Faisal Saeed on Muck Rack

Faisal Saeed

New York, United States
As seen in: Medium, Forbes, Nature, Wiley Online Library, Mashable, Springer, Frontiers, MDPI, BMC Psychology, Diagnostic Pathology and
I have a dream that one day, more people will want to visit the Middle East than escape it. Founder of @IdeasB2 Scholar @CGMPitt Fellow @GwUpOe 🇮🇶🇺🇸🌎

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Faisal Saeed’s Biography

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is an Iraqi born writer, public speaker, community manager, computer geek and a Human rights activist living currently in the United States. He is an advocate for freedom of thought, science, reason and the free market of ideas and economy.

Al-Mutar is the founder of the Global Secular Humanist Movement and Secular Post.

He is on the editorial board of Applied Sentience, which is a multi-university project, and platform for the next generation of Humanist & Secular thinkers and activists, the…

Articles

​The Hidden Fault Line In Enterprise AI

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By Faisal Saeed
| Forbes
Faisal Saeed is the Founder & CEO at Promptev Inc. At the forefront of AI’s next leap. The problem with enterprise AI isn't the technology. It's everything around it. In the spring of 2013, a major U.S. airline experienced a widespread outage in its reservation system that grounded flights and disrupted operations for several hours. Public reporting attributed the incident to a technical failure during a system update.
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Correlation between sugar-sweetened beverage consumption and obesity among adults in Saudi Arabia

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By Faisal Saeed
| Frontiers
Abstract Background: In Saudi Arabia, obesity represents a major population health challenge, driven largely by unhealthy lifestyle behaviors, including excessive intake of sugar-sweetened beverages (SSBs). Nevertheless, there is a lack of evidence on the interactions between different categories of beverages and lifestyle and behavior factors that will determine the risk of obesity.
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PlantCLR: contrastive self-supervised pretraining for generalizable plant disease detection

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By Faisal Saeed, Muhammad Raza, Abdul Rehman, Muhammad Shaheryar, Sangseok Yun, Jae-Mo Kang, II-Min Kim
| Nature
Abstract Deep learning has improved automated plant disease detection by increasing recognition accuracy and robustness compared with traditional vision-based methods. Self-supervised learning (SSL) further reduces dependence on manual labels, but its transferability across heterogeneous agricultural datasets remains insufficiently characterized.
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