Results 111 to 120 of about 113,519 (298)
This study aimed to develop a local dataset of abnormal RBC morphology from confirmed cases of anemia and thalassemia in Thailand, providing a foundation for medical image analysis and future AI-assisted diagnostics.
Bundasak Angmanee +2 more
doaj +1 more source
Solving Data Overlapping Problem Using A Class‐Separable Extreme Learning Machine Auto‐Encoder
The overlapping and imbalanced data in classification present key challenges. Class‐separable extreme learning machine auto‐encoding (CS‐ELM‐AE) is proposed, which is an enhancement of ELM‐AE that better handles overlapping data by clustering points from the same class together. Applying oversampling addresses imbalanced data.
Ekkarat Boonchieng, Wanchaloem Nadda
wiley +1 more source
Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems.
Yanyang Wang +3 more
doaj +1 more source
Electroencephalogram (EEG)-based identification offers a promising biometric solution by leveraging the uniqueness of individual brain activity patterns.
Muhammed Esad Oztemel +1 more
doaj +1 more source
Efficient autonomous material search method combining ab initio calculations, autoencoder, and multi-objective Bayesian optimization [PDF]
Yuma Iwasaki +4 more
openalex +1 more source
Large Language Model‐Based Chatbots in Higher Education
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci +4 more
wiley +1 more source
Climate change has increased the vulnerability of forests to insect-related damage, resulting in widespread forest loss in Central Europe and highlighting the need for effective, continuous monitoring systems.
Maximilian Kirsch +3 more
doaj +1 more source
Forecasting Sequential Data using Consistent Koopman Autoencoders [PDF]
Omri Azencot +3 more
openalex +1 more source
IAR‐Net: Tabular Deep Learning Model for Interventionalist's Action Recognition
This study presents IAR‐Net, a deep‐learning framework for catheterization action recognition. To ensure optimality, this study quantifies interoperator similarities and differences using statistical tests, evaluates the distribution fidelity of synthetic data produced by six generative models, and benchmarks multiple deep‐learning models.
Toluwanimi Akinyemi +7 more
wiley +1 more source

