Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
All of us know that money is important. We constantly need to monitor all expenses to avoid looking back to the bank statement and thinking: “So, where’s my money?”
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Keywords: COVID-19, Medical Imaging, Deep Learning, PACS Viewer, and HealthShare.
Purpose
We are all gripped by this unprecedented Covid-19 pandemic. While supporting our customers in battlefields by any means, we also observed various fighting fronts against Covid-19 by leveraging today's AI powers.
Following up the previous part, it's time to take advantages for IntegratedML VALIDATION MODEL statement, to provide information in order to monitor your ML models. You can watch it in action here
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In this first installment of InterSystems IRIS 2020.1 Tech Talks, we put the spotlight on data science, machine learning (ML), and analytics. InterSystems IntegratedMLTM brings automated machine learning to SQL developers. We'll show you how this technology supports feature engineering and chooses the most appropriate ML model for your data, all from the comfort of a SQL interface. We'll also talk about what's new in our open analytics offerings. Finally, we'll share some big news about InterSystems Reports, our "pixel-perfect" reporting option. See how you can now generate beautiful reports and export to PDF, Excel, or HTML.
This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:
Join us on Wednesday, June 25, to explore innovative, real-world solutions built with InterSystems IRIS, AI, LLMs, and intelligent agent technologies — directly from the developers who created them:
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The introduction of InterSystems' "Vector Search" marks a paradigm shift in data processing. This cutting-edge technology employs an embedding model to transform unstructured data, such as text, into structured vectors, resulting in significantly enhanced search capabilities. Inspired by this breakthrough, we've developed a specialized search engine tailored to companies.
Currently, many digital artists use generative AI technology as a support to accelerate the delivery of their work. Nowadays it is possible to generate a corresponding image from a text sentence. There are several market solutions for this, including some available to be used through APIs. See some at this link: https://www.analyticsvidhya.com/blog/2023/08/ai-image-generators/.
As said in the previous article about the iris-fhir-generative-ai experiment, the project logs all events for analysis. Here we are going to discuss two types of analysis covered by analytics embedded in the project:
Several models, such as DALL-E, Midjourney, and StableDiffusion, became available recently. All these models generate digital images from natural language descriptions. The most interesting one, in my opinion, is StableDiffusion which is open source - released barely a few weeks ago. There's now an entire community trying to leverage it for various use cases.
In this article, I am trying to identify the multiple areas to develop the features we can able to do using python and machine learning.
Each hospital is every moment trying to improve its quality of service and efficiency using technology and services.
The healthcare sector is one of the very big and vast areas of service options available and python is one of the best technology for doing machine learning.
In every hospital, humans will come with some feelings, if this feeling will understand using technology is make a chance to provide better service.
Kidney Disease can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Akshay Singh published a very useful dataset for training ML algorithms in the detection/prediction of kidney disease. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/akshayksingh/kidney-disease-dataset.
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