Publications by authors named "Mustafa Kemal Ozalp"
Article Synopsis
- Recent advancements in machine learning, particularly with architectures like transformers and few-shot learning models, have improved text generation and image analysis tasks.
- The 'no-free lunch' theorem indicates that there's no one-size-fits-all model; different algorithms excel in varying circumstances based on dataset characteristics.
- The study identifies a "goldilocks zone" for model performance: few-shot learning models excel with very small datasets, transformers perform well with small-to-medium and diverse datasets, while classical models are best with larger, sufficiently sized datasets.
Butyrylcholinesterase (BChE) is a target of interest in late-stage Alzheimer's Disease (AD) where selective BChE inhibitors (BIs) may offer symptomatic treatment without the harsh side effects of acetylcholinesterase (AChE) inhibitors. In this study, we explore multiple machine learning strategies to identify BIs , optimizing for precision over all other metrics. We compare state-of-the-art supervised contrastive learning (CL) with deep learning (DL) and Random Forest (RF) machine learning, across single and sequential modeling configurations, to identify the best models for BChE selectivity.
View Article and Find Full Text PDFThe movement of plankton is often dictated by local flow patterns, particularly during storms and in environments with strong flows. Reefs, macrophyte beds, and other immersed structures can provide shelter against washout and drastically alter the distributions of plankton as these structures redirect and slow the flows through them. Advection-diffusion and agent-based models are often used to describe the movement of plankton within marine and fresh water environments and across multiple scales.
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