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Farrukh Nadeem
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- affiliation: King Abdulaziz University, Department of Information Systems, Jeddah, Saudi Arabia
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2020 – today
- 2025
- [j13]Alanoud Alotaibi
, Farrukh Nadeem, Mohamed Hamdy El-Eliemy
Weakly Supervised Deep Learning for Arabic Tweet Sentiment Analysis on Education Reforms: Leveraging Pre-Trained Models and LLMs With Snorkel. IEEE Access 13: 30523-30542 (2025) - 2024
- [j12]Mashail Alsolamy, Farrukh Nadeem
Automated detection and labeling of posterior teeth in dental bitewing X-rays using deep learning. Comput. Biol. Medicine 183: 109262 (2024) - [j11]Norah Alsaeed
A scalable and lightweight group authentication framework for Internet of Medical Things using integrated blockchain and fog computing. Future Gener. Comput. Syst. 151: 162-181 (2024) - 2022
- [j10]Farrukh Nadeem
Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance Indicators. IEEE Access 10: 63245-63257 (2022) - [c11]Eman T. Alharbi, Asma Cherif, Farrukh Nadeem, Tariq Mirza:
Machine Learning Models for Early Prediction of Asthma Attacks Based on Bio-signals and Environmental Triggers. AICCSA 2022: 1-7 - 2021
- [j9]Eman T. Alharbi, Farrukh Nadeem, Asma Cherif:
Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review. BMC Medical Informatics Decis. Mak. 21(1): 345 (2021) - 2020
- [j8]Farrukh Nadeem
A Unified Framework for User-Preferred Multi-Level Ranking of Cloud Computing Services Based on Usability and Quality of Service Evaluation. IEEE Access 8: 180054-180066 (2020) - [j7]Amin Y. Noaman, Abdul Hamid M. Ragab
Predicting and reducing "hospital-acquired infections" using a knowledge-based e-surveillance system. Expert Syst. J. Knowl. Eng. 37(1) (2020)
2010 – 2019
- 2019
- [j6]Farrukh Nadeem
Using Machine Learning Ensemble Methods to Predict Execution Time of e-Science Workflows in Heterogeneous Distributed Systems. IEEE Access 7: 25138-25149 (2019) - [j5]Farrukh Nadeem:
Using radial basis function neural network to predict dynamic resource availability in heterogeneous distributed environments. J. Intell. Fuzzy Syst. 37(4): 5619-5632 (2019) - 2018
- [j4]Sultan Abdullah Algarni, Mohammad Rafi Ikbal, Roobaea Alroobaea
Performance Evaluation of Xen, KVM, and Proxmox Hypervisors. Int. J. Open Source Softw. Process. 9(2): 39-54 (2018) - [c10]Mohammed Matuq Ashi, Muazzam Ahmed Siddiqui, Farrukh Nadeem:
Pre-trained Word Embeddings for Arabic Aspect-Based Sentiment Analysis of Airline Tweets. AISI 2018: 241-251 - [c9]Amin Y. Noaman, Nabeela Al-Abdullah
Knowledge Based e-Health Surveillance System for Predicting Hospital Acquired Infections. COMPSAC (2) 2018: 345-351 - 2017
- [j3]Farrukh Nadeem
Modeling and predicting execution time of scientific workflows in the Grid using radial basis function neural network. Clust. Comput. 20(3): 2805-2819 (2017) - 2015
- [j2]Farrukh Nadeem
An Early Evaluation and Comparison of Three Private Cloud Computing Software Platforms. J. Comput. Sci. Technol. 30(3): 639-654 (2015) - 2013
- [j1]Farrukh Nadeem
Optimizing execution time predictions of scientific workflow applications in the Grid through evolutionary programming. Future Gener. Comput. Syst. 29(4): 926-935 (2013) - 2012
- [c8]Salma Mahgoub, Farrukh Nadeem:
Interactive case based learning in teaching decision support systems and business intelligence. Interacción 2012: 34:1-34:4
2000 – 2009
- 2009
- [c7]Farrukh Nadeem, Thomas Fahringer
Using Templates to Predict Execution Time of Scientific Workflow Applications in the Grid. CCGRID 2009: 316-323 - [c6]Rubing Duan, Farrukh Nadeem
A Hybrid Intelligent Method for Performance Modeling and Prediction of Workflow Activities in Grids. CCGRID 2009: 339-347 - [c5]Farrukh Nadeem
Predicting the execution time of grid workflow applications through local learning. SC 2009 - 2008
- [c4]Farrukh Nadeem
Characterizing, Modeling and Predicting Dynamic Resource Availability in a Large Scale Multi-purpose Grid. CCGRID 2008: 348-357 - [c3]Farrukh Nadeem, Radu Prodan, Thomas Fahringer, Alexandru Iosup
A Framework For Resource Availability Characterization And Online Prediction In The Grids. CoreGRID Integration Workshop 2008: 209-224 - 2007
- [c2]Farrukh Nadeem, Radu Prodan, Thomas Fahringer
Optimizing Performance of Automatic Training Phase for Application Performance Prediction in the Grid. HPCC 2007: 309-321 - [p1]Thomas Fahringer
ASKALON: A Development and Grid Computing Environment for Scientific Workflows. Workflows for e-Science, Scientific Workflows for Grids 2007: 450-471 - 2006
- [c1]Farrukh Nadeem
Soft Benchmarks-Based Application Performance Prediction Using a Minimum Training Set. e-Science 2006: 71
Coauthor Index
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