Blar i Publikasjoner fra Cristin på emneord "Machine learning"
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Anomaly Detection in Cellular IoT with Machine Learning
(Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;Volume 401, Conference object, 2021-01-01)The number of Internet of Things (IoT) devices used in eldercare are increasing day by day and bringing big security challenges especially for health care organizations, IoT service providers and most seriously for the ... -
Artificial intelligence in dry eye disease
(The ocular surface;Volume 23, January 2022, Peer reviewed; Journal article, 2021-12-01)Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. ... -
Artificial intelligence in the fertility clinic: status, pitfalls and possibilities
(Human Reproduction;Volume 36, Issue 9, Peer reviewed; Journal article, 2021-07-29)In recent years, the amount of data produced in the field of assisted reproduction technology [ART] has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, ... -
Cross-Subject emotion recognition from EEG using Convolutional Neural Networks
(Chinese Control Conference (CCC);2020 39th Chinese Control Conference (CCC), Peer reviewed; Journal article, 2020-09-09)Using electroencephalogram (EEG) signals for emotion detection has aroused widespread research concern. However, across subjects emotional recognition has become an insurmountable gap which researchers cannot step across ... -
Deep Learning for Classifying Physical Activities from Accelerometer Data
(Sensors;Volume 21 / Issue 16, Peer reviewed; Journal article, 2021-08-18)Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening ... -
DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine
(Scientific Reports;11, Article number: 21896 (2021), Peer reviewed; Journal article, 2021-11-09)Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic ... -
Design of Reinforced Concrete Isolated Footings Under Axial Loading with Artificial Neural Networks
(EUROGEN;Proceedings of the 14th International Conference on Evolutionary and Deterministic Methods For Design, Optimization and Control, Conference object, 2021)In engineering practice, the design of structural elements is a repetitive task that has proven to be difficult to fully automate. This is mainly because of the complex relations of the design variables and the multiple ... -
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
(Information Fusion;Volume 59, July 2020, Peer reviewed; Journal article, 2020-01-31)In recent years, the rapid advances in machine learning (ML) and information fusion has made it possible to endow machines/computers with the ability of emotion understanding, recognition, and analysis. Emotion recognition ... -
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
(Scientific Reports;11, Article number: 10949 (2021), Peer reviewed; Journal article, 2021-05-26)Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any ... -
File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments
(Information;Volume 12, Issue 10, Peer reviewed; Journal article, 2021-10-18)In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform ... -
HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)
(MMSys: ACM Multimedia Systems;MMSys '21: Proceedings of the 12th ACM Multimedia Systems Conference, Conference object, 2021-09-22)Machine learning research within healthcare frequently lacks the public data needed to be fully reproducible and comparable. Datasets are often restricted due to privacy concerns and legal requirementsthat come with ... -
Improving Phishing Detection with the Grey Wolf Optimizer
(International Conference on Electronics, Information, and Communication (ICEIC);2022 International Conference on Electronics, Information, and Communication (ICEIC), Conference object, 2022-04-11)With the recent epidemic of COVID-19-themed scam and phishing, the efficient automated detection of such attacks is crucial. Although many anti-phishing solutions, such as lists and similarity and heuristic-based approaches ... -
Intelligent Initiatives to Reduce CO2 Emissions in Construction
(Conference object, 2022-11-24)Global warming is one of the most important environmental issues that threatens the living on this globe so far. Carbon dioxide (CO2) emission from the construction industry is one of the major sources of emissions that ... -
Machine learning-based abnormality detection approach for vacuum pump assembly line
(Reliability: Theory & Applications;Special Issue No 2(64), Volume 16, November 2021, Peer reviewed; Journal article, 2021-11-04)The fundamental basis of Industry 4.0 is to make the manufacturing sector more productive and autonomous. In the manufacturing sector, practitioners always long for product quality improvement, reducing reworking costs, ... -
A Machine Learning-based Tool for Passive OS Fingerprinting with TCP Variant as a Novel Feature
(IEEE Internet of Things Journal;Volume: 8, Issue: 5, Peer reviewed; Journal article, 2020-09-15)With the emergence of Internet of Things (IoT), securing and managing large, complex enterprise network infrastructure requires capturing and analyzing network traffic traces in real-time. An accurate passive Operating ... -
Manifold feature fusion with dynamical feature selection for cross-subject emotion recognition
(Brain Sciences;Volume 11 / Issue 11, Peer reviewed; Journal article, 2021-10-23)Affective computing systems can decode cortical activities to facilitate emotional human– computer interaction. However, personalities exist in neurophysiological responses among different users of the brain–computer ... -
A neural network model and framework for an automatic evaluation of image descriptions based on NCAM image accessibility guidelines
(Chapter; Peer reviewed, 2021)Millions of people who are either blind or visually impaired have difficulty understanding the content in an image. To address the problem textual image descriptions or captions are provided separately or as alternative ... -
Predicting High Delays in Mobile Broadband Networks
(IEEE Access;Volume 9: 2021, Peer reviewed; Journal article, 2021-12-24)The number of applications that run over mobile networks, expecting bounded end-to-end delay, is increasing steadily. However, the stochastic and shared nature of the wireless medium makes providing such guarantees ... -
Prediction of cloud fractional cover using machine learning
(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 ... -
SemML: Facilitating development of ML models for condition monitoring with semantics
(Journal of Web Semantics;Volume 71, November 2021, 100664, Peer reviewed; Journal article, 2021-10-22)Monitoring of the state, performance, quality of operations and other parameters of equipment and production processes, which is typically referred to as condition monitoring, is an important common practice in many ...