Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387586658> ?p ?o ?g. }
Showing items 1 to 66 of
66
with 100 items per page.
- W4387586658 endingPage "154" @default.
- W4387586658 startingPage "143" @default.
- W4387586658 abstract "Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases medical analysis and identification. Given a person who stays in a remote area far from a healthcare facility, or doesn’t have the financial means to pay their clinic bill, or don’t have the time to take sick leave from their jobs. In such a case, disease prediction using excessive-cease state-of-the-art equipment can be really beneficial especially when it comes to decision-making. Two distinct research findings are also addressed to highlight the need of having a thorough understanding of procedures when diagnosing a condition. Deep learning requires the use of large neural networks with densely interconnected, each of which can adjust its hyper-parameters in response to incoming input. It is because of this technology that computer architectures are enabled to examine things without the need for particular programming from humans. Here are the most recent tendencies and breakthroughs in the deep learning of the field in this lookup, which might have a huge impact on the effective identification and diagnosis of a variety of ailments. AIM—The aim is to look into the use of deep learning in the accurate evaluation of positive disease risk indicators, with the goal of supporting health workers in their decision-making ( Martini et al. in J Neural Trans, 2017 [1]). This big sample included participants in two distinct fMRI sessions, each of which included behavioral assessment for an hour and an hour-long scan within the same day. If participants had passed all prior testing sessions and did not also meet the following additional exclusion criteria, like any mood-altering medications on the day of the scan, a history of serious medical conditions, and pregnancy-related MRI contraindications, participants were recruited from the parent study to take part in the fMRI section. A deep learning model application in the field of medicine is the proposed notion." @default.
- W4387586658 created "2023-10-13" @default.
- W4387586658 creator A5076249543 @default.
- W4387586658 creator A5085324459 @default.
- W4387586658 creator A5087462896 @default.
- W4387586658 date "2023-01-01" @default.
- W4387586658 modified "2023-10-14" @default.
- W4387586658 title "Neuroinformatics Deep Learning Synthesizer Based on Impulse Control Disorder Using LSTM Cells" @default.
- W4387586658 cites W1541322638 @default.
- W4387586658 cites W2090292104 @default.
- W4387586658 cites W2100649405 @default.
- W4387586658 cites W2131043834 @default.
- W4387586658 cites W2786554073 @default.
- W4387586658 cites W2793303269 @default.
- W4387586658 doi "https://doi.org/10.1007/978-981-99-2602-2_12" @default.
- W4387586658 hasPublicationYear "2023" @default.
- W4387586658 type Work @default.
- W4387586658 citedByCount "0" @default.
- W4387586658 crossrefType "book-chapter" @default.
- W4387586658 hasAuthorship W4387586658A5076249543 @default.
- W4387586658 hasAuthorship W4387586658A5085324459 @default.
- W4387586658 hasAuthorship W4387586658A5087462896 @default.
- W4387586658 hasConcept C108583219 @default.
- W4387586658 hasConcept C116834253 @default.
- W4387586658 hasConcept C119857082 @default.
- W4387586658 hasConcept C154945302 @default.
- W4387586658 hasConcept C160735492 @default.
- W4387586658 hasConcept C162324750 @default.
- W4387586658 hasConcept C202444582 @default.
- W4387586658 hasConcept C33923547 @default.
- W4387586658 hasConcept C41008148 @default.
- W4387586658 hasConcept C50522688 @default.
- W4387586658 hasConcept C59822182 @default.
- W4387586658 hasConcept C86803240 @default.
- W4387586658 hasConcept C9652623 @default.
- W4387586658 hasConceptScore W4387586658C108583219 @default.
- W4387586658 hasConceptScore W4387586658C116834253 @default.
- W4387586658 hasConceptScore W4387586658C119857082 @default.
- W4387586658 hasConceptScore W4387586658C154945302 @default.
- W4387586658 hasConceptScore W4387586658C160735492 @default.
- W4387586658 hasConceptScore W4387586658C162324750 @default.
- W4387586658 hasConceptScore W4387586658C202444582 @default.
- W4387586658 hasConceptScore W4387586658C33923547 @default.
- W4387586658 hasConceptScore W4387586658C41008148 @default.
- W4387586658 hasConceptScore W4387586658C50522688 @default.
- W4387586658 hasConceptScore W4387586658C59822182 @default.
- W4387586658 hasConceptScore W4387586658C86803240 @default.
- W4387586658 hasConceptScore W4387586658C9652623 @default.
- W4387586658 hasLocation W43875866581 @default.
- W4387586658 hasOpenAccess W4387586658 @default.
- W4387586658 hasPrimaryLocation W43875866581 @default.
- W4387586658 hasRelatedWork W2611989081 @default.
- W4387586658 hasRelatedWork W2731899572 @default.
- W4387586658 hasRelatedWork W2898732673 @default.
- W4387586658 hasRelatedWork W3215138031 @default.
- W4387586658 hasRelatedWork W4230611425 @default.
- W4387586658 hasRelatedWork W4294635752 @default.
- W4387586658 hasRelatedWork W4304166257 @default.
- W4387586658 hasRelatedWork W4375867731 @default.
- W4387586658 hasRelatedWork W4380075502 @default.
- W4387586658 hasRelatedWork W4383066092 @default.
- W4387586658 isParatext "false" @default.
- W4387586658 isRetracted "false" @default.
- W4387586658 workType "book-chapter" @default.