Ali Farooq

Ali Farooq
University of Turku | UTU · Department of Computing

D.Sc. (Tech.); MSc (Tech.); MCS; Commonwealth MBA

About

28
Publications
131,818
Reads
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767
Citations
Introduction
I am a researcher with formal education in IT and business. My research focus is "where human meets technology". It intrigues me to understand how people interact with technology, what issues they face during this interaction, and what are the consequences of technology use. To understand these dynamics, I borrow knowledge from disciplines such as sociology, psychology, and economics and run users' studies (both quantitative and qualitative) to improve the human-technology interaction.
Additional affiliations
January 2020 - January 2021
University of Turku
Position
  • PostDoc Position
June 2014 - December 2020
University of Turku
Position
  • Teacher Assistant
June 2014 - December 2019
University of Turku
Position
  • PhD Student
Education
June 2014 - December 2019
University of Turku
Field of study
  • Information Security
September 2011 - July 2013
University of Turku
Field of study
  • Information Security
September 2008 - December 2013
Allama Iqbal Open University
Field of study
  • Business Administration

Questions

Questions (19)
Question
Hello,
I am in a peculiar situation where I need help from experienced researchers.
I am creating a new scale (I know the procedure). After running an EFA (extraction method: principal axis factor (PAF), parallel analysis for identifying the number of factors and oblique rotation), I received a two-factor solution:
Sample: 168
Factor 1 (6 items with loadings between 0.5-0.9, alpha: 0.96, 4% variance)
Factor 2 (10 items with loadings between 0.6-0.8, alpha: 0.97 , 24% variance)
Then I went on and collected a new dataset and ran CFA. Model fit indices are in the attached image.
In your opinion, which solution is better? Or none of them are good?
Kind Regards,
Ali

Network

Cited By

Projects

Projects (5)
Project
The aim of this project is to actively solve the research problems in the domain of Federated Learning. The main research topics includes but not limited to the challenges of privacy-preservation, latency, decentralization, (a)synchronization, personalization, fairness, and bandwidth-optimization. We aim at congregating researchers and practitioners to collaborate with us in showing how federated learning can transform next-generation artificial intelligence applications, and propose solutions to address key federated learning challenges. We are also interested in both survey and original works in unexplored and/or emerging topics in the broad area of federated learning systems, architectures, applications, and algorithms, and in novel findings and/or new insights that build on existing works. Our topics of interest include but not limited to: - Differential Privacy Techniques - Latency-minimal Federated Learning Applications - Bandwidth-Optimization Techniques for Efficient Data Communication - Local and Global Model Personalization - Decentralized Model Training - Fine-grained Federated Learning - Incentive Mechanisms for Large-scale Data Providers - Trust Models in Federated Learning Systems - Reputation Models in Federated Learning Systems - Active Monitoring for Secure and Quality Model Aggregation - Heterogeneity-Awareness Across Federated Learning Systems - Context-Awareness for Data Collection, Model Training, and Aggregation - Model Compression - Adaptive Model Aggregation - Fairness (Algorithmic, Systematic)
Project
Explore the value-added of Technology in Higher Education
Project
Objective: Using Technology Acceptance Model as the base, this project aims at identifying the factors that would affect acceptance of E-Learning System (ELS) among Saudi university students. The factors include both systems' specific factors (Instructor's characteristics, Teaching Material, Learning content, System Quality, Perceived Security) and user specific factors (Gender, Age, Educational Discipline and Level, Experience with computer, internet, and ELS, perceived skill level in computer, internet and ELS, Perceived Interaction and Perceived Engagement). Affect of system and user specific factors will be studied on constructs of TAM (Perceived Usefulness, Perceived Ease of Use, Attitude Towards ELS, and Intention to Use ELS). Method: In this regard, data has been collected from 314 university students of a Saudi University using online and offline survey through random sampling. Analysis: The data will be analyzed using descriptive and regression analysis in SPSS and models will be tested through standard equation modeling (SEM) using smartPLS.