AIMC Topic: Machine Learning

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Machine learning models for predicting malnutrition in NICU patients: A comprehensive benchmarking study.

Computers in biology and medicine
Malnutrition, affecting both adults and children globally, results from inadequate nutrient intake or loss of body mass. Traditional screening tools, reliant on detailed questionnaires, are costly, time-consuming, and often lack accuracy and generali...

Efficient estimation of plant species diversity in desert regions using UAV-based quadrats and advanced machine learning techniques.

Journal of environmental management
Understanding the distribution of plant species diversity(PSD) along spatial and environmental gradients is essential for implementing effective conservation strategies. However, effective monitoring of large-scale PSD in desert regions remain challe...

Explainable AI-based risk assessment for pluvial floods over South Korea.

Journal of environmental management
Analytic Hierarchy Process (AHP) of pluvial flood risk assessment has been widely used, incorporating multiple assessment indices. However, uncertainty assessment of expert judgement-based flood risk remains limited. This study proposes a Machine Lea...

Enhanced slime mould algorithm with chaotic and orthogonal optimization-based learning for improved severity prediction accuracy in malaria patient outcomes.

Computers in biology and medicine
Malaria remains a critical health challenge in developing countries, particularly in Africa, where it disproportionately affects vulnerable populations. Accurate malaria severity prediction is important for proper treatment and improved patient survi...

Recent trends in diabetes mellitus diagnosis: an in-depth review of artificial intelligence-based techniques.

Diabetes research and clinical practice
Diabetes mellitus (DM) is a highly prevalent chronic condition with significant health and economic impacts; therefore, an accurate diagnosis is essential for the effective management and prevention of its complications. This review explores the late...

A multi-objective optimization model integrating machine learning and time-frequency analysis for supporting nitrogen and phosphorus pollution reduction in Guangzhou city, China.

Journal of environmental management
The unbridled discharge of nitrogen and phosphorus (NP) pollutants is believed to have surpassed ecosystem resilience limits for many regions, which is of great concern to research and governmental communities. In this research, a multi-objective opt...

A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE.

Acta psychologica
Seasonal Affective Disorder (SAD) is a mood disorder characterized by recurring depressive episodes during specific seasons, particularly in Fall and Winter. With the rise of social media as a platform for self-expression, user-generated content offe...

Navigating nutrients: A scoping review on real-time food nutrition classification and recommendation systems.

Computers in biology and medicine
In an era where fast-paced lifestyles often conflict with the pursuit of healthy eating, the demand for innovative solutions to aid nutritional decision-making has never been more pressing. Real-time food nutrition classification and recommendation s...

Oral cavity carcinoma detection using BAT algorithm-optimized machine learning models with transfer learning and random sampling.

Computers in biology and medicine
BACKGROUND: Oral cavity carcinoma remains a major public health concern, where early and accurate detection is vital for improving patient outcomes and survival rates. Current diagnostic systems often face challenges such as limited feature selection...

Enhancing osteoporosis risk prediction using machine learning: A holistic approach integrating biomarkers and clinical data.

Computers in biology and medicine
Osteoporosis (OP) affects approximately 18 % of the global population, with osteoporosis-associated fractures impacting up to 37 million people annually. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its limita...