Machine learning is the field that focuses on how computers learn from data. Today, machine learning is playing an integral role in the medical industry. This is due to its ability to process huge datasets beyond the scope of human capability, and then convert the data analyzed into clinical insights that aid physicians in providing care. Machine learning is a powerful, relatively easy to implement tool with numerous possibilities to enhance medical practice. The applications of machine learning in medicine are advancing medicine into a new realm. Therefore, educating the next generation of medical professionals with machine learning is essential. This paper provides a brief introduction to applying machine learning in medicine. Matthew N. O Sadiku | Sarhan M. Musa | Adedamola Omotoso "Machine Learning in Medicine: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd20255.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/20255/machine-learning-in-medicine-a-primer/matthew-n-o-sadiku
How Artificial Intelligence in Transforming PharmaTyrone Systems
Artificial intelligence in Pharma refers to the use of automated algorithms to perform tasks which traditionally rely on human intelligence. Over the last five years, the use of artificial intelligence in the pharma and biotech industry has redefined how scientists develop new drugs, tackle disease, and more.
Given the growing importance of Artificial Intelligence for the pharma industry, we wanted to create a comprehensive report which helps every business leader understand the biggest breakthroughs in the biotech space which are assisted by the deployment of artificial intelligence technologies.
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Cirdan
This presentation was delivered by Ashraf Mina, NSW Pathology at the Pathology Horizons 2017 Conference in Cairns, Australia.
Pathology Horizons 2017 is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information about the event at www.pathologyhorizons.com
The company was founded in 2010 and is headquartered in Lisburn, Northern Ireland and has additional offices in Canada and Australia.
Cirdan is also responsible for organising Pathology Horizons, an annual and open CPD conference on the future of pathology. For more information visit - www.pathologyhorizons.com
Machine learning is the field that focuses on how computers learn from data. Today, machine learning is playing an integral role in the medical industry. This is due to its ability to process huge datasets beyond the scope of human capability, and then convert the data analyzed into clinical insights that aid physicians in providing care. Machine learning is a powerful, relatively easy to implement tool with numerous possibilities to enhance medical practice. The applications of machine learning in medicine are advancing medicine into a new realm. Therefore, educating the next generation of medical professionals with machine learning is essential. This paper provides a brief introduction to applying machine learning in medicine. Matthew N. O Sadiku | Sarhan M. Musa | Adedamola Omotoso "Machine Learning in Medicine: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd20255.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/20255/machine-learning-in-medicine-a-primer/matthew-n-o-sadiku
How Artificial Intelligence in Transforming PharmaTyrone Systems
Artificial intelligence in Pharma refers to the use of automated algorithms to perform tasks which traditionally rely on human intelligence. Over the last five years, the use of artificial intelligence in the pharma and biotech industry has redefined how scientists develop new drugs, tackle disease, and more.
Given the growing importance of Artificial Intelligence for the pharma industry, we wanted to create a comprehensive report which helps every business leader understand the biggest breakthroughs in the biotech space which are assisted by the deployment of artificial intelligence technologies.
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Cirdan
This presentation was delivered by Ashraf Mina, NSW Pathology at the Pathology Horizons 2017 Conference in Cairns, Australia.
Pathology Horizons 2017 is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information about the event at www.pathologyhorizons.com
The company was founded in 2010 and is headquartered in Lisburn, Northern Ireland and has additional offices in Canada and Australia.
Cirdan is also responsible for organising Pathology Horizons, an annual and open CPD conference on the future of pathology. For more information visit - www.pathologyhorizons.com
Everything you want to know about role of artificial intelligence in drug discovery.
Artificial intelligence in health care and pharmacy, drug discovery, tensorflow, python,
deep neural network, GANs
AI in drug discovery and development
AI in clinical trials
Machine learning, health data & the limits of knowledgePaul Agapow
Lecture for Imperial College London's MSc in Health Data Analytics, critiquing a recent paper on COVID diagnosis and moving out to talk about good practices (& limits) in ML and model building
Presentation on how past medical records can be used to provide appropriate and timely treatment for patients using Genetic Algorithm and Feature Selection
ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY "AN OVERVIEW OF AWARENESS"FinianCN
ARTIFICIAL INTELLIGENT IN DRUG DISCOVERY:- AN OVERVIEW OF AWARENESS.
AI is showing the potential to be a faster and more efficient way to find and develop new drugs. A growing number of organizations and universities are focusing to minimize the complexities involved in the classical way of drug discovery by using AI computing to envisage which drug candidate are most likely to be effective treatments.
It is hard to measure the adoption of AI in drug discovery. Pharma and biotech companies tend to not publicly disclose competitive technology use.
While organizations are adopting the technology, there is significant untapped potential for those willing to be more aggressive. Which is depending on the realization of the potential with education and relevant success stories
Hirshberg promise of digital technology astra_zenecaThe Promise of Digital Te...Levi Shapiro
Presentation by Boaz Hirshberg, VP, Clinical Development, Cardiovascular, Renal, Metabolic Disease at AstraZeneca
- The Promise of Digital Technology in Drug Development Clinical Trials. Includes the following:
- The vision for patient-centric medical care delivery
- End-to-end patient experience enhanced by digital technologies
- Digital technologies have a potential to transform clinical trial & medical care delivery
- Example: transforming our understanding of Type 2 diabetes with remote patient monitoring
- Frequent sampling demonstrates glucose lowering very soon after first dose, which might be unappreciated in typical trial design
- Multiple data points reduce uncertainty about the glucose outcome and enable future machine learning of unanticipated relationships
- Lessons learned from CGM pilot: data storage, transfer, and analysis
- Defining the clinical science questions to be answered
- Operational considerations for incorporating digital data into clinical development
- Addressing challenges of digital technologies’ disruption
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Everything you want to know about role of artificial intelligence in drug discovery.
Artificial intelligence in health care and pharmacy, drug discovery, tensorflow, python,
deep neural network, GANs
AI in drug discovery and development
AI in clinical trials
Machine learning, health data & the limits of knowledgePaul Agapow
Lecture for Imperial College London's MSc in Health Data Analytics, critiquing a recent paper on COVID diagnosis and moving out to talk about good practices (& limits) in ML and model building
Presentation on how past medical records can be used to provide appropriate and timely treatment for patients using Genetic Algorithm and Feature Selection
ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY "AN OVERVIEW OF AWARENESS"FinianCN
ARTIFICIAL INTELLIGENT IN DRUG DISCOVERY:- AN OVERVIEW OF AWARENESS.
AI is showing the potential to be a faster and more efficient way to find and develop new drugs. A growing number of organizations and universities are focusing to minimize the complexities involved in the classical way of drug discovery by using AI computing to envisage which drug candidate are most likely to be effective treatments.
It is hard to measure the adoption of AI in drug discovery. Pharma and biotech companies tend to not publicly disclose competitive technology use.
While organizations are adopting the technology, there is significant untapped potential for those willing to be more aggressive. Which is depending on the realization of the potential with education and relevant success stories
Hirshberg promise of digital technology astra_zenecaThe Promise of Digital Te...Levi Shapiro
Presentation by Boaz Hirshberg, VP, Clinical Development, Cardiovascular, Renal, Metabolic Disease at AstraZeneca
- The Promise of Digital Technology in Drug Development Clinical Trials. Includes the following:
- The vision for patient-centric medical care delivery
- End-to-end patient experience enhanced by digital technologies
- Digital technologies have a potential to transform clinical trial & medical care delivery
- Example: transforming our understanding of Type 2 diabetes with remote patient monitoring
- Frequent sampling demonstrates glucose lowering very soon after first dose, which might be unappreciated in typical trial design
- Multiple data points reduce uncertainty about the glucose outcome and enable future machine learning of unanticipated relationships
- Lessons learned from CGM pilot: data storage, transfer, and analysis
- Defining the clinical science questions to be answered
- Operational considerations for incorporating digital data into clinical development
- Addressing challenges of digital technologies’ disruption
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
New tech trends 2016 - How new tech is impacting society around the worldMinter Dial
This presentation is derived from the Netexplo 2016 Trends Reports. The Netexplo Observatory, based out of Paris, hosts an annual Forum and, via a team of experts and sociologists, analyses the usages of new tech via 1000s of initiatives that have been pre-qualified by its network of new tech trend spotters.
SBQS 2013 Keynote: Cooperative Testing and AnalysisTao Xie
SBQS 2013 Keynote: Cooperative Testing and Analysis: Human-Tool, Tool-Tool, and Human-Human Cooperations to Get Work Done http://sbqs.dcc.ufba.br/view/palestrantes.php
“Towards Multi-Step Expert Advice for Cognitive Computing” - Dr. Achim Rettin...diannepatricia
Dr. Achim Rettinger from Karlsruhe Institute of Technology presented this today as part of the Cognitive Systems Institute Speaker Series on October 13, 2016
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need.
However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
Synergy of Human and Artificial Intelligence in Software EngineeringTao Xie
Keynote Talk by Tao Xie at International NSF sponsored Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE 2013) http://promisedata.org/raise/2013/
Patient Safety And Human Factors Engineering Spring2006Carolyn Jenkins
The second Power Point in a 3 part seminar for nursing students during their medical surgical clinical rotation.
Adapted from Dr. John Gosbee MD, MS
VA National Center for Patient Safety
Tool Kit Available at www.patientsafety.gov in 2005.
Challenges of large-scale sensor data processing for autonomous vehicle development, testing and validation using ROS (Robot Operating System). In the presentation, we will show insights from using frameworks for large-scale data processing and distributed applications running on-premise clusters and in the cloud. We will share our experiences and lessons learned on accelerating
the end-to-end engineering process from data ingest and catalog to analysis, development and safety validation. Keywords: Big Data, Data Science, Data Engineering, Deep Learning, Safety Validation, Testing, Automotive R&D
Challenges of Deep Learning in the Automotive Industry and Autonomous DrivingJan Wiegelmann
Talk at AutoSens in Brussels 17-19 September 2019. Development of Autonomous Driving ECUs requires sophisticated neural networks built up from massive training data sets in the process known as Deep Learning. The lifecycle of AD product development will be described, and specific challenges identified.
Data acquisition and conversion from in-car R&D formats into suitable DL formats
Leveraging open-source tools for data management
Using a wide range of analytics / AI frameworks against a common data set
Analysing petabytes of sensor data natively, without converting and copying
Optimising storage infrastructure to get the most out of CPU / GPU / IPU accelerators
Autodeploy a complete end-to-end machine learning pipeline on Kubernetes using tools like Spark, TensorFlow, HDFS, etc. - it requires a running Kubernetes (K8s) cluster in the cloud or on-premise.
Distributed Deep Learning with Hadoop and TensorFlowJan Wiegelmann
Training deep neural nets can take long time and heavy resources. By leveraging an existing distributed versions of TensorFlow and Hadoop can train neural nets quickly and efficiently.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
10 things A.I. can do better than you
1. 10 things A.I. can do better than you
A.I. Artificial intelligence
2. The development of full
A.I. could spell the end
of the human race.
- Stephen Hawking
Machine learning
is the hot new thing.
- John Hennessy,
President, Stanford
4. 4
1) A.I. beats human in games - 2016
Komodo beasts H. Nakamura in 2016AlphaGo beats L. Sedols in 2016
Go 4:1 Chess 2:1
5. 2) Image Classification- 2016
Human Performance A.I. Performance
https://arxiv.org/pdf/1602.07261.pdf
95% 97%
The ability to understand the content of an image by using machine learning
6. 3) Breast Cancer Diagnoses - 2017
Pathologist Performance A.I. Performance
https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
73% 92%
Doctors often use additional tests to find or diagnose breast cancer
The pathologist ended up
spending 30 hours on this
task on 130 slides
A closeup of a lymph node biopsy.
7. 4) Skin Cancer Diagnoses - 2016
Pathologist Performance A.I. Performance
http://www.nature.com/nature/journal/v542/n7639/full/nature21056.html
96,5% 97,1%
If found early 95% of skin cancers can be treated successfully
Pathologist + A.I.
99,5%
8. 5) Face Recognition - 2016
Human Performance A.I. Performance
https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf
https://arxiv.org/pdf/1603.01249v2.pdf
97,5% 97,7%
The ability of a computer to scan, store, and recognize human faces for use in identifying people
9. 6) Lipreading - 2016
We demonstrate lip reading performance that beats a professional lip reader on videos
https://arxiv.org/pdf/1611.05358v1.pdf
Human Performance A.I. Performance
41,3% 57,9%
10. 7) Speech recognition - 2016
Methodologies and technologies that enables the recognition and translation of spoken language
into text by computers
https://arxiv.org/pdf/1610.05256v1.pdf
Human Performance A.I. Performance
41,3% 57,9%
11. 8) Traffic sign recognition - 2011
Technology by which a vehicle is able to recognise the traffic signs put on the road
http://image.diku.dk/igel/paper/MvCBMLAfTSR.pdf
Human Performance A.I. Performance
99,22% 99,46%
12. 9) Solving Verbal Questions in IQ Test - 2016
Verbal comprehension questions appear very frequently in IQ tests, which measure human’s verbal
ability including the understanding of the words with multiple senses, the synonyms and
antonyms, and the analogies among words
https://arxiv.org/pdf/1505.07909.pdf
Human Performance A.I. Performance
46,23% 50,86%
13. 10) Photo Geolocation - 2016
We show that the resulting model attains superhuman levels of accuracy in some cases
https://arxiv.org/pdf/1602.05314.pdf
Human median
localization error
A.I. median
localization error
2321 km 1132 km