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Anis Yazidi

Professor

University of Oslo

Norway

Research Interests

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About

Dr. Anis Yazidi is an Assistant Professor at Oslo Metropolitan University, Norway. His research primarily focuses on the ethical applications of artificial intelligence in medical practice, machine learning for agricultural disease detection, and statistical modeling of wind-speed distributions. Additionally, he explores frameworks for explainable AI and clustering techniques, as well as coverage strategies for UAV-based networks in IoT environments.

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NORWAY

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Daniel Thilo Schroeder

Associate Professor

OsloMet – Oslo Metropolitan University

NORWAY

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Kåre Rønn Richardsen

Vice Dean of Education and Collaboratoin

University of South-Eastern Norway

NORWAY

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HAMIDREZA NAMAZI

MONASH UNIVERSITY

AUSTRALIA

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Britt Elin Øiestad

Professor

OsloMet – Oslo Metropolitan University

NORWAY

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Anis Yazidi

Research Interests

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About

Articles (25)

Chronic pain, psychological distress, and their co-occurrence in Norwegian young adults: Insights from machine learning and explainable AI

The Dynamics of Hate Speech: Assessing Anti-Muslim Hate Speech in Norwegian Social Media

A Comprehensive Review of Explainable AI in Deep Learning Algorithms for EEG Analysis

Attention-driven convolutional neural network for automated well log depth alignment

Comprehensive dataset of features describing eye-gaze dynamics across multiple tasks

Deriving reference limits from historical data - A comparison of four novel methods

Disability pension during emerging adulthood: Insights from the young-HUNT study on psychological distress, chronic pain, and policy reform

Improving Indirect Methods for Calculating Reference Limits for Nerve Conduction Studies From Historical Data

Semi-automated bed boundary detection and log alignment using fast Walsh-Hadamard transform

A Conceptual Framework for Applying Ethical Principles of AI to Medical Practice

COMPLEXITY-BASED ANALYSIS OF THE VARIATIONS IN BRAIN ACTIVITY IN SHORT DEEP BREATHING

A Manifold Representation of the Key in Vision Transformers

A formal technique for automatic resolution of OpenFlow anomalies

DREAMS: A python framework for training deep learning models on EEG data with model card reporting for medical applications

Deep Hierarchical Graph Correlation: A Two-Stage Approach to Well-Log Alignment Using CNNs and Dynamic Programming

Dynamic Graphs Analysis of EEG

Dynamical law behind eye movements: Distinguishing between Lévy and intermittent strategies

EEG Data Classification: Review and Taxonomy

Enhanced visibility graph for EEG classification

Frequency bands EEG Biomarkers for Dementia using Graph Neural Networks

HITS-based Propagation Paradigm for Graph Neural Networks

Wheat Leaf Disease Detection: A Lightweight Approach with Shallow CNN Based Feature Refinement

Modeling Wind-Speed Statistics beyond the Weibull Distribution

A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering

Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment

Collaborators (10)

Mehak Khan

Hårek Haugerud

Bjørnar Berg

Jan Erik Håkegård

Pedro Goncalves Lind

Hugo L. Hammer

Daniel Thilo Schroeder

Kåre Rønn Richardsen

HAMIDREZA NAMAZI

Britt Elin Øiestad

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