Results 101 to 110 of about 13,208 (247)

Predicción de concentraciones de PM2.5 y PM10 utilizando los algoritmos XGboost y LightGBM: un estudio de caso en Lima, Perú

Interfases
La contaminación del aire es un problema importante que afecta tanto a la salud humana como al medio ambiente, causando millones de muertes prematuras anualmente en todo el mundo y degradando severamente el estado del planeta.
Johan Andrés Oblitas Mantilla, Edwin Jhonatan Escobedo Cárdenas   +1 more
doaj   +1 more source

GenoEye: A machine learning‐based framework for the prediction of intermediate eye color phenotypes

Journal of Forensic Sciences, EarlyView.
Abstract Forensic DNA phenotyping (FDP) enables the prediction of externally visible characteristics from genetic data, providing valuable investigative leads. While current approaches achieve high accuracy for blue and brown eye color, the prediction of intermediate phenotypes such as green and hazel remains challenging, particularly in Southern ...
Davide Dalfovo, Giorgia Pallafacchina, Gianluca Occhi, Alberto Marino, Giovanni Vazza, Christian Faccinetto, Alessandro Romanel   +6 more
wiley   +1 more source

Developing Predictive and Explainable Models for Cryptocurrency Delistings: A Case Study of Binance Exchange

Asia-Pacific Journal of Financial Studies, EarlyView.
Abstract This study develops an explainable machine learning model to predict cryptocurrency delistings using Binance data. It combines quantitative indicators (price, volume) with qualitative data from real‐time news and Reddit. Latent Dirichlet Allocation (LDA) is used to extract topic trends and community reactions, which are transformed into time ...
Sungju Yang, Hunyeong Kwon
wiley   +1 more source

A Unified Machine Learning Model for Relapse Prediction in Clinical Stage I Testicular Cancer

Andrology, EarlyView.
ABSTRACT Background Approximately one‐fourth of patients with clinical stage I testicular cancer relapse. For decades, risk stratification has been based on different tumor characteristics for seminomas and non‐seminomas. Previous studies primarily used Cox proportional‐hazards models and included only a limited number of variables.
Thomas Wagner, Ramtin Zargari Marandi, Jakob Lauritsen, Daniel Dawson Murray, Mikkel Bandak, Birgitte Grønkær Toft, Dan Berney, Gedske Daugaard   +7 more
wiley   +1 more source

Artificial intelligence prediction algorithms for refractive error onset and progression in children and adolescents: A systematic review and meta‐analysis

Acta Ophthalmologica, EarlyView.
Abstract This systematic review and meta‐analysis evaluates the performance of artificial intelligence (AI)‐based models for predicting the onset and progression of refractive error (RE) in children and adolescents and quantitatively synthesizes their prediction accuracy.
Athanasia Sandali, Anna Nikolaidou, Theodora Gianni, Andreas Katsimpris, Ioannis D. Apostolopoulos, Lampros Lamprogiannis, Eirini Maliagkani   +6 more
wiley   +1 more source

Automating Archaeological Discovery: Assessing Geospatial Artificial Intelligence (GeoAI) Tools for Stone Wall Identification in Kweneng, South Africa

Archaeometry, EarlyView.
ABSTRACT The discovery of archaeological sites traditionally entails the utilisation of physically demanding exploration methodologies, including terrain surveying and the analysis of historical records. Recent technological developments have led to an increased use of non‐invasive remote sensing techniques, including Google Earth, LiDAR and aerial ...
Mncedisi J. Siteleki
wiley   +1 more source

Macrophage Lipid Homeostasis Drives IVDD via a Senescence‐Dependent Impairment of Efferocytosis

Cell Proliferation, EarlyView.
Intervertebral disc degeneration (IVDD) has been a leading cause of low back pain and lacks disease‐modifying therapies. Although dysregulated disc lipid metabolism is implicated, its impact on the immune microenvironment, particularly macrophages, remains unknown.
Jinyu Wang, Shiyong Ling, Qi Wang, Xuege Qiao, Jianpeng Xing, Siyang Ji, Linhui Han, Chen Yan, Zhishen Niu, Lei Yuan, Baolian Zhao, Kaiqiang Sun, Jiangang Shi   +12 more
wiley   +1 more source

Association of Variability of Monthly Continuous Glucose Monitoring‐Derived Metrics With Diabetic Kidney Disease in Type 1 Diabetes

Diabetes, Obesity and Metabolism, EarlyView.
ABSTRACT Background While HbA1c is the standard for monitoring long‐term glycaemic control, it fails to capture glycaemic variability. We investigated the discriminatory capacity of longitudinal continuous glucose monitoring (CGM) metrics and identified CGM metric patterns associated with diabetic kidney disease (DKD) in individuals with type 1 ...
Byeongjae Kang, Rosa Oh, Taeyoung Kim, Jee Hee Yoo, Danbee Kang, Gyuri Kim, Sang‐Man Jin, Myung Jin Chung, Jae Hyeon Kim   +8 more
wiley   +1 more source

The application of machine learning models to optimal TSP tour length estimation

International Transactions in Operational Research, EarlyView.
Abstract The traveling salesman problem (TSP) is a well‐known NP‐hard problem in combinatorial optimization, with numerous applications in logistics and elsewhere. This paper introduces a machine learning‐based approach to estimate the optimal tour length of the TSP, using linear regression, random forests (RF), and neural networks, including ...
Shuhan Kou, Bruce Golden, Luca Bertazzi
wiley   +1 more source

GBNet: XGBoost and LightGBM PyTorch Modules

GBNet is a Python package that integrates XGBoost and LightGBM with PyTorch. By leveraging PyTorch auto-differentiation, GBNet enables novel architectures for GBMs that were previously exclusive to pure Neural Networks. The result is a greatly expanded set of applications for GBMs and an improved ability to interpret expressive architectures due to the
openaire   +2 more sources