Results 131 to 140 of about 3,463 (178)

Improving emotional connection of human and machine using Deep Maxout Networks optimized through Modified Water Cycle optimizer. [PDF]

Sci Rep
Zhao J, Huang Y, Moattari M.
europepmc   +1 more source

Buzzing with Intelligence: A Systematic Review of Smart Beehive Technologies. [PDF]

Sensors (Basel)
Šabić J, Perković T, Šolić P, Šerić L.   +3 more
europepmc   +1 more source

Prediction of cognitive impairment through speech data analysis: A comparative evaluation of deep learning models. [PDF]

PLoS One
Kim M, Choi H, Shim Y, Ryoo N, Jeong HT, Yun G, Lee H, Kim S, Youn YC.   +8 more
europepmc   +1 more source

The Role of Voice Acoustics in Depression Assessment: Findings From Bibliometric Analysis, Literature Review, and Meta-Analysis. [PDF]

Depress Anxiety
Cantor-Cutiva LC, Carrillo-González A, Yousef AM, Berardi M, Hunter EJ.   +4 more
europepmc   +1 more source

Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls. [PDF]

Sci Rep
Idrisoglu A, Behrens A.
europepmc   +1 more source

Cross-Cultural Identification of Acoustic Voice Features for Depression: A Cross-Sectional Study of Vietnamese and Japanese Datasets. [PDF]

Bioengineering (Basel)
Le PTV, Nakamura M, Higuchi M, Vuu LTM, Huynh N, Tokuno S.   +5 more
europepmc   +1 more source

Hybrid deep learning and YAMNet features for asthma diagnosis from respiratory sounds. [PDF]

Sci Rep
Shatat GAE, Moustafa HE, Saraya MS, Marzouk AA, Hekal AA.   +4 more
europepmc   +1 more source

Optimizing MFCC Parameters for Breathing Phase Detection. [PDF]

Sensors (Basel)
Zhantleuova AK, Makashev YK, Duzbayev NT.   +2 more
europepmc   +1 more source

Speech impairment detection in children using time frequency features of speech and deep learning techniques. [PDF]

Front Hum Neurosci
Manoswini M, Swain BR, Sahoo B, Gourisaria MK, Jha AV, Bizon N, Swetapadma A.   +6 more
europepmc   +1 more source