International Conference on Optimization Algorithms in Data Science
(ICOADS-27)

This track focuses on the latest developments in optimization algorithms applicable to data science. Researchers are invited to present novel approaches that enhance the efficiency and effectiveness of optimization techniques.

This session explores innovative predictive modeling techniques that leverage optimization algorithms to improve accuracy and reliability. Contributions should highlight applications in various engineering domains.

This track examines the integration of optimization algorithms within supervised and unsupervised learning frameworks. Papers should discuss methodologies that enhance learning outcomes and model performance.

This session delves into optimization strategies specifically designed for deep learning architectures. Contributions should address challenges and solutions in training deep neural networks efficiently.

This track focuses on the application of optimization algorithms for effective anomaly detection in large datasets. Researchers are encouraged to present novel techniques that improve detection accuracy and reduce false positives.

This session highlights optimization approaches for feature extraction and selection in data-driven models. Papers should demonstrate how these techniques enhance model interpretability and performance.

This track addresses the challenges of combinatorial optimization in various engineering contexts. Contributions should showcase innovative algorithms and their practical applications in solving complex engineering problems.

This session focuses on gradient-based optimization methods and their applications in data science. Researchers are invited to present advancements that improve convergence rates and solution quality.

This track explores the role of metaheuristics and evolutionary algorithms in solving optimization problems in data science. Papers should discuss their effectiveness in diverse applications and compare them with traditional methods.

This session emphasizes the importance of model evaluation and the development of robust performance metrics. Contributions should focus on optimization techniques that enhance the evaluation process in data science applications.

This track investigates optimization frameworks for effective resource allocation in engineering applications. Researchers are encouraged to present case studies that demonstrate the impact of optimization on resource management.