Early detection of student burnout using data science: a study of behavioral and psychological indicators
This study examined behavioral and psychological predictors of burnout among high school and university students in Pakistan using survey data and machine-learning models. Shorter sleep and greater mental fatigue—especially fatigue—were associated with higher burnout risk, while a Random Forest model successfully identified students at risk of burnout.
Read More...Quantum-inspired neural networks enhance stock prediction accuracy
The authors developed a quantum inspired model for stock market fluctuations.
Read More...Towards multimodal longitudinal analysis for predicting cognitive decline
Understanding and predicting cognitive decline in Alzheimer's disease
Read More...Advancing pediatric cancer predictions through generative artificial intelligence and machine learning
Pediatric cancers pose unique challenges due to their rarity and distinct biological factors, emphasizing the need for accurate survival prediction to guide treatment. This study integrated generative AI and machine learning, including synthetic data, to analyze 9,184 pediatric cancer patients, identifying age at diagnosis, cancer types, and anatomical sites as significant survival predictors. The findings highlight the potential of AI-driven approaches to improve survival prediction and inform personalized treatment strategies, with broader implications for innovative healthcare applications.
Read More...Weather-based power outage prediction in New York City: An ensemble machine learning approach
This study contributes to our understanding of how urban energy systems respond to climate variability and inform strategies for enhancing power grid resilience. The findings can help inform urban planners and infrastructure developers by identifying the factors that make regions within a power grid more vulnerable.
Read More...Predicting sickle cell vaso-occlusion by microscopic imaging and modeling
The authors use blood smears from individuals with sickle cell disease to correlate sickle cell frequency with the occurrence of vaso-occlusive crises.
Read More...Applying machine learning to breast cancer diagnosis: A high school student’s exploration using R
The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.
Read More...Simple solving heuristics improve the accuracy of sudoku difficulty classifiers
Cardiovascular Disease Prediction Using Supervised Ensemble Machine Learning and Shapley Values
The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.
Read More...Building deep neural networks to detect candy from photos and estimate nutrient portfolio
The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.
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