A review of the F-measure: its history, properties, criticism, and alternatives
Methods to classify objects into two or more classes are at the core of various disciplines.
When a set of objects with their true classes is available, a supervised classifier can be …
When a set of objects with their true classes is available, a supervised classifier can be …
A survey on recent named entity recognition and relationship extraction techniques on clinical texts
Significant growth in Electronic Health Records (EHR) over the last decade has provided an
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …
[HTML][HTML] Sentiment Analysis on E-Commerce Product Reviews Using Machine Learning and Deep Learning Algorithms: A Bibliometric Analysisand Systematic …
A Daza, NDG Rueda, MSA Sánchez… - International Journal of …, 2024 - Elsevier
The success of every company is based on the service provided to customers, so it is
essential to know the perception of buyers in relation to specific products, this allows the …
essential to know the perception of buyers in relation to specific products, this allows the …
An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naïve Bayes
Customer churn is a complex challenge for burgeoning competitive organizations,
especially in telecommunication. It refers to customers that swiftly leave a company for a …
especially in telecommunication. It refers to customers that swiftly leave a company for a …
Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight
Pilots of aircraft face varying degrees of cognitive workload even during normal flight
operations. Periods of low cognitive workload may be followed by periods of high cognitive …
operations. Periods of low cognitive workload may be followed by periods of high cognitive …
Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting
The identification of druggable proteins (DPs) is significant for the development of new
drugs, personalized medicine, understanding of disease mechanisms, drug repurposing …
drugs, personalized medicine, understanding of disease mechanisms, drug repurposing …
Multi-label classification to predict antibiotic resistance from raw clinical MALDI-TOF mass spectrometry data
Antimicrobial resistance (AMR) poses a significant global health challenge, necessitating
advanced predictive models to support clinical decision-making. In this study, we explore …
advanced predictive models to support clinical decision-making. In this study, we explore …
A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework
Abstract Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental
disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical …
disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical …
QRFODD: Quaternion Riesz fractional order directional derivative for color image edge detection
Edge detection is the prominent method to determine the discontinuities present in an
image. There exists an issue of loss of information and correlation during the extraction of …
image. There exists an issue of loss of information and correlation during the extraction of …
Identifying risk factors for heart failure: A case study employing data mining algorithms
Heart diseases are increasingly present in the lives of human beings and are diseases that
affect the heart and blood vessels and can lead the person who develops to death. In this …
affect the heart and blood vessels and can lead the person who develops to death. In this …