Farrukh Nadeem - researchr alias

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Viewing Publication 1 - 23 from 23

2024

A scalable and lightweight group authentication framework for Internet of Medical Things using integrated blockchain and fog computingNorah Alsaeed, Farrukh Nadeem, Faisal Albalwy. fgcs, 151:162-181, February 2024. [doi]

2022

Machine Learning Models for Early Prediction of Asthma Attacks Based on Bio-signals and Environmental TriggersEman T. Alharbi, Asma Cherif, Farrukh Nadeem, Tariq Mirza. aiccsa 2022: 1-7 [doi]

Evaluating and Ranking Cloud IaaS, PaaS and SaaS Models Based on Functional and Non-Functional Key Performance IndicatorsFarrukh Nadeem. access, 10:63245-63257, 2022. [doi]

2021

Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic reviewEman T. Alharbi, Farrukh Nadeem, Asma Cherif. midm, 21(1):345, 2021. [doi]

2020

A Unified Framework for User-Preferred Multi-Level Ranking of Cloud Computing Services Based on Usability and Quality of Service EvaluationFarrukh Nadeem. access, 8:180054-180066, 2020. [doi]

Predicting and reducing "hospital-acquired infections" using a knowledge-based e-surveillance systemAmin Y. Noaman, Abdul Hamid M. Ragab, Nabeela Al-Abdullah, Arwa Jamjoom, Farrukh Nadeem, Anser G. Ali. es, 37(1), 2020. [doi]

2019

Using radial basis function neural network to predict dynamic resource availability in heterogeneous distributed environmentsFarrukh Nadeem. jifs, 37(4):5619-5632, 2019. [doi]

Using Machine Learning Ensemble Methods to Predict Execution Time of e-Science Workflows in Heterogeneous Distributed SystemsFarrukh Nadeem, Daniyal M. Alghazzawi, Abdulfattah Mashat, Khalid Faqeeh, Abdullah Al-Malaise Al-Ghamdi. access, 7:25138-25149, 2019. [doi]

2018

2017

Modeling and predicting execution time of scientific workflows in the Grid using radial basis function neural networkFarrukh Nadeem, Daniyal Alghazzawi, Abdulfattah Mashat, Khalid Fakeeh, Abdullah Almalaise, Hani Hagras. cluster, 20(3):2805-2819, 2017. [doi]

2015

An Early Evaluation and Comparison of Three Private Cloud Computing Software PlatformsFarrukh Nadeem, Rizwan Qaiser. jcst, 30(3):639-654, 2015. [doi]

2013

Optimizing execution time predictions of scientific workflow applications in the Grid through evolutionary programmingFarrukh Nadeem, Thomas Fahringer. fgcs, 29(4):926-935, 2013. [doi]

2012

Interactive case based learning in teaching decision support systems and business intelligenceSalma Mahgoub, Farrukh Nadeem. interaccion 2012: [doi]

2009

A Hybrid Intelligent Method for Performance Modeling and Prediction of Workflow Activities in GridsRubing Duan, Farrukh Nadeem, Jie Wang, Yun Zhang, Radu Prodan, Thomas Fahringer. ccgrid 2009: 339-347 [doi]

Using Templates to Predict Execution Time of Scientific Workflow Applications in the GridFarrukh Nadeem, Thomas Fahringer. ccgrid 2009: 316-323 [doi]

Predicting the execution time of grid workflow applications through local learningFarrukh Nadeem, Thomas Fahringer. sc 2009: [doi]

2008

Characterizing, Modeling and Predicting Dynamic Resource Availability in a Large Scale Multi-purpose GridFarrukh Nadeem, Radu Prodan, Thomas Fahringer. ccgrid 2008: 348-357 [doi]

A Framework For Resource Availability Characterization And Online Prediction In The GridsFarrukh Nadeem, Radu Prodan, Thomas Fahringer, Alexandru Iosup. coregrid 2008: 209-224 [doi]

2007

2006

Soft Benchmarks-Based Application Performance Prediction Using a Minimum Training SetFarrukh Nadeem, Muhammad Murtaza Yousaf, Radu Prodan, Thomas Fahringer. eScience 2006: 71 [doi]

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