Predicting California Bearing Ratio with Ensemble and Neural Network Models: A Case Study from T\"urkiye

Researchers at Edith Cowan University are leveraging artificial intelligence (AI) and machine learning to enhance the reliability of solid-state batteries, addressing a significant challenge in battery technology. This initiative aims to improve performance and safety in energy storage solutions.

A new paper has been published on arXiv detailing an unsupervised learning approach for density estimation using a topology-based loss function. This method aims to automate the selection of the optimal kernel bandwidth, a critical hyperparameter that influences the bias-variance trade-off in density estimation, particularly in high-dimensional data where visualization is challenging.

A new automated machine learning framework has been developed to profile the morphology of 316L powder particles for Selective Laser Melting (SLM) in additive manufacturing. This approach utilizes high-throughput imaging, shape extraction, and clustering to analyze approximately 126,000 powder images, significantly enhancing the characterization process compared to traditional methods.

A recent study published in Nature — Machine Learning presents a machine learning model capable of predicting the immune age of mice based on cellular patterns. This innovative approach leverages complex data analysis to enhance understanding of immune system aging, potentially leading to advancements in immunology and age-related research.

Recent advancements in artificial intelligence (AI), particularly in machine learning and deep learning, are significantly enhancing big data analytics and management. This development focuses on large language models (LLMs) like ChatGPT, Claude, and Gemini, which are transforming industries through improved natural language processing and autonomous decision-making capabilities.

A new concept called Interpretive Efficiency has been introduced, which quantifies how effectively data supports interpretive representations in machine learning. This measure is grounded in five axioms and relates to mutual information, providing a framework for assessing the usefulness of data in interpretive tasks.

A novel hybrid framework has been introduced for precise liver tumor segmentation in CT scans, combining an attention-enhanced U-Net with handcrafted radiomics and voxel-wise 3D CNN refinement. This approach aims to improve the accuracy and efficiency of tumor delineation, addressing challenges such as low contrast and blurred boundaries in imaging.

Recent research has focused on the scalability of GPU-accelerated multi-hypothesis tracking, particularly through the Generalized Labeled Multi-Bernoulli (GLMB) filter, which allows for multiple detections per object. This method addresses the computational challenges associated with maintaining multiple hypotheses in multi-target tracking systems, especially in distributed networks of machine learning-based virtual sensors.