Zobrazeno 1 - 10
of 89 369
pro vyhledávání: '"Machine learning and deep learning"'
Autor:
Kannan, Kamala Devi, Jagatheesaperumal, Senthil Kumar, Kandala, Rajesh N. V. P. S., Lotfaliany, Mojtaba, Alizadehsanid, Roohallah, Mohebbi, Mohammadreza
For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) has started playing a significant role. By evaluating complex data from imaging, genetics, and behavior
Externí odkaz:
http://arxiv.org/abs/2412.06147
Autor:
Sublime, Jérémie
In today's world, AI programs powered by Machine Learning are ubiquitous, and have achieved seemingly exceptional performance across a broad range of tasks, from medical diagnosis and credit rating in banking, to theft detection via video analysis, a
Externí odkaz:
http://arxiv.org/abs/2411.18656
The cervix is the narrow end of the uterus that connects to the vagina in the female reproductive system. Abnormal cell growth in the squamous epithelial lining of the cervix leads to cervical cancer in females. A Pap smear is a diagnostic procedure
Externí odkaz:
http://arxiv.org/abs/2411.13535
Autor:
Alaeddini, Maliheh
Emotion detection is pivotal in human communication, as it significantly influences behavior, relationships, and decision-making processes. This study concentrates on text-based emotion detection by leveraging the GoEmotions dataset, which annotates
Externí odkaz:
http://arxiv.org/abs/2411.10328
Star formation rates (SFRs) are a crucial observational tracer of galaxy formation and evolution. Spectroscopy, which is expensive, is traditionally used to estimate SFRs. This study tests the possibility of inferring SFRs of large samples of galaxie
Externí odkaz:
http://arxiv.org/abs/2410.06736
Credit scoring is vital in the financial industry, assessing the risk of lending to credit card applicants. Traditional credit scoring methods face challenges with large datasets and data imbalance between creditworthy and non-creditworthy applicants
Externí odkaz:
http://arxiv.org/abs/2409.16676
Autor:
Chang, Victor1 (AUTHOR) sharuga1225@gmail.com, Sivakulasingam, Sharuga1 (AUTHOR) meghana.ganatra@gmail.com, Wang, Hai2 (AUTHOR) h.wang10@aston.ac.uk, Wong, Siu Tung3 (AUTHOR) tommywong962@gmail.com, Ganatra, Meghana Ashok1 (AUTHOR) j.luo2@aston.ac.uk, Luo, Jiabin1 (AUTHOR)
Publikováno v:
Risks. Nov2024, Vol. 12 Issue 11, p174. 33p.
Autor:
Cao, Yuchen, Dai, Jianglai, Wang, Zhongyan, Zhang, Yeyubei, Shen, Xiaorui, Liu, Yunchong, Tian, Yexin
The global rise in depression necessitates innovative detection methods for early intervention. Social media provides a unique opportunity to identify depression through user-generated posts. This systematic review evaluates machine learning (ML) mod
Externí odkaz:
http://arxiv.org/abs/2410.16204