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 Deep Learning and Reinforcement Learning
aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of
Deep Learning and Reinforcement Learning.
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 Deep Learning and Reinforcement Learning.
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Also, high quality research contributions describing original and unpublished results of conceptual, constructive, empirical, experimental, or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 Deep Learning and Reinforcement Learning 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 Deep Learning and Reinforcement Learning has teamed up with the Special Journal Issue on
Deep Learning and Reinforcement Learning.
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
-
RV-YOLOX: Object Detection on Inland Waterways Based on Optimized YOLOX through Fusion of Vision and 3+1D Millimeter Wave Radar
Zixian Zhang, Shanliang Yao, Zile Huang, Zhaodong Wu, Xiaohui Zhu, Yong Yue, Jieming Ma
-
Safe and Efficient Deep Reinforcement Learning Control Model: A Hydroponics Case Study
Almutasim Billa A. Alanazi, Hal S. Tharp
-
Unveiling the Mathematical Essence of Machine Learning: A Comprehensive Exploration
Randhir Singh Baghel
-
Combining the Deep Neural Network with the K-Means for Traffic Accident Prediction
Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota
-
Personalized Email Marketing Strategy: A Reinforcement Learning Approach
Lei Zhang, Tingting Xu, Jun He, Zhenyu Yan, Roger Brooks
-
Toward Understanding and Testing Deep Learning Information Flow in Deep Learning-Based Android Apps
Jie Zhang, Qianyu Guo, Tieyi Zhang, Zhiyong Feng, Xiaohong Li
-
Automatic Classification of Lung Diseases from CT Images
Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari
-
Artificial Intelligence: A Comprehensive and Systematic Literature Review of Applications and Comparative Technologies
Z. M. Najmi
-
Distributed System Computing Resource Scheduling Algorithm Based on Deep Reinforcement Learning
Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar
-
Breast Cancer Prediction Using Score-Level Fusion of Machine Learning and Deep Learning Models
[email protected]
-
Deep Reinforcement Learning for Optimal Decision-making in Supply Chains
Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol
-
End-to-End Pyramid Based Method for MRI Reconstruction
Omer Cahana, Maya Herman, Ofer Levi
-
Design and Implementation of a Software Platform Based on Artificial Intelligence for Product Recommendation
G. Settanni, A. Panarese, R. Vaira, A. Galiano
-
Reducing the Imbalance Penalty through Artificial Intelligence Methods Geothermal Production Forecasting: A Case Study for Turkey
H. Anıl, G. Kar
Conference venue information will be released soon.
Deep learning
Optimization
Large-scale optimization
Hyper-parameter optimization
Model structure optimization
Regularization
Observation-dependent regularization
Generative models as regularization: semi-supervised learning
Structured learning
Temporal models with long-term dependencies
Deep learning with multiple modalities, including vision, speech and languages
Unsupervised/generative modeling
Efficient (Bayesian) inference for deep learning
Large-scale generative modelling
Reinforcement learning
Learning representations for reinforcement learning
Deep model-based and data-efficient reinforcement learning
| Abstracts/Full-Text Paper Submission Deadline |
|
May 30, 2024 |
| Notification of Acceptance/Rejection |
|
June 12, 2024 |
| Final Paper (Camera Ready) Submission & Early Bird Registration Deadline |
|
January 21, 2025 |
| Conference Dates |
|
February 22-23, 2025 |
