the input data with respect to removing regions with sparse information. We also discuss how preprocessing affects the training and evaluation of a dataset that is limited in size. We will also compare the different inpainting methods with transfer learning using a convolutional neural network.},
	keywords = {classification, Image processing, Machine Leanring},
	author = {Kirker{\o}d, Mathias and Thambawita, Vajira and Riegler, Michael and Halvorsen, P{\r a}l}
}
