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<channel><title><![CDATA[USACFI: Responsible Data Research - AI/ML]]></title><link><![CDATA[https://www.usacfi.net/aiml]]></link><description><![CDATA[AI/ML]]></description><pubDate>Thu, 12 Mar 2026 15:28:11 -0700</pubDate><generator>Weebly</generator><item><title><![CDATA[Biomarker Discovery for Meta-Classification of Melanoma Metastatic Progression Using Transfer Learning]]></title><link><![CDATA[https://www.usacfi.net/aiml/biomarker-discovery-for-meta-classification-of-melanoma-metastatic-progression-using-transfer-learning]]></link><comments><![CDATA[https://www.usacfi.net/aiml/biomarker-discovery-for-meta-classification-of-melanoma-metastatic-progression-using-transfer-learning#comments]]></comments><pubDate>Wed, 07 Dec 2022 08:00:00 GMT</pubDate><category><![CDATA[Uncategorized]]></category><guid isPermaLink="false">https://www.usacfi.net/aiml/biomarker-discovery-for-meta-classification-of-melanoma-metastatic-progression-using-transfer-learning</guid><description><![CDATA[      [...] ]]></description><content:encoded><![CDATA[<div><div class="wsite-image wsite-image-border-none " style="padding-top:10px;padding-bottom:10px;margin-left:0px;margin-right:0px;text-align:center"> <a href='https://www.mdpi.com/2073-4425/13/12/2303?fbclid=IwAR0zATX5Rews2ChFtJZaZsPyHG8GtCgoE-nl4xwmahW8123TwXuEhx6DUDs' target='_blank'> <img src="https://www.usacfi.net/uploads/1/2/7/4/127484311/biomarker-discovery-for-meta-classification-of-melanoma-metastatic-progression-using-transfer-learning_orig.png" alt="Picture" style="width:auto;max-width:100%" /> </a> <div style="display:block;font-size:90%"></div> </div></div>]]></content:encoded></item><item><title><![CDATA[Data Science, Machine Learning, Artificial Intelligence and Lessons from Kaggle - CFI Talks Session 3]]></title><link><![CDATA[https://www.usacfi.net/aiml/data-science-machine-learning-artificial-intelligence-and-lessons-from-kaggle-cfi-talks-session-3]]></link><comments><![CDATA[https://www.usacfi.net/aiml/data-science-machine-learning-artificial-intelligence-and-lessons-from-kaggle-cfi-talks-session-3#comments]]></comments><pubDate>Thu, 27 Oct 2022 07:00:00 GMT</pubDate><category><![CDATA[Uncategorized]]></category><guid isPermaLink="false">https://www.usacfi.net/aiml/data-science-machine-learning-artificial-intelligence-and-lessons-from-kaggle-cfi-talks-session-3</guid><description><![CDATA[       Two of CFI's active researchers in health AI discuss their&nbsp; work and lessons derived from joining an international competition where they both received bronze medals. [...] ]]></description><content:encoded><![CDATA[<div><div class="wsite-image wsite-image-border-none " style="padding-top:10px;padding-bottom:10px;margin-left:0px;margin-right:0px;text-align:center"> <a href='https://vimeo.com/764545535' target='_blank'> <img src="https://www.usacfi.net/uploads/1/2/7/4/127484311/cfi-talks-3-joma-jonathan_orig.png" alt="Picture" style="width:auto;max-width:100%" /> </a> <div style="display:block;font-size:90%"></div> </div></div>  <div class="paragraph"><font color="#000">Two of CFI's active researchers in health AI discuss their&nbsp; work and lessons derived from joining an international competition where they both received bronze medals.</font><br /></div>]]></content:encoded></item><item><title><![CDATA[Bronze Solution to COVID-19 Detection:The Kaggle Experience]]></title><link><![CDATA[https://www.usacfi.net/aiml/bronze-solution-to-covid-19-detectionthe-kaggle-experience]]></link><comments><![CDATA[https://www.usacfi.net/aiml/bronze-solution-to-covid-19-detectionthe-kaggle-experience#comments]]></comments><pubDate>Wed, 26 Oct 2022 07:00:00 GMT</pubDate><category><![CDATA[Uncategorized]]></category><guid isPermaLink="false">https://www.usacfi.net/aiml/bronze-solution-to-covid-19-detectionthe-kaggle-experience</guid><description><![CDATA[			  			  			 			 			 			 			    [CFI Talks 3] Data Science, Machine Learning and Artificial Intelligence - Lessons from Kaggle video presentation.Link to the competition here. [...] ]]></description><content:encoded><![CDATA[<div class="wsite-scribd">			  			  			 			<div title="Scribd: cfi-talks-session-3.pptx" id="doc_627660587" style="background-color:#fff"></div> 			 			 			</div>  <div class="wsite-spacer" style="height:50px;"></div>  <div class="paragraph" style="text-align:left;"><ul><li><font color="#000000"><a href="https://vimeo.com/764545535" target="_blank">[CFI Talks 3] Data Science, Machine Learning and Artificial Intelligence - Lessons from Kaggle video presentation.</a></font><br /></li><br /><li><font color="#000000">Link to the competition <a href="https://www.kaggle.com/competitions/siim-covid19-detection?fbclid=IwAR22niPlnj86Z5bxlv61oQciZtSL_0LuWjUOAIUFsT26J87DzaU5T41VBMg" target="_blank">here</a>.</font><br /></li></ul></div>]]></content:encoded></item><item><title><![CDATA[Using Near-Infrared Spectroscopy and Stacked Regression for the Simultaneous Determination of Fresh Cattle and Poultry Manure Chemical Properties]]></title><link><![CDATA[https://www.usacfi.net/aiml/using-near-infrared-spectroscopy-and-stacked-regression-for-the-simultaneous-determination-of-fresh-cattle-and-poultry-manure-chemical-properties]]></link><comments><![CDATA[https://www.usacfi.net/aiml/using-near-infrared-spectroscopy-and-stacked-regression-for-the-simultaneous-determination-of-fresh-cattle-and-poultry-manure-chemical-properties#comments]]></comments><pubDate>Thu, 25 Aug 2022 07:00:00 GMT</pubDate><category><![CDATA[Uncategorized]]></category><guid isPermaLink="false">https://www.usacfi.net/aiml/using-near-infrared-spectroscopy-and-stacked-regression-for-the-simultaneous-determination-of-fresh-cattle-and-poultry-manure-chemical-properties</guid><description><![CDATA[       Excessive use of animal manure as fertilizers can lead to pollution through the introduction of nitrogen, phosphorus, and other mineral compounds to the environment. Wet chemical analytical methods are traditionally used to determine the precise chemical composition of manure to manage the application of animal waste to the soil. However, such methods require significant resources to carry out the processes. Affordable, rapid, and accurate methods of analyses of various chemical component [...] ]]></description><content:encoded><![CDATA[<div><div class="wsite-image wsite-image-border-none " style="padding-top:10px;padding-bottom:10px;margin-left:0px;margin-right:0px;text-align:center"> <a href='https://link.springer.com/article/10.1007/s40471-022-00303-x#citeas' target='_blank'> <img src="https://www.usacfi.net/uploads/1/2/7/4/127484311/using-near-infrared-spectroscopy-and-stacked-regression_orig.png" alt="Picture" style="width:auto;max-width:100%" /> </a> <div style="display:block;font-size:90%"></div> </div></div>  <div class="paragraph"><font color="#000">Excessive use of animal manure as fertilizers can lead to pollution through the introduction of nitrogen, phosphorus, and other mineral compounds to the environment. Wet chemical analytical methods are traditionally used to determine the precise chemical composition of manure to manage the application of animal waste to the soil. However, such methods require significant resources to carry out the processes. Affordable, rapid, and accurate methods of analyses of various chemical components present in animal manure, therefore, are valuable in managing soil and mitigating water pollution.<br /><br />Download the <a href="https://www.mdpi.com/2227-9040/10/10/410" target="_blank">pdf</a>.</font><br /></div>]]></content:encoded></item><item><title><![CDATA[Data Leakage as a Potential Source of Feature Selection Instability: A Case Study on Lung Cancer Biomarker Discovery]]></title><link><![CDATA[https://www.usacfi.net/aiml/data-leakage-as-a-potential-source-of-feature-selection-instability-a-case-study-on-lung-cancer-biomarker-discovery]]></link><comments><![CDATA[https://www.usacfi.net/aiml/data-leakage-as-a-potential-source-of-feature-selection-instability-a-case-study-on-lung-cancer-biomarker-discovery#comments]]></comments><pubDate>Thu, 25 Aug 2022 07:00:00 GMT</pubDate><category><![CDATA[Uncategorized]]></category><guid isPermaLink="false">https://www.usacfi.net/aiml/data-leakage-as-a-potential-source-of-feature-selection-instability-a-case-study-on-lung-cancer-biomarker-discovery</guid><description><![CDATA[       Mr. Mi&ntilde;oza presented a 2-minute research splash and poster presentation of the study during the Hyper Interdisciplinary Conference in the Philippines 2022 held in Pasay City on November 05, 2022. [...] ]]></description><content:encoded><![CDATA[<div><div class="wsite-image wsite-image-border-none " style="padding-top:10px;padding-bottom:10px;margin-left:0px;margin-right:0px;text-align:center"> <a href='https://link.springer.com/article/10.1007/s40471-022-00303-x' target='_blank'> <img src="https://www.usacfi.net/uploads/1/2/7/4/127484311/data-leakage-as-a-potential-source-of-feature-selection-instability_orig.png" alt="Picture" style="width:auto;max-width:100%" /> </a> <div style="display:block;font-size:90%"></div> </div></div>  <div class="paragraph"><font color="#000">Mr. Mi&ntilde;oza presented a 2-minute research splash and poster presentation of the study during the Hyper Interdisciplinary Conference in the Philippines 2022 held in Pasay City on November 05, 2022.</font><br /></div>]]></content:encoded></item></channel></rss>