Zobrazeno 1 - 10
of 13 993
pro vyhledávání: '"Xgboost"'
Publikováno v:
Journal of Derivatives and Quantitative Studies: 선물연구, 2024, Vol. 32, Issue 4, pp. 266-285.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JDQS-08-2024-0035
Publikováno v:
Journal of Systems and Information Technology, 2024, Vol. 26, Issue 4, pp. 495-527.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JSIT-06-2020-0120
Autor:
Nageswara Rao Gali, Panduranga Vital Terlapu, Yasaswini Mandavakuriti, Sai Manoj Somu, Madhavi Varanasi, Vijay Telugu, Maheswara Rao V V R
Publikováno v:
Proceedings on Engineering Sciences, Vol 6, Iss 4, Pp 1731-1740 (2024)
Breast cancer (BC) ranks the second most prevalent cancer among women globally and is the leading cause of female mortality. The conventional method for BC detection primarily relies on biopsy; this might be time-consuming and error prone. The substa
Externí odkaz:
https://doaj.org/article/1b921bfab8ef4ec7a70d1612394d57df
Autor:
Richa Gupta, Mansi Bhandari, Anhad Grover, Taher Al-shehari, Mohammed Kadrie, Taha Alfakih, Hussain Alsalman
Publikováno v:
BioData Mining, Vol 17, Iss 1, Pp 1-11 (2024)
Abstract This research presents a predictive model aimed at estimating the progression of Amyotrophic Lateral Sclerosis (ALS) based on clinical features collected from a dataset of 50 patients. Important features included evaluations of speech, mobil
Externí odkaz:
https://doaj.org/article/d8c2674708d54ff4a0aed70e2781d6eb
Autor:
Akella Subrahmanya Narasimha Raju, K. Venkatesh, B. Padmaja, CH. N. Santhosh Kumar, Pattabhi Rama Mohan Patnala, Ayodele Lasisi, Saiful Islam, Abdul Razak, Wahaj Ahmad Khan
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-35 (2024)
Abstract Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. This research presents a new method for improving CRC detection using a deep learning ensem
Externí odkaz:
https://doaj.org/article/755363a4b8e34a1f9bcf439d7fd99189
Autor:
Paulo Henrique Couto Simões
Publikováno v:
Cadernos do IME: Série Estatística, Vol 56, Pp 16-38 (2024)
This work proposes a method to rank the contribution of containment measures, vaccination coverage and mobility to contain the evolution of the COVID-19 pandemic in different states of Brazil. The proposed method applies the automatic learning of reg
Externí odkaz:
https://doaj.org/article/1afc3e974dac43c9b387e9675c031811
Publikováno v:
Alexandria Engineering Journal, Vol 109, Iss , Pp 754-767 (2024)
The exponential development of expressways has resulted in increased demand for highway electromechanical (E&M) equipment. However, the constant changes in parameters and models of electromechanical equipment make national quotas outdated and complic
Externí odkaz:
https://doaj.org/article/8b7d0090ada14e7ab1e4a0a5bf8f5e6e
Publikováno v:
Regional Studies, Regional Science, Vol 11, Iss 1, Pp 496-522 (2024)
This research aimed to identify the principal factors predicting social welfare and inequality in the neighbourhoods of Madrid city. A comprehensive dataset representing various socioeconomic metrics of Madrid’s neighbourhoods is analysed utilising
Externí odkaz:
https://doaj.org/article/d2ed328bf9934d9e8a4f5836111469a6
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 3030-3039 (2024)
Current medical research has been demonstrating the roles of miRNAs in a variety of cellular mechanisms, lending credence to the association between miRNA dysregulation and multiple diseases. Understanding the mechanisms of miRNA is critical for deve
Externí odkaz:
https://doaj.org/article/3eca48283a8d4b4786dfaf8899d008ab
Publikováno v:
Journal of Applied Informatics and Computing, Vol 8, Iss 2, Pp 296-303 (2024)
Kanker paru-paru tetap menjadi salah satu penyebab kematian utama di seluruh dunia, dan deteksi dini melalui metode yang akurat dan andal sangat penting untuk meningkatkan prognosis pasien. Studi ini mengusulkan model klasifikasi kanker paru-paru yan
Externí odkaz:
https://doaj.org/article/1999ac9f287a4d90af417422e19a637f