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 and Computer Vision
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 and Computer Vision.
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 and Computer Vision.
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Also, high quality research contributions describing original and unpublished results of conceptual, constructive, empirical, experimental, or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 and Computer Vision 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
19. International Conference on Machine Learning and Computer Vision has teamed up with the Special Journal Issue on
Machine Learning and Computer Vision.
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
-
Advanced Convolutional Neural Network Paradigms-Comparison of VGG16 with Resnet50 in Crime Detection
Taiwo. M. Akinmuyisitan, John Cosmas
-
Design of a Computer Vision Based Exercise Video Game for Senior Citizens
June Tay, Ivy Chia
-
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
-
PredictionSCMS: The Implementation of an AI-Powered Supply Chain Management System
Ioannis Andrianakis, Vasileios Gkatas, Nikos Eleftheriadis, Alexios Ellinidis, Ermioni Avramidou
-
Safe and Efficient Deep Reinforcement Learning Control Model: A Hydroponics Case Study
Almutasim Billa A. Alanazi, Hal S. Tharp
-
AI-Driven Cloud Security: Proactive Defense Against Evolving Cyber Threats
Ashly Joseph
-
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
-
Latency-Based Motion Detection in Spiking Neural Networks
Mohammad Saleh Vahdatpour, Yanqing Zhang
-
Unveiling the Mathematical Essence of Machine Learning: A Comprehensive Exploration
Randhir Singh Baghel
-
Comparison of Machine Learning Techniques for Single Imputation on Audiograms
Sarah Beaver, Renee Bryce
Digital Program consists of the e-proceedings book which is available online-only
and includes the conference communications (proceedings abstracts and papers).
Registered participants can access the digitally available conference
proceedings ( and certificates ) by visiting their profile pages.
Computer Vision
Vision sensors
Early vision
Low-level vision
Biologically motivated vision
Illumination and reflectance modeling
Image based modeling
Physics-based vision
Perceptual organization
Shape modeling and encoding
Computational photography
3D shape recovery
Motion, tracking and video analysis
2D/3D object detection and recognition
Scene understanding
Occlusion and shadow detection
Stereo and multiple view geometry
Reconstruction and camera motion estimation
Vision for graphics
Vision for robotics
Cognitive and embodied vision
Pattern Recognition and Machine Learning
Statistical, syntactic and structural pattern recognition
Machine learning and data mining
Artificial neural networks
Dimensionality reduction and manifold learning
Classification and clustering
Representation and analysis in pixel/voxel images
Support vector machines and kernel methods
Symbolic learning
Active and ensemble learning
Deep learning
Transfer learning
Semi-supervised learning and spectral methods
Model selection
Reinforcement learning and temporal models
| Abstracts/Full-Text Paper Submission Deadline |
|
November 28, 2024 |
| Notification of Acceptance/Rejection |
|
December 13, 2024 |
| Final Paper (Camera Ready) Submission & Early Bird Registration Deadline |
|
December 17, 2024 |
| Conference Dates |
|
January 16-17, 2025 |
