AIMC Topic: Machine Learning

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Threatening language detection from Urdu data with deep sequential model.

PloS one
The Urdu language is spoken and written on different social media platforms like Twitter, WhatsApp, Facebook, and YouTube. However, due to the lack of Urdu Language Processing (ULP) libraries, it is quite challenging to identify threats from textual ...

Customer churn modeling in telecommunication using a novel multi-objective evolutionary clustering-based ensemble learning.

PloS one
Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two essential principles for constructing ensemble cla...

Two-part predictive modeling for COVID-19 cases and deaths in the U.S.

PloS one
COVID-19 prediction has been essential in the aid of prevention and control of the disease. The motivation of this case study is to develop predictive models for COVID-19 cases and deaths based on a cross-sectional data set with a total of 28,955 obs...

Mental health analysis of international students using machine learning techniques.

PloS one
International students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psycholog...

Nonlinear classifiers for wet-neuromorphic computing using gene regulatory neural network.

Biophysical reports
The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational prin...

Examining the Most Important Risk Factors for Predicting Youth Persistent and Distressing Psychotic-Like Experiences.

Biological psychiatry. Cognitive neuroscience and neuroimaging
BACKGROUND: Persistence and distress distinguish more clinically significant psychotic-like experiences (PLEs) from those that are less likely to be associated with impairment and/or need for care. Identifying risk factors that identify clinically re...

Pre-trained language models in medicine: A survey.

Artificial intelligence in medicine
With the rapid progress in Natural Language Processing (NLP), Pre-trained Language Models (PLM) such as BERT, BioBERT, and ChatGPT have shown great potential in various medical NLP tasks. This paper surveys the cutting-edge achievements in applying P...

Towards the automatic calculation of the EQUAL Candida Score: Extraction of CVC-related information from EMRs of critically ill patients with candidemia in Intensive Care Units.

Journal of biomedical informatics
OBJECTIVES: Candidemia is the most frequent invasive fungal disease and the fourth most frequent bloodstream infection in hospitalized patients. Its optimal management is crucial for improving patients' survival. The quality of candidemia management ...

Machine Learning of Cardiac Anatomy and the Risk of New-Onset Atrial Fibrillation After TAVR.

JACC. Clinical electrophysiology
BACKGROUND: New-onset atrial fibrillation (NOAF) occurs in 5% to 15% of patients who undergo transfemoral transcatheter aortic valve replacement (TAVR). Cardiac imaging has been underutilized to predict NOAF following TAVR.

Discovery of antimicrobial peptides in the global microbiome with machine learning.

Cell
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and...