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School of Electronic Engineering and Computer Science

Dr Usman Naeem

Usman

Senior Lecturer

Email: u.naeem@qmul.ac.uk
Telephone: +44(0)20 7882 6171
Room Number: People's Palace, PP5.01
Website: https://www.drusmannaeem.com

Profile

Dr Usman Naeem is a Senior Lecturer (Associate Professor) within the School of Electronic Engineering and Computer Science at Queen Mary University of London. He received his PhD from Queen Mary University of London in 2009.

His research focus is on assistive technologies, which includes machine learning techniques, mobile computing, and ambient intelligent environments.

Usman has taught on a variety of programmes, ranging from traditional programmes such as BSc Computer Science to degree apprenticeships programmes such as BSc Digital & Technology Solutions Professional.

Teaching

Usman has over 15 years of successful teaching experience in higher education, which includes teaching on a variety of undergraduate and postgraduate modules.

Usman is a Senior Fellow of the Higher Education Academy (SFHEA). He also has Postgraduate Certificate in Learning and Teaching in Higher Education.

Undergraduate Teaching

Fundamentals of Web Technology

This is a module designed to offer students practical skills as well as understanding of underlying principles of programming the World Wide Web. Major topics of study include Internet and web server basics; client-side programming using HTML5; Cascading Style Sheets, and Javascript. Students will develop practical skills in server-side programming using PHP and gain an understanding and hands on experience in the practical issues involved when setting up a website.

Project

This final year project is the most crucial element of a degree programme, as it gives students an opportunity to work on an extensive piece of work within the areas of Electronic Engineering and Computer Science. The project also allows students to demonstrate thier problem-solving abilities by being able to apply a range of skills that they have acquired throughout their degree programme.

Project (Work based)

Degree apprentices will have the opportunity to apply the methodologies, approaches and technologies that they have learned during their taught modules to a significant advanced project embedded in their workplace context. The project topic will be appropriate to the degree apprenticeship specialism.

Postgraduate Teaching

MSc Project

The aim of the MSc project is to give students the opportunity to apply to a significant advanced project, the techniques and technologies, that they have learned in their modules. Projects will either be significantly development based, or else have a research focus. All projects will be expected either to investigate or to make use of techniques that are at the leading edge of the field. 

MSc Advanced Research Project 

This project module draws together the knowledge and skills from the taught component to address a research challenge of significant scope to be undertaken independently, under supervision. It focuses on the technical, project management and communication skills needed to successfully execute academic- and/or industry-oriented research. The project entails to apply research methods to solve original problems of fundamental or applied nature. 

MSc by Research Project

This substantial individual research project is taken as part of the MSc by Research programme. Candidates undertake an extended period of research embedded in an appropriate School Research Group. Regular supervision and feedback sessions, combined with active engagement in school research seminars support students individual learning and development of research skills.

 

Research

Research Interests:

Usman's current research interests are within the areas of Pervasive/Ubiquitous Computing. His research is focused on the development of assistive technologies to support independent living.

Research interests also include:
  • Ambient Intelligent Environments
  • Context Awareness
  • Mobile Sensing
  • Gamification
  • Machine Learning Techniques
  • Educational Technologies

Publications

    • Sharma K, Naeem U, Krishnamurthi R et al. (2022), Chapter 1 Lightweight and heavyweight technologies for autonomous vehicles: A survey $nameOfConference


    • Kaushik K, Bathla G, Naeem U et al. (2022), Chapter 16 Cybercriminal approaches in big data models for automated heavy vehicles $nameOfConference


    • Bosman L, Naeem U, Padumadasa E (2021), Entrepreneurially-Minded Program Assessment During Emergency Situations: Using Photovoice to Understand Customer (Engineering Student) Needs Research in Engineering Education Symposium & Australasian Association for Engineering Education Conference

    • Bosman L, Wollega E, Naeem U (2021), HyFlex, Hybrid, and Virtual Synchronous Teaching in the Engineering Classroom: An Autoethnographic Approach 2021 ASEE Virtual Annual Conference Content Access

    • Ihianle IK, Islam S, Naeem U et al. (2021), Exploiting Patterns of Object Use for Human Activity Recognition $nameOfConference


    • Qamar N, Siddiqui N, Ehatisham-ul-Haq M et al. (2020), An Approach towards Position-Independent Human Activity Recognition Model based on Wearable Accelerometer Sensor $nameOfConference


    • Ehatisham-ul-Haq M, Malik MN, Azam MA et al. (2020), Identifying Users with Wearable Sensors based on Activity Patterns $nameOfConference


    • Ehatisham-ul-Haq M, Awais Azam M, Asim Y et al. (2020), Using Smartphone Accelerometer for Human Physical Activity and Context Recognition in-the-Wild $nameOfConference


    • Ehatisham-Ul-Haq M, Azam MA, Amin Y et al. (2020), C2FHAR: Coarse-to-Fine Human Activity Recognition With Behavioral Context Modeling Using Smart Inertial Sensors $nameOfConference


    • Asim Y, Azam MA, Ehatisham-ul-Haq M et al. (2020), Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer $nameOfConference


    • Okoye K, Islam S, Naeem U et al. (2020), Semantic-based process mining technique for annotation and modelling of domain processes $nameOfConference


    • Naeem U, Islam S, Siddiqui A (2019), Improving Student Engagement and Performance in Computing Final Year Projects $nameOfConference


    • Naeem U, Islam S, Siddiqui A (2019), An effective framework for enhancing student engagement and performance in final year projects $nameOfConference


    • Hussain RG, Ghazanfar MA, Azam MA et al. (2019), A performance comparison of machine learning classification approaches for robust activity of daily living recognition $nameOfConference


    • Guinea AS, Seeliger A, Pejović V et al. (2019), UPA’19: 4th international workshop on ubiquitous personal assistance $nameOfConference


    • Ihianle IK, Naeem U, Islam S et al. (2018), Recognising activities of daily living from patterns of object use $nameOfConference


    • Ihianle I, Naeem U, Islam S et al. (2018), A hybrid approach to recognising activities of daily living from object use in the home environment $nameOfConference


    • Ehatisham-ul-Haq M, Azam MA, Naeem U et al. (2018), Continuous authentication of smartphone users based on activity pattern recognition using passive mobile sensing $nameOfConference


    • Sharif MS, Naeem U, Islam S et al. (2018), Functional Connectivity Evaluation for Infant EEG Signals Based on Artificial Neural Network $nameOfConference


    • Pizzamiglio S, Abdalla H, Naeem U et al. (2018), Neural predictors of gait stability when walking freely in the real-world $nameOfConference


    • Okoye K, Islam S, Naeem U (2018), Ontology: core process mining and querying enabling tool $nameOfConference

    • Azam MA, Shahzadi A, Khalid A et al. (2018), Smartphone Based Human Breath Analysis from Respiratory Sounds $nameOfConference


    • Okoye K, Islam S, Naeem U et al. (2018), The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain $nameOfConference


    • Meurisch C, Scholl PM, Naeem U et al. (2018), UPA’18: 3rd International Workshop on Ubiquitous Personal Assistance $nameOfConference


    • Pizzamiglio S, Naeem U, Abdalla H et al. (publicationYear), Neural Correlates of Single- and Dual-Task Walking in the Real World $nameOfConference


    • Ehatisham-ul-Haq M, Azam MA, Loo J et al. (publicationYear), Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing $nameOfConference


    • Pizzamiglio S, Naeem U, Ur Réhman S et al. (2017), A mutlimodal approach to measure the distraction levels of pedestrians using mobile sensing $nameOfConference


    • Islam S, Naeem U, Sharif MS et al. (2017), CrimeSafe: helping you stay safe $nameOfConference


    • Pizzamiglio S, De Lillo M, Naeem U et al. (2017), High-frequency intermuscular coherence between arm muscles during robot-mediated motor adaptation $nameOfConference


    • Ehatisham-ul-Haq M, Azam MA, Naeem U et al. (2017), Identifying Smartphone Users based on their Activity Patterns via Mobile Sensing $nameOfConference


    • Khan MSL, ur Réhman S, Mi Y et al. (2017), Moveable Facial Features in a Social Mediator $nameOfConference


    • Ihianle IK, Naeem U, Islam S (2017), Ontology-driven activity recognition from patterns of object use. $nameOfConference


    • Okoye Dr K, Naeem Dr U, Islam Dr S et al. (2017), Process Models Discovery and Traces Classification: A Fuzzy-BPMN Mining Approach. $nameOfConference

    • Ihianle IK, Naeem U, Islam S et al. (2017), Recognising activities of daily living from patterns of object use $nameOfConference

    • Okoye K, Naeem U, Islam S (2017), Semantic Fuzzy Mining: Enhancement of process models and event logs analysis from Syntactic to Conceptual Level $nameOfConference

    • Islam S, Sharif MS, Naeem U et al. (2017), SignalSense: towards quality service. $nameOfConference


    • Meurisch C, Naeem U, Scholl PM et al. (2017), SmartGuidance’17: 2nd workshop on intelligent personal support of human behavior $nameOfConference


    • Naeem U, Islam S, Sharif MS et al. (2017), Taskification: gamification of tasks $nameOfConference


    • Ali W, Azam MA, Naeem U et al. (2017), Variance Based Pattern Detection for Inferring Activities of Daily Living $nameOfConference

    • Nasreen S, Azam MA, Naeem U et al. (2016), Recognition Framework for Inferring Activities of Daily Living Based on Pattern Mining $nameOfConference


    • Okoye K, Tawil A-RH, Naeem U et al. (2016), A semantic reasoning method towards ontological model for automated learning analysis $nameOfConference


    • Raana A, Azam MA, Ghazanfar MA et al. (2016), C++ BUG CUB: Logical Bug Detection for C++ Code $nameOfConference

    • Okoye K, Tawil A-RH, Naeem U et al. (2016), Discovery and Enhancement of Learning Model Analysis through Semantic Process Mining* $nameOfConference

    • Kingsley O, Tawil A-R, Naeem U et al. (2016), Discovery and enhancement of learning model analysis through semantic process mining $nameOfConference

    • Naeem U, Tawil A-R, Semelis I et al. (2016), Inference Engine Based on a Hierarchical Structure for Detecting Everyday Activities Within the Home $nameOfConference


    • Ihianle IK, Naeem U, Tawil A-R (2016), Recognition of activities of daily living from topic model $nameOfConference


    • Ihianle IK, Naeem U, Tawil A-R et al. (2016), Recognizing activities of daily living from patterns and extraction of web knowledge $nameOfConference


    • Okoye K, Tawil A-RH, Naeem U et al. (2016), Semantic-based model analysis towards enhancing information values of process mining: Case study of learning process domain $nameOfConference


    • Meurisch C, Naeem U, Azam MA et al. (2016), Smarticipation: intelligent personal guidance of human behavior utilizing anticipatory models $nameOfConference


    • Kingsley O, Tawil A-RH, Naeem U et al. (2016), Using semantic-based approach to manage perspectives of process mining: Application on improving learning process domain data $nameOfConference


    • Zahra S, Ghazanfar MA, Khalid A et al. (2015), Novel centroid selection approaches for KMeans-clustering based recommender systems $nameOfConference


    • Nasreen S, Azam MA, Naeem U et al. (2015), Inference of activities with unexpected actions using pattern mining $nameOfConference


    • Kennedy II, Naeem U, Tawil A-R (2015), A dynamic segmentation based activity discovery through topic modelling $nameOfConference

    • Naeem U, Bashroush R, Anthony R et al. (2015), Activities of daily life recognition using process representation modelling to support intention analysis $nameOfConference


    • Kingsley O, Tawil A-R, Naeem U et al. (2015), Process mining towards automated learning: a semantic rule-based approach $nameOfConference

    • Okoye K, Tawil ARH, Naeem U et al. (2015), Semantic Process Mining Towards Discovery and Enhancement of Learning Model Analysis $nameOfConference


    • Naeem U, Anthony R, Tawil A-R et al. (2015), The role of ambient intelligent environments for tracking functional decline $nameOfConference


    • Okoye K, Tawil A-RH, Naeem U et al. (2014), A semantic rule-based approach supported by process mining for personalised adaptive learning $nameOfConference


    • Okoye K, Tawil ARH, Naeem U et al. (2014), A semantic rule-based approach towards process mining for personalised adaptive learning $nameOfConference


    • Mann KS, Bansal EA, Sokullu R et al. (2014), EUSPN 2014 Papers $nameOfConference

    • Nasreen S, Azam MA, Shehzad K et al. (2014), Frequent pattern mining algorithms for finding associated frequent patterns for data streams: A survey $nameOfConference


    • Naeem U, Tawil A-RH, Semelis I et al. (2014), Inference of Hygiene Behaviours While Recognising Activities of Daily Living $nameOfConference

    • Tawil A-RH, Taweel A, Naeem U et al. (2014), Integration operators for generating RDF/OWL-based user defined mediator views in a grid environment $nameOfConference


    • Lee SW, Naeem U, Anthony R et al. (2014), Tracking functional decline using ambient intelligence for Alzheimer’s patients $nameOfConference

    • Lau BT, Wong MLD, Naeem U et al. (2013), An Indoor Prototype Framework for Recognition of Activities of Daily Life $nameOfConference

    • Azam MA, Loo J, Naeem U et al. (2013), Recognising indoor/outdoor activities of low entropy people using Bluetooth proximity and object usage data $nameOfConference


    • Sarantinos N, Al-Nemrat A, Naeem U (2013), Statistical Sampling Approach to Investigate Child Pornography Cases $nameOfConference


    • Azam MA, Loo J, Naeem U et al. (2012), A framework to recognise daily life activities with wireless proximity and object usage data $nameOfConference

    • Naeem U, Tawil A, Bashroush R et al. (2012), Achieving Model Completeness for Hierarchally Structured Activities of Daily Life $nameOfConference

    • Awais Azam M, Loo J, Kashif Ashraf Khan S et al. (2012), Behavioural patterns analysis of low entropy people using proximity data $nameOfConference

    • Naeem U (2009), A hierarchal framework for recognising activities of daily life $nameOfConference

    • Naeem U, BIGHAM J (2009), Activity Recognition in the Home using a Hierarchal Framework with Object Usage Data $nameOfConference


    • Naeem U, BIGHAM J (2009), Recognising Activities of Daily Life through the Usage of Everyday Objects around the Home $nameOfConference


    • Naeem U, Bigham J (2008), A Hierarchal Approach to Activity Recognition in the Home Environment based on Object Usage $nameOfConference

    • Naeem U, Bigham J (2008), Activity Recognition using a Hierarchical Framework $nameOfConference


    • Naeem U, Bigham J (2008), Activity recognition using a hierarchical framework $nameOfConference


    • Naeem U, BIGHAM J (2008), Recognising Activities of Daily Life through the Usage of Everyday Objects around the Home Proceedings of the 2008 Networking and Electronic Commerce Research Conference (NAEC 2008)


    • Naeem U, Bigham J (2007), A comparison of two hidden Markov approaches to task identification in the home environment $nameOfConference


    • Naeem U, Bigham J, Wang JF (2007), Recognising activities of daily life using hierarchical plans $nameOfConference


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