AIMC Topic: Machine Learning

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Integrating machine learning and remote sensing for long-term monitoring of chlorophyll-a in Chilika Lagoon, India.

Environmental monitoring and assessment
Chlorophyll-a (Chla) is recognized as a key indicator of water quality and ecological health in aquatic ecosystems, offering valuable insights into ecosystem dynamics and changes over time. This study aimed to to develop and validate a robust ML mode...

Spatiotemporal analysis of airborne pollutants and health risks in Mashhad metropolis: enhanced insights through sensitivity analysis and machine learning.

Environmental geochemistry and health
The study delved into an extensive assessment of outdoor air pollutant levels, focusing specifically on PM, SO, NO, and CO, across the Mashhad metropolis from 2017 to 2021. In tandem, it explored their intricate correlations with meteorological condi...

Soil temperature estimation at different depths using machine learning paradigms based on meteorological data.

Environmental monitoring and assessment
Knowledge of soil temperature (ST) is important for analysing environmental conditions and climate change. Moreover, ST is a vital element of soil that impacts crop growth as well as the germination of the seeds. In this study, four machine-learning ...

Machine learning-based pipeline for automated intracerebral hemorrhage and drain detection, quantification, and classification in non-enhanced CT images (NeuroDrAIn).

PloS one
BACKGROUND AND PURPOSE: External drainage represents a well-established treatment option for acute intracerebral hemorrhage. The current standard of practice includes post-operative computer tomography imaging, which is subjectively evaluated. The im...

Analyzing the impact of socioeconomic indicators on gender inequality in Sri Lanka: A machine learning-based approach.

PloS one
This study conducts a comprehensive analysis of gender inequality in Sri Lanka, focusing on the relationship between key socioeconomic factors and the Gender Inequality Index (GII) from 1990 to 2022. By applying machine learning techniques, including...

Machine Learning Analysis of the Orbitofrontal Cortex Transcriptome of Human Opioid Users Identifies Shisa7 as a Translational Target Relevant for Heroin Seeking Leveraging a Male Rat Model.

Biological psychiatry
BACKGROUND: Identifying neurobiological targets predictive of the molecular neuropathophysiological signature of human opioid use disorder (OUD) could expedite new treatments. OUD is characterized by dysregulated cognition and goal-directed behavior ...

Mucoepidermoid carcinoma: Enhancing diagnostic accuracy and treatment strategy through machine learning models and web-based prognostic tool.

Journal of stomatology, oral and maxillofacial surgery
BACKGROUND: Oral cancer, particularly mucoepidermoid carcinoma (MEC), presents diagnostic challenges due to its histological diversity and rarity. This study aimed to develop machine learning (ML) models to predict survival outcomes for MEC patients ...

Development of a machine learning model to classify polycystic ovarian syndrome.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundOne of the main causes of infertility among women nowadays is Polycystic Ovarian Syndrome, or PCOS. A decision support strategy for supporting medical specialists through PCOS monitoring is presented in the suggested work. A feature selecti...

BCD-TransNet: Automatic breast cancer detection and classification using transfer learning approach.

Technology and health care : official journal of the European Society for Engineering and Medicine
Breast Cancer (BC) is a predominant form of cancer diagnosed in women and one of the deadliest diseases. The important cause of death owing to the cancer amongst women is BC. However, the existing ML techniques are very challenge evaluate the perform...