Big Data and Named Entity Recognition Approaches for Urdu Language

Autor: Qudsia Jamil, Muhammad Rehman Zafar

Publikováno v: EAI Endorsed Transactions on Scalable Information Systems, Vol 5, Iss 16, Pp 1-5 (2018)

Nowadays data is stored in digital form and Terabyte of data is generated on daily basis. It is difficult task to extract useful information from Big data efficiently. From unstructured text Information extraction is a technique which used to extract

Externí odkaz: https://doaj.org/article/ca18fb6a55c7435cb31b0cf9a4a539ef

Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability

Autor: Naimul Mefraz Khan, Muhammad Rehman Zafar

Publikováno v: Machine Learning and Knowledge Extraction
Volume 3
Issue 3
Pages 27-541
Machine Learning and Knowledge Extraction, Vol 3, Iss 27, Pp 525-541 (2021)

Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to increase the interpretability and explainability of black box Machine Learning (ML) algorithms. LIME typically creates an explanation for a single prediction by any

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86b4468b5ac1a4d0cab9041b06b29334
https://doi.org/10.3390/make3030027

Scheduling on Heterogeneous Multi-core Processors Using Stable Matching Algorithm

Autor: Muhammad Rehman Zafar, Muhammad Asfand-e-yar

Publikováno v: International Journal of Advanced Computer Science and Applications. 7

Heterogeneous Multi-core Processors (HMP) are better to schedule jobs as compare to homogenous multi-core processors. There are two main factors associated while analyzing both architectures i.e. performance and power consumption. HMP incorporates co

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0223cb53ca7fc0ced1a6ad34877ddab9
https://doi.org/10.14569/ijacsa.2016.070666