This research introduces a sophisticated transfer learning model based on Google's MobileNetV2 for breast cancer tumor classification into normal, benign, and malignant categories, utilizing a dataset of 1576 ultrasound images (265 normal, 891 benign, 420 malignant). The model achieves an accuracy of 0.82, precision of 0.83, recall of 0.81, ROC-AUC of 0.94, PR-AUC of 0.88, and MCC of 0.74. It examines image intensity distributions and misclassification errors, offering improvements for future applications. Addressing dataset imbalances, the study ensures a generalizable model. This work, using a dataset from Baheya Hospital, Cairo, Egypt, compiled by Walid Al-Dhabyani et al., emphasizes MobileNetV2's potential in medical imaging, aiming to improve diagnostic precision in oncology. Additionally, the paper explores Streamlit-based deployment for real-time tumor classification, demonstrating MobileNetV2's applicability in medical imaging and setting a benchmark for future research in oncology diagnostics.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A hot air balloon pilot died this week after he became trapped underneath the balloon's basket and fell to his death, the Vermont State Police said. Longtime pilot Brian Boland, 72, had left Post Mills Airport in Vermont with four passengers when the balloon started to descend rapidly and touched down in a field. The basket tipped and one of the passengers fell out but wasn't hurt, police said.

Bergan Flannigan, of Plattsburgh, N.Y., says she used to "get a lot of stares" when she wore her prosthetic leg with the metal pipe exposed. "I feel like people don't look as much" with the cover, she says, "which I like." Bergan Flannigan, of Plattsburgh, N.Y., says she used to "get a lot of stares" when she wore her prosthetic leg with the metal pipe exposed. "I feel like people don't look as much" with the cover, she says, "which I like." Prosthetic limbs for people who have lost an arm or a leg have come a long way in the past decade.

Bergan Flannigan, of Plattsburgh, N.Y., says she used to "get a lot of stares" when she wore her prosthetic leg with the metal pipe exposed. "I feel like people don't look as much" with the cover, she says, "which I like." Bergan Flannigan, of Plattsburgh, N.Y., says she used to "get a lot of stares" when she wore her prosthetic leg with the metal pipe exposed. "I feel like people don't look as much" with the cover, she says, "which I like." Prosthetic limbs for people who have lost an arm or a leg have come a long way in the last decade.

For humans and automation to effectively collaborate and perform tasks, all participants need access to a common representation of potentially relevant situational information, or context. This article describes a general framework for building context-aware interactive intelligent systems that comprises three major functions: (1) capture human-system interactions and infer implicit context; (2) analyze and predict user intent and goals; and (3) provide effective augmentation or mitigation strategies to improve performance, such as delivering timely, personalized information and recommendations, adjusting levels of automation, or adapting visualizations. Our goal is to develop an approach that enables humans to interact with automation more intuitively and naturally that is reusable across domains by modeling context and algorithms at a higher-level of abstraction. We first provide an operational definition of context and discuss challenges and opportunities for exploiting context. We then describe our current work towards a general platform that supports developing context-aware applications in a variety of domains. We then explore an example use case illustrating how our framework can facilitate personalized collaboration within an information management and decision support tool. Future work includes evaluating our framework.