Zobrazeno 1 - 10
of 5 987
pro vyhledávání: '"Agu, A"'
Autor:
Akamike, Ifeyinwa C., Agu, Ifunanya C., Agu, Chibuike, Eze, Irene I., Eigbiremolen, Godstime O., Mbachu, Chinyere O., Onwujekwe, Obinna
Publikováno v:
African Journal of Reproductive Health / La Revue Africaine de la Santé Reproductive, 2024 Aug 01. 28, 83-92.
Externí odkaz:
https://www.jstor.org/stable/27332721
Autor:
Agu, Chibuike, Agu, Ifunanya, Mbachu, Chinyere, Agu, Ozioma, Molen, God'stime, Onwujekwe, Obinna
Publikováno v:
African Journal of Reproductive Health / La Revue Africaine de la Santé Reproductive, 2024 Aug 01. 28, 74-82.
Externí odkaz:
https://www.jstor.org/stable/27332720
Autor:
Agu, Ifunanya C., Eze, Irene I., Agu, Chibuike I., Agu, Ozioma, Mbachu, Chinyere O., Onwujekwe, Obinna
Publikováno v:
African Journal of Reproductive Health / La Revue Africaine de la Santé Reproductive, 2024 Aug 01. 28, 51-61.
Externí odkaz:
https://www.jstor.org/stable/27332718
Publikováno v:
ICMLA 2024
Human Activity Recognition (HAR) is essential in ubiquitous computing, with far-reaching real-world applications. While recent SOTA HAR research has demonstrated impressive performance, some key aspects remain under-explored. Firstly, HAR can be both
Externí odkaz:
http://arxiv.org/abs/2410.21300
Publikováno v:
IMWUT 2023
Human Activity Recognition (HAR) is a challenging, multi-label classification problem as activities may co-occur and sensor signals corresponding to the same activity may vary in different contexts (e.g., different device placements). This paper prop
Externí odkaz:
http://arxiv.org/abs/2409.18481
Publikováno v:
PerCom 2023
Context-aware Human Activity Recognition (CHAR) is challenging due to the need to recognize the user's current activity from signals that vary significantly with contextual factors such as phone placements and the varied styles with which different u
Externí odkaz:
http://arxiv.org/abs/2409.17483
Autor:
Busaranuvong, Palawat, Agu, Emmanuel, Kumar, Deepak, Gautam, Shefalika, Fard, Reza Saadati, Tulu, Bengisu, Strong, Diane
To detect infected wounds in Diabetic Foot Ulcers (DFUs) from photographs, preventing severe complications and amputations. Methods: This paper proposes the Guided Conditional Diffusion Classifier (ConDiff), a novel deep-learning infection detection
Externí odkaz:
http://arxiv.org/abs/2405.00858
Autor:
Anosike, Chibueze, Osefo, Rita Chinenye, Isiogugu, Nnanyelugo Ogechukwu, Nwachukwu, Emmanuel Chijiekwu, Agu, Ugonna Kyrian, Nwaji, Jonathan Chimaobi, Ogbu, Mario-Ephraim Afam
Publikováno v:
Mental Health and Social Inclusion, 2024, Vol. 28, Issue 6, pp. 1263-1273.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/MHSI-12-2023-0138
HCM-Echo-VAR-Ensemble: Deep Ensemble Fusion to Detect Hypertrophic Cardiomyopathy in Echocardiograms
Publikováno v:
IEEE Open Journal of Engineering in Medicine and Biology, Vol 6, Pp 193-201 (2025)
Goal: To detect Hypertrophic Cardiomyopathy (HCM) from multiple views of Echocardiogram (cardiac ultrasound) videos. Methods: we propose HCM-Echo-VAR-Ensemble, a novel framework that performs binary classification (HCM vs. no HCM) of echocardiogram v
Externí odkaz:
https://doaj.org/article/f8764e52ff3243078418b3e4b38552ef
Autor:
Palawat Busaranuvong, Emmanuel Agu, Deepak Kumar, Shefalika Gautam, Reza Saadati Fard, Bengisu Tulu, Diane Strong
Publikováno v:
IEEE Open Journal of Engineering in Medicine and Biology, Vol 6, Pp 20-27 (2025)
Goal: To accurately detect infections in Diabetic Foot Ulcers (DFUs) using photographs taken at the Point of Care (POC). Achieving high performance is critical for preventing complications and amputations, as well as minimizing unnecessary emergency
Externí odkaz:
https://doaj.org/article/71657890aeb74f83994357471ee449e9