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Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.
Viruses
November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
View Article and Find Full Text PDFConstruction of the First Russian Recombinant Live Attenuated Vaccine Strain and Evaluation of Its Protection Efficacy Against Two African Swine Fever Virus Heterologous Strains of Serotype 8.
Vaccines (Basel)
December 2024
Federal Research Centre for Virology and Microbiology, Academician Bakoulov Street 1, 601125 Volginsky, Russia.
The spread of African swine fever virus (ASFV) has led to major economic losses to pork worldwide. In Russia, there are no developed or registered vaccines against ASFV genotype II, which is associated with numerous ASFV outbreaks in populations of domestic pigs and wild boars in the country. We introduced deletions of the six MGF360 and MGF505 genes of the ASFV virulent Stavropol_01/08 strain, isolated in Russia in 2008.
View Article and Find Full Text PDFChallenges of Multidrug-Resistant Tuberculosis Meningitis: Current Treatments and the Role of Glutathione as an Adjunct Therapy.
Vaccines (Basel)
December 2024
College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, CA 91766-1854, USA.
Multidrug-resistant tuberculosis (MDR-TB) poses a significant global health threat, especially when it involves the central nervous system (CNS). Tuberculous meningitis (TBM), a severe manifestation of TB, is linked to high mortality rates and long-term neurological complications, further exacerbated by drug resistance and immune evasion mechanisms employed by Mycobacterium tuberculosis (Mtb). Although pulmonary TB remains the primary focus of research, MDR-TBM introduces unique challenges in diagnosis, treatment, and patient outcomes.
View Article and Find Full Text PDFTowards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data.
Sensors (Basel)
December 2024
School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool L3 3AF, UK.
Camera traps offer enormous new opportunities in ecological studies, but current automated image analysis methods often lack the contextual richness needed to support impactful conservation outcomes. Integrating vision-language models into these workflows could address this gap by providing enhanced contextual understanding and enabling advanced queries across temporal and spatial dimensions. Here, we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps.
View Article and Find Full Text PDFDynamic Road Anomaly Detection: Harnessing Smartphone Accelerometer Data with Incremental Concept Drift Detection and Classification.
Sensors (Basel)
December 2024
Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
Effective monitoring of road conditions is crucial for ensuring safe and efficient transportation systems. By leveraging the power of crowd-sourced smartphone sensor data, road condition monitoring can be conducted in real-time, providing valuable insights for transportation planners, policymakers, and the general public. Previous studies have primarily focused on the use of pre-trained machine learning models and threshold-based methods for anomaly classification, which may not be suitable for real-world scenarios that require incremental detection and classification.
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