Skip to main content

Project : Breast Cancer Prediction

Using AI to Predict Future Risk of Breast Cancer | Technology Networks

When A Brilliant Mathematician Dies From Breast Cancer At Age 40

Maryam Mirzakhani, an Iranian mathematician who was the only woman ever to win a Fields Medal, the most prestigious honor in mathematics, died on Friday. She was 40.

The cause was breast cancer, said Stanford University, where she was a professor. The university did not say where she died.

Her death is “a big loss and shock to the mathematical community worldwide,” said Peter C. Sarnak, a mathematician at Princeton University and the Institute for Advanced Study.

The Fields Medal, established in 1936, is often described as the Nobel Prize of mathematics. But unlike the Nobels, the Fields are bestowed only on people aged 40 or younger, not just to honor their accomplishments but also to predict future mathematical triumphs. The Fields are awarded every four years, with up to four mathematicians chosen at a time.


In this study, my task is to classify tumors into malignant (cancer) or benign using features obtained from several cell images.

You can find the data here:  https://www.kaggle.com/uciml/breast-cancer-wisconsin-data

Let's look at the project at the Kaggle site:  https://www.kaggle.com/samansiadati/breast-cancer-prediction

Using A.I. to Transform Breast Cancer Care - The New York Times

Comments

Popular posts from this blog

Artificial Neural Network

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The data structures and functionality of neural nets are designed to simulate associative memory. Neural nets learn by processing examples, each of which contains a known "input" and "result," forming probability-weighted associations between the two, which are stored within the data structure of the net itself. (The "input" here is more accurately called an input set, since it generally consists of multiple independent variables, rather than a single value.) Thus, the "learning" of a neural net from a given example is the difference in the state of the net before and after processing the example. After being given a sufficient number of examples, the net becomes capable of predicting results from inputs, using the associations built from the example set. If a feedback loop is prov...

Which AWS Certifications Exist for AI? A Practical Map

 If you want to prove you can work with AI on AWS, the certification catalog can feel crowded. This post gives you a map: which credentials are aimed at AI, who each one is for, what the exams look like, and in which order a sensible person would take them. Every figure below comes from the official AWS certification pages as they stood on 25 October 2026, so always double-check the live pages before you book anything. The short version Two certifications are built squarely around AI on AWS, and they sit at opposite ends of the difficulty scale: AWS Certified AI Practitioner (AIF-C01) is the foundational, concept-level exam. AWS Certified Generative AI Developer – Professional (AIP-C01) is the advanced, build-it-in-production exam. Around them sits a ring of neighbouring certifications that make good stepping stones, plus a newer business-focused AI credential and a set of hands-on "Demonstrated" credentials. At a glance Detail AI Practitioner GenAI Developer ...

Project: Heartbeat computing

In computer science, a heartbeat is a periodic signal generated by hardware or software to indicate normal operation or to synchronize other parts of a computer system. Usually, a heartbeat is sent between machines at a regular interval in the order of seconds; a heartbeat message. If the endpoint does not receive a heartbeat for a time—usually a few heartbeat intervals—the machine that should have sent the heartbeat is assumed to have failed. Heartbeat messages are typically sent non-stop on a periodic or recurring basis from the originator's start-up until the originator's shutdown. When the destination identifies a lack of heartbeat messages during an anticipated arrival period, the destination may determine that the originator has failed, shutdown, or is generally no longer available. Heartbeat messages may be used for high-availability and fault-tolerance purposes.