Identifying biological markers in the gut microbiome associated with celiac disease using machine learning

Finding Biomarkers for Type 2 Diabetes

Empirical Investigation of Learning Curves: Assessing Convexity Characteristics

Bridging the Knowledge Gap: Identifying Essential Machine Learning Concepts for Effective Progression in Follow-Up Courses

A Comparative Analysis of Learning Curve Models and their Applicability in Different Scenarios: Finding datasets patterns which lead to certain parametric curve model

Annotation Practices in Machine Learning Research On Depression

Using Nearest-Neighbors to Evaluate Overlap in Causal Inference

Architectural Pipeline - Pipeline Architecture: An experiment into the role of topological graphs in the early stages of architectural design in the era of machine learning

Machine-learning based methods to accelerate design optimization of perforated monopiles

ClimAIte Control: Improving Building Operation Through AI

Data-Driven Empirical Analysis of Correlation-Based Feature Selection Techniques

Genetic algorithm-based program synthesizer for the construction of machine learning pipelines

Big slopes, little data: data-driven nowcasting of deep-seated landslide deformation

Machine learning for post-storm profile predictions: Using XBeach and convolutional neural network structure U-Net to predict 1D dune erosion profile shapes at the Holland Coast

Design and Application of MGA Analysis Package: A Python-based clustering package using machine learning algorithms to analyze near optimal energy systems

Parallel cost-aware optimization of multidimensional black-box functions

Detecting Fog using Machine Learning and Investigating the Possibilities of Generating Synthetic Data

Self-supervised Learning for Tumor Microenvironment Analysis: Addressing Label Scarcity in Multiplexed Immunofluorescence Imaging with Novel Feature Extraction Techniques

Enriching Machine Learning Model Metadata: Collecting performance metadata through automatic evaluation

Host- Microbiome Omics Integration for Cancer Analysis and Diagnostics: Investigating the added value of integrating microbial and host omics information for cancer diagnostics using prediction models