Automated Bone Cancer Detection Using Deep Learning on X-Ray Images.

In recent days, bone cancer is a life-threatening health issue that can lead to death. However, physicians use CT-scan, X-rays, or MRI images to recognize bone cancer, but still require techniques to increase precision and reduce human labor. These methods face challenges such as high costs, time consumption, and the risk of misdiagnosis due to the complexity of bone tumor appearances.

Mitigating adversarial manipulation in LLMs: a prompt-based approach to counter Jailbreak attacks (Prompt-G).

Large language models (LLMs) have become transformative tools in areas like text generation, natural language processing, and conversational AI. However, their widespread use introduces security risks, such as jailbreak attacks, which exploit LLM's vulnerabilities to manipulate outputs or extract sensitive information. Malicious actors can use LLMs to spread misinformation, manipulate public opinion, and promote harmful ideologies, raising ethical concerns.

Predicting cervical cancer risk probabilities using advanced H20 AutoML and local interpretable model-agnostic explanation techniques.

Background: Cancer is positioned as a major disease, particularly for middle-aged people, which remains a global concern that can develop in the form of abnormal growth of body cells at any place in the human body. Cervical cancer, often known as cervix cancer, is cancer present in the female cervix. In the area where the endocervix (upper two-thirds of the cervix) and ectocervix (lower third of the cervix) meet, the majority of cervical cancers begin.