University of Missouri research team develops an artificial intelligence model that is 92% accurate at detecting melanoma.

Abstract: Skin cancer is a common cancer, with melanoma being the most dangerous type. Early diagnosis is crucial for effective treatment and better outcomes. Dermatologists have widely used digital ...

Abstract: Fire events threaten life and property safety and require fire prevention and firefighting. Traditional methods such as smoke detectors and thermometers often suffer from low accuracy and ...

A 2025 study projects that over one in 20 women worldwide will be diagnosed with breast cancer in her lifetime, and by 2050 ...

Abstract: The rapid advancement of generative AI has enabled the creation of highly realistic forged facial images, posing significant threats to AI security, digital media integrity, and public trust ...

Abstract: Breast cancer is still the very communal and fatal diseases that women throughout the globe suffer from. Timely and accurate finding through medical imaging especially ultrasound is crucial ...

Abstract: Addressing the dual challenges of class imbalance and manual hyperparameter tuning in network intrusion detection, this paper proposes a CNN-BiGRU detection model integrating Adversarial ...

Cancer remains one of the leading causes of death among women worldwide, yet awareness of the risk is still low. Breast, ...

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Abstract: Skin lesion classification using deep learning techniques is challenged by insufficient samples and class imbalances in datasets. This study introduces a novel framework, the class expert ...

Abstract: Skin cancer is one of the most prevalent lifethreatening diseases worldwide, necessitating accurate and efficient techniques for early detection and precise classification. Although machine ...