DeSE2024 is organised and sponsored by eSystems Engineering society and will cover a mix of topics aimed to address current research issues in the design, engineering and adoption of eSystems. DeSE2023 conference comprises of stimulating tracks:
- Advanced Robotics
- Nanomaterials and Energy
- Sensors, Sensors Applications and Sensors Platforms
- Internet of Everything and its Applications
- AI and its Applications
- Biomedical Intelligence, Image Processing and Medical Imaging & Clinical Data Analysis
- Bio-informatics, Health Informatics, and Bio-Computing
- Computational intelligence
- Decision Support Systems
- Engineering Management
- Water Engineering Technologies
- Genetic Algorithms
- Novel Data Processing and Analytics, Tools and Systems
- Big Data Systems, Mining and Management, Tools and Applications
- Machine Learning, Web-based Decision Making
- Deep Learning Methods and Techniques
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eSystems Engineering (Main Track)
Prospective authors are invited to submit full-length papers (not exceeding 6 pages) conform to the IEEE format . All papers will be handled and processed electronically via the EDAS online submission system.
Submission implies the willingness of at least one of the authors to register and present their papers.
DeSE2024 is pleased to announce the following provisionally accepted special sessions from our research partners:
- Special Session 1 – Decision Support System in Medical Microbiology, Turkiya.
- Special Session 2 – AI Applications in Medical Imaging. UAE.
- Special Session 3 – AI and its Applications in Healthcare, UAE.
- Special Session 4 – Digital Media: Augmented and Virtual Reality, Malaysia.
- Special Session 5 – Emerging Trends in Data Science Theory and Multidisciplinary Applications, KSA.
- Special Session 6 – The Impact of Artificial Intelligence on the Future of Higher Education, Iraq.
- Special Session 7 – Deep Learning Techniques and Applications, Jordan.
- Special Session 8 – Artificial Intelligence for Improving Patient and Medication Safety.
- Special Session 9 – Generative Adversarial Attacks and Defence Strategies Against Deep Networks