Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network.
Sign Up to like & getrecommendations! Published in 2022 at "Molecular informatics"
DOI: 10.1002/minf.202100247
Abstract: The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. In this work, we developed three different deep neural network models (DNN) to predict the antibacterial property… read more here.
Keywords: dnn model; graph clustering; model; network ... See more keywords
Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Cleaner Production"
DOI: 10.1016/j.jclepro.2021.127672
Abstract: Abstract In recent years, the strong development of urban areas and rapid population growth have contributed significantly to environmental pollution issues, especially SW. Of those, municipal solid waste (MSW) is considered a major concern of… read more here.
Keywords: energy recovery; waste; dnn model; energy ... See more keywords
Optimum Selection of DNN Model and Framework for Edge Inference
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2869929
Abstract: This paper describes a methodology to select the optimum combination of deep neural network and software framework for visual inference on embedded systems. As a first step, benchmarking is required. In particular, we have benchmarked… read more here.
Keywords: optimum selection; selection dnn; framework; model framework ... See more keywords
Assessing Optimizer Impact on DNN Model Sensitivity to Adversarial Examples
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2948658
Abstract: Deep Neural Networks (DNNs) have been gaining state-of-the-art achievement compared with many traditional Machine Learning (ML) models in diverse fields. However, adversarial examples challenge the further deployment and application of DNNs. Analysis has been carried… read more here.
Keywords: sensitivity adversarial; impact; dnn model; model ... See more keywords
An Effective Training Scheme for Deep Neural Network in Edge Computing Enabled Internet of Medical Things (IoMT) Systems
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3000322
Abstract: At present times, the real-time requirement on the multiaccess healthcare monitoring system, information mining, and efficient disease diagnosis of health conditions is a difficult process. The recent advances in information technology and the internet of… read more here.
Keywords: internet medical; ets dnn; edge computing; dnn model ... See more keywords
Predicting transient diesel engine NOx emissions using time-series data preprocessing with deep-learning models
Sign Up to like & getrecommendations! Published in 2021 at "Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering"
DOI: 10.1177/09544070211005570
Abstract: Deep-learning models were developed and evaluated for predicting the engine-out emission of NOx—one of the main pollutants emitted from diesel engines—under transient conditions, that is, the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). Phenomena of… read more here.
Keywords: learning models; time; deep learning; model ... See more keywords
Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neurology"
DOI: 10.3389/fneur.2022.869915
Abstract: Background Based on the assumption that systemic metabolic disorders affect cognitive function, we have developed a deep neural network (DNN) model that can estimate cognitive function based on basic blood test data that do not… read more here.
Keywords: age; dnn model; systemic metabolic; cerebral atrophy ... See more keywords
An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data
Sign Up to like & getrecommendations! Published in 2020 at "Applied Sciences"
DOI: 10.3390/app10113785
Abstract: This paper describes the development of an optimized corn yield prediction model under extreme weather conditions for the Midwestern United States (US). We tested six different artificial intelligence (AI) models using satellite images and meteorological… read more here.
Keywords: weather conditions; dnn model; corn; extreme weather ... See more keywords
Assessment of Therapeutic Responses Using a Deep Neural Network Based on 18F-FDG PET and Blood Inflammatory Markers in Pyogenic Vertebral Osteomyelitis
Sign Up to like & getrecommendations! Published in 2022 at "Medicina"
DOI: 10.3390/medicina58111693
Abstract: Background and Objectives: This study investigated the usefulness of deep neural network (DNN) models based on 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and blood inflammatory markers to assess the therapeutic response in pyogenic vertebral osteomyelitis (PVO).… read more here.
Keywords: neural network; assessment; deep neural; dnn model ... See more keywords
A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps
Sign Up to like & getrecommendations! Published in 2020 at "Sustainability"
DOI: 10.3390/su12072914
Abstract: Improper refrigerant charge amount (RCA) is a recurring fault in electric heat pump (EHP) systems. Because EHP systems show their best performance at optimum charge, predicting the RCA is important. There has been considerable development… read more here.
Keywords: ehp systems; charge amount; model; refrigerant charge ... See more keywords