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Salim Diwani
Salim Diwani
在 udom.ac.tz 的电子邮件经过验证 - 首页
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引用次数
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年份
Diabetes forecasting using supervised learning techniques
SA Diwani, A Sam
Adv Comput Sci an Int J 3, 10-18, 2014
432014
Overview applications of data mining in health care: The case study of Arusha region
S Diwani, S Mishol, D Kayange, D Machuve, A Sam
IJCER, 2013
342013
A novel holistic disease prediction tool using best fit data mining techniques
SA Diwani, ZO Yonah
Int. J. Com. Dig. Sys 6 (2), 63-72, 2017
132017
AI for Anglophone Africa: Unlocking its adoption for responsible solutions in academia-private sector
R Sinde, S Diwani, J Leo, T Kondo, N Elisa, J Matogoro
Frontiers in Artificial Intelligence 6, 1133677, 2023
82023
Framework for Data Mining In Healthcare Information System in Developing Countries: A Case of Tanzania
S Diwani, A Sam
IJTEEE, 2013
72013
Data Mining Awareness and Readiness in Healthcare Sector: a case of Tanzania
SA Diwani, A Sam
Advances in Computer Science: an International Journal 3 (1), 37-43, 2014
42014
Artificial intelligence in Africa: a bibliometric analysis from 2013 to 2022
TS Kondo, SA Diwani
Discover Artificial Intelligence 3 (1), 34, 2023
32023
Exploring the status of artificial intelligence for healthcare research in Africa: a bibliometric and thematic analysis
TS Kondo, SA Diwani, AS Nyamawe, MM Mjahidi
AI and Ethics, 1-22, 2023
32023
Holistic diagnosis tool for prediction of benign and malignant breast cancer using data mining techniques
SA Diwani, Z O Yonah
International Journal of Computing and Digital Systems 10, 1-10, 2021
32021
Performance analysis of a high throughput LDPC decoder using sum product and min sum algorithm
S Kakde, A Khobragade
Int. J. Com. Dig. Sys 6 (2), 89-95, 2017
32017
Holistic Diagnosis Tool for Early Detection of Breast Cancer
SA Diwani, ZO Yonah
Int. J. Com. Dig. Sys 10 (2), 2021
22021
DiscoverArtificial Intelligence
TS Kondo, SA Diwani
Discover 3, 34, 2023
2023
Theoretical Factors underlying Data Mining Techniques in Developing Countries: a case of Tanzania
SA Diwani, A Sam
2014
Practical Machine Learning: A Beginner's Guide with Ethical Insights
AS Nyamawe, MM Mjahidi, NE Nnko, SA Diwani, GG Minja, K Malyango
Chapman and Hall/CRC, 0
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