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International Conference on Deep Learning and Data Science Techniques

ICDLDT

1st Oct – 2nd Oct 2026 Vancouver, Canada

Official Invitation Letter Available

An official invitation letter will be provided upon successful registration for your participation in the conference.

Benefits of Registering as Listener

Access to All Conference Sessions

Plenary, keynote and parallel sessions

Networking Opportunities

Connect with global educators & researchers

Certificate of Participation

Digital certificate of participation

Invitation Letter Support

Official invitation letter after successful registration

Conference Kit / Digital Materials

E-proceedings & resource materials

Access to Keynote Sessions

Learn from leading experts & scholars

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Standard Registration Closed
The deadline for Standard Participation has ended. Participants may continue with Virtual Registration to join the conference remotely.
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Terms & Condition

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Conference Session Tracks

UN SDG Wheel

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

SDG 8 SDG 9 SDG 11 SDG 12
01 Advancements in Deep Learning Techniques +
This track focuses on the latest developments in deep learning methodologies, emphasizing novel architectures and optimization strategies. Researchers are encouraged to present their findings on how these advancements can enhance various applications in engineering.
SDG 9 SDG 12
02 Artificial Intelligence Applications in Data Science +
This session explores the integration of artificial intelligence techniques within data science frameworks. Papers addressing practical implementations and case studies that demonstrate AI's impact on data-driven decision-making are particularly welcome.
SDG 8 SDG 9
03 Convolutional Neural Networks in Engineering +
This track delves into the application of convolutional neural networks (CNNs) in engineering disciplines, particularly in image and signal processing. Contributions that showcase innovative uses of CNNs for solving complex engineering problems are encouraged.
SDG 9 SDG 11
04 Recurrent Neural Networks for Time-Series Analysis +
This session highlights the utilization of recurrent neural networks (RNNs) for analyzing time-series data in engineering contexts. Researchers are invited to share insights on the effectiveness of RNNs in forecasting and anomaly detection.
SDG 9 SDG 12
05 Generative Adversarial Networks in Data Generation +
This track examines the role of generative adversarial networks (GANs) in creating synthetic data for various engineering applications. Papers that discuss the challenges and successes of GANs in data augmentation and simulation are encouraged.
SDG 9 SDG 12
06 Unsupervised Feature Learning Techniques +
This session focuses on unsupervised learning methods for feature extraction and representation in complex datasets. Contributions that demonstrate the effectiveness of these techniques in enhancing model performance are highly sought after.
SDG 9 SDG 12
07 Transfer Learning in Engineering Applications +
This track investigates the application of transfer learning techniques to improve model performance in engineering tasks. Researchers are invited to present studies that illustrate the benefits of leveraging pre-trained models in specific domains.
SDG 9 SDG 12
08 Reinforcement Learning for Optimization Problems +
This session explores the application of reinforcement learning algorithms to solve optimization challenges in engineering. Papers that present novel approaches and real-world applications of reinforcement learning are particularly welcome.
SDG 9 SDG 12
09 Predictive Analytics in Engineering Systems +
This track focuses on the use of predictive analytics techniques to enhance decision-making in engineering systems. Contributions that showcase the integration of machine learning models for predictive maintenance and system optimization are encouraged.
SDG 9 SDG 12
10 Computer Vision Techniques in Engineering +
This session highlights the application of computer vision technologies in various engineering fields. Researchers are invited to present innovative solutions that leverage computer vision for automation, inspection, and analysis.
SDG 9 SDG 12
11 Natural Language Processing in Engineering Contexts +
This track explores the application of natural language processing (NLP) techniques in engineering-related tasks. Papers that discuss the use of NLP for technical documentation, sentiment analysis, and communication enhancement are welcome.
SDG 9 SDG 12