NHLBI – Heart Research & Education’s Post

🎉 Excited to share that my paper, "Edge-Based Heart Disease Prediction Using Federated Learning," has been published in IEEE! 🌟 In this research, we explored innovative edge computing techniques to enhance the accuracy and efficiency of heart disease prediction. By leveraging real-time data processing at the edge of the network, our approach aims to provide secure, faster and more reliable health monitoring solutions. Check out the publication here https://lnkd.in/gPBvdCRc #Research #IEEE #EdgeComputing #HeartDiseasePrediction #HealthcareInnovation #federatedlearning

Just published in IEEE Transactions on Emerging Topics in Computational Intelligence with Murtadha Hssayeni: Our latest work tackles deep regression modeling for imbalanced time-series data using cGANs, significantly improving prediction accuracy for datasets like Parkinson's disease. https://bit.ly/43EIDua

Slant in news coverage can be easier to spot than bias in selecting what to cover. A machine learning study of vaccine news over the last six years quantifies both selection and narrative biases in news outlets—and analyses consumption. In PNAS Nexus: https://ow.ly/4BAs50U2Egt

https://lnkd.in/gKjvRaJs This edition has some great articles about causes, treatments, and the US National Plan to End PD!

🌟 Big News! 🌟 Research Paper Title : "EXPLORING THE PATH: MACHINE LEARNING APPROACHES TO CARDIOVASCULAR RISK ASSESSMENT" Our team, including Deepak Tallapudi , Malladi Srikari, Mahitha Chimata, just published a paper in IEEE! Huge thanks to Dr.Murali Krishna Enduri sir for their invaluable contribution. 👀 Our project focuses on using machine learning to accurately predict cardiovascular disease risk. By analyzing diverse datasets, we aim to develop models that identify individuals at higher risk, leading to proactive interventions and improved health outcomes. #ML #Health #IEEE #Conference https://lnkd.in/grkgduEK

👉🏼 Intentional binding - Is it just causal binding? A replication study of Suzuki et al. (2019) 🤓 Michael Wiesing 👇🏻 https://lnkd.in/eEFwSPNZ 🔍 Focus on data insights: - The study found systematically greater compression for active than passive trials. - Replication attempts showed more pronounced temporal compression in active trials compared to passive ones. 💡 Main outcomes and implications: - Intentions rather than causality cause temporal binding. - Results challenge previous findings by Suzuki et al. (2019). 📚 Field significance: - Understanding the mechanisms behind intentional binding. - Implications for studies on temporal perception and causality. 🗄️: [#intentionalbinding #causalbinding #temporalcompression #replicationstudy]

🚀 Just In: 📚 Our research paper, "Hybrid Approach for Heart Disease Detection using Classification Algorithms" is out now! 🎉 Dig into the full paper here: https://lnkd.in/dfzcY9_8 #ieee #datascience #ML #algorithm #ResearchCommunity

Artificial intelligence-based evaluation of prognosis in cirrhosis: In the decompensated stage, prognostic assessment systems for cirrhosis are based on the machine-learning-enhanced version of the Clif-C-ACLF scoring ... http://dlvr.it/TFKYNV

🌟 Exciting News! 🌟 I'm thrilled to share that our research paper, "Solanum Lycopersicum Disease Detection using AlexNet," has been published by IEEE in the 2024 International Conference on Smart Systems for Applications in Electrical Sciences (ICSSES)! 🚀📚 Sri Srujana Sadineni 🔗 Read the full paper here: DOI: 10.1109/ICSSES62373.2024.10561324 https://lnkd.in/gBbnmR8y #Research #Publication #DeepLearning #CNN #AlexNet #IEEE #ICSSES2024 #ComputerScience #AI #MachineLearning

Modeling viral evolution: A novel SIRSVIDE framework with application to SARS-CoV-2 dynamics https://lnkd.in/gMw7yB8H