A reinforcement learning approach for reducing traffic congestion using deep Q learning.

Nowadays, traffic congestion is a significant issue globally. The vehicle quantity has grown dramatically, while road and transportation infrastructure capacities have yet to expand proportionally to handle the additional traffic effectively. Road congestion and traffic-related pollution have increased, which is detrimental to society and public health.

A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method.

In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics.

RETRACTED: Nobel et al. Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis. 2024, , 105-132.

The Editorial Office retracts the article "Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis" [...

Vacancy induced enhanced photocatalytic activity of nitrogen doped CuO NPs synthesized by Co-precipitation method.

The chemical co-precipitation method, an effective approach in the synthesis of nanomaterials, was used to synthesize CuO nanoparticles (NPs). Structural and morphological modification of undoped and nitrogen (N) doped CuO nanoparticles were studied thoroughly using X-ray diffraction (XRD), FT-IR and field emission scanning electron microscope (FE SEM). Doping effect on defects was investigated using X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and photoluminescence (PL) spectroscopy.

RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis.

Ovarian cancer, a deadly female reproductive system disease, is a significant challenge in medical research due to its notorious lethality. Addressing ovarian cancer in the current medical landscape has become more complex than ever. This research explores the complex field of Ovarian Cancer Subtype Classification and the crucial task of Outlier Detection, driven by a progressive automated system, as the need to fight this unforgiving illness becomes critical.