{"id":574,"date":"2024-06-09T14:16:51","date_gmt":"2024-06-09T14:16:51","guid":{"rendered":"https:\/\/flexboxai.com\/?page_id=574"},"modified":"2024-10-15T12:04:59","modified_gmt":"2024-10-15T12:04:59","slug":"publications","status":"publish","type":"page","link":"https:\/\/flexboxai.com\/ar\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

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Slide 1<\/div>

Flexbox AI Publications<\/h2><\/div>

At Flexbox AI, we actively contribute to conferences and seminars by presenting our latest research and innovations. Our involvement in keynotes, panels, and workshops helps shape AI advancements and fosters valuable discussions within the tech community.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

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Our Publications<\/h2>\n\n
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\n \"Publication\n
2023<\/div>\n
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L\u2019apport de l\u2019intelligence artificielle pour l\u2019innovation: pr\u00e9dire le succ\u00e8s ou l\u2019\u00e9chec d\u2019un produit<\/h3>\n

-This section was written for the book “L’Afrique de l’innovation: Politiques nationales et partenariats panafricains”.
-This chapter discusses the role of AI, especially neural networks, in creating innovative and sustainable products and services.
\n-It highlights how companies use innovation to stay competitive nationally and internationally.
\n-The authors propose predicting product success by analyzing patents, publications, and revenues.
\n-Neural networks estimate success rates, helping companies make better investment decisions and improve product development.
-\u00c9ditions L’Harmattan\n<\/p>\n

\n FlexBox AI Publication<\/span>
\n Research<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \"Publication\n
2021<\/div>\n
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Enhancing Decision-Making in Product Development: Predicting a medicine-based treatment for a new disease using a Multidimensional Neural Network<\/h3>\n

-This article focuses on the COVID-19 pandemic.
\n-It proposes using multidimensional neural networks to predict cure and mortality rates from Big Data analysis of symptoms, treatments, and indicators.
\n-The study compares three models\u2014Wide (WNN), Deep (DNN), and Wide and Deep (WDNN)\u2014with WDNN achieving 98.79% accuracy, aiding pharmaceutical and clinical decisions.
\n-Indexing: Scopus, Web Of Science, etc.<\/p>\n

\n FlexBox AI Publication<\/span>
\n Medical<\/span>\n COVID-19<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \"Publication\n
2020<\/div>\n
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Enhancing Decision-Making in New Product Development: Forecasting technologies revenues using a Multidimensional Neural Network<\/h3>\n

-Part of the “Lecture Notes in Business Information Processing” series.
\n-Published by Springer in Information Systems.
\n-Proposes a neural network-based approach to predict the success of tech products using patents, publications, and revenue growth.
\n-RNN models outperform WDNN in prediction accuracy, aiding decision-making in digital innovation and product development.
\n-Indexed by ISI Proceedings, DBLP, EI, and Scopus.\n<\/p>\n

\n FlexBox AI Publication<\/span>
\n Technology<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \"Publication\n
2019<\/div>\n
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Predicting Technology Success based on Patent Data, using a Wide and Deep Neural Network and a Recurrent Neural Network<\/h3>\n

-The paper proposes a method for predicting the success of innovative technologies using Neural Networks to analyze patent data from the United States Patent and Trademark Office (USPTO).
-Comparing Wide and Deep Neural Networks (WDNN) with Recurrent Neural Networks (RNN), the study finds that RNN achieves better performance and accuracy, especially with small datasets, when predicting patent growth for technologies like Cloud\/Client computing and Autonomous Vehicles.
\n-Indexing: Scopus, Web Of Science, Engineering Village \u00b7 <\/p>\n

\n FlexBox AI Publication<\/span>
\n Innovation<\/span>\n Patent Prediction<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n <\/div>\n<\/div>\n\n

Us Speaking in Conferences<\/h2>\n\n
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\n \"Conference\n
2021<\/div>\n
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\u0645\u0624\u062a\u0645\u0631 \u0625\u062f\u0627\u0631\u0629 \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0627\u0644\u062a\u062c\u0627\u0631\u064a\u0629 \u0627\u0644\u062f\u0648\u0644\u064a (\u0627\u0644\u0645\u0624\u062a\u0645\u0631 \u0627\u0644\u0633\u0627\u0628\u0639 \u0648\u0627\u0644\u062b\u0644\u0627\u062b\u0648\u0646 \u0644\u0640 IBIMA)<\/h3>\n

\u0627\u0644\u0645\u0624\u062a\u0645\u0631 \u0627\u0644\u062f\u0648\u0644\u064a \u0644\u0625\u062f\u0627\u0631\u0629 \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0648\u0627\u0644\u0623\u0639\u0645\u0627\u0644 (IBIMA) \u0647\u0648 \u062d\u062f\u062b \u0645\u0647\u0645 \u0641\u064a \u0645\u062c\u0627\u0644 \u0627\u0644\u0623\u0639\u0645\u0627\u0644 \u0648\u0625\u062f\u0627\u0631\u0629 \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062a\u060c \u0648\u0645\u0639\u0631\u0648\u0641 \u0631\u0633\u0645\u064a\u064b\u0627...<\/p>\n

\n FlexBox AI Conference<\/span>
\n Speaking<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \"Conference\n
2020<\/div>\n
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\u0646\u0638\u0645 \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062a: \u0627\u0644\u0645\u0624\u062a\u0645\u0631 \u0627\u0644\u0623\u0648\u0631\u0648\u0628\u064a \u0648\u0627\u0644\u0645\u062a\u0648\u0633\u0637\u064a \u0648\u0627\u0644\u0634\u0631\u0642 \u0627\u0644\u0623\u0648\u0633\u0637 \u0627\u0644\u0633\u0627\u0628\u0639 \u0639\u0634\u0631 (EMCIS 2020)<\/h3>\n

\u062a\u0628\u0631\u0632 \u0645\u0624\u062a\u0645\u0631 \u0627\u0644\u0623\u0646\u0638\u0645\u0629 \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062a\u064a\u0629 \u0627\u0644\u0623\u0648\u0631\u0648\u0628\u064a \u0648\u0627\u0644\u0645\u062a\u0648\u0633\u0637\u064a \u0648\u0627\u0644\u0634\u0631\u0642 \u0623\u0648\u0633\u0637\u064a \u0627\u0644\u0633\u0627\u0628\u0639 \u0639\u0634\u0631 (EMCIS 2020) \u0643\u0631\u0627\u0626\u062f \u0641\u064a...<\/p>\n

\n FlexBox AI Conference<\/span>
\n Presentation<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \"Conference\n
2019<\/div>\n
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\u0645\u0624\u062a\u0645\u0631 \u0625\u062f\u0627\u0631\u0629 \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0627\u0644\u062a\u062c\u0627\u0631\u064a\u0629 \u0627\u0644\u062f\u0648\u0644\u064a (\u0627\u0644\u062b\u0627\u0644\u062b \u0648\u0627\u0644\u062b\u0644\u0627\u062b\u0648\u0646 - IBIMA)<\/h3>\n

\u0627\u0644\u0645\u0624\u062a\u0645\u0631 \u0627\u0644\u062f\u0648\u0644\u064a \u0627\u0644\u062b\u0627\u0644\u062b \u0648\u0627\u0644\u062b\u0644\u0627\u062b\u0648\u0646 \u0644\u062c\u0645\u0639\u064a\u0629 \u0625\u062f\u0627\u0631\u0629 \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0627\u0644\u062a\u062c\u0627\u0631\u064a\u0629 (IBIMA) \u0647\u0648 \u062d\u062f\u062b \u0645\u062a\u0645\u064a\u0632 \u0641\u064a \u0645\u062c\u0627\u0644 \u0627\u0644\u0623\u0639\u0645\u0627\u0644...<\/p>\n

\n Conference<\/span>
\n Keynote<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n <\/div>\n<\/div>\n\n\n
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\n <\/span> \u0646\u0638\u0631\u0629 \u0639\u0627\u0645\u0629\n <\/h3>\n