Hybrid Conferencee

International Conference on Bioinformatics in Environmental Biosensing and Engineering (ICBEBE - 26)

7th - 8th November 2026 | Reykjavik, Iceland

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Call for Papers Extended:
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Certificate of Presentation:
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Abstract Submissions Open:
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Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Reykjavik conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Reykjavik, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Reykjavik."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Conference Session Tracks

SDG Wheel

Aligned with

UN Sustainable Development Goals

This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.

SDG 6
SDG 6 Clean Water and Sanitation
SDG 9
SDG 9 Industry, Innovation and Infrastructure
SDG 11
SDG 11 Sustainable Cities and Communities
Track 01

Advancements in Bioinformatics for Environmental Monitoring

This track focuses on the latest bioinformatics methodologies applied to environmental monitoring. It aims to explore the integration of sensor data analytics and predictive modeling for enhanced environmental assessments.

Track 02

Machine Learning Techniques in Environmental Biosensing

This session will delve into supervised and unsupervised learning techniques tailored for environmental biosensing applications. Participants will discuss the efficacy of various algorithms in processing and interpreting sensor data.

Track 03

Deep Learning Applications in Environmental Engineering

This track highlights the role of deep learning in addressing complex challenges in environmental engineering. Case studies will showcase innovative applications that leverage deep learning for predictive maintenance and resource optimization.

Track 04

Anomaly Detection in Environmental Sensor Networks

This session will explore methodologies for anomaly detection within environmental sensor networks. Researchers will present novel approaches to identify and mitigate unexpected behaviors in sensor data streams.

Track 05

Feature Extraction Techniques for Environmental Data

This track will cover advanced feature extraction techniques essential for analyzing environmental data. Discussions will focus on how these techniques enhance model performance in various biosensing applications.

Track 06

Workflow Automation in Environmental Biosensing Systems

This session will address the automation of workflows in environmental biosensing systems. Participants will examine tools and frameworks that facilitate seamless data processing and analysis.

Track 07

Model Evaluation and Validation in Bioinformatics

This track emphasizes the importance of model evaluation and validation in bioinformatics applications. Presentations will cover best practices and metrics for assessing model performance in environmental contexts.

Track 08

Industrial IoT and Environmental Data Analytics

This session will explore the intersection of industrial IoT and environmental data analytics. Researchers will discuss how IoT technologies enhance data collection and analysis for improved environmental outcomes.

Track 09

Pathway Analysis in Environmental Biosensing

This track will focus on pathway analysis techniques used in environmental biosensing. Participants will explore how these methods contribute to understanding complex biological interactions in environmental contexts.

Track 10

Simulation Modeling for Environmental Engineering Solutions

This session will highlight the role of simulation modeling in developing solutions for environmental engineering challenges. Case studies will illustrate the application of simulation techniques in real-world scenarios.

Track 11

Digital Twin Technologies in Environmental Monitoring

This track will investigate the application of digital twin technologies in environmental monitoring. Discussions will focus on how digital twins can enhance predictive capabilities and system monitoring in environmental engineering.