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	<title>Derin öğrenme &#8211; BIOENGINEER.ORG</title>
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<site xmlns="com-wordpress:feed-additions:1">72741379</site>	<item>
		<title>Terahertz Spectroscopy and AI Reveal Hidden Explosives</title>
		<link>https://bioengineer.org/terahertz-spectroscopy-and-ai-reveal-hidden-explosives/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Thu, 22 Jan 2026 15:57:51 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Chemical Detection]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[Derin öğrenme]]></category>
		<category><![CDATA[Explosive imaging]]></category>
		<category><![CDATA[Gizli patlayıcı tespiti]]></category>
		<category><![CDATA[Güvenlik teknolojileri]]></category>
		<category><![CDATA[İşte bu yazı için 5 uygun etiket (virgülle ayrılmış): **Terahertz spectroscopy]]></category>
		<category><![CDATA[Kimyasal madde analizi]]></category>
		<category><![CDATA[Security screening** * **Terahertz spectroscopy:** Teknolojinin temelini oluşturan analiz yöntemi. * **Deep learning:** Terahertz verilerinin analizinde kullanılan yapay zeka tekniği (]]></category>
		<category><![CDATA[Terahertz spektroskopisi]]></category>
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					<description><![CDATA[In a groundbreaking advancement poised to revolutionize security and chemical detection, researchers have unveiled a cutting-edge method that synergizes terahertz time-domain spectroscopy with the power of deep learning. This novel approach allows unprecedented detection and imaging of chemicals as well as concealed explosives with remarkable precision and speed, promising to dramatically enhance safety measures in [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">319339</post-id>	</item>
		<item>
		<title>Boosting Link Prediction in Biomedical Knowledge Graphs</title>
		<link>https://bioengineer.org/boosting-link-prediction-in-biomedical-knowledge-graphs/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Tue, 20 Jan 2026 18:27:40 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[BioPathNet]]></category>
		<category><![CDATA[Biyomedikal bilgi grafikleri]]></category>
		<category><![CDATA[Deep Learning in Biomedicine]]></category>
		<category><![CDATA[Derin öğrenme]]></category>
		<category><![CDATA[Drug Discovery** **Açıklama:** 1. **Biomedical Knowledge Graphs:** Makalenin temel konusu ve verilerin temsil edildiği yapı. 2. **Link Prediction:** Makalenin çözmeye çalış]]></category>
		<category><![CDATA[İlaç keşfi]]></category>
		<category><![CDATA[İşte 5 uygun etiket (virgülle ayrılmış): **Biomedical Knowledge Graphs]]></category>
		<category><![CDATA[link prediction]]></category>
		<guid isPermaLink="false">https://bioengineer.org/boosting-link-prediction-in-biomedical-knowledge-graphs/</guid>

					<description><![CDATA[In an exciting advance for the field of biomedical informatics, researchers have unveiled a groundbreaking method aimed at enhancing link prediction within biomedical knowledge graphs through a novel framework called BioPathNet. This innovative approach addresses a significant challenge in the field—making accurately informed predictions about potential relationships and interactions between biological entities, which can have [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">319084</post-id>	</item>
		<item>
		<title>AI Predicts Tooth Extraction with Limited Imaging Data</title>
		<link>https://bioengineer.org/ai-predicts-tooth-extraction-with-limited-imaging-data/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Fri, 16 Jan 2026 01:40:45 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[clinical decision-making** **Açıklama:** 1. **AI in dentistry:** Çalışmanın temel alanını (yapay zekanın diş hekimliğindeki uygulamasını) doğrudan belirtir]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[Derin öğrenme]]></category>
		<category><![CDATA[Diş hekimliğinde yapay zeka]]></category>
		<category><![CDATA[İşte 5 uygun etiket]]></category>
		<category><![CDATA[İşte içerik için 5 uygun etiket (virgülle ayrılmış): **AI in dentistry]]></category>
		<category><![CDATA[Klinik karar destek** **Açıklama:** 1. **Diş çekimi tahmini:** Makalenin temel konusu ve ara]]></category>
		<category><![CDATA[limited imaging data]]></category>
		<category><![CDATA[Sınırlı görüntü verisi]]></category>
		<category><![CDATA[tooth extraction prediction]]></category>
		<category><![CDATA[virgülle ayrılmış olarak: **Diş çekimi tahmini]]></category>
		<guid isPermaLink="false">https://bioengineer.org/ai-predicts-tooth-extraction-with-limited-imaging-data/</guid>

					<description><![CDATA[In an innovative stride towards the integration of artificial intelligence in dentistry, a group of researchers led by R.D. Escobar-Torres has made significant advancements in utilizing deep learning to predict tooth extraction decisions. This pioneering study, titled “Deep Learning Prediction of Tooth Extraction Decisions from Limited Intraoral and Extraoral Image Data,” proposes a novel approach [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">317360</post-id>	</item>
		<item>
		<title>AI Models Enhance Prognosis and Immunotherapy in Gastric Cancer</title>
		<link>https://bioengineer.org/ai-models-enhance-prognosis-and-immunotherapy-in-gastric-cancer/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sat, 27 Dec 2025 09:56:40 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Derin öğrenme]]></category>
		<category><![CDATA[Dijital Patoloji** * **Mide Kanseri:** Makalenin ana konusu. * **Derin Öğrenme:** Araştırmanın temel teknolojisi ve metodolojisi. * **İmmü]]></category>
		<category><![CDATA[immünoterapi]]></category>
		<category><![CDATA[İşte bu içerik için uygun 5 etiket: **Mide Kanseri]]></category>
		<category><![CDATA[Prognoz Tahmini]]></category>
		<guid isPermaLink="false">https://bioengineer.org/ai-models-enhance-prognosis-and-immunotherapy-in-gastric-cancer/</guid>

					<description><![CDATA[In a groundbreaking study published in the Journal of Translational Medicine, a team of researchers led by Nguyen et al. has unveiled innovative deep learning models aimed at enhancing risk stratification for patients diagnosed with gastric cancer. This pivotal research taps into the realm of digital pathology, wherein high-resolution images are analyzed to derive complex [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">311688</post-id>	</item>
		<item>
		<title>Deep Learning Powers Gradient Optimization of Nanoparticles</title>
		<link>https://bioengineer.org/deep-learning-powers-gradient-optimization-of-nanoparticles/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Wed, 10 Dec 2025 23:19:05 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Derin öğrenme]]></category>
		<category><![CDATA[Gradyan Tabanlı Optimizasyon]]></category>
		<category><![CDATA[Heterojen Grafik Sinir Ağları]]></category>
		<category><![CDATA[Nanoparçacık Optimizasyonu]]></category>
		<category><![CDATA[Upconverting Nanoparçacıklar]]></category>
		<guid isPermaLink="false">https://bioengineer.org/deep-learning-powers-gradient-optimization-of-nanoparticles/</guid>

					<description><![CDATA[Recent advances in deep learning have opened new avenues for the design and optimization of nanomaterials, particularly in the realm of core-shell upconverting nanoparticles (UCNPs). These innovative nanostructures hold tremendous potential across various fields including biosensing, super-resolution microscopy, and three-dimensional printing. UCNPs can convert low-energy near-infrared light into higher-energy visible and ultraviolet emissions, making them [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">305257</post-id>	</item>
		<item>
		<title>AI Revolutionizes Microbial Detection in Deep Seafloor Samples</title>
		<link>https://bioengineer.org/ai-revolutionizes-microbial-detection-in-deep-seafloor-samples/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Fri, 28 Nov 2025 17:21:23 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Astrobiyoloji * **Derin Öğrenme:** Makalenin temel teknolojisi olan CNN'ler ve otomatik analiz vurgulanıyor. * **Denizaltı Mikrobiyolojisi:** Çalışmanın örnek]]></category>
		<category><![CDATA[Denizaltı Mikrobiyolojisi]]></category>
		<category><![CDATA[Derin öğrenme]]></category>
		<category><![CDATA[Hücre Tanıma]]></category>
		<category><![CDATA[Mikrobiyal Çeşitlilik]]></category>
		<guid isPermaLink="false">https://bioengineer.org/ai-revolutionizes-microbial-detection-in-deep-seafloor-samples/</guid>

					<description><![CDATA[In an unprecedented leap in microbial research, a groundbreaking study published in Scientific Reports has harnessed deep learning technologies to identify and analyze microbial life within the enigmatic terrains of deep subseafloor samples. This study, spearheaded by researchers including T. Nishimura, Y. Iwamoto, and H. Nagahashi, not only enhances our understanding of microbial ecosystems lying [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">302914</post-id>	</item>
		<item>
		<title>Revolutionary AI Classifies Blood Cell Morphology Deeply</title>
		<link>https://bioengineer.org/revolutionary-ai-classifies-blood-cell-morphology-deeply/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Wed, 19 Nov 2025 13:40:46 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Derin öğrenme]]></category>
		<category><![CDATA[Hematoloji]]></category>
		<category><![CDATA[Kan Hücresi Morfolojisi]]></category>
		<category><![CDATA[Tıbbi Görüntü Analizi]]></category>
		<category><![CDATA[Üretici Modeller]]></category>
		<guid isPermaLink="false">https://bioengineer.org/revolutionary-ai-classifies-blood-cell-morphology-deeply/</guid>

					<description><![CDATA[Advancements in medical technology are rapidly reshaping the way we approach diagnostics and treatment, particularly in the realm of blood cell analysis. A recent study published in Nature Machine Intelligence explores an innovative approach utilizing deep generative models for classifying blood cell morphologies. Conducted by a team of researchers—Deltadahl, Gilbey, Van Laer, and their colleagues—this [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">298685</post-id>	</item>
		<item>
		<title>AI Software for Pediatric Fracture Detection: A Comparison</title>
		<link>https://bioengineer.org/ai-software-for-pediatric-fracture-detection-a-comparison/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Tue, 18 Nov 2025 12:51:45 +0000</pubDate>
				<category><![CDATA[Cancer]]></category>
		<category><![CDATA[Derin öğrenme]]></category>
		<category><![CDATA[Otomatik kırık tespiti]]></category>
		<category><![CDATA[Pediatrik radyoloji]]></category>
		<category><![CDATA[Tıbbi tanı doğruluğu]]></category>
		<category><![CDATA[Yapay zeka karşılaştırması]]></category>
		<guid isPermaLink="false">https://bioengineer.org/ai-software-for-pediatric-fracture-detection-a-comparison/</guid>

					<description><![CDATA[In an age where technology continues to transform healthcare, the integration of artificial intelligence (AI) into medical diagnostics stands out as a particularly revolutionary development. The latest research, led by a team of scientists including Altmann-Schneider, Geiger, and Kellenberger, examines the efficacy of multiple AI software applications aimed at automating fracture detection in pediatric patients. [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">298229</post-id>	</item>
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