Humans and artificial neural networks exhibit some similar patterns during learning

Past psychology and behavioral science studies have identified various ways in which people's acquisition of new knowledge ...
Tech Xplore

Intelligent photodetectors 'sniff and seek' like retriever dogs to recognize materials directly from light spectra

Researchers at the University of California, Los Angeles (UCLA), in collaboration with UC Berkeley, have developed a new type ...
eLife

TSvelo: Comprehensive RNA velocity by modeling the cascade of gene regulation, transcription and splicing

This study presents a valuable tool named TSvelo, a computational framework for RNA velocity inference that models transcriptional regulation and gene-specific splicing. The evidence supporting the ...
AlphaGalileo

Smart tea agriculture: machine learning opens new pathways for quality and sustainability

A research team has mapped how machine learning is transforming the global tea industry, revealing that data-driven ...
EurekAlert!

An interpretable machine learning model for predicting 5‐year survival in breast cancer based on integration of proteomics and clinical data

Breast cancer is a highly heterogeneous malignancy among women worldwide. Traditional prognostic models relying solely on ...
IEEE

Investigation on PCA and LDA Feature Extraction Algorithms with KNN Classifier Targetting to Improve Diagnostic Accuracy into Detection of Breast Cancer Malignancy

Abstract: This work aims to compare two different Feature Extraction Algorithms (FEAs) viz. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), using a K-Nearest Neighbor (KNN) ...
Journal of Medical Internet Research

Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study

Objective: This study aimed to develop and evaluate a machine learning (ML)–based algorithm to predict whether an initial vancomycin dose falls within the therapeutic range of the 24-hour area under ...
IEEE

MPDA: A Massively Parallel Learning and Dependency-Aware Scheduling Algorithm for Data Processing Clusters

Abstract: In the era of large-scale machine learning, large-scale clusters are extensively used for data processing jobs. However, the state-of-the-art heuristic-based and Deep Reinforcement Learning ...