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Fadi Al Machot

Associate Professor

Norwegian University of Life Sciences

Norway

Research Interests

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About

Fadi Al Machot is an Associate Professor at the Norwegian University of Life Sciences in Norway. His research focuses on computational intelligence applications, particularly in areas such as wind turbine aerodynamics, disease classification in agriculture using deep learning, and robust systems for analog circuits. Recent publications highlight his work in transfer learning performance assessment and advancements in bioinformatics tools.

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Collaborators (9)

Mohamed Abouhawwash

College of Engineering

UNITED STATES

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Kyandoghere Kyamakya

Full Professor for Transportation Informatics

Alpen-Adria-Universität Klagenfurt

AUSTRIA

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Kristian Hovde Liland

Norwegian University of Life Sciences

NORWAY

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Jonas Kusch

Associate Professor in Scientific Computing

RWTH Aachen University

GERMANY

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Faouzi Alaya Cheikh

Associate Professor

NTNU Fakultet for ingeniorvitenskap og teknologi Trondheim

NORWAY

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Muhammad Salman Siddiqui

Full Professor

Norwegian University of Life Sciences

NORWAY

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Ngai-Man Cheung

Singapore University of Technology and Design

SINGAPORE

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Jean Chamberlain Chedjou

University of Klagenfurt

AUSTRIA

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Martin Thomas Horsch

Associate Professor in Data Science

Norwegian University of Life Sciences

NORWAY

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Fadi Al Machot

Research Interests

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About

Articles (27)

Towards Sustainable Universal Deepfake Detection with Frequency-Domain Masking

Cyber-Physical GraphRAG: Safe, Real-Time, and Scalable Orchestration of Large-Scale Autonomous Fleets

Building Trustworthy AI: Transparent AI Systems via Language Models, Ontologies, and Logical Reasoning (TranspNet)

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach

Advances in computational intelligence for floating offshore wind turbines aerodynamics: Current state review and future potential

Deep Learning-Based Models for Paddy Disease Classification and Segmentation: An Experimental Review

FragFM: Fragmented Fourier Matrix for multi-seasonality extraction in Univariate Long-Term Time Series Forecasting

Editorial: Synopsis of Core Concepts for Explainable-AI-Ready Data and Models

Scope of Physics-Based Simulation Artefacts

Bridging Logic and Learning: A Neural-Symbolic Approach for Enhanced Reasoning in Neural Models (ASPER)

Recognizing Hand-Based Micro Activities Using Wrist-Worn Inertial Sensors: A Zero-Shot Learning Approach

RevCD: Reversed Conditional Diffusion for Generalized Zero-Shot Learning

SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning

Towards Explainable Ensemble Learning: Adaptive Feature Space Pruning and Optimized Classifier Selection

Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

A Graph Attention Network Based System for Robust Analog Circuits’ Structure Recognition Involving a Novel Data Augmentation Technique

Deep Learning Based Models for Paddy Disease Identification and Classification: A Systematic Survey

A Robust Automated Analog Circuits Classification Involving a Graph Neural Network and a Novel Data Augmentation Strategy

Modeling and Simulation of Shared Electric Automated and Connected Mobility Systems with Autonomous Repositioning: Performance Evaluation and Deployment

An Integral Projection-Based Semantic Autoencoder for Zero-Shot Learning

Human Emotion: A Survey focusing on Languages, Ontologies, Datasets, and Systems

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

HFM: A Hybrid Feature Model Based on Conditional Auto Encoders for Zero-Shot Learning

Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme

Document-Image Related Visual Sensors and Machine Learning Techniques

Human emotion modeling (HEM): an interface for IoT systems

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

Collaborators (9)

Mohamed Abouhawwash

Kyandoghere Kyamakya

Kristian Hovde Liland

Jonas Kusch

Faouzi Alaya Cheikh

Muhammad Salman Siddiqui

Ngai-Man Cheung

Jean Chamberlain Chedjou

Martin Thomas Horsch

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