Real-Time Plankton Monitoring with RAPID
Developing AI-driven tools for real-time plankton analysis at sea
Posts by Collection
portfolio
publications
Microaneurysm Detection in Fundus Images Using a Two-Step Convolutional Neural Network
Published in Biomedical Engineering Online, 2019
This study presents a novel approach for detecting microaneurysms in fundus images, enhancing early diagnosis in diabetic retinopathy.
Recommended citation: N. Eftekhari, H. Pourreza, M. Masoudi, K. G. Shirazi, and E. Saeedi, "Microaneurysm detection in fundus images using a two-step convolutional neural network," Biomedical Engineering Online, 2019.
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Optimizing Plankton Image Classification with Metadata-Enhanced Representation Learning
Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024
This research explores the integration of metadata for improving the classification of plankton images, enhancing real-time environmental monitoring.
Recommended citation: M. Masoudi, S. Verma, N. Eftekhari, M. Massot-Campos, J. O. Irisson, B. Thornton, "Optimizing plankton image classification with metadata-enhanced representation learning," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024.
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Cross-Attention Enables Deep Learning on Limited Omics-Imaging-Clinical Data of 130 Lung Cancer Patients
Published in Cell Reports Methods, 2024
This study introduces a cross-attention mechanism for multi-modal deep learning on limited data in lung cancer research.
Recommended citation: S. Verma, G. Magazzù, N. Eftekhari, A. Occhipinti, C. Angione, "Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients," Cell Reports Methods, 2024.
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Improving In Situ Real-Time Classification of Long-Tail Marine Plankton Images for Ecosystem Studies
Published in ECCV, CV4E Workshop, 2024
This paper presents a model for enhancing in situ, real-time classification of long-tail marine plankton species, essential for biodiversity monitoring.
Recommended citation: N. Eftekhari, S. Pitois, M. Masoudi, R. E. Blackwell, J. Scott, S. L. C. Giering, "Improving in situ real-time classification of long-tail marine plankton images for ecosystem studies," ECCV, CV4E Workshop, 2024.
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Improving In Situ Real-Time Classification of Long-Tail Marine Plankton Images for Ecosystem Studies
Published in ECCV, CV4E Workshop, 2024
At the CV4E workshop during ECCV, I presented a poster on enhancing real-time classification of marine plankton images, particularly focusing on long-tail species. The discussion centered around AI advancements that support more accurate ecosystem studies through improved image classification in real-world conditions.
talks
Real-time Particle Analysis
Published:
This talk focused on real-time particle analysis using advanced machine learning techniques. I presented methods for processing high-dimensional marine particle data, highlighting AI-driven innovations in ecosystem monitoring.
Plankton Classification Using Edge Artificial Intelligence
Published:
At this international symposium, I discussed the use of edge AI for in situ plankton classification, presenting how real-time image analysis tools can support marine ecosystem research. This approach enhances data availability directly in the field, supporting timely ecological assessments.
AI Biodiversity Monitoring: Developing Computer Vision Tools to Assess Ecosystem Health
Published:
This poster presentation highlighted the development of computer vision tools for biodiversity monitoring, showcasing how AI-driven methodologies can be used to assess ecosystem health effectively. It covered novel approaches in visual data analysis for environmental sustainability.
Improving In Situ Real-Time Classification of Long-Tail Marine Plankton Images for Ecosystem Studies
Published:
At the CV4E workshop during ECCV, I presented a poster on enhancing real-time classification of marine plankton images, particularly focusing on long-tail species. The discussion centered around AI advancements that support more accurate ecosystem studies through improved image classification in real-world conditions.
teaching
Teaching Experience: Artificial Intelligence and Machine Learning
Undergraduate and Bootcamp Courses, Teesside University, School of Computing, Engineering and Digital Technologies, 2021
During my time as a part-time lecturer at Teesside University, I taught several courses in artificial intelligence and machine learning to both undergraduate students and professionals in intensive bootcamp formats. These courses provided students with a hands-on understanding of core AI principles, applications in business, and foundations of machine learning.