Machine learning
A team of researchers has developed a system that enables the clustering and standardizing of structured data records generated using an extraction neural network. The system, patented by Xyla Inc., utilizes a knowledge-based approach to process unstructured data and generate structured data records. The innovation leverages machine learning models to
Machine learning
A new study from Jazan University has revealed the effectiveness of an artificial neural network-based approach to improve patient quality and safety in healthcare. The research proposed an exponentially weighted moving average (EWMA) control chart, derived from residuals of the neural network model, to comprehensively analyze actual cardiac surgery patient
Machine learning
Researchers from Pontifical University Javeriana in Bogota, Colombia, have developed and validated three machine learning models combined with Natural Language Processing (NLP) techniques to accurately identify graft loss in Electronic Medical Records (EMRs) of kidney transplant recipients. The study analyzed 2712 patients transplanted between July 2008 and January 2023, examining
Machine learning
A team of researchers has developed a generative artificial intelligence (AI) tool, NanoSafari, to automatically extract knowledge from the biomedical nanoscience literature and address scientific queries. The tool, which uses the Grouped Iterative Validation based Information Extraction (GIVE) method, was able to extract contextual information on nanoparticle characteristics from 20,
Machine learning
Researchers from Comillas Pontifical University have developed a system using machine learning and natural language processing (NLP) to detect gender bias in job advertisements. The system, which combines NLP with Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA), analyzes the language used in job postings to identify words
Machine learning
Researchers have conducted a systematic review to evaluate the effectiveness of machine learning algorithms in predicting anemia among various populations in Ethiopia. The study found that machine learning algorithms, particularly random forest, offer promising tools for accurately predicting anemia in Ethiopia by integrating a wide range of socio-demographic and health-related
Machine learning
New research from the University of Parma has found that a combination of design of experiments and machine learning algorithms can effectively optimize 3D printing parameters for Fused Deposition Modeling (FDM) technology. The study, published in the journal Scientific Reports, explores the influence of various printing parameters on the mechanical
Machine learning
A recent study published in the journal Applied Research in Quality of Life investigates online discourse on dehumanization and violence among polarized and non-apathetic neutral individuals. Researchers analyzed over 39 million user-generated comments from two online forums in Hong Kong during the 2019 social unrest. The study found that non-apathetic
Machine learning
Vall d'Hebron Institute of Oncology's Radiomics Group has developed SALSA, a deep learning-driven tool for the precise and automated detection and monitoring of liver tumors, also known as hepatocellular carcinoma. This innovative tool uses medical images, including computed tomography (CT) scans, to detect and delineate liver
Machine learning
Researchers at the Kumoh National Institute of Technology have made significant strides in reducing the environmental impact of additive manufacturing (AM) through the development of a novel part decomposition method. This innovative approach, called D-ecomposer, utilizes machine learning-based life cycle assessment to minimize the carbon footprint of 3D printed parts.
Machine learning
Researchers at the University of Genoa in Italy have made significant strides in the diagnosis of liver diseases using a novel approach that combines ultrasound imaging and machine learning techniques. This innovative method, which focuses on analyzing the morphology of Glisson's capsule, has demonstrated promising results in accurately
Machine learning
Researchers from Beijing University of Chinese Medicine have conducted a study to analyze the molecular mechanisms driving adrenocortical carcinoma (ACC) progression. The study employed Weighted Gene Co-Expression Network Analysis (WGCNA) to identify gene co-expression modules associated with ACC progression. The results revealed that pathways associated with cell division, protein synthesis,
