Zobrazeno 1 - 10
of 71 884
pro vyhledávání: '"A. Safdar"'
Autor:
Shahid, Yousra1 yousrashahid786@gmail.com, Ashiq, Aqsa1, Anwar, Shanza1, Mushtaq, Zainab1, Zaheer, Sadia2
Publikováno v:
Biomedica. 2022, Vol. 38 Issue 3, p183-189. 7p.
Publikováno v:
BioMedica, Vol 38, Iss 3, Pp 183-189 (2022)
Background and Objective: A high incidence of burnout, depression, and anxiety is found among medical undergraduate and postgraduate students worldwide with the increasing prevalence of stress. The objective of this study was to analyze the correlati
Externí odkaz:
https://doaj.org/article/50f0b6aecebe4d7980b0b88bab3f2745
Autor:
Kim, KiHwan, Chung, Hyunsun, Ahn, Seonghoon, Park, Junhyeok, Jamil, Safdar, Byun, Hongsu, Lee, Myungcheol, Choi, Jinchun, Kim, Youngjae
Log-Structured Merge (LSM) tree-based Key-Value Stores (KVSs) are widely adopted for their high performance in write-intensive environments, but they often face performance degradation due to write stalls during compaction. Prior solutions, such as r
Externí odkaz:
http://arxiv.org/abs/2410.21760
Publikováno v:
Journal of Pakistan Association of Dermatologists. Apr-Jun2020, Vol. 30 Issue 2, p327-330. 4p.
The significant portion of diabetic patients was affected due to major blindness caused by Diabetic retinopathy (DR). For diabetic retinopathy, lesion segmentation, and detection the comprehensive examination is delved into the deep learning techniqu
Externí odkaz:
http://arxiv.org/abs/2409.16721
Autor:
Khan, Asifullah, Sohail, Anabia, Fiaz, Mustansar, Hassan, Mehdi, Afridi, Tariq Habib, Marwat, Sibghat Ullah, Munir, Farzeen, Ali, Safdar, Naseem, Hannan, Zaheer, Muhammad Zaigham, Ali, Kamran, Sultana, Tangina, Tanoli, Ziaurrehman, Akhter, Naeem
Deep supervised learning models require high volume of labeled data to attain sufficiently good results. Although, the practice of gathering and annotating such big data is costly and laborious. Recently, the application of self supervised learning (
Externí odkaz:
http://arxiv.org/abs/2408.17059
Various machine learning (ML)-based in-situ monitoring systems have been developed to detect anomalies and defects in laser additive manufacturing (LAM) processes. While multimodal fusion, which integrates data from visual, audio, and other modalitie
Externí odkaz:
http://arxiv.org/abs/2408.05307
Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and Artificial Intelligence (AI) contexts t
Externí odkaz:
http://arxiv.org/abs/2407.18827
Autor:
Xie, Jiarui, Safdar, Mutahar, Mircea, Andrei, Zhao, Bi Cheng, Lu, Yan, Ko, Hyunwoong, Yang, Zhuo, Zhao, Yaoyao Fiona
Machine learning (ML)-based cyber-physical systems (CPSs) have been extensively developed to improve the print quality of additive manufacturing (AM). However, the reproducibility of these systems, as presented in published research, has not been tho
Externí odkaz:
http://arxiv.org/abs/2407.04031