Fadi Al Machot

Associate Professor

Norwegian University of Life Sciences

Norway

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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.

Fadi Al Machot

Research Interests

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About

Articles (15)

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

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 (5)

Mohamed Abouhawwash

Kyandoghere Kyamakya

Faouzi Alaya Cheikh

Muhammad Salman Siddiqui

Jean Chamberlain Chedjou

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