The International Research Conference Aims and Objectives
The International Research Conference is a federated organization dedicated to bringing together a significant number of diverse scholarly events for presentation
within the conference program. Events will run over a span of time during the conference depending on the number and length of the presentations.
With its high quality, it provides an exceptional value for students, academics and industry researchers.
International Conference on Machine Learning Methods in Ecological and Environmental Sciences
aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of
Machine Learning Methods in Ecological and Environmental Sciences.
It also provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations,
trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Machine Learning Methods in Ecological and Environmental Sciences.
Call for Contributions
Prospective authors are kindly encouraged to contribute to and help shape the conference through submissions of their research abstracts, papers and e-posters.
Also, high quality research contributions describing original and unpublished results of conceptual, constructive, empirical, experimental, or
theoretical work in all areas of Machine Learning Methods in Ecological and Environmental Sciences are cordially invited for presentation at the conference.
The conference solicits contributions of abstracts, papers and e-posters that address themes and topics of the conference, including figures, tables and references of
novel research materials.
Guidelines for Authors
Please ensure your submission meets the conference's strict guidelines for accepting scholarly papers.
Downloadable versions of the check list for
Full-Text Papers and
Abstract Papers.
Please refer to the
Paper Submission Guideline,
Abstract Submission Guideline and
Author Information
before submitting your paper.
Conference Proceedings
All submitted conference papers will be blind peer reviewed by three competent reviewers.
The peer-reviewed conference proceedings are indexed in the Open Science Index,
Google Scholar,
Semantic Scholar,
Zenedo,
BASE,
WorldCAT,
Sherpa/RoMEO,
and other index databases. Impact Factor Indicators.
Special Journal Issues
20. International Conference on Machine Learning Methods in Ecological and Environmental Sciences has teamed up with the Special Journal Issue on
Machine Learning Methods in Ecological and Environmental Sciences.
A number of selected high-impact full text papers will also be considered for the special journal issues.
All submitted papers will have the opportunity to be considered for this Special Journal Issue.
The paper selection will be carried out during the peer review process as well as at the conference presentation stage.
Submitted papers must not be under consideration by any other journal or publication.
The final decision for paper selection will be made based on peer review reports by the Guest Editors and the Editor-in-Chief jointly.
Selected full-text papers will be published online free of charge.
Conference Sponsor and Exhibitor Opportunities
The Conference offers the opportunity to become a conference sponsor or exhibitor.
To participate as a sponsor or exhibitor, please download and complete the
Conference Sponsorship Request Form.
Selected Papers
-
A Study on the Application of Machine Learning and Deep Learning Techniques for Skin Cancer Detection
Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra, Abdus Sobur
-
Using Historical Data for Stock Prediction of a Tech Company
Sofia Stoica
-
Identifying Autism Spectrum Disorder Using Optimization-Based Clustering
Sharifah Mousli, Sona Taheri, Jiayuan He
-
Environmental Impact of Sustainability Dispersion of Chlorine Releases in Coastal Zone of Alexandra: Spatial-Ecological Modeling
Mohammed El Raey, Moustafa Osman Mohammed
-
Climate Change in Albania and Its Effect on Cereal Yield
L. Basha, E. Gjika
-
A Text Classification Approach Based on Natural Language Processing and Machine Learning Techniques
Rim Messaoudi, Nogaye-Gueye Gning, François Azelart
-
Safe and Efficient Deep Reinforcement Learning Control Model: A Hydroponics Case Study
Almutasim Billa A. Alanazi, Hal S. Tharp
-
PredictionSCMS: The Implementation of an AI-Powered Supply Chain Management System
Ioannis Andrianakis, Vasileios Gkatas, Nikos Eleftheriadis, Alexios Ellinidis, Ermioni Avramidou
-
Socio-Economic Influences on Soilless Agriculture
G. V. Byrd, B. B. Ghaley, E. Hayashi
-
Accelerating Quantum Chemistry Calculations: Machine Learning for Efficient Evaluation of Electron-Repulsion Integrals
Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty
-
Imputing Missing Data in Electronic Health Records: A Comparison of Linear and Non-Linear Imputation Models
Alireza Vafaei Sadr, Vida Abedi, Jiang Li, Ramin Zand
-
Comparison of Machine Learning Techniques for Single Imputation on Audiograms
Sarah Beaver, Renee Bryce
-
Unveiling the Mathematical Essence of Machine Learning: A Comprehensive Exploration
Randhir Singh Baghel
-
AI-Driven Cloud Security: Proactive Defense Against Evolving Cyber Threats
Ashly Joseph
-
Metrology-Inspired Methods to Assess the Biases of Artificial Intelligence Systems
Belkacem Laimouche
Conference venue information will be released soon.
Advances in Climate Science
Application of machine learning to agriculture, hydrology and atmospheric modeling
Applications in environmental sciences
Applications of extreme learning machines, gaussian process regression models, genetic programming algorithms, support vector regressions and neural networks
Applications of Machine Learning in Environmental Engineering
Artificial Intelligence Methods in the Environmental Sciences
Classical data analysis
Combining geostatistics and remote sensing
Combining machine learning and remote sensing
Comparison of dynamic (physical) models versus other machine learning models for predictive modeling
Comparison of hydrological models with machine learning models
Datasets in Climate/Environmental Science
Downscaling of Global Climate Model outputs using machine learning (data-driven) algorithms
Ecological Data Analysis with Machine Learning Methods
Feed-forward neural network models
Geoinformatics for modeling of climate data and analysis
Geostatistics and Machine Learning Applications in Climate and Environmental Sciences
Kernel methods
Learning and generalization
Linear multivariate statistical analysis
Machine Learning Algorithms for Environmental Applications
Machine Learning and Data Mining for Environmental Sciences
Machine learning applications in climate and environmental science
Machine Learning Applications in Habitat Suitability Modeling
Machine Learning in Ecological Science and Environmental Policy
Machine Learning Methods for Ecological Applications
Machine Learning Methods in the Environmental Sciences
Machine Learning Techniques in Ecology and Earth Science
Modeling of rainfall, floods, streamflow, drought, heatwaves, flood and other natural disasters using machine learning
Nonlinear canonical correlation analysis
Nonlinear classification
Nonlinear optimization
Nonlinear principal component analysis
Nonlinear regression
Sampling optimization in climate/environmental science
Software advances in spatial analysis
Spatio-temporal interpolation of climate/environmental data
Spatio-temporal variations of climate variables
Time series analysis
Training, testing and error assessment issues in predictive modeling for environmental applications
| Abstracts/Full-Text Paper Submission Deadline |
|
February 13, 2025 |
| Notification of Acceptance/Rejection |
|
February 27, 2025 |
| Final Paper (Camera Ready) Submission & Early Bird Registration Deadline |
|
May 11, 2026 |
| Conference Dates |
|
June 10-11, 2026 |
