FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4324319975> ?p ?o ?g. }

• W4324319975 endingPage "106103" @default.
• W4324319975 startingPage "106103" @default.
• W4324319975 abstract "Mental disorders are neural issues that influence brain cognition and social connectivity. The significant increase in mental disorders needs prompt detection for effective treatment. Psychiatric disorders can be partially diagnosed with constant counseling by using anti-depressants and other inhibitors. However, this is a laborious and also time-consuming process. This leads to investigating the computational techniques that can be applied to detect psychiatric illness in its premature phases. Various works are proposed to detect mental disorders from four data forms: neuro-images, EEG signals, textual data, and gene data. The researchers aim to investigate various models that can detect mental illness using advanced deep learning techniques. Through exploring authoritative databases, the authors gathered papers and studied classic machine learning and primary deep learning techniques for anticipating mental health issues. As the results of various models, the study also presented the difficulties and constraints that machine learning researchers have encountered while studying psychological disorders. The collected articles are categorized into four forms based on the data type used for the analysis. This paper proposes four different hybrid models to detect mental disorders in the early stages from four different data forms. Standardized metrics like area under the receiver operating characteristic curve (AUC–ROC) score and average classification accuracy (ACA) are used to evaluate the models. The proposed models outperformed all the state-of-the-art models in disorder prediction. Furthermore, the study discusses specific suggestions for future studies and development in the use of learning algorithms in the field of mental health." @default.
• W4324319975 created "2023-03-16" @default.
• W4324319975 creator A5039049132 @default.
• W4324319975 creator A5091577454 @default.
• W4324319975 date "2023-06-01" @default.
• W4324319975 modified "2023-09-30" @default.
• W4324319975 title "Deep Learning based techniques for Neuro-degenerative disorders detection" @default.
• W4324319975 cites W1457602677 @default.
• W4324319975 cites W1539633355 @default.
• W4324319975 cites W1648141552 @default.
• W4324319975 cites W1968272910 @default.
• W4324319975 cites W1969959732 @default.
• W4324319975 cites W2036735145 @default.
• W4324319975 cites W2052271957 @default.
• W4324319975 cites W2058818054 @default.
• W4324319975 cites W2074830260 @default.
• W4324319975 cites W2075094338 @default.
• W4324319975 cites W2094425026 @default.
• W4324319975 cites W2122328291 @default.
• W4324319975 cites W2124260444 @default.
• W4324319975 cites W2129359809 @default.
• W4324319975 cites W2130140789 @default.
• W4324319975 cites W2133304638 @default.
• W4324319975 cites W2133502275 @default.
• W4324319975 cites W2238387778 @default.
• W4324319975 cites W2263825730 @default.
• W4324319975 cites W2557410492 @default.
• W4324319975 cites W2569531558 @default.
• W4324319975 cites W2729451077 @default.
• W4324319975 cites W2734832579 @default.
• W4324319975 cites W2737280991 @default.
• W4324319975 cites W2762517398 @default.
• W4324319975 cites W2794320209 @default.
• W4324319975 cites W2902565975 @default.
• W4324319975 cites W2926974277 @default.
• W4324319975 cites W2940179095 @default.
• W4324319975 cites W2946763314 @default.
• W4324319975 cites W2953303127 @default.
• W4324319975 cites W2969579165 @default.
• W4324319975 cites W2990575831 @default.
• W4324319975 cites W3003719033 @default.
• W4324319975 cites W3004330282 @default.
• W4324319975 cites W3005049612 @default.
• W4324319975 cites W3010030563 @default.
• W4324319975 cites W3012875109 @default.
• W4324319975 cites W3015188018 @default.
• W4324319975 cites W3016847450 @default.
• W4324319975 cites W3021203631 @default.
• W4324319975 cites W3031744338 @default.
• W4324319975 cites W3033432616 @default.
• W4324319975 cites W3034674374 @default.
• W4324319975 cites W3037114781 @default.
• W4324319975 cites W3045705243 @default.
• W4324319975 cites W3081983074 @default.
• W4324319975 cites W3088134661 @default.
• W4324319975 cites W3126917856 @default.
• W4324319975 cites W3129234057 @default.
• W4324319975 cites W3196871682 @default.
• W4324319975 cites W3211062931 @default.
• W4324319975 cites W4205970017 @default.
• W4324319975 cites W4223610593 @default.
• W4324319975 cites W4223969733 @default.
• W4324319975 cites W4224087309 @default.
• W4324319975 cites W4224247713 @default.
• W4324319975 cites W4286462213 @default.
• W4324319975 cites W4292855834 @default.
• W4324319975 doi "https://doi.org/10.1016/j.engappai.2023.106103" @default.
• W4324319975 hasPublicationYear "2023" @default.
• W4324319975 type Work @default.
• W4324319975 citedByCount "0" @default.
• W4324319975 crossrefType "journal-article" @default.
• W4324319975 hasAuthorship W4324319975A5039049132 @default.
• W4324319975 hasAuthorship W4324319975A5091577454 @default.
• W4324319975 hasConcept C108583219 @default.
• W4324319975 hasConcept C111919701 @default.
• W4324319975 hasConcept C118552586 @default.
• W4324319975 hasConcept C119857082 @default.
• W4324319975 hasConcept C134362201 @default.
• W4324319975 hasConcept C154945302 @default.
• W4324319975 hasConcept C15744967 @default.
• W4324319975 hasConcept C202444582 @default.
• W4324319975 hasConcept C2776674806 @default.
• W4324319975 hasConcept C33923547 @default.
• W4324319975 hasConcept C41008148 @default.
• W4324319975 hasConcept C50644808 @default.
• W4324319975 hasConcept C58471807 @default.
• W4324319975 hasConcept C9652623 @default.
• W4324319975 hasConcept C98045186 @default.
• W4324319975 hasConceptScore W4324319975C108583219 @default.
• W4324319975 hasConceptScore W4324319975C111919701 @default.
• W4324319975 hasConceptScore W4324319975C118552586 @default.
• W4324319975 hasConceptScore W4324319975C119857082 @default.
• W4324319975 hasConceptScore W4324319975C134362201 @default.
• W4324319975 hasConceptScore W4324319975C154945302 @default.
• W4324319975 hasConceptScore W4324319975C15744967 @default.
• W4324319975 hasConceptScore W4324319975C202444582 @default.
• W4324319975 hasConceptScore W4324319975C2776674806 @default.
• W4324319975 hasConceptScore W4324319975C33923547 @default.

• next